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Article Type: Original Article
Do Political Connections Weaken Tax Enforcement Effectiveness?*
Kenny Z. Lin, Lillian F. Mills,† Fang Zhang, and Yongbo Li
September 2017
Kenny Z. Lin
Department of Accountancy, Lingnan University, Hong Kong
Email: klin@ln.edu.hk
Lillian F. Mills
McCombs School of Business, University of Texas at Austin, Austin, TX 78712
Email: Lillian.mills@mccombs.utexa.edu
Fang Zhang
Department of Accountancy and Law, Hong Kong Baptist University, Hong Kong
Email: fang@hkbu.edu.hk
Yongbo Li
Department of Accounting, Hong Kong University of Science and Technology, Hong Kong
Email: ylics@connect.ust.hk
* Accepted by Jeffrey Pittman. We appreciate comments from the Arizona Tax Readings
Group, 2015 EAA Conference, Wei Cui, Lisa De Simone, Michael Devereux, Michael
Donohoe (discussant at the 2014 AAA Annual Meeting), Katharine Drake, Matthew
Erickson, Nathan Goldman, Jeffrey Gramlich, Jeffrey Hoopes, Kenneth Klassen, Colin
Koutney, Stacie Laplante, Kelvin Law, Georgia Maffini, Edmund Outslay (discussant at the
2015 NTA Annual Conference), Jeffrey Pittman, Braden Williams, Jing Xing, Li Xu, and
students and faculty at The University of Paderborn, Washington State University, Xiamen
University, and Zhongnan University of Economics and Law.
†
Corresponding author: Lillian.mills@mccombs.utexa.edu
This is an Accepted Article that has been peer-reviewed and approved for publication in the
Contemporary Accounting Research, but has yet to undergo copy-editing and proof
correction. Please cite this article as an “Accepted Article”; doi: 10.1111/1911-3846.12360
This article is protected by copyright. All rights reserved.
Do Political Connections Weaken Tax Enforcement Effectiveness?
ABSTRACT
This paper examines whether ties to politicians by corporate boards of directors weaken the
effectiveness of tax authorities in constraining tax avoidance in China. We use a unique data
set to measure geographic time-variant tax enforcement, including the probability of income
tax audit, the expertise of tax officers, and the consequences of underreporting tax liabilities.
Based on a sample of 11,121 firm-years from 2003 to 2013, we find that the deterrent effect
of the probability that a firm’s taxable income understatement will be detected and lead to
heavy penalties is significantly undermined if the board is politically connected. To enhance
our analysis, we use opportunities for income shifting, the most likely mechanism through
which Chinese firms avoid taxes on an ongoing basis, to illustrate how connected boards
exert power to unwind the constraining effect of tax enforcement. Overall, our results suggest
that a board’s ties to politicians can be a significant challenge to the effective enforcement of
tax compliance in a politically controlled economy.
Keywords Effective tax rates, income shifting, political connections, tax avoidance, tax
enforcement.
JEL Descriptors H25 H26 M40
This draft: September 2017
Do Political Connections Weaken Tax Enforcement Effectiveness?
1.
Introduction
We investigate whether politicians tied to a firm’s board wield sufficient power to mitigate or
even completely offset tax enforcement effectiveness. Prior research has taken a piecemeal
approach to examining the association between tax authority enforcement and tax avoidance
(Hoopes et al. 2012) and between corporate political connections and tax aggressiveness
(e.g., Brown et al. 2015; Kim and Zhang 2016). However, few studies to date have examined
whether less scrutiny from revenue authorities of the aggressive tax positions of politically
connected firms is also a reason for corporate tax avoidance, or whether government
enforcement is less effective for firms with political connections.1 Kim and Zhang (2016)
1
Desai et al. (2007) find a sudden increase in tax enforcement (i.e., increased tax payments, decreased related
party trades, and reduced tax haven entities) in Russia following the election of Vladimir Putin as prime
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call for research to examine the interplay of political ties and tax enforcement. We respond to
this call directly by examining whether board of director ties to politicians, a widespread
phenomenon in China, limit the ability of revenue enforcers to constrain tax avoidance. We
also answer Crocker and Slemrod’s (2005) call to extend the relatively sparse literature on the
effects of tax authority monitoring on corporate tax behavior (El Ghoul et al. 2011; Guedhami
and Pittman 2008; Hoopes et al. 2012).
Our question deserves particular attention because tax enforcement affects government
revenues available to fund public expenditures. By studying the political economy of tax
enforcement, we push the boundaries of prior research that separately examines whether
political connections facilitate tax avoidance while enforcement efforts impede it, with little
regard to the possibility that the process of enforcing tax laws is itself prone to political
influence.
We measure tax avoidance with the one-year book effective tax rate (ETR) that equals
the ratio of total income taxes to pre-tax income before special items (Hoopes et al. 2012).
We use a novel data set sourced from the China Tax Audits Yearbook, published annually by
the State Administration of Taxation (SAT, akin to the IRS), to measure aggregate tax
enforcement characteristics for all provinces and large cities in China. We use detailed and
summary measures of enforcement for each region-year: the probability of tax audits, the
expertise of tax officers, and the consequences of underreporting taxable income. We
consider both the existence and intensity of a board’s political connections, namely whether
the chairman is connected and the number and percentage of connected board directors. To
improve identification, we also consider a change in the political connection status from not
minister in 2000. Chen et al. (2015) examine three channels through which the political contributions of firms
affect their tax burden in the United States and find evidence consistent with the influence of political
contributions on IRS audit rates and hence on the tax burden through enforcement of tax laws and regulations.
However, Chinese firms do not establish political ties through political campaign contributions or corporate
lobbying expenditures but through hiring board members with political backgrounds. The current
organizational structure of Chinese tax administration places local tax offices at risk of intervention by local
politicians.
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connected to connected. We predict that firms with a well-connected board are subject to less
scrutiny so that enforcement of tax avoiders is less effective in the presence of a politically
connected board.
We report the following findings based on a sample of 11,121 firm-years from 2003 to
2013. First, corporate political connections attenuate the constraining effect of tax authorities
on tax avoidance in pooled cross-sectional tests. Holding various firm characteristics
constant, the existence of political connections on the board reduces the sensitivity of ETR to
tax enforcement by approximately 86 percent to 88 percent, depending on model
specifications. To strengthen the causal inference, we then analyze the effect of a connection
status change triggered by firm-specific and market-wide factors. Specifically, we test
whether the incremental association between ETR and Enforcement is weaker for firms with
an increase in connected directors than for firms without a similar increase. We also use the
2012 nationwide anti-corruption campaign as a natural experiment to implement the
difference-in-differences (DID) analysis. If connected firms have the ability to obtain or
maintain lenient regulatory treatment, then, arguably, firms that lose a connection following
the crackdown on corrupt officials will experience a drop in this ability. Using a DID
procedure, we find that the positive ETR-Enforcement relation weakens after a firm acquired
political ties, but strengthens after a change in the political landscape that diminished the
rent-seeking and favor-exchanging ability of both politicians and managers. Triangulating our
cross-sectional and time-series differences helps confirm that our results are not due to
common shocks or omitted variables.
In supplemental tests, we find that our results are robust to measuring tax avoidance
using a cash ETR or a three-year book ETR adjusted for firm size and industry effects
(Balakrishnan et al. 2014; Kim and Zhang 2016). To help us better understand the underlying
mechanism, we also examine the most common opportunity for tax avoidance in China: the
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shifting of income between member firms in a business group (Lo et al. 2010; Shevlin et al.
2012). China requires consolidated financial reporting but separate legal entity tax filing.
Such an arrangement, similar to the separate reporting under the territorial system (Markle
2016; Altshuler and Grubert 2001), creates strong shifting incentives. We model the
opportunity for such shifting as a joint function of the spread in tax rates among members of
the consolidated group and the magnitude of intragroup transactions, like Jacob (1996). Based
on a reduced sample of 7,869 group firm-years from 2003 to 2013, we find that tax avoidance
associated with income-shifting opportunities is constrained by tax enforcement but unwound
when firms are politically connected.
China provides a good setting for conducting this research. First, despite its rapid
transition to a market-based economy, China remains a hierarchical, relationship-based
society, with political connections (an important form of relationship) being critical to firm
survival and success (Xin and Pearce 1996). Second, the current arrangement that local tax
authorities are under dual supervision from central and local governments exposes them to a
great risk of interference by local politicians. Third, while the country has uniform tax laws,
enforcement often deviates from written law in some regions (PricewaterhouseCoopers
2012). Our study also provides several enhancements in research design. First, in sharp
contrast to prior non-U.S. research that suffers from not having data on when political
connections are formed or severed (e.g., Chaney et al. 2011; Guedhami et al. 2014), we
improve identification by exploiting changes in the political connection status. In an attempt
to alleviate the concern that endogeneity biases our main findings, we also take a number of
steps, including DID analysis, a Heckman two-step treatment effects procedure, and a
propensity score matching approach. Second, our tax enforcement proxies benefit from
varying on multiple dimensions in comparison to extensive cross-country research that relies
on proxies that only vary on a single dimension (e.g., Dyck and Zingales 2004; Haw et al.
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2004; Desai et al. 2007). Finally, the various institutional characteristics (e.g., extensive
government involvement in business, varying statutory tax rates across industries, separate
legal entity tax filing, and widespread intragroup transactions) that we describe later enable
us to examine the research question in a high-power testing ground.
Although our single-country focus inevitably undermines external validity of our results,
our description of informal institutions and ineffective law enforcement, which are common
in the BRIC and emerging economies (Estrin and Prevezer 2011; Faccio 2006; Khwaja and
Mian 2005), helps dispel this validity concern. This study contributes to the very limited
literature exploring the interaction between politics and tax administration receptive to
bureaucratic influence. Our empirical results raise questions about the effectiveness of the
deterrent role of tax authorities and highlight the importance of understanding the political
economy of tax enforcers in a transition economy. From a policy viewpoint, the results
suggest that reforms to increase government revenue should not be limited to the
strengthening of tax administration within the revenue system alone, but should also take the
political economy of tax enforcers into account.
2.
Tax aggressiveness and tax enforcement in China
The most common methods by which Chinese firms avoid income tax include underreporting
sales revenue, fabricating and inflating expenses, abusing tax credits, using unsanctioned
different policies for book and tax purposes, managing discretionary accruals, and
transferring profits (across periods within the same firm or across member firms in a business
group) (Cai and Liu 2009; Lin et al. 2012; Lin et al. 2014).2 As the first four methods
represent tax evasion, it is unlikely that firms can apply these methods for an extended
period. In contrast, the last two could represent legitimate tax planning that allows managers
2
According to Benari (2016), tax avoidance schemes in the United States fall into three broad categories:
“schemes involving financial transactions (e.g., debt buying, overpayment, use of dividends, deductions on
employee stock options); schemes involving geographical locations (e.g., tax havens, outward domestication);
and schemes involving cost of goods (e.g., false invoicing, transfer pricing).” Comparatively, outbound
investments, overseas tax havens, and deductions of employee stock options are less applicable in China.
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to shift earnings via accounting accruals or real activities for long periods. Moreover, tax
authorities usually find it difficult to detect and unravel income shifting, because it is subject
to managerial choices that are difficult for external parties to challenge. Therefore, accruals
management and income shifting are more prevalent than other methods of avoiding income
tax over the long run in China.
