Parent-subsidiary dispersion and executive excess perks consumption

Author Details
Bin Li 
Associate Professor, School of Economics and Finance, 
Xi’an Jiaotong University
Xi’an, Shaanxi, China 
libin2010@mail.xjtu.edu.cn

Yao Yao 
Ph.D. Candidate, School of Economics and Finance, 
Xi’an Jiaotong University
Xi’an, Shaanxi, China 
565121670@qq.com

Yasir Shahab (Corresponding author)
Associate Professor, 
School of Accounting, Xijing University 
Xi’an, Shaanxi, China
Contact Number: +8613126584641
roulett360@yahoo.com; 20180223@xijing.edu.cn

Hai-Xia Li, Associate Professor, 
School of Accounting, Xijing University 
Xi’an, Shaanxi, China
lihaixia@xijing.edu.cn

Collins G. Ntim 
Professor of Accounting
Centre for Research in Accounting, Accountability, and Governance (CRAAG)
 Department of Accounting
 Southampton Business School
University of Southampton
Southampton, United Kingdom
C.G.Ntim@soton.ac.uk

Cite paper as follows: Li, B., Yao, Y., Shahab, Y., Li, H. and Ntim, C.G. (2020). Parent-subsidiary dispersion and executive excess perks consumption. International Review of Financial Analysis, Forthcoming. 

Acknowledgments: 
The corresponding author acknowledges the support from the “Funds for High-Level Talents of Xijing University (2019)”, Grant/Award Number: XJ19B02. 

Parent-subsidiary dispersion and executive excess perks consumption

Abstract
We investigate the impact of parent-subsidiary dispersion on the corporate executives’ excess perks consumption using comprehensive data of 1,784 Chinese listed firms over the 2003–2017 period (i.e., total firm-year observations of 24,976). We argue that firms with greater geographic and institutional environment dispersion are subject to an acute information asymmetry problem, which makes it difficult and costly for shareholders and the public to monitor managerial actions and hence, facilitates managers to consume perks excessively. Consistent with this assertion, we find a positive relationship between parent-subsidiary company dispersion and executives’ excess perks consumption. These patterns are not likely to be driven by the firms’ endogenous choice and remain robust to various proxies for parent-subsidiary dispersion and executives’ excess perks consumption. In addition, our results also show that firms with a greater dispersion between parent and subsidiary companies have higher investor recognition. However, investor recognition does not appear to negatively impact the rate of executives’ excess perks consumption, which may explain why executives’ excess perks consumption is not mitigated in firms with greater parent-subsidiary dispersion.   

Keywords: Parent-subsidiary dispersion; corporate executives; excess perks; information asymmetry; investor recognition. 

JEL classification: G12; G14; G30; G32

1. Introduction

We investigate whether parent-subsidiary dispersion is positively or negatively correlated with corporate executives’ excess perks consumption. As one of the traditional compensation packages to often supplement insufficient nominal salaries under Chinese corporate culture, we argue that rational perquisite consumption potentially is able to enhance employee productivity, improve executive efficiency and consequently firm performance (Chen et al., 2009; Kato and Long, 2006; Lee et al., 2018). In contrast, we conjecture that excess perks consumption (which is different from rational perks consumption that can be used for corporate development or as alternative to monetary compensation) is more likely to impact negatively on firm value as a consequence of an agency problem (Cai et al., 2011; Grossman and Hart, 1980; Jensen, 1986). 
Recent typical examples in China, include the liquor scandal of China Petrochemical Corporation (SINOPEC) and the banquet event of the GREENcorporation. These examples have highlighted the negative impact of excess perks consumption on different groups of corporate stakeholders (e.g., shareholders, local communities, customers, employees and central/local government), and thus, have captured a great deal of attention from the media and regulators. Accordingly, the Chinese government and regulators have introduced a number of measures to combat executives’ excess perks consumption. For instance, the stricter supervision and monitoring by the head of state-owned enterprises (SOEs), jointly promulgated by the central ministry of finance and State-owned Assets Supervision and Administration Commission of the State Council (SASAC), is an on-going attempt to mitigate executives’ excess perks consumption. Further, the Central Committee of the Communist Party of China (CPC) has issued guidelines (e.g., eight regulations and six prohibitions) requiring state-owned firms to control excess perks consumption by corporate executives. Hence, in the light of these important background developments, our study seeks to offer a timely investigation into the factors that may explain the propensity for corporate executives’ to consume excess perks in China. 
Meanwhile, prior research indicates that geographic diversification has a significant impact on corporate decision-making (Camelia and Yuval, 2016; Riste and Matej, 2018; Sabri et al., 2015; Young and Ike, 2008). In particular, geographical diversification strategy, which may be implemented by diversified firms in order to improve their competitive power can lead to a significant expansion of subsidiaries and subsequently results in larger parent-subsidiary dispersion. Firms with subsidiaries throughout different cities face not only large geographical dispersion, but also significant differences in institutional/economic environments arising from the legacy of unbalanced regional development in China. Observably, China offers the ideal context to conduct this study because it is: (i) the single largest country by population in the world; (ii) the 4th largest country in the world in terms of geographic landmass; and (iii) the second biggest economy with the largest number of companies in the World. Thus, we exploit these ideal contextual conditions to examine parent-subsidiary dispersion from two dimensions: (a) geographic dispersion, and (b) institutional environmental difference. In particular, we determine whether corporate executives’ excess perks consumption is facilitated or mitigated by parent-subsidiary dispersion. A number of Chinese firms, such as SINOPEC and GREEN, where executives’ excess perks consumption is reported to have reduced corporate performance in the form of crash risk (Xu et al., 2014), operate in distant locations and have greater parent-subsidiary dispersion (Li et al., 2019). One argument is that the geographic dispersion increases the cost of face-to-face communication among internal personnel and creates additional organizational complexity, which can lead to higher information asymmetry (Jensen and Meckling, 1976; Yermack, 2006). This information asymmetry may stimulate managers to engage in a higher degree of perks consumption (Li et al., 2019). However, it has been argued further that geographically dispersed firms also have a larger investor base and higher investor recognition, which may mitigate manager’s perks consumption by exposing them to greater outside scrutiny (Garcia and Norli, 2012; Shi et al., 2015). Consequently, the central research question is: does parent-subsidiary dispersion, in part, be responsible for corporate executives’ excess perks consumption in diversified firms? 
In this case, we propose two competing theoretical hypotheses to explain parent-subsidiary dispersion–corporate executives’ excess perks consumption nexus as follows: (a) information asymmetry (Jensen and Meckling, 1976; Yermack, 2006); and (b) investor recognition (Garcia and Norli, 2012). First, the information asymmetry hypothesis argues that parent-subsidiary dispersion (including geographic dispersion and institutional environment difference) affects the quantity and speed of information transmission, and thereby resulting in the existence of an information distortion/asymmetry between shareholders and managers. Information asymmetry makes it difficult and costly for shareholders and the public to monitor managerial actions, which facilitates managers to consume perks excessively. Hence, the information asymmetry hypothesis predicts a positive relation between parent-subsidiary dispersion and excess perks consumption. 
In contrast, the investor recognition offers a competing alternative hypothesis to the information asymmetry one. The investor recognition hypothesis argues that firms with greater parent-subsidiary dispersion may have a larger investor base and higher investor recognition. Therefore, firms are subject to greater public scrutiny, which is likely to constrain excess perks consumption by strengthening external supervision and improving public “outrage” cost. Thus, the investor recognition hypothesis predicts an inverse relation between parent-subsidiary dispersion and excess perks consumption.
Our empirical findings support the “information asymmetry hypothesis.” First, while using a large dataset of Chinese listed firms over the 2003–2017 period, our results suggest that parent-subsidiary dispersion facilitates corporate executives’ excess perks consumption in China. This result implies that executives in widely dispersed firms have more opportunity to consume perks excessively due to apparently to the high information asymmetry and high cost associated with monitoring managerial actions. Our findings are robust to the alternative measures of corporate executives’ excess perks consumption and parent-subsidiary dispersion. In addition, our results are consistent after accounting for potential endogeneity issues. Second, our findings imply that though firms with greater parent-subsidiary dispersion have higher investor base and recognition, investor recognition alone is ineffective at mitigating excess perks consumption by corporate executives, as predicted by investor recognition hypothesis. 
We contribute to the extant international corporate finance literature in the following ways. First, we contribute to the corporate financial and geographical diversification literature by providing evidence that offers new insights on the perspective of parent-subsidiary dispersion. A number of studies in the corporate financial and geographical diversification have regarded corporation as a whole, and therefore, have often not focused on the distance between parent and subsidiaries. Based on the measures of parent-subsidiary dispersion, we found that parent-subsidiary dispersion positively affects this specific type of agency cost, namely excess perks consumption by corporate executives. 
Second, the existing international corporate finance literature on perks consumption is mainly devoted to the internal determinants of firms, including corporate ownership, corporate governance, and managerial power (Andrews et al., 2017; Bebchuk, 2002; Luo et al., 2011; Shahab et al., 2020). Hence, in contrast to the ongoing debate on the consequences of corporate executives’ excess perks consumption (Cai et al., 2011; Gul et al., 2011; Xu et al., 2014; Huang and Lin, 2016; Xu et al., 2014; Yermack, 2006), we extend the international corporate finance literature further by examining an important, yet unexplored external determinant of corporate executives’ excess perks consumption (i.e., parent-subsidiary dispersion). Specifically, we contribute to this literature by demonstrating further that corporate executives’ excess perks consumption may be facilitated in firms with greater parent-subsidiary dispersion. Our findings supplement the growing literature on international corporate finance with specific focus on corporate economic geography, and provide new insights into the role of parent-subsidiary dispersion in managerial decisions about perquisite consumption.
The paper is organized as follows. Section 2 presents the contextual settings of the study. Section 3 provides a literature review and discusses the two competing theoretical hypotheses. Section 4 discusses the data, sample, measures and regression model used in the study. Section 5 reports the empirical findings. Section 6 concludes the paper.  

2. Contextual settings of the study
 
2.1. Regional development strategy and parent-subsidiary dispersion

In order to narrow the regional development gap and promote regionally coordinated development, the Chinese government has implemented a series of regional development strategy since the reforms and opening-up the economy in the 1990s. The latest strategies include “The Belt and Road initiative,” “Beijing-Tianjin-Hebei integration strategy,” “Yangtze River economic zone,” and “Guangdong-Hong Kong-Macao Greater Bay Area development strategy,” amongst others. In addition, the precise poverty alleviation policy fully implemented by the Chinese government in 2015 has also promoted the location of enterprises in poorer areas and neighbourhoods. In a nutshell, regional development strategies have been an important component of building a modernized economic system in China. 
With the full implementation of regional development strategies, the government has formulated a series of preferential policies, including tax incentives, government subsidies, and financing incentives, to encourage companies to migrate. Under this background, more and more companies have expanded their operations far beyond their headquarters’ regions/locations and now operate in wide areas, which we argue can further lead to greater parent-subsidiary dispersion. Thus, parent-subsidiaries dispersion is an important factor in studying international corporate finance that is rooted in agency conflicts and information asymmetry problems in China.