In an effort to attract investment to specific regions and industries, China offers
preferential tax rates (typically 15 percent) to domestic firms operating in the designated
zones
and
industries.
As
a
result,
Chinese
statutory
tax
rates
have
wide
cross-sectional/regional and temporal variation, similar to international variation. A Chinese
parent firm must consolidate the income of its subsidiaries for book but file separate tax
returns on a legal entity basis.3 Therefore, the parent firm can reallocate taxable income
within the group without affecting consolidated pre-tax book income. Chinese group firms
usually have a large, powerful parent (e.g., a listed firm) whose subsidiaries and affiliates
operate in different regions and industries subject to varying tax rates. The parent firm
exercises its authority over subsidiaries through multiple layers to achieve various objectives
(Fan et al. 2007). For example, it can strategically transfer income within the pyramid to
reduce the tax burden for the group (Lo et al. 2010; Shevlin et al. 2012).
Transfer pricing, also referred to as base erosion and profit sharing, is a major tool for
corporate tax misreporting, 4 because applying the arm’s length principle is challenging
particularly for an emerging economy whose tax administration is handicapped by
3
4
Separate taxation is practiced in developed (e.g., Canada, Norway, Sweden, and the United Kingdom) and
developing countries (e.g., Brazil, Greece, India, Korea, Russia [for small groups], and South Africa)
(taxsummaries.pwc.com/).
In September 2014, the OECD approved the Base Erosion and Profit Sharing (BEPS) Action Plan to create a
single set of international tax rules to minimize the erosion of tax bases and the artificial shifting of profits for
tax purposes. The OECD issued its report, Measuring and Monitoring BEPS, in October 2015, and China
plans to localize the BEPS recommendations (PricewaterhouseCoopers 2015). Transfer pricing issues have led
to some of the largest U.S. tax settlements, and firms filing an IRS Schedule UTP (uncertain tax position) must
disclose whether the position involves transfer pricing (Towery 2017). Lin et al. (2012), Lin et al. (2014), Lo
et al. (2010), and Shevlin et al. (2012) document that Chinese firms avoid subnational tax through domestic
income shifting.
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underdeveloped financial markets and inadequate financial resources and human expertise
(Dharmapala and Hines 2009; He et al. 2012; Piotroski and Wong 2012). In this institutional
setting (i.e., differential tax rates, separate tax reporting, a multilayered pyramid structure,
and insufficient market-supporting infrastructure), one can expect tax avoidance through
income shifting to be rampant in China. China’s revenue agency has been stepping up efforts
to challenge tax avoidance via transfer pricing both inside China and in cross-border
transactions since 2000. Transfer pricing audits frequently result in tax deficiencies with
proposed adjustment, interest levy, and penalties.5 The high ex ante threat of a tax audit,
heavy penalties, and ex post incidence of closer monitoring should deter firms from taking
aggressive tax positions.
Recent evidence suggests that tax authority oversight can constrain opportunistic
managerial reporting. 6 However, there is anecdotal evidence that casts doubt on the
capability or willingness of China’s tax administration to detect and constrain sophisticated
tax avoidance arrangements. As China decentralized decision rights, local governments
acquired more autonomy in economic matters (Li and Zhou 2005) and as a result had goal
incongruence with the central government. The Chinese saying “the mountains are high and
the emperor far away” aptly captures the autonomy that is abused by some local officials. The
decentralization also created a wide disparity in economic and institutional development
across regions (Fan et al. 2011). Consequently, there are differences in government
involvement, professionalism of business intermediaries, and the quality of local government
5
6
For example, in 2014, Xuzhou SAT of Jiangsu province claimed that an agreement for sharing management
and R&D expenses lacked economic substance and represented abusive transfers of profit between two
differently taxed affiliates. These two firms had to pay an extra tax charge of RMB 5 million (Huang and Zhao
2015). Adjustments are subject to a special interest levy based on the RMB loan base rate applicable to the
relevant period of tax delinquency plus five percentage points. A daily surcharge of 0.05 percent is levied on
the underpaid taxes and interest levies. What is worse, tax agents will follow up closely for five years after any
adjustment, during which period post-adjustment firms must submit contemporaneous transfer pricing
documentation by June 20 of each year.
For example, active enforcement of tax laws results in lower corporate tax avoidance (Atwood et al. 2012;
Hoopes et al. 2012), reduces rent diversion by managers (Desai et al. 2007), improves the quality of financial
reporting (Guenther and Young 2000; Hanlon et al. 2014), and lowers the cost of debt and implied equity
capital (El Ghoul et al. 2011; Guedhami and Pittman 2008; Houston et al. 2014).
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(World Bank 2006). Government quality influences the administrative effectiveness of local
tax bureaus. Although tax laws are centrally legislated, local tax offices may interpret and
enforce them differently (PricewaterhouseCoopers 2012). In some areas, tax officials
interpret tax laws and the arm’s length principle more strictly and take a tougher stance
against noncompliance, while in other areas they are more lenient in imposing penalties,
fines, and surcharges. As a result of uneven economic growth across regions, some tax
offices also have more financial resources than others to attract and retain top talent, enforce
tax laws, improve tax administration infrastructure, and advocate for voluntary tax
compliance as a form of corporate social responsibility.
3.
Research hypothesis: Political connections constrain tax enforcement effectiveness
Since the 1978 reforms, China has developed a unique hybrid economy melding socialist and
capitalistic ideas by balancing government ownership of firms with the rapidly growing
private sector. However, China remains a hierarchical, relationship-based society, with
political connections being an important form of relationship. The government still has
absolute power over the allocation of massive state resources (e.g., licenses and permits,
initial and subsequent public offerings, bank loans, subsidies, and government contracts) and
effective control of large-scale state-owned enterprises (SOEs) and major banks, which
continue to dominate key sectors of the economy. The Communist Party has ruled the
country since 1949, without either challenge by opposition or scrutiny by the media. In the
absence of political reform, corporate ties with bureaucrats have become even more
important to take advantage of the many new business opportunities created by the economic
reform. To compete with SOEs for economic resources and reduce the risk of expropriation,
many Chinese private firms hire managers who are government officials or current members
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of the People’s Congresses or Political Consultative Conference (Li et al. 2006).7 Unlike
many mature markets that impose reasonable restrictions on a civil servant’s rights to work
after leaving office, China has no specific post-service employment rules for civil servants.
Moses (2015) estimates 2 percent of the bureaucratic workforce left government for better
opportunities in the yearly 2000s. Thus, many firms could hire former government officials
and tax experts to act as a protective shield against fines, litigation, loss of explicit and
implicit contracts, negative publicity, and damaged relations with the government.8
China has a two-tier tax administration system of national and local tax bureaus. The
SAT is the highest tax authority. The tax organizations at and below the provincial level are
divided into the offices of the SAT and local tax bureaus, with the former (latter) collecting
corporate income taxes from centrally (locally) controlled enterprises. Local tax bureaus are
under dual leadership from central and local governments, but the local government
determines director appointments, organizational structure and size, staffing levels and cadre
promotions, and operational budgets and employee pay scales. Such arrangements place local
tax agencies at risk of interference by local politicians. Both anecdotal and empirical
evidence suggests that Chinese tax administration is subject to political pressure. 9 For
example, Liu et al. (2015) find that political leaders tend to tighten collection to generate
7
8
9
The People’s Congresses or Political Consultative Conference have a significant influence on and a close
relation with government officials. Many members of these organizations are current or former key
government officials. Although the membership of the two organizations includes non-Communists (e.g.,
movie superstar Jackie Chan), all government officials must be Communist Party members. The Chinese
Communist Party reached almost 88 million members in 2014 (South China Morning Post, June 30, 2016),
representing 6 percent of the Chinese population. Party members include factory workers, nurses, and teachers,
so party membership alone does not equate to the political power to influence corporate affairs.
Cases attracting media attention include the governor of Shandong province and the deputy head of the SAT
joining Sinotruk Hong Kong Ltd., the vice-mayor of Xinghua city in Jiangsu province working in China
Pacific Construction Group Ltd. while still in office, senior officials at the national securities regulatory
authority joining the private sector, and anti-tax avoidance experts from the SAT joining leading international
accounting firms as well as public and private corporations.
A local government can affect the operation of local tax bureaus via its finance bureau, which controls
resource allocation and staff welfare subsidies to all bureaucratic agencies. A change in government officials
can change tax enforcement behavior. For instance, when Xilai Bo was removed from the Central Committee
of the Communist Party of China, on March 15, 2012, the Chongqing municipal government immediately
instructed its Bureau of Finance to heighten tax enforcement (21st Century Business Herald, February 1,
2013).
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immediate effects on fiscal revenue during the early part of their terms. Chen et al. (2015)
also find that newly appointed local leaders are inclined to collect more taxes to expand fiscal
expenditure. Chen (2014) shows that under the influence of the local Communist Party
secretary, local tax authorities impose lax enforcement of value-added tax collection on
capital-intensive industries that participate in corruption. Deng and Luo (2011) provide
evidence that when bureaucrats are evaluated on collections, local tax departments collude
with government-friendly businesses by timing tax collections to achieve their annual tax
budget.
Tax auditors are also likely motivated and shaped by private interests. Families of local
tax officials face welfare and social issues, such as housing, schooling, education, healthcare,
and employment, the resolution of which depends, to a large extent, on the ability of
bureaucrats to cultivate a wide social network of contacts with firm managers. Chinese tax
agents may subject the tax positions of favored or connected firms to less scrutiny or
arbitrarily enforce tax regulations to promote these firms over others (Shevlin et al. 2012).
Tax and non-tax research suggests that political connections help individual firms,
particularly in economies where the government exercises tight control over resource
allocation. Such value includes government bailouts in the event of financial distress (Faccio
2006), increased firm value (Fisman 2001; Johnson and Mitton 2003), better access to the
legal system and bank loans (Khwaja and Mian 2015), preferential tax treatment (Adhikari et
al. 2006; Faccio 2006), and lower regulatory penalties and relaxed government oversight
(Correia 2014; Wu et al. 2016). Desai et al. (2007) model a political economy game that
involves the state, insiders, and outside shareholders in the determination of tax liabilities, in
which managers may bribe the tax authority to make it an accomplice to their diversion. The
U.S.-based results suggest that politically connected firms can shape IRS audit intensity in
their favor through influencing the allocation of enforcement-related resources and execution
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of tax laws and regulations (Bagchi 2012; Chen et al. 2015; Young et al. 2001). Connected
firms face lower penalties if they are prosecuted by the SEC (Correia 2014) and accrue
greater future tax benefits (Brown et al. 2015; Francis et al. 2012; Kim and Zhang 2016).
Kim and Zhang (2016) call for research investigating the underlying mechanisms with which
connected firms avoid tax.
The preceding discussion suggests that the extent to which revenue authorities can
constrain tax avoidance, as observed by Hoopes et al. (2012), could be subject to political
influence and that connected firms are more tax aggressive, as observed by Kim and Zhang
(2016), presumably because they can influence the probability of tax audits and the
magnitude of post-audit adjustments and sanctions. To summarize, we expect the political
influence of firms to mitigate the positive effect of tax authority enforcement in deterring tax
avoidance. We state our composite hypothesis in the alternative as follows:
HYPOTHESIS. Corporate political ties decrease the impact of tax authorities in
constraining tax avoidance.
Although our hypothesis is directional, we recognize there are countervailing incentives
pushing politically connected firms to forgo current tax savings to reap other benefits later
(Bradshaw et al. 2016; Erickson et al. 2004; Mills et al. 2013). There is also evidence casting
doubt on the role of political relations in tax reporting. For example, the tax collection effort
in China is influenced more by municipal leaders’ career advancement incentives and
regional budgets, and less by firm-level political motivations (Chen et al. 2015; Deng and
Luo 2011; Liu et al. 2015). To the extent that firms exploit political ties for priorities other
than minimizing tax scrutiny, such ties would be unrelated to enforcement or even enhance
the enforcement effect on tax payments. Moreover, as the political relationship is a personal
asset that is based on reputational capital, connected board members have incentives to
ensure that their personal reputation and goodwill in relation to the government is not eroded
due to corporate tax avoidance. Such reputation concerns could make board members
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reluctant to hinder tax enforcement. Whether political ties decrease tax authorities’ role in
constraining tax avoidance is thus an empirical issue.
4.
Research methodology
Data and sample selection
We focus on A-share firms listed on the Shanghai and Shenzhen stock exchanges and start with
a sample of 15,070 firm-year observations from 2003 to 2013. We begin in 2003, the year
following the 2002 tax revenue sharing reform and the implementation of strengthened rules
on income transfers, and end in 2013, the last year complete tax enforcement data are available.
We obtain firm financial data from the China Stock Market and Accounting Research
(CSMAR) and WIND databases. We manually collect background information about top
executives and board members from corporate annual reports. To prevent outliers from
unduly affecting our results, we winsorize the top and bottom one percentile of all scaled
variables and exclude (i) 157 firm-years in the financial industry; (ii) 2,824 firm-years with
negative income tax expense or negative pre-tax income; (iii) 611 firm-years with an ETR
equal to or greater than 1; (iv) 269 observations with no financial information to calculate the
ETR; and (v) 88 observations from the Tibet Autonomous Region because only state tax is
collected in this particular region. Our final sample contains 11,121 firm-year observations.
Table 1 describes the sample distribution by year and by industry. Our sample firms
cross multiple industries, with 50.87 percent in manufacturing and 9.25 percent in the real
estate industry. Observations are fairly evenly spread across years. Untabulated results show
that (i) listed firms are geographically dispersed with a maximum number of 132 in
Guangdong and Shanghai, and a minimum of 10 in Qinghai and Ningxia; (ii) the country’s
lowest-tax province is Ningxia (15.6 percent) and the highest is Liaoning (27.7 percent); and
(iii) agriculture and real estate are the lowest and highest taxed industries, respectively (12.5
percent vs. 29.8 percent).
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Model specification and definition of variables
To test our hypothesis, we estimate the following regression model:
ETRit = β0 + β1Enforcementkt + β2Connectedit + β3Enforcementkt×Connectedit + βkXikt + ԑit,
(1)
where i, k, and t are firm, region, and year indicators, respectively. ETR is the one-year
effective tax rate, defined as the ratio of annual income tax expense to annual pre-tax income
before special items (Hoopes et al. 2012). We consider whether our main results are sensitive
to other measures of tax avoidance, including the one-year and three-year cash ETR and
three-year book ETR, adjusted or unadjusted for size and industry effects (Balakrishnan et al.
2014; Kim and Zhang 2016).
Tax enforcement
We use aggregate data from the China Tax Audits Yearbook (2003–2013) to construct our tax
enforcement measures. The yearbook is published annually by the SAT and contains detailed
tax enforcement data for each province and major city in China. Specifically, the data include
the number of permanent employees, tax inspectors, and employees with a bachelor’s degree
and/or a professional qualification (certified accountants, certified tax agents, and lawyers)
and their Communist Party membership and age range; corporate taxpayers; corporate tax
returns audited; audit departments; suspicious cases; cases prosecuted; and cases closed. It
also includes the amount of regional tax revenue, tax deficiencies settled, overdue tax
surcharges, and tax penalties, interest, and fines. Following Hoopes et al. (2012), we examine
the contemporaneous relation between enforcement and avoidance assuming that managers
have rational expectations of the actual audit rates from public statistics. However, we find
our results are robust to using the one-year lagged enforcement.
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We perform principal component analysis to identify and interpret common factors. This
procedure reduces the dimensionality of the data by combining the individual aspects of tax
enforcement into a parsimonious set of linear combinations that explain the shared variance
among the characteristics. Our analysis produces three common factors, which we label
Probability, Expertise, and Outcome, which we then combine into an aggregate measure
called Enforcement. Appendix 1 describes our factor analysis in detail.
Figure 1 shows which provinces fall into each quartile of Enforcement for the period
2003–2013. Firms in regions with the darkest shade are most likely to be audited, confront a
well-trained and skilled audit workforce, and face the toughest fines for underreporting.
[Figure 1 here]
Political connection
To measure political connections of the board, we collect data from corporate annual reports.
We classify board members as politically affiliated if they serve or formerly served in a
city-level government agency (e.g., taxation/finance bureaus) or are current or ex-members of
the People’s Congress or the People’s Political Consultative Conference (Fan et al. 2007).10
We consider both the existence and the strength of connections. We code
ConnectedChair as one for firm-years with a politically connected chairman and zero
otherwise. ConnectedBoard equals one if the firm-year’s number of connected board
members exceeds the industry-year median and zero otherwise. We measure the strength of
political connections for each firm/year with (i) the number of connected board members
(Connected#), defined as the natural logarithm of one plus the number of connected members
10
Although the central government has ultimate power over local government decisions, the local governments
have the incentive to help rent-seeking firms. Kim and Zhang (2016) also use corporate campaign
contributions and corporate lobbying expenditures to construct the data set. As there are no opposition parties
in China, all political connections are with government officials from the Communist Party and Consultative
Party.
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in the year; and (ii) the percentage of connected members on board (Connected%).11 Using a
continuous variable assumes a log-linear relation between the variables of interest and avoids
imposing a subjective and arbitrary cutoff point, whereas using an indicator variable relaxes
this assumption and allows one to observe whether the slope of a variable differs across the
levels of another variable.
To test our hypothesis, we interact the political connection variables with the
enforcement variables. The coefficient of Enforcement captures the difference in ETR for
unconnected firms facing different levels of tax enforcement, whereas the sum of the
coefficients of Enforcement and its interaction with the connection variables captures that for
connected firms. A significantly negative coefficient for the interaction term will indicate
political ties attenuate enforcement.
We include multiple control variables known to affect tax avoidance (e.g., Chen et al.
2010; Gupta and Mills 2002; Kim and Zhang 2016; Mills et al. 2013; Wu et al. 2007). We
include the following firm-specific characteristics: profitability (ROA), the ratio of net
income to total assets, and its standard deviation (Std. dev. of ROA); firm size (Size), the
natural logarithm of the firm’s year-end total assets; asset liquidity (Liquidity), the current
assets over current liabilities; financial leverage (Leverage), the total debt over total assets;
plant, property, and equipment (PPE), the ratio of net PPE to total assets; growth (Growth),
the market value scaled by net book value of assets; intangibles (Intangible), the ratio of
intangible assets to total assets; inventory (Inventory), the ratio of inventory to total assets;
cash holdings (Cash), the ratio of year-end cash holdings to lagged assets; and ownership
structure (Ownership), the percentage of equity held by the government. We include
discretionary accruals (Accruals), estimated from the modified cross-sectional Jones model,
to control for the effect of financial reporting aggressiveness (Frank et al. 2009).
11
The percentage of political connected boards is the same for a board with one connected member out of five
or for another board with two connected members out of 10. However, as one may argue that the latter board
has more political clout than the former, we present both scenarios.
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We also include three factors to control for differences in regional economic and
institutional conditions and the effect of local leader turnover (Chen et al. 2015): GDP, the
natural logarithm of a province’s gross domestic product; Institution, an index score that
reflects a region’s institutional characteristics (Fan et al. 2011); and LeaderTurnover, an
indicator variable that takes the value of one if the province where the firm is headquartered
experiences governor or party secretary turnover, and zero otherwise. Finally, we include
year and industry fixed effects to control for macroeconomic conditions and changes in tax
regulations that differ across years and industries. Appendix 2 summarizes the regression
variable specifications.
5.
Empirical results
Descriptive statistics on tax enforcement and political ties
Table 2 describes our dependent and explanatory variables. Mean ETR is 23.3 percent and
varies substantially, but not due to connected firms clustering in lower-tax industries or being
smaller in size, nor by regions with the above-median enforcement level having more
tax-favored industries or smaller firms. We also describe the raw components of enforcement.
On average, 1.6 percent of corporate tax returns filed are selected for an audit (Ratio 1), 1.2
percent are prosecuted (Ratio 2), 17 tax officials are in charge of each listed firm (Ratio 3),
92.4 percent of the audit workforce have at least a bachelor’s degree (Ratio 4), 8 percent of
the personnel are professionally qualified (Ratio 5 to Ratio 9), and 1.5 percent of a region’s
tax revenue comes from deficiencies, penalties, interest, and surcharges (Ratio 8 and Ratio 9).
A typical corporate board has about 15 directors, and about 66 percent have at least one
connected director. The average number and percentage of connected board members are
1.28 and 8.75 percent.
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Untabulated descriptive statistics suggest enforcement varies across region and time. For
example, the highest audit rate is 15.7 percent for Hubei and 0.04 percent for Inner Mongolia.
Jilin and Hainan are far more effective than other provinces in settling tax deficiencies, while
Gansu and Ningxia impose heavier fines for tax noncompliance than Shanghai and Shandong.
However, the proportion of the tax administration’s workforce with a university degree
and/or a professional qualification does not differ significantly across regions. In addition, the
three tax enforcement factors are highly correlated, suggesting that although each of the three
factors captures different aspects of tax enforcement, they classify the provinces in a similar
way.
We match regional enforcement with the location of a listed firm’s headquarters, because
this is where many key business decisions are made and is thus often the focus of inquiry by
tax authorities. Further, the local tax administration in the headquarters’ region can change
the tax owed by subsidiaries in other regions, so the strength of the tax office where a firm is
headquartered matters. Our headquarters are widely dispersed across the six regions (the
Southeast, Bohai, Central, Northeast, Southwest, and Northwest). Substantial cross-office
variations in enforcement intensity ensure that our statistical tests have sufficient power to
assess the effect of government scrutiny on corporate tax reporting.
Univariate tests
To provide univariate tests of our hypothesis, we rank all provinces in ascending order of
Enforcement, group them into quartiles, and partition the sample at the median of
Connected#. We compare the mean ETR of the two groups of firms within each of the four
quartiles. Panel A of Table 3 shows that in areas with the lowest enforcement level (1st
quartile), political connections do not explain variation in effective taxes. However,
well-connected firms in the highest enforcement regions (3rd and 4th quartiles) report
significantly lower mean ETR, consistent with our prediction.