2.2. Regulated monetary compensation and excess perks consumption

The monetary compensation of top executives is generally constrained in China compared with developed countries, such the US, especially in Chinese state-owned enterprises (SOEs) due to a few reasons. First, due to the monopolistic rights enjoyed by some companies in certain industries, as well as the specific non-profit operating objectives of Chinese SOEs, the causal relationship between corporate performance and executive efforts is often ambiguous (Li et al., 2019; Xu et al., 2014). This exacerbates the information asymmetry problem between the SOEs and the government in particular, and thus, makes it more difficult for the government to have effective monitoring of corporate executives. One monitoring tool that the government, therefore, uses is by making regulatory restrictions on cash compensation of executives. Second, the government has rejected high levels of cash compensation as they tend to create unwarranted managerial privilege and encourage corrupt behaviour. Moreover, the significant gap in monetary compensation between top executives and average employees challenges the equality of income distribution and attract significant public criticism. Consequently, the Chinese government has issued different regulations since 1986. The latest one is “reform scheme of the compensation system for the Central enterprises’ responsible person” officially implemented in 2015 to restrict the cash compensation of executives in SOEs. 
Specifically, the cash compensation of a top executive in an SOE consists of three parts: (i) base salary; (ii) performance-based bonus; and (iii) tenure incentive income. The executives appointed by the government in different SOEs have the same base salary in principle. In addition to Chinese SOEs, other listed companies also have different levels of cash compensation constraints in order to avoid public criticisms that may be caused by unfair income distribution (Luo et al., 2011; Xu et al., 2014). Due to the regulatory restrictions on cash compensation of executives, Chinese companies need an alternative tool to motivate and compete for potential executives in the market. Perks consumption is less likely to be observed by public and a certain level of perks attract less criticisms. Especially under the influence of Chinese traditional culture, perks consumption is often easily accepted by executives as a substitute for cash consumption (Luo et al., 2011). Thus, perks consumption constitutes a significant element other than cash compensation in the total executive compensation package and plays a more important role in China than that in other countries (Luo et al., 2011; Xu et al., 2014). Nevertheless, excess perks consumption may lead to common agency problems in Chinese companies due to lack of public knowledge.

3.	The theoretical framework

Most of the existing studies on executives’ perks consumption and firm geographic dispersion have mainly employed single theoretical perspective (e.g., Andrews et al., 2017; Luo et al., 2011; Shi et al., 2015; Vasilescu and Millo, 2016). Moreover, few studies have made the study in an integrative and competitive perspective in this area (Jiraporn et al., 2008; Li et al., 2019). However, no study so far has examined how the parent subsidiary dispersion influences the executives’ perks consumption in China. Following the arguments of prior literature (Jiraporn et al., 2008; Li et al., 2019; Shi et al., 2015), we contend that studies analyzed from single theoretic perspective may not be powerfully predictive in the examination of executives’ excess perk consumption in connection with the parent-subsidiary dispersion, which can be ascertained by integrating multiple different theoretical lenses. Also and as noted previously, the special institutional and environmental background of China provides us with the ideal setting to combine agency theory (Jensen and Meckling, 1976), information asymmetry theory (Grossman and Stiglitz, 1976; Grossman and Stiglitz, 1980) and the investor recognition theory (Garcia and Norli, 2012; Merton, 1978; Shi et al., 2015) in our analysis. According to the agency theory, company principals (e.g., shareholders and board of directors), as the owners of the enterprises’ resources has a contractual relationship and target difference with the company agents (e.g., managers), who are the managers of corporate resources. Utility maximizing managers tend to engage in misappropriating the surplus funds of the firm and maximizing their own compensation (Jensen and Meckling, 1976;Yermack，2006). For protecting the interests of them and avoid impacting negatively on the firm value, shareholders can strive to mitigate the managerial actions by using some methods, such as equity and compensation incentives and supervision reinforcement from the board of directors and board of supervisors (Andrews et al., 2017). However, managers still have opportunities to take managerial actions because of the information asymmetry problem and the high information costs.
Further, the information asymmetry theory (Grossman and Stiglitz, 1976; Grossman and Stiglitz, 1980; Jensen and Meckling, 1976) indicates that a huge information difference exists among different stakeholders in a company. Managers, who have more business information are in a comparatively dominant position, while the owners, who have less information are at a disadvantage. The party with more information can take advantage of this information difference to benefit from the market.
We, therefore, first integrate agency theory and the information asymmetry theory perspectives. We argue that firms with greater parent-subsidiary dispersion, including geographic dispersion and institutional environment difference, are more likely to suffer acute information asymmetry between shareholders and managers. Thus, higher information asymmetry in such firms weakens shareholders' ability to monitor managerial actions and increases agency costs, which can motivate mangers to consume perks excessively. 
Moreover, the investor recognition theory (Garcia and Norli, 2012; Merton, 1978; Shi et al., 2015) indicates that under the condition of incomplete information, more investor recognition can reduce the information barrier of enterprises and the expected deviation of investors, which can improve the market value or reduce the capital cost of enterprises. Accordingly, we argue that parent subsidiary dispersion leads to a larger investor base and hence, improve the investors’ construal level to judge and perceive the financial and management information of the enterprise, which results in higher investor recognition. Further, by strengthening supervision and improving “outrage” cost from the public, investor recognition can reduce the agency costs and executives’ excess perks consumption.

4. Literature review and hypotheses development

4.1. Literature review 

With the continuous implementation of enterprise diversification strategy, corporate dispersion, including geographic and institutional environmental dispersion,  has been shown to play a significant role with respect to firm value and performance (Kang and Kim, 2008; Gao et al. 2008; Young and Ike, 2008), stock returns (Garcia and Norli, 2012; Riste and Matej, 2018), capital cost and risk taking (Hann et al., 2013) and corporate management decision-making (Camelia and Yuval, 2016; Shi et al., 2015). The findings of these stuidies indicate that economic geography of firms have a significant impact on a firm’s information asymmetry, social networks and investor recognition conditions, amongst others, which further affect executives’ decisions/actions (Li et al., 2019; Sabri et al., 2015; Shi et al., 2015). 
Meanwhile, executives’ perks consumption has received some level of attention from scholars due to the increasing occurrence and revelation of corporate executives’ perks consumption scandals. Noticeably, the existing literature has mainly focused on the effect of executives’ perk consumption on shareholder returns and stock price cash risk (Xu et al., 2014; Yermack, 2006), quality of financial reporting (Gul et al., 2011) and firm value/financial performance (Cai et al., 2011; Lee et al., 2018), amongst others. In terms of the determinants of executives’ excess perks consumption (Andrews et al., 2017; Luo et al., 2011), the existing literature has rarely investigated the relationship between parent-subsidiary dispersion and executives’ opportunistic behaviour. Jiraporn et al. (2008) examined whether earnings management is exacerbated or alleviated by corporate dispersion based on the information asymmetry hypothesis. They document that a combination of industrial and global diversification helps alleviate earnings management by 2.5%, which supports the view that parent-subsidiary dispersion can help to mitigate excess opportunistic behaviour. Similarly, Li et al. (2019) investigate the relationship between parent-subsidiary geographic dispersion and earnings management by empirically examining listed firms in China. From the information asymmetry perspective, they argue that parent-subsidiary geographic dispersion is positively associated with earnings management. Duru and Reeb (2002b) explore the relationship between corporate dispersion and CEO compensation. They find that both geographic and industrial diversification are associated with a greater use of incentive-based compensation.
However, no study so far has studied the relation between the parent-subsidiary dispersion and executives’ excess perks consumption. In addition to this, the limited evidence so far has mainly been conducted in the developed markets (Duru and Reeb, 2002b; Jiraporn et al. 2008), whilst only few studies pay attention to the parent-subsidiary dispersion in an economy like China (as argued by Li et al., 2019). In the context of China, (a) firms can be expected to be more proactive to migrate and operate in wide areas under the influence of regional development strategies, especially to where preferential policies have been introduced by the government, and (b) managers can be expected to accept perk consumption as a substitute for cash compensation because of the strict cash compensation controls, especially in SOEs. Arguably, this unique institutional context provides interesting platform to examine the relationship between parent-subsidiary dispersion and executives’ perk consumption.

4.2. Hypotheses development

Inferring from above theoretical perspectives and empirical findings, we suggest two competing hypotheses that potentially explain the relationship between the parent-subsidiary dispersion and executives’ excess perk consumption. These hypotheses are discussed below.

4.2.1. Parent-subsidiary dispersion and excess perks consumption: The information asymmetry hypothesis
  
We examine parent-subsidiary dispersion from two dimensions: (a) geographic dispersion and (b) institutional environment difference. Firms with greater geographic dispersion between the parent and subsidiary company usually need more sophisticated firm structures to control the operations of subsidiaries located in different regions (Jiraporn et al., 2008). The firm complexity caused by difficulties inherent in developing and coordinating leads to higher information asymmetry between shareholders and managers, and makes it more difficult for shareholders to distinguish excess perk consumption from normally increased perk consumption, which provides cover for managers’ excess perks consumption. For example, managers may use growing business or doing an investigation as an excuse to equip themselves with premiums car or go on a travel, which is hard to be discovered by shareholders. Additionally, the limitations of natural geographical conditions may set obvious obstacles to face-to-face communication among the shareholders, managers and employees, which may make communication difficult, and hence can lead to higher information costs of monitoring managerial behavior (Riste and Matej, 2018). All of these information asymmetry problems that may be caused by geographic dispersion can weaken shareholders ability to effectively supervise managers and thus resulting in greater executive excess perks consumption.
 In addition, each subsidiary (located in different areas with special institutional environment, such as differing legal system, political relationship and others) may have particular culture and mode of operation. The complex managerial decision-making environment may not only provides incentives for managers to increase perks consumption, such as hospitality and transportation expenses, but also make it difficult for shareholders to acquire more real-time information about subsidiaries operations and interpret the information they have (Duru and Reeb, 2002b; Mehdi and Seboui, 2011). Hence, information asymmetry problem may be exacerbated by the parent-subsidiary institutional environment difference that may make managerial actions more difficult to be observed and monitored, which may facilitate excess perks consumption by corporate executives.
Furthermore, firms with greater parent-subsidiary dispersion, including geographic dispersion and institutional environment difference, are subject to acute information asymmetry problem more than closely located firms. Finally, higher information asymmetry in such firms (with greater parent-subsidiary dispersion) facilitates managers to consume perks excessively. Some empirical studies support the close association between excess perks consumption and higher information asymmetry (Alchian and Demsetz, 1972; Duru and Reeb, 2002b). Consequently, the information asymmetry hypothesis predicts a positive relationship between parent-subsidiary dispersion and excess perks consumption. Therefore, our first hypothesis is as follows:

Hypothesis 1. Parent-subsidiary dispersion is positively associated with executives’ excess perks consumption.

4.2.2. Parent-subsidiary dispersion and excess perks consumption: The investor recognition hypothesis 
Firms with larger geographic dispersion between parent and subsidiary company operate in wide areas and are likely to establish contact with more investors; therefore, may have a larger investor base. Garcia and Norli (2012) argued that geographically dispersed firms have larger investor base and higher investor recognition than local firms, whose view is in favor of the connection between larger geographic dispersion and higher investor recognition. In addition, investors affected by the particular institutional environment in different areas would have specific thought pattern and habits. Notably, firms with larger institutional environment difference are likely to be faced with diversified investors who observe the firm from a more diverse and different perspective, which make the managers' behavior easier to be detected. Thus, firms with large institutional environment difference may have high investor recognition. Finally, firms with greater parent-subsidiary dispersion, including geographic dispersion and institutional environment difference, are more likely to possess higher investor recognition.
Consistent with this assertion, we argue that since firms with larger parent-subsidiary dispersion have a higher investor recognition, it is obvious that they are subject to more public attention from institutional holdings, financial analysts and media coverage (Dyck and Zingales, 2004; Shi et al., 2015). Moreover, a firm that spans several provinces may be subject to strict disclosure regulations and may make it easier for investors to monitor such a firm. We further argue that, if managers in firms with greater parent-subsidiary dispersion engage in excess perks consumption, they would be more exposed to outside scrutiny and potentially have a greater possibility of being detected by auditors and outside financial analysts. Consequently, managers consuming perks excessively are likely to be penalized by causing reputational damage, and lose investments from external investors, as well as a reduced support from shareholders in promotion tournaments (Bebchuk, 2002), which may make managers deviate from the goals of maximizing personal benefits and suffer more loss. Therefore, by strengthening supervision and improving “outrage” cost, investor recognition can reduce executives’ excess perks consumption. Accordingly, the investor recognition hypothesis predicts a negative relationship between parent-subsidiary dispersion and excess perks consumption. Consequently, our second and final hypothesis is as follows:

Hypothesis 2. Parent-subsidiary dispersion is negatively associated with executives’ excess perks consumption.