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Panel B shows that both connected and unconnected firms are dispersed across regions
with different levels of tax enforcement. Panel C shows the descriptive statistics and
univariate test results for control variables. For the full sample, connected firms report
significantly larger return on equity (ROA), total assets (Size), inventory (Inventory), cash
(Cash), and discretionary accruals (Accruals), but significantly smaller amounts of property,
plant, and equipment (PPE), lower growth opportunities (Growth), and government-owned
shares (Ownership).12 The differences in firm attributes between the two groups suggest the
need for multivariate analysis.
Multivariate tests
We include the three enforcement variables (Probability, Expertise, and Outcome) separately
(columns 1–3 of Table 4) and together (column 4), and then use our aggregate measure
Enforcement (column 5).13 Across all specifications, the enforcement variables are positive
and statistically significant. To determine which enforcement measure has relatively more
explanatory power, we perform Vuong (1989) tests by running the baseline without any
enforcement variables and then comparing its R2 value to the ones with each of the three
variables. Untabulated results indicate that the consequence of tax audits has the
12
There are two opposing directional predictions about the tax sensitivity of SOEs (Cui 2015). The first is that
because taxing SOEs simply involves the transfer of money from one pocket of the government to another,
SOEs are insensitive to income tax and hence do not pursue tax avoidance. The second prediction is that SOE
managers, like managers of private firms, are tax-averse; therefore, SOEs (even those that are wholly owned)
are likely to be tax-sensitive and do engage in tax avoidance to increase after-tax cash flow. If we expect
businesses to be sensitive to taxation, then excluding tax-insensitive (tax-sensitive) SOEs from our sample will
enhance (weaken) our results. Nonetheless, our main results are invariant to the exclusion of SOEs from our
sample. The central government’s share of corporate tax revenues collected from local SOEs increased from 0
percent before 2002 to 50 percent in 2002 and 60 percent thereafter, which may have induced local SOEs to
shift income to lower-tax subsidiaries to increase after-tax cash flows. However, Shevlin et al. (2012) find no
evidence that income shifting varies as a function of whether the largest shareholder is either the central or
local government, either before or after 2002. Our sample period includes no policy changes in the tax-sharing
rule.
13
As the same firm can appear several times in our sample and the residuals may be correlated across
observations, we use the Huber-White standard errors clustered at the firm level for all regressions (Petersen
2009). For simplicity, we do not report the yearly and industry indicator variables. The highest Pearson
correlation among the independent variables is −0.508 between PPE and Inventory and 0.533 between GDP
and Institution. The remaining correlations are all below 0.40, which is far below 0.80, the point beyond which
the threat of multicollinearity becomes a real concern (Judge et al. 1988).
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most incremental ability to explain variation in ETR, followed by the probability of audits,
although the differences are small. This is reasonable because the decision of whether to
initiate an audit and to impose post-audit penalties (if so, how much) is more vulnerable to
external influences.14 The weaker results on auditor expertise could be due to insufficient
year-to-year variation in auditors’ personal characteristics (e.g., academic degree and
professional qualifications) or more expert auditors being less vulnerable to influence.
Column 4 shows all three variables load similarly to the separate models (columns 1–3), in
spite of collinearity. Our coefficient estimate on Enforcement in column 5 implies that raising
the enforcement intensity from 37 percent (the 25th percentile in our data) to 61 percent (the
75th percentile) increases ETR, on average, by 1.1 percent, translating into an extra annual
tax payment of USD 1.08 million (equivalent to 5.08 percent of the reported tax expense) for
the average firm. Therefore, we conclude that Chinese tax authorities substantially deter tax
avoidance, consistent with the results in Hoopes et al. (2012) for U.S. data.
The sign and significance of the control variable coefficients are generally consistent
with prior literature (Chen et al. 2010; Hanlon and Heitzman 2010; Kim and Zhang 2013;
Mills et al. 2013; Wu et al. 2007). Specifically, higher ETRs are associated with firms
reporting higher levels of cash, inventory, and tangible and intangible assets, and locating in
regions with more developed economies and less mature institutional environments, but
reporting lower levels of net income and discretionary accruals.
14
Because our audit data are regional, not firm-specific, we cannot examine whether connected firms are
associated with a lower probability of being audited, although anecdotal evidence suggests so (Shevlin et al.
2012). To shed light on whether connected firms face lesser penalties conditional on an enforcement action
being taken, we hand-collect government penalty data from financial statement footnotes. We identify 6,460
firm-years with misconduct penalties ranging from tax avoidance and evasion, environmental pollution, food
safety, product defects, security fraud, and false information disclosures. In untabulated results, we find firms
with a connected board enjoy smaller penalties (scaled by assets).
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To test our hypothesis, we interact the three enforcement measures with the political
connection variables. However, for brevity, in Table 5 and throughout the rest of the paper,
we present the results using the aggregate Enforcement variable, rather than the separate
enforcement measures. 15 Columns 1, 2, 3, and 4, respectively, use ConnectedChair,
ConnectedBoard, Connected#, and Connected% for political connectedness.
Across columns 1–4, Enforcement explains higher ETR, consistent with Table 4.
Consistent with our hypothesis, the coefficient on Enforcement×Connected is negative and
significant, which implies a weakening of the responsiveness from 0.059 to 0.066 for
unconnected firms to about 0.008 for connected firms, a drop of 86 percent to 88 percent (see
columns 1–2 and the F-statistics therein). The coefficient on the interaction variable in
column 4 implies that a one standard deviation increase in the percentage of connected board
members is associated with a decrease in ETR of 0.059 (= –0.002×9.244 percent). This value
is larger in magnitude than the coefficient on Enforcement in the last column of Table 4 (i.e.,
0.047), which suggests that such an increase in political ties would entirely offset the positive
effect of tax enforcement on tax costs. Collectively, these results are consistent with our
prediction that connected firms can make the tax authority more lenient in enforcing
corporate tax reporting or that the revenue agency experiences greater difficulty in enforcing
tax compliance for firms with a connected board. Results for the control variables in Table 5
are generally consistent with those in Table 4.
15
As a zero value of enforcement is not meaningful, to increase interpretability we center Enforcement first (i.e.,
subtract the annual mean from each case) and then compute Enforcement×Connected. Centering does not
change what a model means or what it predicts, but by centering we can easily interpret the effect of political
connections for firms in regions with an average level of enforcement. Further, if the variable is not centered,
its product used in computing the interaction may be highly correlated with other variables, thereby inducing
multicollinearity (Aiken and West 1991, 32–33). For example, the correlation between
Enforcement×ConnectedBoard and ConnectedBoard is 0.941 before centering and 0.007 after centering. The
average variance inflation factors (VIFs) also reduce from 3.49–3.57 to 2.95–2.98 in our centered models.
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Changes in political connections
To strengthen our analysis and help address the endogeneity concern related to omitted
variables (e.g., corporate attitudes toward politics and taxation and government spending on
tax education), we employ a DID procedure to analyze the effect of a political connection
status change. Such a change could arise from either unknown firm-specific preferences
changing, or from an exogenous shock to corporate political ties. For brevity, we do not
report the results for control variables.
First, we examine the effects of new politically connected director appointments. We
identify 242 firms that change their connection status from zero (i.e., ConnectedBoard = 0) to
one (i.e., ConnectedBoard = 1) and 595 firms that remain politically unconnected during our
sample period. We match each treatment firm (i.e., with a connection status change) with an
unconnected control firm based on firm size (using the logarithm of sales revenue to identify
the closest firm) and ownership (SOEs and non-SOEs).16 After matching, we have 1,828 × 2
firm-years. We then estimate the following DID regression:
ETRit = β0 + β1Enforcementkt + β2Treatmenti + β3Postt + β4Enforcementkt×Treatmenti
+ β5Enforcementkt×Postt + β6Treatmenti×Postt +
β7Enforcementkt×Postt×Treatmenti
+ βkXikt + ԑit,
(2)
where Treatment is an indicator variable that equals one for firms that increase their political
connections, and zero otherwise. Post equals one for firm-years in the post-change period,
and zero otherwise. Using this specification, we can investigate whether tax enforcement
becomes less effective for firms that newly obtain political connections than for control firms
that remain politically unconnected. Thus, by using a matched control sample, we control for
the multiple other factors not related to the connection change that could be responsible for
We considered allowing 1:N matching to increase our power and gain more confidence in our inferences.
However, due to a small pool of control firms we are unable to expand the 1:1 to even a 1:2 match for our
treatment firms, so we only implement this test with a 1:1 treatment-control group.
16
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the difference in the effect of enforcement on ETR.
Our interpretation of the primary coefficients follows. The coefficient on Enforcement
(β1) measures whether government enforcement affects ETR. A positive Enforcement
coefficient suggests higher tax enforcement is associated with higher effective taxes for
control firm-years in the pre-change period. The interaction term Enforcement×Treatment
(β4) captures any cross-sectional variation in enforcement effectiveness between treatment
and control firms pre-change. Enforcement×Post (β5) captures the temporal trend in
enforcement effectiveness for control firms, unrelated to a change in the board’s connection
status. As the board’s connection status is the same for both groups of firms pre-change or
remains unchanged for control firms post-change (meeting the parallel trends assumption),
we expect β4 and β5 to be insignificant. 17 Finally, Enforcement×Treatment×Post (β7)
captures how the estimated coefficients on Enforcement evolve over time for treatment firms
(i.e., β5 + β7) or for treatment versus control firms (i.e., β4 + β7). A negative β7 indicates firms
that obtain political connections (i.e., treatment firms) enjoy reduced enforcement
effectiveness compared to those that remain politically unconnected (control firms).
Table 6, panel A1, reports the DID regression results. Panel A2 presents a two-by-two
matrix to illustrate changes in enforcement effectiveness over time for control versus
treatment firms.18 We conduct an F-test to determine whether a group of variables is jointly
17
To better meet the parallel trends assumption, we also use a propensity score matching approach. We
estimate a probit model using Treatment as the dependent variable and several fundamental firm
characteristics (Size, ROA, Leverage, Liquidity, Growth, and SOEs) and year fixed effects as the explanatory
variables. We use the predicted propensity score to match each treated observation with the control
observation. This procedure generates 278 matched pairs. The t-test results for the between-group differences
show that the two groups of firms are very similar in fundamental firm characteristics, indicating that our
matching is effective. We re-estimate this model and find that Enforcement has a significantly positive
coefficient (0.152, t = 2.14), and more importantly, our DID (Enforcement×Treatment×Post) coefficient
continues to be negative and significant (−0.645, t = −1.84).
18
We thank a reviewer for suggesting this matrix to help readers interpret our results. Although the DID
method produces estimates of changes that are more plausible than those based on a single difference, results
from triple interactions are harder to interpret. To simplify the analysis, we regress ETR on Enforcement, Post,
Enforcement×Post, and control variables based only on the sample of firms that changed their political
connection status from zero to one. We find (untabulated) that the coefficients are positive for Enforcement
(t-stat = 1.93, p-value = 0.053) and negative for the interaction variable (t-stat = −2.41, p-value = 0.016),
consistent with the DID results.
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significant. From the second row/column of the matrix, we observe that enforcement
effectiveness declines for firms that evolve from having no political connection to acquiring a
new connection.19 This suggests that firms gain tax-favored treatment in the enforcement
process following the establishment of a political connection.