5. Data, sample, measures and regression model

5.1. Data and sample selection

The initial sample is obtained from the China Securities Market Accounting Research Database (CSMAR) and the RESSET database over the period from 2003 to 2017. All financial variables are in RMB. We obtain parent-subsidiary geographic dispersion data by hand collecting the latitudes and longitudes of the parent and their subsidiaries, referencing the registration sites’ information disclosed in the financial reports of the firm. The unit of distance between parent company and subsidiary company is kilometer. We measured institutional environment difference (as a proxy for parent-subsidiary dispersion) by employing marketization index data from “NERI INDEX” of Marketization of China’s Provinces 2013 Report (compiled by Fan et al., 2011).
Then, we excluded firms from the finance and insurance industry because these industries are subject to different accounting and financial regulations. We further excluded firms that have been listed for less than a year and have not continuously disclosed their financial data for at least two years. Finally, firms with insufficient information for the estimation of the model are discarded. The final sample consists of 24,976 firm-year observations from 1,784 Chinese listed firms over the period from 2003 to 2017. All continuous variables are winsorized at the top and bottom 1 percent to avoid the influence of extreme observations. We have drawn our sample from 15 different industries of China. Table 1 depicts the industry and the year distribution of total sample. Firms from “manufacturing industry” constitute around 59.79% of the total sample. Also, there are more observations in latest year, which indicate that more firms are listed and have disclosed more information about perquisite consumption in recent years.     
INSERT “TABLE 1” ABOUT HERE

5.2. Measuring parent-subsidiary dispersion
  
We employ both geographic dispersion (GOD) and institutional environmental difference (IND) as a proxy for the parent-subsidiary dispersion (DIS). 

5.2.1. Measuring geographic dispersion
Following Pantzalis and Park (2014), Jensen et al. (2015), we estimate geographic dispersion (GOD) between a parent and its subsidiaries by the following steps. First, based on the previously obtained longitude and latitude data of each company, the geographic distance between parent company ‘p’ and its subsidiary ‘j’ (Dpj) is calculated from Eq. (1). Then, we calculate the standard deviation of geographic distance between the parent company and each subsidiary of it (Dpj) by using Eq. (2). Moreover, the standard deviation is multiplied by 0.001 in order to avoid the impact of large values. The variable GODit measures the geographic dispersion, i.e., the bigger the variable GODit, the larger the parent-subsidiary geographic dispersion. 

Dpj=arcos[cos(latj)×sin(lonj)×cos(latp)×sin(lonp)+cos(latj)×cos(lonj)×cos(latp)
×cos(lonp)+sin(latj)×sin(latp)]×πr/180							(1)
  
                                      	(2)
Where Dpj is the geographic distance between the parent company ‘p’ and its subsidiary company ‘j’ . ‘’ is the average of geographic distance. ‘lat’ and ‘lon’ are the latitude and longitude of the company. ‘π’ is the circumference ratio and ‘r’ is equatorial radius which is valued at 6378 km. For the firm, GODit is the geographic dispersion between the parent company and its subsidiaries in year ‘t’ . ‘n’ is the number of subsidiaries belonging to the parent company ‘p’ .

5.2.2. Measuring the institutional environmental difference
Similar to the measurement of geographic dispersion, we estimate institutional environmental difference (IND) by calculating the standard deviation of the institutional environmental difference between the parent company and its subsidiary. First, we calculate the difference of marketization index (Mpj) between the province where the parent company ‘p’ is located and the province where its subsidiary ‘j’ is located. Then, we employ Mpj to calculate the standard deviation based on Eq. (3) and then, we get the variable INDit, which measures the institutional environmental difference between the parent and its subsidiary company. The bigger the INDit is, the larger the parent-subsidiary companies’ institutional environmental difference is.

                      			          (3)

Where Mpj is the institutional environment difference between the parent company ‘p’ and it's subsidiary ‘j’ . ‘’ is the average of the difference of marketization index. For firm, INDit is the institutional environmental difference between parent and its subsidiary companies in year ‘t’ .‘n’ is the number of subsidiaries belonging to the parent company ‘p’.

5.3. Measuring executives’ excess perks consumption

We obtain the excess perks consumption that executive receives by three alternative methods. In the first method, we measure excess perks consumption based on the sum of eight categories of cash payment, which is similar to Chen et al. (2016). China Accounting Standards (CAS) require listed companies to disclose cash payment related to major operating activities in the column of “other cash payment related to operating activities” in the cash flow statement. Using this information, we collect the following eight categories as potential payment for perks consumption: (1) business travel expenses, (2) business entertainment expenses, (3) training abroad expenses, (4) board meeting expenses, (5) office administrative expenses, (6) communication expenses, (7) company car and chauffeur service, and (8) conference expenses. ‘Mpay1’ in equation (4) is the sum of the above eight categories of cash payment. We use the following model (see Eq. (4)) to regress Mpay1 on several firm characteristics and estimate the normal level of such cash payment for each industry within each year, and then calculate the residual values as our first proxy for excess perks consumption (EPC1).

         (4)

Following Gul et al. (2011), the second method to measure excess perks consumption is based on the sum of following six categories of cash payment: traveling expenses, business entertainment expenses, overseas training expenses, board meeting expenses, meeting expenses, and company car expenses. Compared with the first approach, we exclude two controversial items, i.e., office administrative expenses and communication expenses, to avoid company misclassifying their regular business expenses. ‘Mpay2’ in equation (5) is the sum of the above six categories of cash payment. We use Eq. (5) to regress Mpay2 and use the residual values as our second proxy for excess perks consumption (EPC2).  

         (5)

Moreover, the third measure that we use is abnormal administration expense following Luo et al. (2011). We obtain information on administration expense from the firm’s income statement, and then, we exclude bad debt expenses, unrealized holding gain or loss for inventory, and direct compensation for directors and top executives out of the total administration expense. The Mpay3 in Eq. (6) is the remainder of administrative expense. We use the Eq. (6) to estimate the normal level of administrative expenses for each industry within each year, and then use the residual values as the abnormal administrative expenses of a company to create our last proxy for excess perks consumption (EPC3).

        (6)

Where ‘Asseti, t-1’ is lagged total assets for firm. ‘Sal’ is change in sales, which is used as a proxy for productivity to estimate the normal administrative expenses. ‘PPE’ is the net value of property, plant, and equipment, which is the proxy for depreciation and amortization. ‘Inv’ is the year-end value of inventories to proxy the operating needs of administrative expenses. Finally, ‘Lnemp’ is the natural log of the number of employees, which is a proxy for expenses of coordination, training and employee developments. In addition, the EPC1, EPC2 and EPC3 are multiplied by 100 in order to avoid the impact of small values on the empirical results.

5.4. Regression model and control variables
   
The purpose of this study is to ascertain whether excess perks consumption is facilitated or mitigated by parent-subsidiary dispersion. To analyze the relationship between them, we formulate Eq. (7) to investigate the competing hypothesis.

EPCit=β0+β1DISit+β2SIZEit+β3GROit+β4SHRit+β5ROAit+β6LEVit+β7BOSit+β8BOAit+β9IPBit+β10SOEit+β11BIGit+β12OPIit+β13Mediait+β14∑YEARt+β15∑INDUi+νit 	(7)

  
Dependent variable ‘EPC’ is the proxy of excess perks consumption, and the independent variable ‘DIS’ is the proxy of parent-subsidiary dispersion. In addition, many firm characteristic variables are controlled in our analysis following Luo et al. (2011), Andrews et al. (2017), and Gul et al. (2011). Firm size (SIZE) is measured by the natural logarithm of the total assets of the previous period. Growth opportunity (GRO) is measured by operating income growth rate. Equity concentration ratio (SHR) is measured by the shareholding ratio of the largest shareholder. Profitability (ROA) is measured by the ratio of operating profit to total assets of the previous period. Capital structure (LEV) is measured by the ratio of total debt to total assets. Duality status (BOS) is a dummy variable, where the value is ‘1’ if the Chairman is also the general manager and otherwise is ‘0’ (Xu et al., 2014). Board size (BOA) is measured by the natural logarithm of the number of directors on the board. Board independence (IPB) is measured by the proportion of independent directors over the total number of directors on the board. Property rights (SOE) is a dummy variable where the value is ‘1’ if the firm is state-owned and otherwise is ‘0’. Accounting firm (BIG) that can reflect the status of external supervision as a dummy variable where value is ‘1’ if the accounting firm belongs to the top ten accounting firms and ‘0’ otherwise. The type of audit opinion (OPI) is a dummy variable where the value is ‘1’ if the financial statements are given a standard unqualified opinion and ‘0’ otherwise. The media coverage (Media) is measured by the natural logarithm of the sum of the number of online media reports covering the firms in year t for firm i and 1. We add Media coverage as an important control variable because media coverage as a crucial external monitoring tool can play a corporate governance role and, thus, are likely to have a negative impact on the executives’ perks consumption (Borochin and Cu, 2018; Core et al., 2008). In addition, we used the China Research Data Service Platform (CNRDS) database to collect the data relating to the media coverage variable. β0 is the intercept of the regression line. βi is the coefficient of the variables and νit is the error term. In addition, we also controlled for industry (INDUi) and year (YEARt) fixed effects in our analyses.  
If the coefficient β1 is significantly positive, it means that a positive relation is found between parent-subsidiary dispersion and excess perks consumption, and our empirical result would reveal evidence in favour of the information asymmetry hypothesis. If the coefficient β1 is significant negative, it means that the larger the parent-subsidiary dispersion, the lower the excess perks consumption and thus, our empirical result support the competing hypothesis.   
 
6. Empirical results

6.1. Descriptive statistics and correlation analysis

Table 2 presents the results of the descriptive statistics for the variables used in the study. The mean of the two measures (GOD and IND) for the parent-subsidiary dispersion (DIS) are 0.459 and 1.192 (with the median are 0.467 and 1.247), which indicate that firms in China have a higher geographic dispersion and institution environment difference between parent and its subsidiary. Moreover, the standard deviation of GOD and IND are 0.308 and 0.833, indicating that a big difference in dispersion between parent and its subsidiary exist for different firms. The mean of three proxy for excess perks consumption (EPC1, EPC2, and EPC3) are 0.013, 0.036 and 0.176 (with the median are -0.033, -0.023 and -0.241), meaning that it is widespread for managers to consume perquisite excessively in listed-firms. These mean values for excess perks consumption are quite in range with the previous literature on excess perks from China (e.g., Gul et al., 2011; Xu et al., 2014). As for the control variables, the mean of SIZE, GRO, SHR, and ROA are 21.760, 0.223, 0.355 and 0.049, showing that sampled firms have comparatively large operation scale and low profitability. The mean of LEV indicates that sampled firms have moderate debt and, the mean values of BOS and BOA show that sampled firms have a large number of directors on the board. The mean values of IPB, BIG and OPI indicate that sampled firms are subject to strict internal and external supervision. The mean of SOE shows that 62.3% of sampled firms are state-owned. The mean and standard deviation of the Media coverage variable indicates that most sampled firms have high media attention, but the number of media reports has a big difference among different sampled firms.
INSERT “TABLE 2” ABOUT HERE

If parent-subsidiary dispersion (DIS) has an impact on excess perks consumption (EPC), we should expect EPC vary across firms with different parent-subsidiary dispersion. To test this, we divide the sample into three groups on the basis of the tri-sectional quantiles split of parent-subsidiary dispersion (DIS) and then compare how the groups differ for certain variables. Firms below the 33rd percentile are referred to have “low dispersion” between parent and subsidiary, and firms over the 66th percentile are referred to have “high dispersion.” The firms in the middle are referred to have “medium dispersion.” Panel A in Table 3 shows the distribution of geographic dispersion (GOD) and EPC across three groups. We can easily see that the mean of EPC1, EPC2 and EPC3 in the group of “low geographic dispersion” are -0.064, -0.051 and -0.099, while the mean among the medium group are 0.043, 0.047 and 0.315, and the mean among the group of “high geographic dispersion” are 0.060, 0.112 and 0.313, respectively. The mean and median differences are statistically significant. Although it is not straightforward to assess the economic meaning of these measures of EPC because they are residuals from regression analyses, it is apparent that the EPC in firms with low geographic dispersion are on average below the average (the mean of EPC1, EPC2 and EPC3 reported in Table 2 respectively is 0.013, 0.036 and 0.176). But those in firms with medium and high geographic dispersion are above the average. Panel B in Table 3 shows the distribution of institutional environmental difference (IND) and EPC across three groups. Similar as a result in Panel A, the mean and median differences in Panel B are also statistically significant. All of these results suggest that firms in high dispersion group have considerably higher excess perks consumption than those in the low dispersion group.
INSERT “TABLE 3” ABOUT HERE

Table 4 displays a correlation matrix with ‘Spearman coefficients’ in the upper triangle and ‘Pearson coefficients’ in the lower triangle for parent-subsidiary dispersion (DIS) and excess perks consumption (EPC). As we expected, there is a significant positive correlation between DIS and EPC, implying that greater parent-subsidiary dispersion is associated with higher excess perks consumption. The correlation coefficients of three measures for excess perks consumption (EPC1, EPC2, and EPC3) and two measures for parent-subsidiary dispersion (GOD and IND) are high, but these measures do not appear in the same regression model. In addition, we conducted a multicollinearity test and find that the variance inflation factor (VIF) is less than 10 and thus, no serious multicollinearity issues can be found.
INSERT “TABLE 4” ABOUT HERE

6.2. Regression results analysis 

To investigate our competitive hypotheses, we examine the impact of parent-subsidiary dispersion (DIS) on excess perks consumption (EPC) based on the Eq. (7). Table 5 displays the multivariate regression results where dependent variables are three measures of excess perk consumption (EPC). In the column (1) and (2), the dependent variables are EPC1, which is the first proxy for excess perks consumption, and the independent variable is DIS which has two proxies, GOD and IND. The coefficients of GOD and IND are 0.195 and 0.091, respectively, which are positive and significant at the 1% level. Similarly, in the column (3) and (4), and column (5) and (6), all the coefficients of GOD and IND are positive and significant at 1% level. These results show a positive relationship between the parent subsidiary dispersion and executives’ excess perk consumption. The policy of our findings is that, on average, firms with greater parent subsidiary dispersion are more likely to have a higher executives’ excess perk consumption. The economic meaning of our results is not straightforward, but can be understood indirectly, because the measure of EPC is based on the residual of the regression analysis and it is actually a ratio of the absolute amount of excess perks consumption to total assets at the beginning of the period, and the measure of DIS is a standard deviation. Taking the regression result of EPC1 as an example (shown in column (1) and (2) in table 5), the economic meaning of it is that each additional unit of the geographical dispersion and institutional environment dispersion between the parent and subsidiaries will increase executives’ excess perks consumption, respectively by 0.195 and 0.091 unit. Consider that the measure of excess perks consumption is a ratio to total asset, we can actually observe from the above regression results that there is a significant increase in the amount of excess perks consumption, when parent-subsidiary dispersion becomes larger, which confirms the economic significance of our finding. 
Theoretically, our finding is consistent with the information asymmetry hypothesis rather than the investor recognition hypothesis. Specifically, our evidence confirms that geographic dispersion and institutional environmental difference increases the cost and complexity of communication between the shareholders and managers, which leads to acute information asymmetry (Jiraporn et al., 2008; Li et al., 2019; Mehdi and Seboui, 2011). Further, the supervision difficulties caused by information asymmetry will stimulate managers to engage in a higher degree of perk consumption. Although the firms with great parent subsidiary dispersion may have a large investor base and higher investor recognition according to the investor recognition hypothesis (Shi et al., 2015), our evidence validates parent subsidiary dispersion is not negatively associated with executives’ excess perk consumption. Previously, Lim et al. (2008) examine the impact of corporate diversification on the earning management based on the similar information asymmetry hypothesis and supported that managers of diversified firms may exploit the additional information asymmetry and engage in more earnings management than those of focused firms. However, our evidence further extends the literature by presenting the positive relationship between the parent subsidiary dispersion and excess perk consumption based on the information asymmetry theory, which provides a new research perspective.
In the case of control variables, ROA and BIG were found to have a significantly positive influence on excess perks consumption (EPC), indicating that the firms are more inclined to consume perks excessively with higher profitability or audited by big accounting firms. However, Media coverage was also found to have a significant positive relationship with EPC, which is completely different from the result we predicted earlier. We argue that although media coverage has a certain external monitoring effect on firms, it may also positively relate to EPC due to following reasons. Firstly, firms with more media coverage may have better reputation, larger operating scales and better economic performance, resulting in higher executive compensation and perks consumption. Some evidence from China can prove this (Chen et al., 2013). In addition, more media attention may prompt managers to seeking higher perks consumption that is more difficult to detect as a substitute for higher cash compensation. SIZE and GRO were found to have a significantly negative influence on EPC, which means that the firms with large scale operations and high growth rate tend to have less excess perks consumption.      
INSERT “TABLE 5” ABOUT HERE

6.3. Endogeneity test

We perform additional analysis of the endogeneity test to investigate the robustness of the association between parent-subsidiary dispersion (DIS) and excess perks consumption (EPC). We argue that the firms with large parent-subsidiary dispersion have high excess perks consumption, however, the causality issue may arise since it is also possible that managers who excess perk consumption may have greater management power and they may choose to operate in a wide areas in order to misappropriate more funds from firms and to maximize the benefits, which lead to a large parent-subsidiary dispersion. To address this reverse causality issue, we resort to the instrumental variable method by employing a 2SLS regression technique. Following Shi et al. (2015), we use the average value of parent-subsidiary dispersion of other firms that parent company located in the same province but operate in other industries as the instrumental variable for parent-subsidiary dispersion. The parent-subsidiary dispersion of other firms that parent company located in the same province closely related to the dispersion of research firms because other firms that parent company in the same province (or the same market) have similar resource and institutional environment with the research firms, which may lead to similar parent-subsidiary dispersion. What’s more, managers’ excess perk consumption in research firms hard to affect or be affected by parent-subsidiary dispersion of other firms operated in other industries, which can control for the potential reverse causality issue between parent-subsidiary dispersion and excess perk consumption.
Table 6 reports the empirical results using the 2SLS regression to control for the endogeneity between parent-subsidiary dispersion (DIS) and excess perks consumption (EPC). Panel A in Table 6 displays the first-stage regression results. The F-statistics in the test for the relevance of instrument are 128.12 and 36.56 with 1% significance level, and the partial R2 are 0.040 and 0.009, which indicate that the instrument variable is appropriate. In the weak identification test, the Kleibergen–Paap F-statistic respectively is 128.12 and 36.56 with 1% significant level, which exceed the critical value of 16.38 and reject the weak identification hypothesis. In other words, the instrument we are using is not weak. The results of the under-identification test and weak-instrument robust inference test also show that the instrument variable for parent-subsidiary dispersion is valid. The empirical results of the second-stage in 2SLS regression are tabulated in Panel B of Table 6. All the coefficients of the fitted value of parent-subsidiary dispersion (DIS hat) are positive and significant at 5% or 1% level, meaning that parent-subsidiary dispersion is significantly associated with the significant at consumption and confirming the previous results. 
INSERT “TABLE 6” ABOUT HERE

6.4. Robustness check: Alternative measures of parent-subsidiary dispersion
 
To further check the robustness of results, we use alternative measures of parent-subsidiary dispersion. The alternative measure of geographic dispersion between parent and subsidiary company (AGOD) is based on the Eq. (8), which uses the mean deviations of Dpj calculated in Eq.(1) instead of the standard deviation. Similarly, the alternative measure of institutional environmental difference (AIND) based on the Eq. (9) uses the mean deviations of Mpj in Eq. (2) instead of its standard deviation.

                                 (8)

                                        (9)

Table 7 reports the regression results using the alternative measures of parent-subsidiary dispersion. The coefficient of AGOD (geographic dispersion) and AIND (institutional environmental difference) is positive and statistically significant at 1% level, suggesting that larger parent-subsidiary companies’ dispersion is associated with more excess perks consumption. Thus, when we replace parent-subsidiary dispersion with alternative measures, the evidence we obtained reaffirms the previous results and appears to be robust.
INSERT “TABLE 7” ABOUT HERE

6.5. Robustness check: Alternative measures of excess perk consumption

To reaffirm the relation between parent-subsidiary dispersion and excess perks consumption, we employ three methods to obtain alternative proxies for excess perks consumption (EPC). In the first method, perquisite consumption includes the following eight categories: business travel expenses, business entertainment expenses, training abroad expenses, board meeting expenses, office administrative expenses, communication expenses, company car and chauffeur service, and conference expenses. By following Gul et al. (2011), Xu et al. (2014), Chen et al. (2016) and using the Eq. (10), we measure the first alternative proxy of excess perks consumption (EPC4). Specifically, Expperk1 in the Eq. (10) is the ratio of the sum of the above eight categories to sales revenue. We estimate the Eq. (10) for the firms in each industry for each year and compute the expected value of Expperk1, and then employ the residuals from the expected model to generate our first alternative proxy for excess perks consumption (EPC4).

Expperk1it=α1LnTotalCompit+α2LnAssetit+α3LnTotalIncPerCapit+εit 	(10)

In the second method, perquisite consumption includes the following six categories: traveling expenses, business entertainment expenses, overseas training expenses, board meeting expenses, meeting expenses, and company car expenses. Same as the first method, we estimate the expected value of Expperk2 based on the Eq. (11) and use the residual values as our second alternative measure for excess perks consumption (EPC5). Expperk2 in Eq. (11) is the ratio of the sum of the six categories to sales revenue.

Expperk2it=α1LnTotalCompit+α2LnAssetit+α3LnTotalIncPerCapit+εit 	(11)

In the third method, we use abnormal administration expense to measure the alternative proxy for excess perks consumption based on the Eq. (12). Out of the total administrative expenses, we exclude bad debt expenses, unrealized holding gain or loss for inventory, and direct compensation for directors and top executives. Expperk3 in Eq. (12) is the ratio of the remainder of administrative expense to sales revenue. Then, we estimate the normal level of administrative expenses (the expected value of Expperk3) by using Eq. (12) and use the residual values as the abnormal administrative expenses, i.e., our last alternative proxy for excess perks consumption (EPC6).