Second, we examine whether a plausibly exogenous change in the political landscape
moderates the sensitivity of ETRs to a tax audit. Corruption has plagued China since the
economic reforms started in the late 1970s. To crack down on “tigers” (high-level officials)
and “flies” (local civil servants), President Xi Jinping initiated an unprecedented countrywide
anti-corruption campaign that has prosecuted many corrupt officials since 2012. 20 The
campaign signaled an exogenous shock to the rent-seeking and favor-exchanging ability of
both politicians and managers. Undoubtedly, many firms lost their established political ties as
a result of the post-2011 crackdown on corruption. To examine the effect of this political
environment change, we estimate the following regression:
ETRit = β0 + β1Enforcementkt+β2ConnectedBoardit + β3Post2011t
+ β4Enforcementkt×ConnectedBoardit + β5Enforcementkt×Post2011t
+ β6ConnectedBoardit×Post2011t +
β7Enforcementkt×ConnectedBoardit×Post2011t
+ βkXikt + ԑit,
(3)
19
We also consider comparing the difference between firms that undergo a change in their connection status
from one to zero and firms that remain politically connected. We find 175 firms that experience such a change
during our sample period. However, matching unconnected firms with their counterparts on the basis of firm
size and ownership significantly reduces our sample size. We fail to find an incremental increase in the
Enforcement coefficient following political disconnections, probably because the disconnection is due to the
regular turnover (e.g., retirement) of board members, which does not immediately terminate the existing
bureaucratic ties. This is why we next turn to a more powerful test of the effect of political cessations caused
by extrinsic events.
20
According to the Corruption Perceptions Index reported by Transparency International, the corruption index
in China averaged 33.7 points from 1995 to 2015, reaching an all-time high of 40 points in 2013. The Chinese
Communist Party’s 18th Congress held in November 2012 marked a key power transition. The official records
reveal that more than 336,000 party officials at various levels have been investigated or arrested, including 98
vice ministers or officials above that level (http://www.transparency.org/whatwedo/publication/cpi_2014).
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where Post2011 equals one for the 2012–2013 observations and zero for the 2010–2011
observations.21 The incremental cross-sectional and time-series variations in the Enforcement
coefficients are captured by β4, β5, and β7. β4 indicates the incremental difference in the effect
of enforcement on ETR between connected and unconnected firms pre-2011. A negative sign
on β4 is consistent with political ties influencing tax audit outcomes more favorably for
connected firms. β5 captures any temporal change in the effect of enforcement for firms that
are not influenced by regime change. We have no prediction on the sign of this coefficient. β7
captures the incremental difference in the enforcement effectiveness between connected and
unconnected firms in the post- versus pre-2011 period (i.e., β4 + β7 vs. β4) or between the two
periods for connected versus unconnected firms (i.e., β5 + β7 vs. β5). Our predicted sign for
this DID coefficient (β7) is positive, consistent with political regime change causing
connected firms to lose some of their ability to influence government decisions in their favor.
Table 6, panel B1, reports the DID regression results, and panel B2 presents a
two-by-two matrix to show the sensitivity of tax to enforcement classified by political
connectedness in both pre- and post-2011 periods. While environmental change has little
effect on enforcement effectiveness for unconnected firms (first row), its effect on connected
firms is substantial (second row). The two columns of the matrix convey a similar message:
following a regime change in 2012, the political unwinding of the deterrent effect of tax
enforcement has completely diminished (β4 + β7 = 0.046, F-stat = 0.60, p-value = 0.438).
Collectively, our cross-sectional and cross-temporal comparisons reinforce our main results
that the impact of government agencies in constraining tax avoidance weakens after a firm’s
political connection is established and strengthens after this connection is severed/scrutinized.
21
The decision regarding the time frame to examine the effect of an event involves a trade-off: using a longer
window will include more observations thereby increasing the statistical power but will also increase the
confounding effect of other uncontrolled factors (as the environment changes with time), while using a shorter
window will minimize the confounding effect but will include fewer firm-years. We balance this trade-off by
comparing two years before and two years after the event. Our coefficients of interest remain substantially
unchanged, both in levels and in statistical significance, if we use the full sample.
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These results also provide some assurance that our core results are unlikely to suffer from an
endogeneity bias.
Self-selection bias
Our results so far show that the strong positive association between ETR and tax enforcement
is moderated by political connectedness. A serious concern, however, is that our results may
be biased if a firm’s decision to become connected is affected by its tax burden.22 As firms
choose whether and how deeply to connect with politicians, it is possible that a comparison of
the tax sensitivity for firms with versus without a connection biases the estimated sensitivity.
To alleviate the concern of the endogenous choice of establishing political ties, we employ
the Heckman two-stage model, similar to Kim and Zhang (2016). Lennox et al. (2012) stress
the importance of imposing exclusion restrictions in implementing the Heckman model. To
justify the validity of the exclusion restriction, we follow Kim and Zhang (2016) by using an
instrumental variable, Industry % of Connected Firms, defined as the percentage of firms
with a politically connected board in a firm’s industry group. In the first stage, we regress
ConnectedBoard against Industry % of Connected Firms and the same set of control variables
from Table 4 using probit analysis.
Table 7 shows that the instrumental variable loads significantly positive (coeff. = 3.043,
t-stat = 7.82). In spite of our instrument being strongly significant, we acknowledge that the
overall explanatory power of the first stage is modest (R2 = 6 percent).23 Other results
22
We think that a firm’s decision to select a headquarters location is less likely to be affected by its tax burden.
An individual firm is less likely to be able to influence provincial-level tax enforcement through its own effort,
so we do not expect enforcement to be endogenous in our analysis. Tax enforcement would be endogenous to
firm-specific avoidance only to the extent that one single firm is able to influence tax audit decisions in the
region (see Hoopes et al. 2012 for a similar argument), or firms respond to enforcement intensity by relocating
their headquarters from region to region. However, corporate location decisions are more likely to be driven
by exogenous factors (e.g., proximity to customers, suppliers, and production inputs) than by decisions to
escape tax investigation (Hoopes et al. 2012; Loughran 2008); and once determined, the location of the
corporate headquarters seldom changes.
23
To improve the R2 value, we consider other predictors known to be associated with political ties but not with
effective taxes. We employ distance to the provincial capital because the closer the capital, the greater the
likelihood to enter politics (e.g., Guedhami et al. 2014; Kim and Zhang 2016). We consider market
development and business competition at the regional level because as markets develop, competition between
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indicate that larger and more profitable firms and firms with less leverage and a lower
government stake are more likely to seek political ties. In the second stage, when we include
the
inverse
Mills
ratio
(Inverse
Mills
Ratio),
the
coefficient
on
Enforcement×ConnectedBoard continues to load negatively at the 5 percent level (coeff. =
−0.058, t-stat = −2.18).
Notwithstanding the foregoing analysis, we stress that we do not attempt to identify a
causal effect of political influence on tax burdens. Rather, we test whether such influence
affects tax avoidance by shaping the enforcement action of tax authorities, and we conduct
our test through an interaction variable. We think the interaction term is less prone to induce
endogeneity, unless the individual firm has full control over the joint outcome of the two
variables.
The mechanism through which firms avoid taxes
We next focus on the most common type of tax aggressiveness within China—domestic
income shifting within a book-consolidated business group—to examine whether political
relations weaken the effectiveness of enforcement against tax-motivated income shifting.24
Separate tax reporting by member firms creates strong tax incentives for them to shift income
firms intensifies and the uncertainty facing firms increases, motivating firms to develop ties to bureaucrats in
order to reduce this uncertainty (Haveman et al. 2017). Regrettably, we do not find significant improvement to
the R2 value. As the interpretations of the significant variables are the same for both high and low R2 models,
we instead pay more attention to the appropriateness of the IV and the presence of multicollinearity in the
second-stage model. Our IV is an industry-level variable exogenous to an individual firm’s influence; its effect
on the first-stage dependent variable is significant (coeff = 3.043, t = 7.82); and it has low correlation with the
ETR (0.009), suggesting this IV is valid according Lennox et al. (2012) and Larcker and Rusticus (2010). We
also find that the VIFs are 1.08 for ConnectedBoard and 9.07 for Inverse Mills Ratio, implying that the model
does not face serious multicollinearity problem according to Greene (2008). Nonetheless, we acknowledge our
compromises and limitations in pursuit of answering interesting questions (Bloomfield et al. 2016) about the
effect of political ties.
24
Most income-shifting studies have been done at the international level. Subnational research has the
advantage that, while tax rates may differ across regions and industries, nontax factors are more homogenous
between regions than between nations (Gupta and Mills 2002). Relative to multinational income shifting,
subnational shifting is less costly and easier to execute because income shifters are not constrained by import
and export tariffs, foreign exchange controls, or dividend repatriation restrictions. Although prior studies
provide extensive evidence on the tax effect of both types of income shifting, our study permits granular detail
on enforcement and political connections that would be much harder to obtain across multiple countries.
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within the group. Chinese tax authorities employ many ways to detect and deter tax
avoidance via transfer pricing. For example, recent tax returns require nine transfer
pricing-related forms that report the amounts and types of related party transactions and the
methodologies the firm uses to arrive at an arm’s length price. When selecting targets for
transfer pricing audits, tax authorities focus on firms with the following characteristics: (i) a
significant amount or numerous types of related party transactions; (ii) consecutive losses,
low profitability, or a fluctuating pattern of profit/loss; (iii) profitability lower than others in
the same industry; (iv) business dealings with a related party in a tax haven; and (v) lack of
tax return disclosures relating to transfer pricing. 25 However, they pay less attention to
domestic income shifts that are taxed at the same rate (SAT [2009], Circular 2). Tax audits
conclude with a final assessment that includes the proposed adjustment, interest levy, and
penalties (Chan et al. 2010). The high ex ante threat of a tax audit, heavy penalties, and ex
post incidence of closer monitoring should deter firms from taking aggressive tax positions.
We obtain domestic related party operating transactions from the CSMAR database.
Using the firm-years for our main results as the starting point, we delete observations with no
related transactions and no tax rate differentials within the group. We also exclude
observations whose related party transactions exceed consolidated total assets. Our reduced
sample for this test contains 7,869 observations. Following Jacob (1996), we model a firm’s
opportunity to shift income for tax reasons as a joint function of the spread in tax rates among
members of the consolidated group and the magnitude of intragroup transactions. This design
is important because firms would have little tax incentives to shift profits if such rate differences do
25
In the United States, the IRS instructs its agents to scrutinize firms reporting significant book-tax differences
(U.S. Department of the Treasury 1999). Mills (1998) documents a positive association between book-tax
differences and proposed tax audit adjustments. The largest U.S. firms are subject to a higher audit rate
(Hoopes et al. 2012). In the Chinese context, Lennox et al. (2015) find that firms are more likely to receive a
tax audit if they report low ETRs, large book-tax differences, and negative discretionary accruals. Chan et al.
(2010) find that the larger the difference between book and tax incomes, the larger the adjustment of tax
audits. Lin et al. (2014) find that private firms are more likely than public firms to make income-decreasing
accruals when the tax rate is about to decrease. To the extent that income-decreasing accruals signal a
potential underreporting of taxable incomes, they are more likely to trigger a tax audit. However, our
book-based ETR measure will not directly address avoidance via book-tax differences.