Expperk3it=α1LnTotalCompit+α2LnAssetit+α3LnTotalIncPerCapit+εit 	(12)

Where LnTotalComp is the log of total compensation for all employees of the firm, LnAsset is the natural log of the book value of total assets, and the LnTotalIncPerCap is the natural log of total income per capital of the region in which the firm is located. Similarly, the EPC4, EPC5 and EPC6 are also multiplied by 100 in order to avoid the impact of small values on empirical result.
Results are reported in Table 8. All the coefficients for the different additional proxies of excess perks consumption (include EPC4, EPC5, and EPC6) are positive and statistically significant at 1% level. Collectively, the evidence shows that the firms with larger parent-subsidiary dispersion have higher excess perks consumption, which is in agreement with previous results and appears to be robust.
INSERT “TABLE 8” ABOUT HERE

6.6. Further evidence on the investor recognition hypothesis

The previous empirical evidence is supportive of information asymmetry hypothesis and is against the investor recognition hypothesis. In this section, we explore further why the investor recognition hypothesis fails to explain the relationship between parent-subsidiary dispersion and excess perks consumption. The investor recognition hypothesis argues that firms with large parent-subsidiary dispersion may have a larger investor base and a higher investor recognition, which makes the firms subject to more public attention and supervision, and then constrain excess perks consumption. Therefore, there are two possibilities why there is no evidence in favor of the hypothesis. First, it is possible that a firm with large parent-subsidiary dispersion does not possess higher investor recognition, so no more external supervision constrain the excess perks consumption of managers. Second, it might be the case that the firm with large parent-subsidiary dispersion has high investor recognition. However, the attention and supervision from external investors have no deterrent effect on managers and therefore, do not result in a reduction of excess perks consumption. We provide further evidence on this issue by testing the relation among parent-subsidiary dispersion, investor recognition, and excess perks consumption.
We utilize three alternative measures of investor recognition, including analyst coverage (ALC), institutional shareholding (INS) and shareholder base (SHB). Following Fang and Peress (2009), we measure analyst coverage as the natural log of the number of analysts plus one, representing a higher degree of investor recognition. The second proxy of investor recognition is institutional shareholding, which is measured by the percentage of the sum of institutional investors' shareholdings and is similar to the one used in Garcia and Norli (2012). Firms with high investor recognition are expected to have a higher institutional shareholding. The third proxy for investor recognition is shareholder base, similar to the one used in Amihud et al. (1999), and Bodnaruk and Ostberg (2009). It is measured as the proportion of investors in the economy that are company shareholders. Firms with high investor recognition are expected to show a larger shareholder base. 
Panel A in Table 9 presents the regression results for the impact of parent-subsidiary dispersion (DIS) on the proxies of investor recognition (ALC, INS and SHB) on. In the column (1) and (2), the dependent variable is ALC. The coefficients of GOD and IND are 0.671 and 0.416, respectively, which are positive and significant at 10% level or 1% level. In the column (3) and (4), the dependent variable is INS. The coefficients of GOD and IND are 0.003 and 0.001, respectively, which are positive and significant at 10% or 5% level. In the column (5) and (6), the dependent variable is SHB. The coefficients of GOD are positive and significant at the 10% level, but the coefficient of IND is statistically insignificant. All of these results suggest that the firm with large parent-subsidiary dispersion has higher investor recognition.
In Panel B of Table 9, we provide regression analysis result for testing the relationship between investor recognition (include ALC, INS and SHB) and excess perks consumption (EPC). In the column (1) and (2), the dependent variable is EPC1. The coefficients of the proxy for investor recognition (ALC, INS and SHB) in the column (1) are 0.057, 0.523 and 10.696, which are positive and significant at 1% level (except SHB). The coefficients of ALC, INS and SHB in the column (2) are 0.056, 0.521 and 14.129 respectively, which are also positive and significant at 1% level (except SHB). Similar results can be found when dependent variables are EPC2 (in column (3) and (4)) and EPC3 (in column (5) and (6)). The results indicate that there is no negative relationship between investor recognition and excess perks consumption. In other words, firms with higher investor recognition have more excess perks consumption, negating the investor recognition hypothesis which argues a negative relationship between them.
In conclusion, the results suggest that there is no negative relation between investor recognition and excess perks consumption as predicted by the investor recognition hypothesis. Hence, the investor recognition hypothesis fails to explain the relationship between parent-subsidiary dispersion and excess perks consumption, because higher investor recognition fails to reduce the excess perks consumption,
INSERT “TABLE 9” ABOUT HERE

7. Conclusion

Excess perks consumption by corporate executives has attracted more and more public attention due to the increasing recent corporate cases, especially in China. This has also resulted in regulators paying increasing attention to the issue of excess perks consumption, and consequently, made several regulatory changes. In order to gain more resources and greater competitive advantages, a number of firms tend to implement geographical diversity strategy by operating over wide geographic areas, which results in larger parent-subsidiary dispersion. Therefore, the purpose of our study is to offer a timely evidence by examining whether corporate executives’ excess perks consumption is facilitated or mitigated by parent-subsidiary dispersion. We suggest and empirically test two competing hypotheses to explain the relation between parent-subsidiary dispersion and excess perks consumption (i.e., the information asymmetry and investor recognition hypotheses).
Specifically and using a sample of Chinese-listed firms between 2003 and 2017, we examine the relationship between parent-subsidiary dispersion and excess perks consumption by corporate executives. The empirical results reveal evidence that supports the information asymmetry hypothesis. In particular, our findings indicate that there is a positive relationship between parent-subsidiary dispersion and excess perks consumption by corporate executives. Specifically, the parent-subsidiary dispersion seems to facilitate excess perks consumption by corporate executives. These patterns are robust to a number of alternative proxies of our main variables and endogeneity issues.
Our study extends the international corporate finance and economic geography literature both within the geographical diversification and executive compensation, especially corporate executives’ excess perks consumption areas. In particular, we contribute to the finance and economic geographical diversification literature by drawing insights directly from the perspective of parent-subsidiary dispersion. Our study also enriches the literature on corporate executives’ excess perks consumption by suggesting another important determinant (i.e., parent-subsidiary dispersion), which may affect management’s use of excess perks consumption. We show a positive relationship between excess perks consumption by corporate executives and the parent-subsidiary dispersion. Our findings have practical implications for the management of consumption of executives’ excess perks that are useful for shareholders, corporate boards and regulatory bodies. 

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Appendix
Variable 	Description 	
(a) Dependent Variables	
EPC	Three proxies (EPC1, EPC2, and EPC3) for excess perks consumption is calculated on the basis of the sum of eight categories, the sum of six categories, and abnormal administrative expenses. Specifically, the eight categories include: business travel expenses, business entertainment expenses, training abroad expenses, board meeting expenses, office administrative expenses, communication expenses, company car and chauffeur service, and conference expenses. The six categories include: traveling expenses, business entertainment expenses, overseas training expenses, board meeting expenses, meeting expenses, and company car expenses.	
(b) Independent Variables	
DIS	We employ both geographic dispersion (GOD) and institutional environmental difference (IND) as proxies for the parent-subsidiary dispersion.	
GOD	Geographic dispersion between a parent and its subsidiaries are estimated by 0.001 multiplying the standard deviation of the geographic distance between parent company ‘p’ and its subsidiary ‘j’ (Dpj).	
IND	We estimate institutional environmental difference by calculating the standard deviation of the institutional environmental difference between the parent company and its subsidiary.	
(c) Control Variables	
SIZE	Firm size is measured by the natural logarithm of the total asset of the previous period.	
GRO	Growth opportunity is measured by operating income growth rate.	
SHR	The equity concentration ratio is measured by the shareholding ratio of the largest shareholders.	
ROA	Profitability is measured by the ratio of operating profit to total assets of the previous period.	
LEV	Capital structure is measured by the ratio of total debt to total assets.	
BOS	Duality status is a dummy variable, where the value is ‘1’ if the Chairman is also the CEO and ‘0’ otherwise. 	
BOA	Board size is measured by the natural logarithm of the total number of directors on the board.	
IPB	Board independence is measured by the proportion of independent directors over the total number of directors on the board.	
SOE	A dummy variable where the value is ‘1’ if the firm is state-owned and ‘0’ otherwise.	
BIG	A dummy variable where value is ‘1’ if the accounting firm belongs to the top ten accounting firms and ‘0’ otherwise.	
OPI	The type of audit opinion is a dummy variable where the value is ‘1’ if the financial statements are given a standard unqualified opinion and ‘0’ otherwise.	
Media	Media coverage is measured by the natural logarithm of the sum of the number of online media reports covering the firms in year ‘t’ for firm ‘i' and 1.	
(d) Variables related to investor recognition hypothesis	
ALC	Analyst coverage is measured by the natural log of the number of analysts plus one.	
INS	Institutional shareholding is measured by the percentage of the sum of institutional investors' shareholdings.	
SHB	Shareholder base is measured by the proportion of investors in the economy that are company shareholders.	

Table 1
Sample distribution. 
Panel A shows the distribution of sample firms in 2003-2017 across industries based on the Guidance on the Industry Category of listed Companies (Revised in 2012) issued by the CSRC, where A=Agriculture, forestry, husbandry and fishery, B=Mining, C=Manufacturing, D=Electricity, thermal, gas and water, E=Construction, F=Wholesale and retail trade, G=Transport, storage and post, H=Hotels and catering service, I=Information transmission, software and information technology, K=Estate, L=Leasing and business Services, N=Water conservancy, environment and public facilities management, P=Education, Q=Health and social work, S=Comprehensive. Panel B shows the sample distribution by year. 
Panel A: Industry distribution	
	A	B	C	D	E	F	G	H	I	K	L	N	P	Q	S	Total	
N	404	744	14932	1163	707	1706	982	108	1531	1577	402	318	28	87	287	24976	
%	1.62	2.98	59.79	4.66	2.83	6.83	3.93	0.43	6.13	6.31	1.61	1.27	0.11	0.35	1.15	100	

Panel B: Yearly distribution	
	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	Total	
N	309	542	925	1153	1263	1378	1461	1606	1943	2186	2305	2343	2424	2566	2572	24976	
%	1.24	2.17	3.7	4.62	5.06	5.52	5.85	6.43	7.78	8.75	9.23	9.38	9.71	10.27	10.3	100	

Table 2
Descriptive statistics.
This table reports the descriptive statistics for the whole sample. The sample consists of Chinese listed firms in the period from 2003-2017. All the continuous variables are winsorized at 1% level. Variables are defined in the Appendix.
Variable	N	Mean	Median	Min	Max	Std. Dev	Variance	
EPC1	24976	0.013 	-0.033 	-1.547 	4.238 	0.798 	0.636 	
EPC2	24976	0.036 	-0.023 	-1.879 	3.680 	0.739 	0.547 	
EPC3	24976	0.176 	-0.241 	-7.028 	12.574 	3.150 	9.923 	
GOD	24976	0.459 	0.467 	0.000 	1.357 	0.308 	0.095 	
IND	24976	1.192 	1.247 	0.000 	3.508 	0.833 	0.693 	
SIZE	24976	21.760 	21.621 	19.065 	25.600 	1.268 	1.607 	
GRO	24976	0.223 	0.124 	-0.678 	4.297 	0.596 	0.355 	
SHR	24976	0.355 	0.334 	0.088 	0.743 	0.152 	0.023 	
ROA	24976	0.049 	0.041 	-0.218 	0.362 	0.082 	0.007 	
LEV	24976	0.473 	0.471 	0.054 	1.262 	0.227 	0.052 	
BOS	24976	0.217 	0.000 	0.000 	1.000 	0.412 	0.170 	
BOA	24976	2.166 	2.197 	1.609 	2.708 	0.203 	0.041 	
IPB	24976	0.368 	0.333 	0.273 	0.571 	0.052 	0.003 	
SOE	24976	0.623 	1.000 	0.000 	1.000 	0.485 	0.235 	
BIG	24976	0.519 	1.000 	0.000 	1.000 	0.500 	0.250 	
OPI	24976	0.947 	1.000 	0.000 	1.000 	0.223 	0.050 	
Media	24976	3.822 	4.249 	0.001 	6.528 	1.681 	2.826 	