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not exist; or even if they do, group firms have little or no intragroup transactions (we find neither
component alone is sufficient to explain ETRs). We estimate the following model to examine
whether enforcement constrains tax-induced income shifting and political influence reduces
that constraint:
ETRit = β0 + β1Shiftingit + β2Enforcementkt + β3ConnectedBoardit
+ β4Shiftingit×Enforcementkt + β5Shiftingit×ConnectedBoardit
+ β6Enforcementkt×ConnectedBoardit
+ β7Shiftingit×Enforcementkt×ConnectedBoardit + βkXikt + ԑit,
(4)
where Shifting is a product of the difference between the highest and lowest statutory tax
rates faced by any group member (i.e., the rate range) and the aggregate dollar amount of
related transactions (scaled by the lagged assets of the group). As the rate range is highly
skewed with a break in the distribution around 90th percentile, prior to computing Shifting we
transform the range into a dummy variable that equals one if the group is in the top decile of
the rate range and zero otherwise. To facilitate interpretation, we convert Enforcement into an
indicator variable: for each year during our sample, we classify provinces with the
enforcement value falling into the upper quartile of the sample distribution as regions with
strong tax enforcement (= 1) and the remaining three quartiles as weak enforcement regions
(= 0). We obtain (untabulated) the same inferences when we use a continuous variable. Other
variables are as previously defined. Our ex ante prediction about the coefficients of interest is
as follows.
The baseline coefficient measures the association between Shifting and ETR for
unconnected firms in weak enforcement regions; a negative β1 means greater Shifting is
associated with lower ETR. β4 measures the incremental effect of tax enforcement and should
have a positive sign if unconnected firms with greater Shifting are associated with
incrementally smaller declines in ETR when enforcement becomes stronger. β5 indicates a
change in this association for connected firms versus unconnected firms in regions with weak
enforcement. We have no prediction on the sign of this coefficient because firms have less of
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a need to exploit their political ties for tax benefits when government enforcement is
ineffective. The main coefficient of interest for testing whether political ties weaken the
effectiveness of government enforcement against tax-induced income shifting is β7. This
represents the incremental association between Shifting and ETR for connected versus
unconnected firms in strong enforcement regions or for connected firms in regions with
strong versus weak enforcement. Our predicted sign for this coefficient is negative, consistent
with firms establishing political ties to influence the outcomes of tax enforcement actions in
their favor.
Table 8 (panel A) reports how tax enforcement and political connections separately and
jointly moderate the firm’s incentive to shift income for tax purposes. Across all
specifications, the coefficients that exhibit statistical significance in the predicted direction
are β1, β2, β4, and β7. They indicate that government enforcement mitigates or inhibits
income-shifters’ capability to avoid tax, while political ties enhance it. Panel B presents
whether the Shifting coefficients vary according to political connectedness and enforcement
strength. The matrix shows evidence similar to our core results: corporate political ties can
make tax enforcement less effective in constraining tax avoidance (an incremental difference
of 0.018), or put differently, government enforcement is less effective for firms with a
politically connected board (an incremental difference of 0.160).
6.
Sensitivity tests and other supplemental analysis
We evaluate the sensitivity of our main results to different definitions of our dependent and
test variables. For brevity, we do not tabulate these results.
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We first confirm that our results are not sensitive to alternative measures of ETR for the
dependent variable. We estimate annual cash ETRs (CashETR) using the ratio of cash taxes
paid to pre-tax income.26 We estimate the three-year book ETR (3-year BookETR) using the
ratio of the three-year sum of income tax expense to the three-year sum of pre-tax income in
years t, t+1, and t+2. Using 3-year BookETR avoids significant year-to-year variation in the
annual ETR (Dyreng et al. 2008; Kim and Zhang 2016) and also reflects the likelihood that
the tax bureau may audit additional years if it discovers underreporting in a certain year. We
similarly estimate a 3-year CashETR. We also use 3-year adjusted BookETR following
Balakrishnan et al. (2014) and Kim and Zhang (2016) by subtracting each firm’s 3-year
BookETR from the average 3-year BookETR for firms in the same industry and quartile of
total assets. An unusually low ETR compared to similar firms can be viewed as more tax
aggressive (Balakrishnan et al. 2014), and a positive value of the adjusted ETR implies more
tax avoidance because that firm reports less tax than its size-industry peers. Our results are
not sensitive to these alternative ETR measures.
We also evaluate alternative measures of the explanatory variables. Our results are robust
to using the one-year lagged enforcement data or to using the raw enforcement data rather
than the more complex principal component analysis. Results are qualitatively similar when
redefining Enforcement using the number of income tax audits conducted scaled by the
number of income tax returns filed in a given region/year, the number of income tax cases
prosecuted scaled by the number of income tax returns filed in a given region/year, the
percentage of tax officers with a professional qualification, or the sum of additional tax
payments and penalties, interest, and surcharges scaled by regional tax revenues.
26
As Chinese firms do not disclose cash income tax payment information, we estimate this amount by taking
total tax expense plus beginning taxes payable minus ending taxes payable, following Bradshaw et al. (2016).
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Finally, we apply propensity score matching to help dispel the concern that differences in
firm attributes between firms with and without political relations are spuriously responsible
for our core results. We first estimate a logistic regression using ConnectedBoard as the
dependent variable.27 Using a predicted propensity score from this logistic regression, we
then match (without replacement) each connected firm with a nonconnected firm based on
the closest propensity score within a caliper distance of 10 percent. We are able to identify
3,528 matched pairs. Our main results are invariant to this control for endogeneity issues
associated with political relations.
7. Conclusion
There has been a call to contribute to the scant literature examining the interaction between
politics and tax enforcement agencies susceptible to political influence. We answer this call
directly by studying whether political ties in the form of board interlocks weaken the ability
of revenue agencies to hamper tax avoidance. Using aggregate government data, we develop
a new composite measure of tax enforcement that captures the geographic and annual
variation in the probability of detecting and prosecuting tax avoiders, the sufficiency and
expertise of tax auditors, and the consequences and penalties of tax noncompliance. We find
that while audit coverage and penalty rates deter an individual firm from tax avoidance, their
deterrent effects are significantly undermined in the presence of a politically connected board
of directors. Using an event study approach that mitigates the concerns over endogeneity, we
find robust evidence that the ability of firms to affect their tax burden is higher following the
addition of politically affiliated board members, but lower after a change in political
environment that constrains the ability of politicians and firm managers to exchange favors.
The independent variables include various firm characteristics (i.e., Industry % of Connected Firms,
cross-listing, size, profitability, leverage, government shareholding) known to affect the likelihood of political
ties in the prior literatures (e.g., Faccio 2006; Kim and Zhang 2016). We also expand our model to include
other firm characteristics (e.g., growth, accruals, cash holding) and variables that proxy for regional economic
and institutional conditions. In this extended model, we match 3,516 pairs and obtain very similar results
(untabulated).
27
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Our study contributes to the scarce literature by providing evidence that because of political
influence, revenue agencies subject the tax aggressiveness of connected firms to less scrutiny
and light punishment, making their tax burden significantly lower than the statutory rate.
To better understand the mechanism underlying tax avoidance, we use income-shifting
opportunities to shed light on whether tax enforcement constrains this form of noncompliance
while political connections assist it. We exploit the unique situation in which differently
taxed entities within a consolidated group file their tax returns as independent legal entities in
China to examine the extent to which taxation motivates the shifting of income within the
affiliated group. We find evidence consistent with our main hypothesis. This additional
analysis contributes to the very limited amount of literature addressing the combined effects
of tax-induced income shifting, the deterrent role of tax authorities in this shifting, and the
political unwinding of this role.
Politically controlled economies represent a significant portion of the world economy
(Faccio 2006), and the role of politics in shaping the government-business relationship is
much more salient in the relations-based Chinese environment than the market-based U.S.
environment. From a policy viewpoint, our findings imply that because the administrative
effectiveness of tax systems in developing countries to protect revenues is influenced by both
formal (e.g., the justice system and tax enforcement) and informal (e.g., personal
relationships and social networks) institutions, successful tax reform should not be limited to
strengthening the tax administration within the revenue system. Reforms must also
encompass a country’s political and business environments, including government-business
relations, the political economy of regulatory agencies, and the incentives and behavior of
different actors within and outside the tax system.
Our study faces several limitations, each of which represents a potential research agenda.
First, as our analysis is based on the conventional estimation of ETR, the usual caution
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applies in interpreting the results (Hanlon and Heitzman 2010). For example, our estimation
of ETR does not pick up conforming tax avoidance because the measure uses book income as
the denominator and we cannot generalize our inferences to unprofitable firms because firms
with negative pre-tax income are excluded. Second, because the tax authority does not
publicly disclose firm-level audit data, our tests of political connections on tax enforcement
are based on aggregated audit data and hence are indirect. Third, to the extent that our
empirical strategies are unlikely to completely solve the endogeneity issue, we urge readers to
be cautious in interpreting our results. Finally, although we try to ensure that political
connections are sought by the firm, rather than imposed by the government as in SOEs, we
are unable to tease out the effect of government ownership from that of political connections.
As most Chinese listed firms have some government ownership, local governments play dual
but conflicting roles as both state tax collector and residual income claimant of their
controlling firm. This represents a study in itself: how does the government enforce taxes
when it is also the claimant of a firm’s before- and after-tax income? Future work can
examine how local governments in a transition economy such as that of China trade off their
tax collection efforts and tax avoidance incentives.
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Figure 1 Mean tax enforcement for each province (2003–2013)
Notes: To help readers understand the enforcement intensity across geographic locations, we use
four shades to show how many provinces fall into each quartile of Enforcement for the period
2003–2013. Provinces in the darkest shade are those with the highest enforcement level. Firms in
these regions are most likely to be audited, confront a well-trained and skilled audit workforce, and
face the toughest fines for underreporting tax income.
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Appendix 1
Principal component analysis proxies for tax enforcement
We use aggregate data from the China Tax Audits Yearbook (2003–2013) to construct our tax
enforcement measures. The yearbook is published annually by the SAT and contains detailed tax
enforcement information for each provincial (SAT and local) tax bureau in China. Specifically,
the disclosed information includes the quantity of (i) permanent employees, (ii) tax inspectors,
and (iii) employees with a bachelor’s degree and/or a professional qualification (certified
accountants, certified tax agents, and lawyers) and their Communist Party membership and age
range; (iv) corporate taxpayers; (v) corporate tax returns audited; (vi) audit departments; (vii)
suspicious cases; (viii) cases prosecuted; (ix) cases closed; (x) regional tax revenue; (xi) tax
deficiencies settled; (xii) overdue tax surcharges; and (xiii) tax penalties, interest, and fines. If
information about a specific item is missing for a certain year, we replace the missing information
with data from earlier or later years if it is not available from other sources.
To extract common factors from a pooled data set, we use principal component analysis, a
tool used to analyze exploratory (rather than confirmatory) data and adopted in many prior
studies (Dechow et al. 1996; Bushee 1998; Kim and Zhang 2016). We construct nine ratios using
the data described above. Specifically, Ratio 1 and Ratio 2 are the audit coverage and the
prosecution rate, respectively, while Ratio 3 captures the manpower sufficiency to monitor listed
firms. Ratio 4 to Ratio 7 reflect the educational and professional level of the tax officers in a
region, and Ratio 8 and Ratio 9 measure the economic consequences of the tax audits in terms of
additional tax payments and monetary punishment for tax underreporting. To mitigate the
difficulty of drawing conclusions from one ratio at a time and to alleviate the multicollinearity
concern of using multiple ratios in the same regression, we perform principal component analysis
with an oblique (rather than an orthogonal) rotation to identify and interpret common factors.
This procedure reduces the dimensionality of the data by combining the nine individual aspects
into a parsimonious set of linear combinations that explain the shared variance among the
characteristics.
Our analysis produces three common factors with an eigenvalue greater than one. These
three factors retain 69 percent of the total variance of the original data. For ease of interpretation,
we associate each factor with those variables that have post-rotation loadings larger than 0.60 in
absolute value and are statistically different from zero at conventional levels. Factor 1 is
associated with the number of tax audits divided by the number of tax returns filed in the same
region (factor loading = 0.917) and the number of cases prosecuted divided by the number of tax
returns filed (factor loading = 0.933). Clearly, Factor 1 represents the probability of facing a tax
audit and the likelihood of being prosecuted in a region-year. Labeled Probability, this factor
explains the largest proportion (33 percent or three units) of the total variance.
Factor 2 is associated with the percentage of tax officers holding the qualification of certified
public accountant (factor loading = 0.877), certified tax agent (factor loading = 0.848), or lawyer
(factor loading 0.716). This factor, which we label Expertise, measures the proficiency of tax
officers and explains 22 percent of the total variance. Higher percentages represent higher-quality
audit personnel in the region, and, plausibly, a greater likelihood that tax misreporting will be
detected. Factor 3 is associated with the dollar value of tax deficiencies settled (factor loading =
0.906) and the dollar value of surcharges, penalties, and fines (factor loading = 0.876),
respectively, over the amount of regional tax revenue. This factor, labeled Outcome, measures the
outcomes or consequences of tax audits and explains 14 percent of the total variance. As the
remaining factors account for only trivial amounts of variance, they are not retained.
To form an aggregate enforcement measure, which we label Enforcement, we first rank each
factor into percentile groups and then calculate the mean ranking of the three factors for each
region and year (2003–2013). A higher aggregate value indicates higher enforcement intensity.
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Appendix 2
Variable definitions and construction
Dependent variable
ETR
GAAP effective tax rate, annual income tax expense scaled by annual pre-tax income before
special items
Explanatory variables
Tax enforcement measures at the regional-year level:
Input variables
Ratio 1
Number of corporate income tax audits conducted over the number of corporate income tax
returns filed
Ratio 2
Number of income tax cases prosecuted over the number of income tax returns filed
Ratio 3
Number of tax officers over the number of listed firms
Ratio 4
Number of tax officers with a bachelor’s degree or higher over the number of tax officers
Ratio 5
Number of tax officers with a CPA qualification over the number of tax officers
Ratio 6
Number of tax officers with a certified tax agent qualification over the number of tax
officers
Ratio 7
Number of tax officers with a lawyer qualification over the number of tax officers
Ratio 8
Amount of tax deficiencies settled over the amount of regional tax revenues
Ratio 9
Amount of penalties, interest, and fines over the amount of regional tax revenues
Factor extraction and variables used in regression analysis
Factor 1
Probability of tax audits, measured by ratios 1, 2, and 3
Factor 2
Expertise of tax officers, measured by ratios 4 to 7
Factor 3
Outcome or consequence of tax audits, measured by ratios 8 and 9
Enforcement
Average percentile ranking of factors 1 to 3
Political connection measures at the firm-year level:
ConnectedChair One if the chairman of the board of directors is politically connected and zero otherwise
ConnectedBoard One if the number of politically connected board members is above the industry-year
median and zero otherwise
Connected#
Natural logarithm of one plus the number of connected members on board
Connected%
Percentage of connected board members on board
Control variables
ROA
Ratio of consolidated net income to consolidated total assets
Std. dev. of ROA
Standard deviation of ROA
Size
Natural logarithm of year-end total assets
Liquidity
Ratio of current assets to current liabilities
Leverage
Ratio of total debts to total assets
PPE
Ratio of property, plant, and equipment to total assets
Growth
Ratio of market value to book value of net assets
Intangible
Ratio of intangible assets to total assets
Inventory
Ratio of inventory to total assets
Cash
Ratio of year-end cash holdings to lagged assets
Accruals
Discretionary current accruals estimated from the modified cross-sectional Jones model
Ownership
Percentage of shares owned by the government
GDP
Natural logarithm of provincial annual GDP
Institution
Index that reflects institutional characteristics of a province, based on the National
Economic Research Institute (NERI) Index of Marketization of China’s provinces
LeaderTurnover
One if the province where the firm is headquartered experiences a governor or party
secretary turnover and zero otherwise
Industry
Set of dummy variables indicating industry sector membership based on the CSRC
classification
Year
Set of dummy variables indicating years
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TABLE 1
Sample distribution by year and industry
2003 2004
A. Agriculture
13
13
B. Natural resources
2005
16
2006
17
2007
16
2008
17
2009
15
2010
21
2011
16
2012
21
2013
17
All
182
%
1.637
23
27
24
26
34
34
40
42
44
45
46
385
3.462
482
504
498
520
538
454
488
543
538
535
558
5,658
D. Utilities
52
52
56
55
60
43
60
60
57
61
67
623
50.87
7
5.602
E. Construction
12
15
18
20
20
19
21
23
24
26
29
227
2.041
F. Transportation
42
45
47
46
55
50
53
58
61
61
60
578
5.197
G. Information technology
43
47
38
49
49
39
47
52
56
55
55
530
4.766
H. Wholesale and retail
82
86
83
85
91
82
85
89
90
92
95
960
8.632
J. Real estate
84
78
74
83
91
88
103
104
108
106
110
1,029
9.253
K. Services
31
34
28
31
35
32
29
36
35
38
42
371
3.336
L. Communication
14
14
10
9
14
11
10
16
17
20
21
156
1.403
M. Conglomerates
42
37
33
36
38
34
38
37
44
41
42
422
3.795
920
952
925
977
1,041
903
989
1,08
1
1,090
1,101
1,142
11,121
100
C. Manufacturing
Total
Notes:
This table presents the sample distribution by year and industry following the 13 CSRC industry classifications.
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TABLE 2
Descriptive statistics of main variables
Mean
P25
Median
P75
Std. dev.
ETR
0.233
0.135
0.205
0.293
0.158
Tax enforcement measures
Input variables
Ratio 1
0.016
0.003
0.009
0.021
0.020
Ratio 2
0.012
0.003
0.005
0.013
0.016
Ratio 3
16.863
6.043
12.500
22.917
13.682
Ratio 4
0.924
0.903
0.935
0.958
0.049
Ratio 5
0.015
0.009
0.014
0.020
0.009
Ratio 6
0.059
0.038
0.046
0.076
0.038
Ratio 7
0.006
0.002
0.004
0.008
0.007
Ratio 8
0.013
0.006
0.010
0.017
0.011
Ratio 9
0.002
0.001
0.001
0.002
0.001
Factor extraction and variables used in regression analysis
Audit Probability (Factor 1)
-0.168
-0.785
-0.486
0.137
0.925
Auditor Expertise (Factor 2)
0.547
-0.159
0.403
1.087
1.107
Audit Outcome (Factor 3)
-0.023
-0.623
-0.304
0.308
0.940
Enforcement (All factors)
0.511
0.372
0.517
0.606
0.152
Political connection measures
ConnectedChair
0.231
0.000
0.000
0.000
0.421
ConnectedBoard
0.343
0.000
0.000
1.000
0.475
Connected#
0.064
0.000
0.693
1.099
0.559
Connected%
8.749
0.000
7.143
13.333
9.244
Notes:
The sample statistics are based on 11,121 firm-year observations from 2003 to 2013. See Appendix 2
for variable definitions and construction.
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TABLE 3
Univariate tests
All firms
Connected
Nonconnected
Panel A: Comparison of the mean ETR between connected and nonconnected firms
1st quartile Enforcement (weakest)
0.225
0.224
0.225
2nd quartile Enforcement
0.232
0.228
0.231
3rd quartile Enforcement
0.238
0.231
0.243
4th quartile Enforcement (strongest)
0.238
0.230
0.245
Difference
Panel B: Comparison of the mean tax enforcement between connected and nonconnected firms
Audit Probability
-0.168
-0.183
-0.160
Auditor Expertise
0.547
0.550
0.546
Audit Outcome
-0.023
-0.043
-0.013
Enforcement (overall)
0.511
0.512
0.510
-0.001
-0.003
-0.012*
-0.015**
-0.023
0.004
-0.031
0.002
Panel C: Comparison of the mean firm characteristics between connected and nonconnected firms
ROA
0.045
0.049
0.042
0.007***
Std. dev. of ROA
0.030
0.028
0.031
-0.003**
Size
21.890
22.258
21.699
0.558***
Liquidity
1.568
1.572
1.566
0.006
Leverage
0.504
0.508
0.502
0.006
PPE
0.275
0.269
0.279
-0.009**
Intangible
0.043
0.043
0.044
-0.001
Growth
3.171
3.031
3.243
-0.213***
Inventory
0.182
0.187
0.180
0.007**
Cash
0.160
0.163
0.159
0.005**
Accruals
0.012
0.016
0.010
0.006***
Ownership
0.206
0.198
0.211
-0.012**
GDP
9.454
9.444
9.460
-0.016
Institution
7.547
7.588
7.526
0.061
LeaderTurnover
0.358
0.361
0.354
0.007
N
11,121
3,810
7,311
Notes:
This table shows the differences between the two sample means for ETR, tax enforcement, and firm
characteristics and the two-sample t-test results. Politically connected firm-years are defined as those
firm-years that the number of politically connected board members is above the industry-year median.
See Appendix 2 for variable definitions and construction. ***, **, and * indicate significance at the 0.01,
0.05, and 0.10 levels, respectively.