Table 3
Descriptive statistics based on a grouped sample.
Panel A presents the descriptive statistics for firms with low, medium and high geographic dispersion between parent and subsidiary companies. Panel B presents the descriptive statistics for firms with short, medium and large institutional environmental difference between parent and subsidiary companies.
All the continuous variables are winsorized at 1% level. See the Appendix for variable definitions. ***, **, * statistically significant at the 1%, 5%, 10% levels, respectively. T-statistics and z-statistics are presented in parentheses.
Panel A: Descriptive statistics for firms with short, medium and large geographic distance between parent and subsidiary companies
Variables	Low geographic dispersion	Medium geographic dispersion	High geographic dispersion	Mean difference	Median difference	
	N	Mean	Median	N	Mean	Median	N	Mean	Median		t-value		z-value	
EPC1	8327	-0.064 	-0.035 	8324	0.043 	-0.033 	8325	0.060 	-0.029 	-0.124***	(-10.654) 	-0.006***	(-7.322) 	
EPC2	8327	-0.051 	-0.031 	8324	0.047 	-0.023 	8325	0.112 	-0.017 	-0.163***	(-15.162) 	-0.014***	(-12.464) 	
EPC3	8327	-0.099 	-0.419 	8324	0.315 	-0.114 	8325	0.313 	-0.197 	-0.412***	(-8.488) 	-0.222***	(-7.166) 	
SIZE	8327	21.542 	21.450 	8324	21.857 	21.693 	8325	21.880 	21.711 	-0.337*** 	(-17.552) 	-0.260*** 	(-14.973) 	
GRO	8327	0.201 	0.107 	8324	0.243 	0.130 	8325	0.226 	0.135 	-0.025*** 	(-2.852) 	-0.027*** 	(-6.646) 	
SHR	8327	0.369 	0.354 	8324	0.353 	0.333 	8325	0.344 	0.318 	0.025*** 	(10.682) 	0.036*** 	(10.699) 	
ROA	8327	0.047 	0.039 	8324	0.051 	0.042 	8325	0.049 	0.041 	-0.002 	(-1.412) 	-0.002** 	(-2.196) 	
LEV	8327	0.474 	0.474 	8324	0.481 	0.482 	8325	0.465 	0.456 	0.009*** 	(2.564) 	0.018** 	(2.060) 	
BOS	8327	0.184 	0.001 	8324	0.218 	0.001	8325	0.247 	0.000 	-0.063*** 	(-9.909) 	0.001*** 	(-9.880) 	
BOA	8327	2.179 	2.197 	8324	2.162 	2.197 	8325	2.159 	2.197 	0.020*** 	(6.195) 	0.001*** 	(7.458) 	
IPB	8327	0.364 	0.333 	8324	0.368 	0.333 	8325	0.372 	0.333 	-0.007*** 	(-9.136) 	0.001*** 	(-8.594) 	
SOE	8327	0.700 	1.000 	8324	0.608 	1.000 	8325	0.561 	1.000 	0.139*** 	(18.780) 	0.001*** 	(18.585) 	
BIG	8327	0.475 	0.000 	8324	0.542 	1.000 	8325	0.540 	1.000 	-0.066*** 	(-8.497) 	-1.000*** 	(-8.479) 	
OPI	8327	0.940 	1.000 	8324	0.952 	1.000 	8325	0.951 	1.000 	-0.011*** 	(-3.098) 	0.001 ***	(-3.097) 	
Media	8327	3.470	3.891	8324	3.932	4.357	8325	4.062	4.477	0.692***	(-22.751)	-0.586	(-23.550)	

Panel B: Descriptive statistics for firms with short, medium and large institutional environmental distance between parent and subsidiary companies	
Variable	Short institutional 
environmental difference	Medium institutional environmental difference	Large institutional environmental difference	Mean difference	Median difference	
	N	Mean	Median	N	Mean	Median	N	Mean	Median		t-value		z-value	
EPC1	8328	-0.063 	-0.036 	8323	0.029 	-0.029 	8325	0.074 	-0.032 	-0.137***	(-11.457) 	-0.004***	(-6.782) 	
EPC2	8328	-0.052 	-0.031 	8323	0.057 	-0.019 	8325	0.104 	-0.020 	-0.156***	(-14.383) 	-0.011***	(-10.777) 	
EPC3	8328	-0.091 	-0.394 	8323	0.353 	-0.154 	8325	0.267 	-0.168 	-0.358***	(-7.800) 	-0.226***	(-7.161) 	
SIZE	8328	21.507 	21.419 	8323	21.873 	21.709 	8325	21.899 	21.751 	-0.391***  	(-20.776) 	-0.333***  	(-19.035) 	
GRO	8328	0.198 	0.104 	8323	0.243 	0.133 	8325	0.229 	0.134 	-0.031***  	(-3.469) 	-0.030***  	(-6.826) 	
SHR	8328	0.366 	0.349 	8323	0.352 	0.328 	8325	0.348 	0.326 	0.018***  	(7.498) 	0.023***  	(7.480) 	
ROA	8328	0.048 	0.040 	8323	0.049 	0.040 	8325	0.050 	0.042 	-0.002***  	(-1.899) 	-0.002***  	(-2.491) 	
LEV	8328	0.456 	0.448 	8323	0.493 	0.495 	8325	0.470 	0.465 	-0.015***  	(-4.167) 	-0.017***  	(-4.583) 	
BOS	8328	0.200 	0.000 	8323	0.219 	0.000 	8325	0.231 	0.000 	-0.032***  	(-4.954) 	0.001***  	(-4.950) 	
BOA	8328	2.174 	2.197 	8323	2.162 	2.197 	8325	2.163 	2.197 	0.011***  	(3.550) 	0.001***  	(4.519) 	
IPB	8328	0.365 	0.333 	8323	0.370 	0.333 	8325	0.369 	0.333 	-0.004***  	(-5.063) 	0.001***  	(-4.389) 	
SOE	8328	0.671 	1.000 	8323	0.627 	1.000 	8325	0.571 	1.000 	0.100***  	(13.344) 	0.001***  	(13.274) 	
BIG	8328	0.479 	0.000 	8323	0.528 	1.000 	8325	0.549 	1.000 	-0.070***  	(-9.069) 	-1.000***  	(-9.047) 	
OPI	8328	0.947 	1.000 	8323	0.946 	1.000 	8325	0.949 	1.000 	-0.003 	(-0.762) 	0.001 	(-0.762) 	
Media	8328	3.487 	3.912 	8323	3.950 	4.394 	8325	4.028 	4.443 	-0.541	(-20.820)	-0.531	(-22.487)	

Table 4 
Correlation matrix.
Spearman coefficients are in the upper triangle, and Pearson coefficients are in the lower triangle. All the continuous variables are winsorized at 1% level. See the Appendix for variable definitions. ***, **, * statistically significant at the 1%, 5%, 10% levels, respectively. T-statistics and z-statistics are presented in parentheses.
	GOD	IND	EPC1	EPC2	EPC3	SIZE	GRO	SHR	ROA	LEV	BOS	BOA	IPB	SOE	BIG	OPI	Media	
GOD	1	0.628***	0.052***	0.083***	0.048***	0.103***	0.042***	-0.069***	0.018***	-0.015**	0.06***	-0.044***	0.054***	-0.122***	0.053***	0.026***	0.162***	
IND	0.618***	1	0.049***	0.079***	0.054***	0.112***	0.043***	-0.06***	0.011*	0.014**	0.04***	-0.039***	0.035***	-0.107***	0.068***	0.001	0.156***	
EPC1	0.066***	0.081***	1	0.612***	0.286***	-0.119***	-0.002	-0.021***	0.085***	-0.024***	0.006	0.039***	-0.04***	-0.029***	-0.021***	0.024***	-0.093***	
EPC2	0.092***	0.100***	0.717***	1	0.222***	-0.151***	-0.02***	-0.052***	0.082***	-0.112***	0.035***	-0.007	-0.022***	-0.11***	-0.014**	0.023***	-0.055***	
EPC3	0.045***	0.053***	0.285***	0.240***	1	-0.153***	-0.038***	-0.025***	0.121***	-0.112***	0.038***	-0.018***	-0.012*	-0.024***	0.06***	-0.026***	0.021***	
SIZE	0.099***	0.101***	-0.120***	-0.113***	-0.163***	1	-0.016**	0.191***	0.02***	0.346***	-0.137***	0.209***	0.026***	0.222***	0.19***	0.144***	0.286***	
GRO	0.012*	0.017***	0.001	-0.018***	-0.007	-0.081***	1	0.034***	0.382***	-0.007	0.025***	0.016**	-0.007	-0.086***	-0.002	0.14***	0.031***	
SHR	-0.070***	-0.061***	-0.040***	-0.054***	-0.033***	0.224***	0.031***	1	0.137***	0.038***	-0.064***	0.036***	-0.01	0.145***	0.063***	0.112***	-0.062***	
ROA	0.009	0.004	0.112***	0.097***	0.085***	0.020***	0.333***	0.146***	1	-0.359***	0.041***	0.02***	-0.02***	-0.148***	0.064***	0.265***	0.106***	
LEV	-0.021***	0.004	-0.085***	-0.117***	-0.070***	0.284***	0.022***	0.022***	-0.377***	1	-0.129***	0.139***	-0.026***	0.306***	-0.028***	-0.2***	-0.059***	
BOS	0.056***	0.040***	0.044***	0.053***	0.039***	-0.133***	0.007	-0.069***	0.030***	-0.117***	1	-0.19***	0.101***	-0.255***	0.042***	-0.012**	0.087***	
BOA	-0.032***	-0.037***	-0.006	-0.024***	-0.017***	0.225***	-0.019***	0.046***	0.024***	0.117***	-0.179***	1	-0.45***	0.244***	-0.004	0.024***	-0.074***	
IPB	0.049***	0.041***	-0.019***	-0.013**	-0.014**	0.046***	0.009	0.005	-0.007	-0.026***	0.108***	-0.462***	1	-0.097***	0.041***	0.002	0.127***	
SOE	-0.115***	-0.107***	-0.093***	-0.138***	-0.030***	0.224***	-0.035***	0.150***	-0.111***	0.289***	-0.255***	0.247***	-0.094***	1	-0.058***	-0.035***	-0.184***	
BIG	0.042***	0.066***	0.019***	0.022***	0.043***	0.213***	-0.017***	0.063***	0.066***	-0.039***	0.042***	0.004	0.048***	-0.057***	1	0.063***	0.214***	
OPI	0.024***	-0.001	0.037***	0.046***	-0.045***	0.154***	0.057***	0.107***	0.316***	-0.294***	-0.012*	0.027***	0.003	-0.035***	0.063***	1	0.106***	
Media	0.139***	0.142***	0.023***	0.046***	0.01	0.300***	0.004	-0.072***	0.136***	-0.110***	0.089***	-0.072***	0.138***	-0.188***	0.216***	0.135***	1	