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TABLE 4
The effect of tax enforcement on firms’ ETR
(1)
Audit Probability
0.006**
(2.16)
Auditor Expertise
Audit Outcome
(2)
(3)
0.004*
(1.94)
0.009***
(3.22)
(4)
0.006**
(2.06)
0.004*
(1.87)
0.009***
(3.21)
Enforcement
ROA
St. dev. of ROA
Size
Liquidity
Leverage
PPE
Intangibles
Growth
Inventory
Cash
Accruals
Ownership
GDP
Institution
LeaderTurnover
Intercept
Year fixed effects
Industry fixed effects
N
Adj. R2
F-statistic
-1.247***
(-18.03)
-0.051
(-1.14)
-0.003
(-1.18)
0.001
(0.46)
0.011
(0.57)
0.060***
(3.74)
0.099***
(2.64)
0.003***
(2.85)
0.127***
(5.16)
0.106***
(4.50)
-0.057***
(-3.55)
-0.006
(-0.68)
0.023***
(5.24)
-0.005***
(-2.63)
-0.003
(-0.85)
-0.015
(-0.25)
Yes
Yes
11,121
0.158
22.62
-1.254***
(-18.13)
-0.048
(-1.07)
-0.003
(-1.07)
0.001
(0.40)
0.009
(0.47)
0.058***
(3.66)
0.096**
(2.56)
0.003***
(2.95)
0.127***
(5.14)
0.103***
(4.38)
-0.056***
(-3.53)
-0.005
(-0.56)
0.023***
(5.27)
-0.007***
(-3.18)
-0.002
(-0.76)
-0.008
(-0.14)
Yes
Yes
11,121
0.158
22.98
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-1.251***
(-18.04)
-0.050
(-1.11)
-0.002
(-0.87)
0.001
(0.35)
0.009
(0.43)
0.058***
(3.62)
0.090**
(2.40)
0.003***
(2.97)
0.126***
(5.14)
0.105***
(4.47)
-0.057***
(-3.58)
-0.002
(-0.23)
0.021***
(4.85)
-0.004*
(-1.76)
-0.002
(-0.58)
-0.030
(-0.51)
Yes
Yes
11,121
0.159
22.89
-1.250***
(-18.07)
-0.049
(-1.08)
-0.002
(-0.84)
0.001
(0.48)
0.010
(0.50)
0.058***
(3.68)
0.092**
(2.47)
0.003***
(3.06)
0.128***
(5.24)
0.105***
(4.49)
-0.056***
(-3.52)
-0.003
(-0.34)
0.022***
(5.10)
-0.004**
(-2.15)
-0.002
(-0.76)
-0.040
(-0.68)
Yes
Yes
11,121
0.160
22.21
(5)
0.047***
(3.43)
-1.249***
(-18.05)
-0.049
(-1.10)
-0.002
(-0.96)
0.001
(0.45)
0.010
(0.48)
0.058***
(3.64)
0.096**
(2.56)
0.003***
(2.96)
0.128***
(5.22)
0.105***
(4.48)
-0.056***
(-3.51)
-0.005
(-0.58)
0.022***
(5.05)
-0.005***
(-2.64)
-0.002
(-0.75)
-0.014
(-0.24)
Yes
Yes
11,121
0.159
23.01
Notes:
This table reports the regression results for the effect of aggregate and dimensional tax enforcement on firms’
ETR. t-statistics are reported in parentheses below the coefficient. All variables are defined in Appendix 2. ***,
**, and * indicate significance at the 0.01, 0.05, and 0.10 levels (two-tailed), respectively.
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TABLE 5
The effect of tax enforcement on firms’ ETR conditional on political connectedness
ConnectedChair
ConnectedBoard
Connected#
(1)
(2)
(3)
***
***
Enforcement (β1)
0.059
0.066
0.076***
(3.71)
(3.88)
(3.46)
Connected (β2)
0.002
0.004
0.005
(0.33)
(0.83)
(1.22)
-0.051*
-0.058**
-0.044*
Enforcement×Connected (β3)
(-1.69)
(-2.17)
(-1.85)
ROA
-1.251***
-1.250***
-1.251***
(-18.07)
(-18.01)
(-18.01)
-0.051
-0.051
-0.050
St. dev. of ROA
(-1.12)
(-1.12)
(-1.11)
Size
-0.002
-0.003
-0.003
(-1.05)
(-1.16)
(-1.33)
Liquidity
0.001
0.001
0.001
(0.49)
(0.48)
(0.51)
Leverage
0.010
0.011
0.012
(0.51)
(0.55)
(0.61)
PPE
0.059***
0.060***
0.060***
(3.70)
(3.76)
(3.75)
Intangibles
0.096**
0.096**
0.095**
(2.55)
(2.56)
(2.55)
Growth
0.003***
0.003***
0.003***
(2.97)
(2.93)
(2.89)
***
***
Inventory
0.129
0.130
0.129***
(5.25)
(5.30)
(5.27)
Cash
0.105***
0.105***
0.105***
(4.50)
(4.50)
(4.50)
Accruals
-0.056***
-0.057***
-0.057***
(-3.54)
(-3.57)
(-3.60)
Ownership
-0.005
-0.005
-0.005
(-0.49)
(-0.52)
(-0.49)
GDP
0.022***
0.023***
0.023***
(5.11)
(5.16)
(5.15)
Institution
-0.005***
-0.005***
-0.005***
(-2.70)
(-2.73)
(-2.75)
LeaderTurnover
-0.002
-0.002
-0.002
(-0.77)
(-0.78)
(-0.78)
Intercept
-0.012
-0.009
-0.002
(-0.20)
(-0.15)
(-0.03)
Year and industry fixed effects
Yes
Yes
Yes
N
11,121
11,121
11,121
Adj. R2
0.159
0.160
0.160
F-statistic
22.09
22.11
22.02
Test of joint significance (columns 1 and 2)
Coeff.
F-stat
Coeff.
F-stat
β 1 + β3 = 0
0.008
0.10
0.008
0.14
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Connected%
(4)
0.068***
(3.53)
0.000
(1.61)
-0.002*
(-1.69)
-1.252***
(-18.04)
-0.050
(-1.11)
-0.003
(-1.35)
0.001
(0.52)
0.012
(0.62)
0.060***
(3.77)
0.095**
(2.55)
0.003***
(2.87)
0.129***
(5.27)
0.105***
(4.50)
-0.058***
(-3.62)
-0.004
(-0.41)
0.023***
(5.18)
-0.005***
(-2.77)
-0.002
(-0.79)
-0.002
(-0.04)
Yes
11,121
0.160
22.05
Notes:
This table reports the regression results for the (aggregate) tax enforcement effect on firms’ ETR conditional on
different measures of political connections. t-statistics are reported in parentheses. All variables are defined in
Appendix 2. ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels (two-tailed), respectively.
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TABLE 6
The effect of changes in political connections
Panel A1: The effect of gaining a new connection
Enforcement (β1)
0.076***
(2.76)
Treatment (β2)
0.001
(0.11)
Post (β3)
0.003
(0.39)
-0.019
Enforcement×Treatment (β4)
(-0.49)
0.002
Enforcement×Post (β5)
(0.05)
-0.016*
Treatment×Post (β6)
(-1.83)
-0.111
Enforcement×Treatment×Post (β7)
(-1.85)*
Control variables
Yes
Year fixed effects
Yes
Industry fixed effects
Yes
N
3,656
Adj. R2
0.128
F-statistic
15.96
Panel A2: Tests of coefficient differences
Pre-period Post-period
Diff.
β1
β1+β5
β5
Control firms
Enforcement×ConnectedBoard×Post2011
(β7)
Control variables
Year fixed effects
Industry fixed effects
N
Adj. R2
F-statistic
Panel B2: Tests of coefficient differences
Pre-2011 Post-2011
β1
β1+β5
Unconnected firms
Treatment firms
Connected firms
Diff.
= 0.076***
β1+β4
= 0.057**
β4
= -0.019
= 0.078**
β1+β4+β5+β7
= -0.052
β4+β7
= -0.130***
= 0.002
β5+β7
= -0.109***
β7
= -0.111*
Panel B1: The effect of a regime change
Enforcement (β1)
ConnectedBoard (β2)
Post2011 (β3)
Enforcement×ConnectedBoard (β4)
Enforcement×Post2011 (β5)
ConnectedBoard×Post2011 (β6)
Diff.
0.053*
(1.76)
-0.003
(-0.35)
0.017**
*
(3.10)
-0.120*
**
(-2.61)
-0.061
(-1.53)
0.006
(0.70)
0.166**
(2.41)
Yes
No
Yes
4,414
0.186
14.916
Diff.
β5
= 0.053*
= -0.008
= -0.061
β1+β4
β1+β4+β5+β7 β5+β7
= -0.067*
= 0.038
= 0.105*
β4
β4+β7
β7
= -0.120***
= 0.046
= 0.166**
Notes:
This table reports the regression results for the effect of changes in political connections. The same set of control
variables as in Table 5 is included in both regressions, but the results are not reported here for brevity. Panels A1
and B1, respectively, show the results on the effect of obtaining a new connection and changing political
landscape. Panels A2 and B2, respectively, show the coefficient differences between the two groups of firms
across time. t-statistics are reported in parentheses. Treatment is an indicator variable that equals one for firms that
increase their political connections, and zero otherwise. Post equals one for firm-years in the post-change period,
and zero otherwise. Post2011 equals one for the 2012–2013 observations and zero for the 2010–2011
observations. Other variables are defined in Appendix 2. ***, **, and * indicate significance at the 0.01, 0.05, and
0.10 levels (two-tailed), respectively.
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TABLE 7
Heckman two-step treatment effects procedure to correct for endogeneity
First Stage
DV = ConnectedBoard
Enforcement
0.218
(1.32)
ConnectedBoard
Enforcement×ConnectedBoard
Inverse Mills Ratio
Industry % of Connected Firms
Intercept
3.043***
(7.82)
-6.002***
(-7.87)
Yes
Yes
Yes
11,121
0.057
217.7
Second Stage
DV = ETR
0.069***
(4.01)
0.004
(0.87)
-0.058**
(-2.18)
0.015
(0.79)
-0.074
(-0.76)
Yes
Yes
Yes
11,121
0.160
21.70
Control variables
Year fixed effects
Industry fixed effects
N
Pseudo/Adjusted R2
Wald χ2
Notes:
This table presents the regression results of the Heckman two-step procedure. The same set of control
variables as in Table 5 is included in both regressions, but the results are not reported here for brevity.
t-statistics are reported in parentheses. Industry % of Connected Firms is defined as the percentage of
firms with a politically connected board in a firm’s industry group. Other variables are defined in
Appendix 2. *** and ** indicate significance at the 0.01 and 0.05 levels (two-tailed), respectively.
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TABLE 8
Panel A: Joint effect of political connections and tax enforcement on income shifting
(1)
(2)
Shifting (β1)
-0.063***
-0.081***
(-3.13)
(-4.07)
Enforcement (β2)
0.007*
(2.08)
ConnectedBoard (β3)
Shifting×Enforcement (β4)
Shifting×ConnectedBoard (β5)
Enforcement×ConnectedBoard (β6)
Shifting×Enforcement×ConnectedBoard (β7)
Intercept
0.072***
(3.50)
(3)
-0.068**
(-2.69)
0.012*
(1.89)
0.007*
(1.90)
0.143***
(3.21)
-0.035
(-0.92)
-0.013
(-1.23)
-0.125*
(-1.86)
0.086
(1.68)
Yes
7,869
0.155
16.514
0.088
0.083
(1.73)
(1.61)
Control variables
Yes
Yes
N
7,869
7,869
Adj. R2
0.154
0.154
F-statistic
18.172
17.690
Panel B: Tests of coefficient differences between groups
Weak enforcement
Strong enforcement
Diff.
Unconnected firms
β1
β1 + β4
β4
= -0.068**
= 0.075
= 0.143***
Connected firms
β1 + β5
β1 + β4 + β5 + β7
β4 + β7
***
***
= -0.103
= -0.085
= 0.018
Diff.
β5
β5 + β7
β7
= -0.035
= -0.160***
= -0.125*
Notes:
This table reports the regression results of the joint effect of political connections and tax enforcement
on income shifting (panel A) and the test for the coefficient differences between connected and
unconnected firms (panel B). The same set of control variables as in Table 5 is included in all the
regressions but not reported here for brevity. t-statistics are reported in parentheses. Enforcement is an
indicator variable that equals one if the province’s estimated Enforcement value is in the top quartile in
that year, and zero otherwise. Other variables are defined in Appendix 2. ***, **, and * indicate
significance at the 0.01, 0.05, and 0.10 levels (two-tailed), respectively.
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