Table 5 
Regressions results of the impact of parent-subsidiary dispersion (DIS) on excess perks consumption (EPC).
The dependent variable excess perks consumption (EPC) has three alternative measures including EPC1, EPC2 & EPC3. The independent variable parent-subsidiary dispersion has two proxies which are GOD and IND. See the Appendix for the variable definitions. ***, **, * statistically significant at the 1%, 5%, 10% levels, respectively. T-statistics are based on clustered (by firm) standard errors & are presented in parentheses. 
	(1)	(2)	(3)	(4)	(5)	(6)	
	 EPC=EPC1	 EPC= EPC2	 EPC=EPC3	
	DIS=GOD	DIS=IND	DIS=GOD	DIS=IND	DIS=GOD	DIS=IND	
DIS	0.195***	0.091***	0.179***	0.082***	0.659***	0.269***	
	(7.024)	(8.369)	(7.785)	(9.309)	(5.097)	(5.939)	
SIZE	-0.103***	-0.103***	-0.065***	-0.065***	-0.579***	-0.579***	
	(-10.108)	(-10.142)	(-7.428)	(-7.488)	(-12.617)	(-12.650)	
GRO	-0.082***	-0.083***	-0.089***	-0.090***	-0.334***	-0.336***	
	(-6.896)	(-7.009)	(-6.318)	(-6.397)	(-5.635)	(-5.669)	
SHR	-0.097	-0.096	-0.113*	-0.112*	0.031	0.023	
	(-1.327)	(-1.324)	(-1.794)	(-1.801)	(0.100)	(0.073)	
ROA	1.144***	1.140***	0.768***	0.764***	4.740***	4.723***	
	(8.069)	(8.049)	(6.282)	(6.258)	(7.653)	(7.643)	
LEV	0.061	0.052	0.020	0.012	0.311	0.282	
	(1.249)	(1.065)	(0.460)	(0.270)	(1.428)	(1.301)	
BOS	0.029	0.031	0.018	0.020	0.095	0.102	
	(1.046)	(1.130)	(0.792)	(0.878)	(0.957)	(1.033)	
BOA	0.111*	0.115*	0.066	0.070	0.359	0.371	
	(1.846)	(1.927)	(1.365)	(1.451)	(1.363)	(1.407)	
IPB	-0.129	-0.113	-0.199	-0.184	-0.121	-0.066	
	(-0.646)	(-0.568)	(-1.177)	(-1.094)	(-0.146)	(-0.080)	
SOE	-0.089***	-0.088***	-0.116***	-0.115***	0.260**	0.260**	
	(-3.023)	(-3.005)	(-4.741)	(-4.734)	(2.413)	(2.406)	
BIG	0.057***	0.054**	0.045**	0.043**	0.463***	0.454***	
	(2.624)	(2.491)	(2.508)	(2.360)	(5.708)	(5.599)	
OPI	0.091**	0.098***	0.123***	0.129***	-0.745***	-0.724***	
	(2.551)	(2.742)	(3.901)	(4.081)	(-4.144)	(-4.019)	
Media	0.027***	0.027***	0.029***	0.029***	0.130***	0.133***	
	(3.797)	(3.857)	(4.670)	(4.735)	(3.850)	(3.923)	
Constant	1.799***	1.804***	1.201***	1.205***	11.255***	11.251***	
	(8.024)	(8.088)	(6.344)	(6.393)	(10.288)	(10.306)	
Year	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	
Industry	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	
N	24976	24976	24976	24976	24976	24976	
adj. R2	0.049	0.052	0.091	0.093	0.054	0.054	
F-statistics	13.771***	13.910***	28.528***	28.767***	9.795***	10.080***	

Table 6
Endogeneity analysis using 2SLS technique.
Panel A presents empirical results of the First-stage in 2SLS regression, and panel B presents empirical results of the second-stage in 2SLS regression. DIS hat is the fitted value of parent-subsidiary dispersion (DIS) based on the instrument variables. GOD hat is the fitted value of geographic dispersion (GOD) based on the instrument variables, and IND hat is the fitted value of institutional environmental difference (IND) based on the instrument variables. ***, **, * statistically significant at the 1%, 5%, 10% levels, respectively. T-statistics are based on clustered (by firm) standard errors and are presented in parentheses. See the Appendix for variable definitions.
Panel A: Empirical results of the First-stage in 2SLS regression
Variables	(1)	(2)	
	First-stage	
	DIS=GOD	DIS=IND	
Instrumental variable	0.684***	0.444***	
	(11.32)	(6.047)	
SIZE	0.034***	0.083***	
	(8.002)	(7.221)	
GRO	0.010***	0.036***	
	(2.827)	(3.680)	
SHR	-0.131***	-0.300***	
	(-4.350)	(-3.622)	
ROA	-0.028	-0.076	
	(-0.598)	(-0.600)	
LEV	-0.009	0.070	
	(-0.402)	(1.089)	
BOS	0.015*	0.013	
	(1.689)	(0.532)	
BOA	-0.006	-0.061	
	(-0.242)	(-0.975)	
IPB	0.024	-0.059	
	(0.304)	(-0.264)	
SOE	-0.056***	-0.124***	
	(-5.307)	(-4.106)	
BIG	-0.003	0.021	
	(-0.405)	(0.945)	
OPI	0.006	-0.073*	
	(0.411)	(-1.674)	
Media 	0.012***	0.024**	
	(3.689)	(2.546)	
Constant	-0.501***	-1.014***	
	(-4.717)	(-3.571)	
Year & Industry	Controlled	Controlled	
Observations	24976	24976	
Adj-R2	0.105	0.075	
Tests for the relevance of instruments	
First-stage F-statistic	128.12***	36.56***	
Partial R2	0.040	0.009	
Under-identification test			
Kleibergen–Paap rk LM statistic (x2)	97.55***	34.86***	
Weak identification test			
Kleibergen–Paap Wald rk F-statistic
(Critical value = 16.38)	128.12***	36.56***	
Weak-instrument robust inference test	
Anderson–Rubin Wald test (F-stat)	4.73**	4.89**	
Stock–Wright LM S statistic (x2)	4.75**	4.85**	
Panel B: Empirical results of the second-stage in 2SLS regression

	(1)	(2)	(3)	(4)	(5)	(6)	
	EPC=EPC1	EPC=EPC2	EPC=EPC3	
	DIS hat=GOD hat	DIS hat=IND hat	DIS hat=GOD hat	DIS hat=IND hat	DIS hat=GOD hat	DIS hat=IND hat	
DIS hat	0.363**	0.294**	0.339**	0.280**	1.894**	2.256***	
	(2.136)	(2.078)	(2.398)	(2.322)	(2.521)	(3.335)	
SIZE	-0.109***	-0.120***	-0.070***	-0.082***	-0.622***	-0.744***	
	(-9.405)	(-7.895)	(-7.152)	(-6.217)	(-12.153)	(-9.984)	
GRO	-0.084***	-0.091***	-0.091***	-0.098***	-0.349***	-0.413***	
	(-6.880)	(-6.829)	(-6.387)	(-6.551)	(-5.886)	(-6.293)	
SHR	-0.075	-0.037	-0.091	-0.054	0.195	0.606	
	(-0.959)	(-0.422)	(-1.388)	(-0.734)	(0.595)	(1.513)	
ROA	1.155***	1.161***	0.779***	0.784***	4.821***	4.919***	
	(8.080)	(8.014)	(6.313)	(6.227)	(7.742)	(7.403)	
LEV	0.065	0.041	0.023	0.001	0.338	0.177	
	(1.330)	(0.800)	(0.541)	(0.024)	(1.535)	(0.705)	
BOS	0.025	0.027	0.016	0.017	0.072	0.070	
	(0.933)	(0.999)	(0.670)	(0.732)	(0.723)	(0.636)	
BOA	0.112*	0.126**	0.067	0.081	0.363	0.478	
	(1.847)	(2.047)	(1.365)	(1.599)	(1.376)	(1.630)	
IPB	-0.140	-0.105	-0.210	-0.176	-0.204	0.010	
	（-0.699）	（-0.514）	（-1.227）	（-1.005）	（-0.246）	（0.011）	
SOE	-0.080***	-0.063*	-0.108***	-0.091***	0.323***	0.503***	
	(-2.576)	(-1.909)	(-4.152)	(-3.243)	(2.819)	(3.459)	
BIG	0.057***	0.048**	0.046**	0.037**	0.465***	0.396***	
	(2.638)	(2.147)	(2.519)	(1.967)	(5.711)	(4.240)	
OPI	0.090**	0.113***	0.122***	0.144***	-0.747***	-0.573***	
	(2.544)	(3.013)	(3.902)	(4.298)	(-4.138)	(-2.751)	
Media	0.025***	0.022***	0.027***	0.024***	0.114***	0.084**	
	(3.325)	(2.733)	(4.123)	(3.432)	(3.177)	(1.981)	
Constant	1.835***	1.909***	1.236***	1.307***	11.522***	12.278***	
	(8.083)	(8.136)	(6.403)	(6.426)	(10.525)	(9.925)	
Year	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	
Industry	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	
N	24976	24976	24976	24976	24976	24976	
adj. R2	0.045	0.010	0.086	0.047	0.040	0.033	
chi2	518.158***	488.625***	1088.537***	938.998***	391.891***	327.987***	

Table 7
Regression results of the alternative measures of parent-subsidiary dispersion (AGOD and AIND) on the excess perks consumption (EPC).
AGOD and AIND are two alternative measures for parent-subsidiary dispersion. ***, **, * statistically significant at the 1%, 5%, 10% levels, respectively. T-statistics are based on clustered (by firm) standard errors and are presented in parentheses. See the Appendix for variable definitions.
	(1)	(2)	(3)	(4)	(5)	(6)	
	EPC=EPC1	EPC=EPC2	EPC=EPC3	
	DIS=AGOD	DIS=AIND	DIS=AGOD	DIS=AIND	DIS=AGOD	DIS=AIND	
DIS	0.146***	0.060***	0.122***	0.049***	0.677***	0.220***	
	(4.264)	(4.345)	(4.165)	(4.278)	(4.303)	(3.917)	
SIZE	-0.102***	-0.101***	-0.063***	-0.063***	-0.584***	-0.577***	
	(-10.020)	(-9.866)	(-7.259)	(-7.181)	(-12.692)	(-12.527)	
GRO	-0.082***	-0.082***	-0.089***	-0.089***	-0.337***	-0.336***	
	(-6.884)	(-6.922)	(-6.285)	(-6.309)	(-5.668)	(-5.648)	
SHR	-0.105	-0.107	-0.121*	-0.123*	0.026	0.004	
	(-1.433)	(-1.454)	(-1.935)	(-1.961)	(0.085)	(0.012)	
ROA	1.137***	1.134***	0.761***	0.759***	4.724***	4.706***	
	(8.013)	(7.991)	(6.218)	(6.195)	(7.624)	(7.605)	
LEV	0.058	0.051	0.017	0.011	0.302	0.275	
	(1.181)	(1.038)	(0.389)	(0.256)	(1.386)	(1.261)	
BOS	0.030	0.032	0.020	0.022	0.097	0.105	
	(1.094)	(1.165)	(0.851)	(0.919)	(0.977)	(1.064)	
BOA	0.112*	0.114*	0.067	0.069	0.364	0.370	
	(1.862)	(1.899)	(1.383)	(1.420)	(1.383)	(1.403)	
IPB	-0.122	-0.111	-0.192	-0.183	-0.105	-0.057	
	(-0.612)	(-0.556)	(-1.137)	(-1.084)	(-0.127)	(-0.069)	
SOE	-0.091***	-0.091***	-0.119***	-0.118***	0.262**	0.256**	
	(-3.099)	(-3.095)	(-4.855)	(-4.861)	(2.422)	(2.369)	
BIG	0.057***	0.056**	0.045**	0.044**	0.463***	0.458***	
	(2.610)	(2.557)	(2.493)	(2.440)	(5.697)	(5.641)	
OPI	0.090**	0.092***	0.122***	0.124***	-0.750***	-0.740***	
	(2.517)	(2.585)	(3.852)	(3.909)	(-4.167)	(-4.107)	
Media	0.028***	0.028***	0.030***	0.030***	0.131***	0.135***	
	(3.896)	(3.968)	(4.779)	(4.854)	(3.861)	(3.973)	
Constant	1.824***	1.823***	1.218***	1.217***	11.421***	11.355***	
	(8.115)	(8.133)	(6.408)	(6.418)	(10.408)	(10.332)	
Year	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	
Industry	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	
N	24976	24976	24976	24976	24976	24976	
adj. R2	0.046	0.047	0.087	0.087	0.053	0.052	
F-statistics	13.048***	12.799***	27.543***	27.407***	9.656***	9.534***	

Table 8
Regression results of parent-subsidiary dispersion (DIS) on the alternative measures for excess perk consumption (EPC4, EPC5 and EPC6).
EPC4, EPC5, and EPC6 are three alternative measures for excess perks consumption. ***, **, * statistically significant at the 1%, 5%, 10% levels, respectively. T-statistics are based on clustered (by firm) standard errors and are presented in parentheses. See the Appendix for variable definitions.
	(1)	(2)	(3)	(4)	(5)	(6)	
	EPC=EPC4	EPC=EPC5	EPC=EPC6	
	DIS=GOD	DIS=IND	DIS=GOD	DIS=IND	DIS=GOD	DIS=IND	
DIS	0.470***	0.237***	0.326***	0.160***	4.953***	2.540***	
	(7.167)	(9.228)	(6.938)	(8.961)	(4.664)	(6.490)	
SIZE	0.078***	0.075***	0.057***	0.056***	3.180***	3.141***	
	(3.528)	(3.375)	(3.708)	(3.574)	(7.901)	(7.798)	
GRO	-0.105***	-0.109***	-0.076***	-0.078***	-1.228*	-1.265**	
	(-4.114)	(-4.286)	(-4.280)	(-4.449)	(-1.923)	(-1.985)	
SHR	-0.574***	-0.567***	-0.413***	-0.409***	0.139	0.227	
	(-3.770)	(-3.742)	(-3.755)	(-3.740)	(0.059)	(0.097)	
ROA	0.009	0.002	0.029	0.024	-7.781	-7.856	
	(0.032)	(0.006)	(0.141)	(0.115)	(-1.499)	(-1.514)	
LEV	-0.653***	-0.676***	-0.420***	-0.436***	-2.030	-2.273	
	(-5.035)	(-5.240)	(-4.538)	(-4.729)	(-0.741)	(-0.833)	
BOS	0.062	0.067	0.022	0.026	1.279*	1.329*	
	(1.088)	(1.182)	(0.546)	(0.632)	(1.694)	(1.763)	
BOA	0.308**	0.319**	0.189**	0.196**	4.852**	4.970**	
	(2.457)	(2.571)	(2.174)	(2.282)	(2.469)	(2.537)	
IPB	0.631	0.672	0.444	0.472	7.280	7.709	
	(1.502)	(1.611)	(1.486)	(1.593)	(1.129)	(1.196)	
SOE	-0.102	-0.097	-0.088**	-0.085*	2.124**	2.182***	
	(-1.607)	(-1.537)	(-1.966)	(-1.908)	(2.571)	(2.645)	
BIG	0.133***	0.125***	0.105***	0.099***	1.566***	1.482**	
	(2.940)	(2.773)	(3.244)	(3.082)	(2.691)	(2.540)	
OPI	-0.457***	-0.439***	-0.285***	-0.272***	-13.256***	-13.055***	
	(-3.543)	(-3.438)	(-3.038)	(-2.935)	(-4.422)	(-4.385)	
Media	0.071***	0.072***	0.052***	0.053***	0.814***	0.818***	
	(4.398)	(4.439)	(4.646)	(4.695)	(2.604)	(2.625)	
Constant	-1.696***	-1.676***	-1.206***	-1.194***	-72.981***	-72.741***	
	(-3.368)	(-3.342)	(-3.438)	(-3.415)	(-7.792)	(-7.744)	
Year	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	
Industry	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	
N	24976	24976	24976	24976	24976	24976	
adj. R2	0.027	0.032	0.027	0.031	0.033	0.034	
F-statistics	7.503***	8.855***	7.397***	8.789***	10.945***	11.215***	

Table 9
The regression results of further analysis on investor recognition hypothesis.
Panel A presents the regression results for the impact of parent-subsidiary dispersion (DIS) on the proxies of investor recognition (ALC, INS and SHB). Panel B presents the regression results of the proxies of investor recognition (ALC, INS and SHB) on the excess perks consumption (EPC). Analyst coverage (ALC), institutional shareholding (INS) and shareholder base (SHB) are three proxies for investor recognition. ***, **, * statistically significant at the 1%, 5%, 10% levels, respectively. T-statistics are based on clustered (by firm) standard errors and are presented in parentheses. See the Appendix for variable definitions.
Panel A: The regression results for the impact of parent-subsidiary dispersion (DIS) on the proxies of investor recognition (ALC, INS and SHB)	
	(1)	(2)	(3)	(4)	(5)	(6)	
	ALC	INS	SHB	
	DIS=GOD	DIS=IND	DIS=GOD	DIS=IND	DIS=GOD	DIS=IND	
DIS	0.671*	0.416***	0.003*	0.001**	0.001*	-0.001	
	(1.913)	(3.098)	(1.710)	(2.011)	(1.654)	(-0.216)	
SIZE	3.670***	3.659***	0.005***	0.005***	0.001***	0.001***	
	(23.659)	(23.478)	(7.793)	(9.052)	(25.548)	(25.444)	
GRO	-0.815***	-0.823***	-0.001**	-0.001**	0.001***	0.001***	
	(-5.222)	(-5.265)	(-2.347)	(-2.169)	(7.743)	(7.791)	
SHR	-3.126***	-3.093***	-0.044***	-0.042***	-0.001***	-0.001***	
	(-3.305)	(-3.264)	(-10.835)	(-10.106)	(-8.125)	(-8.201)	
ROA	55.880***	55.877***	0.147***	0.146***	-0.001***	-0.001***	
	(28.335)	(28.346)	(18.995)	(19.247)	(-9.540)	(-9.561)	
LEV	0.193	0.156	0.010***	0.009***	-0.001***	-0.001***	
	(0.315)	(0.256)	(3.453)	(3.210)	(-10.586)	(-10.608)	
BOS	0.864***	0.870***	0.001	0.001	0.001	0.001	
	(2.821)	(2.843)	(0.980)	(0.898)	(0.063)	(0.115)	
BOA	1.031	1.051	-0.005	-0.004	0.001	0.001	
	(1.402)	(1.431)	(-1.494)	(-1.140)	(0.562)	(0.557)	
IPB	0.321	0.382	-0.010	-0.007	0.001***	0.001***	
	(0.119)	(0.142)	(-0.888)	(-0.669)	(4.312)	(4.325)	
SOE	-2.765***	-2.749***	0.001	0.001	0.001***	0.001***	
	(-8.235)	(-8.198)	(0.591)	(0.912)	(4.619)	(4.515)	
BIG	0.561**	0.548**	-0.001	-0.001	0.001	0.001	
	(2.279)	(2.224)	(-0.113)	(-0.028)	(1.361)	(1.361)	
OPI	-2.809***	-2.776***	0.003	0.003	-0.001***	-0.001***	
	(-5.941)	(-5.887)	(1.297)	(1.228)	(-6.208)	(-6.205)	
Media	1.384***	1.383***	0.004***	0.003***	0.001***	0.001***	
	(12.436)	(12.463)	(6.901)	(6.889)	(3.751)	(3.827)	
Constant	-71.939***	-71.869***	-0.054***	-0.066***	-0.005***	-0.005***	
	(-20.865)	(-20.831)	(-3.716)	(-4.277)	(-19.433)	(-19.414)	
Year	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	
Industry	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	
N	24976	24976	24976	24976	24976	24976	
adj. R2	0.397	0.397	0.098	0.106	0.462	0.462	
F-statistics	77.368***	77.696***	39.582***	27.850***	43.209***	42.998***	
Panel B: The regression results of the proxies of investor recognition (ALC, INS and SHB) on the excess perks consumption (EPC)
	(1)	(2)	(3)	(4)	(5)	(6)	
	EPC=EPC1	EPC=EPC2	EPC=EPC3	
	DIS=GOD	DIS=IND	DIS=GOD	DIS=IND	DIS=GOD	DIS=IND	
ALC	0.057***	0.056***	0.048***	0.047***	0.325***	0.322***	
	(5.813)	(5.725)	(5.735)	(5.641)	(8.952)	(8.860)	
INS	0.523***	0.521***	0.231	0.230	1.310*	1.312*	
	(2.699)	(2.689)	(1.409)	(1.398)	(1.695)	(1.693)	
SHB	10.696	14.129	-9.120	-5.920	-8.255	3.623	
	(0.528)	(0.695)	(-0.504)	(-0.325)	(-0.080)	(0.035)	
DIS	0.190***	0.088***	0.176***	0.080***	0.635***	0.255***	
	(6.878)	(8.157)	(7.686)	(9.112)	(4.983)	(5.678)	
SIZE	-0.131***	-0.132***	-0.083***	-0.084***	-0.715***	-0.716***	
	(-9.686)	(-9.744)	(-7.042)	(-7.114)	(-12.268)	(-12.313)	
GRO	-0.079***	-0.080***	-0.086***	-0.087***	-0.315***	-0.318***	
	(-6.679)	(-6.807)	(-6.146)	(-6.237)	(-5.348)	(-5.388)	
SHR	-0.066	-0.064	-0.103	-0.101	0.109	0.104	
	(-0.905)	(-0.886)	(-1.643)	(-1.633)	(0.347)	(0.333)	
ROA	0.774***	0.778***	0.475***	0.479***	2.832***	2.837***	
	(5.649)	(5.673)	(3.965)	(3.990)	(4.522)	(4.539)	
LEV	0.075	0.067	0.028	0.020	0.383*	0.358	
	(1.510)	(1.351)	(0.631)	(0.466)	(1.736)	(1.630)	
BOS	0.023	0.025	0.014	0.016	0.063	0.071	
	(0.833)	(0.917)	(0.592)	(0.681)	(0.645)	(0.722)	
BOA	0.098	0.102*	0.055	0.059	0.280	0.291	
	(1.626)	(1.706)	(1.128)	(1.214)	(1.065)	(1.109)	
IPB	-0.135	-0.121	-0.195	-0.182	-0.128	-0.083	
	(-0.677)	(-0.611)	(-1.153)	(-1.082)	(-0.156)	(-0.101)	
SOE	-0.069**	-0.069**	-0.098***	-0.098***	0.378***	0.375***	
	(-2.386)	(-2.384)	(-4.080)	(-4.088)	(3.515)	(3.491)	
BIG	0.054**	0.051**	0.043**	0.040**	0.445***	0.437***	
	(2.485)	(2.355)	(2.377)	(2.233)	(5.532)	(5.426)	
OPI	0.093***	0.101***	0.123***	0.130***	-0.734***	-0.712***	
	(2.623)	(2.815)	(3.924)	(4.107)	(-4.109)	(-3.980)	
Media	0.016**	0.016**	0.020***	0.021***	0.073**	0.076**	
	(2.217)	(2.287)	(3.256)	(3.330)	(2.143)	(2.217)	
Constant	2.358***	2.369***	1.574***	1.584***	14.001***	14.028***	
	(8.412)	(8.477)	(6.529)	(6.587)	(10.743)	(10.769)	
Year	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	
Industry	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	
N	24976	24976	24976	24976	24976	24976	
adj. R2	0.056	0.058	0.095	0.097	0.064	0.065	
F-statistics	13.773***	13.754***	26.936***	27.060***	10.628***	10.971***