Inter-regional M&As, home bias and the post-merger performance

ABSTRACT The under-performance of mergers and acquisitions (M&As) resulting from impairment of goodwill is of great interest to regulators and investors. This study investigates the performance of M&As from the perspective of home bias. The results indicate that (1) Post-merger performance is worse when purchased companies are in high-level executives’ hometowns. (2) When located in the executives’ hometown, listed companies pay higher premiums, and further loss of goodwill occurs in the following years. Integration efficiency after M&As is lower, as reflected in the higher degree of excess employees. (3) Cross-sectional analyses indicate that M&A under-performance only occurs when the province has a low level of market development, listed companies are non-state-owned enterprises, or listed companies’ executives are older. (4) Interactive effect test results suggest that fund holding, analyst following and media coverage can alleviate the negative impact of home bias on long-term performance.


Introduction
Spurred by the trend of adjusting the mode and structure of China's economic development, mergers and acquisitions (M&As) have become an important means for listed companies to realise industrial integration. Policy-making institutions also vigorously support enterprises' pursuit of M&A activities to improve the function of the capital market in the interest of the development of the real economy. In March 2014, the State Council issued the 'Opinions on Further Optimising the Market Environment of Mergers and Reorganisations', which promoted M&As in China's capital market. According to Sina Corporation's 2017 financial data, the M&A transaction amount of listed companies was 889.2 billion yuan in 2013 and increased to 2.39 trillion yuan by 2016, with an average annual growth rate of 41.14%, ranking second in the world. 1 Under this background, listed companies have reported huge goodwill after M&As, which brought the risk of impairment. For example, in 2018, a total of 882 listed companies made provision To test this research question with tension, 1460 M&As initiated by listed companies from 2005 to 2016 were used as research samples. There are two types of samples in this study. If the acquired enterprise is located in the province where the executives of listed companies were born, it will be called 'M&As-in-Hometown'. The other is called 'other M&As'. The results indicate that the long-term market performance of M&As-in-Hometown is worse, but only among executives at the level of chairman or CFO. This finding remains valid after considering five alternative explanations. Furthermore, the results using short-term market performance, Cumulative Abnormal Return (CAR), as an alternative indicator of M&As' performance also support the finding. Second, the poor performance of M&As-in-Hometown is partly due to higher premiums paid by listed companies and higher impairment losses of goodwill following M&As. The integration effect is also found to be one of the mechanisms that affect performance. The results imply that the degree of excess employees is significantly higher after listed companies acquire enterprises in the hometowns of executives. In addition, the home bias of executives is found to not be limited to executives' city of birth but is also reflected in entire provinces. Furthermore, the cross-sectional analysis indicates that the under-performance of M&As-in-Hometown is only evident in the sample group in which the province where the target is located has a low level of marketisation, the listed company is a non-state-owned enterprise or senior executives are older. Finally, fund holding, analyst following and media coverage are found to alleviate the negative impact of executives' home bias on M&As' performance.
The contributions of this paper are threefold. First, existing literature mostly examines M&As' performance from the institutional background perspective. After an extensive review, it is apparent that no paper has investigated this issue from the perspective of home bias. This paper clarifies the mechanism of this influence. (1) Listed firms pay higher and unreasonable premiums for M&As-in-Hometown, leading to listed firms reporting higher goodwill impairment losses in the year following the merger. (2) Home bias affects the efficiency of post-merger integration; that is, the degree of excess employees following M&As-in-Hometown is significantly higher. This paper supplements the literature in the field by identifying factors that affect M&A performance.
Second, western scholars generally find that home bias brings information advantages to capital market participants. Chinese scholars have investigated home bias from the perspective of credit or investment (Cao et al., 2018). In contrast to research confirming that home bias brings information advantage and mutual trust, the informal system embedded in individual consciousness demonstrates a stronger hometown identity in the context of M&As in China. This paper is a useful supplement to investigations of the relationship between the informal system of home bias and the operational efficiency of China's capital market.
Finally, this paper offers policy implications for regulators. Under the increasing trend of M&As, listed companies can gain advantages in production capacity or technology through merging with other enterprises and have a role in improving the capital market to serve the development of the real economy. In this context, theoretical and practical researchers should clarify the influences of various formal and informal systems on the performance of M&As, in addition to clarifying how to manage or improve such factors. This paper suggests that internal and external governance mechanisms can mitigate the negative impact of home bias on M&As and provides a practical, evidence-based reference.

Literature on M&As' performance
Western scholars mainly focus on the degree of information asymmetry between enterprises participating in M&As (Megginson et al., 2004;Portes & Rey, 2005) and post-merger synergy (Bradley et al., 1988). China's unique institutional factors can affect the performance of Chinese enterprises following M&As (Fang, 2008;Pan & Yu, 2011;Qiao, 2012). The degree of development of factors in the market can also have an impact on M&A behaviour. For example, Fang (2009) found that marketisation improvement effectively alleviated the barriers to cross-provincial M&As. In addition to formal institutional factors, senior executives' motivation for personal political promotion, personal social capital and abilities in information communication are also key factors affecting M&As. For example, when executives of state-owned enterprises are faced with greater opportunities for political promotion, enterprises will choose the growth mode of M&As more (Chen et al., 2015).  find that CEO's cultural background has a significant impact on M&A performance. CEOs with northern cultural backgrounds have better short-and long-term M&A performance.
In summary, Chinese scholars have studied the performance of M&As from perspectives of institutional factors (local protection, government intervention, factor marketisation and other considerations) and executives' personal characteristics. At present, minimal research has investigated M&As' long-term performance from the perspective of home bias. To better understand the influence of informal systems on China's M&A market, the following describes the literature in the field of home bias.

Literature on home bias
Early research sought to explain the reasons for the existence of home bias from the perspective of information advantage; that is, local investors' information advantages over local enterprises or assets (Nieuwerburgh & Veldkamp, 2009). Previous research demonstrated the phenomenon of home bias in terms of equity investment, bond investment and venture capital markets (Dong & Xiao, 2011;Hau & Rey, 2008;Hochberg & Rauh, 2013). Nevertheless, information advantage cannot fully explain the reasons why home bias exists in capital markets. For example, Pool et al.'s (2012)study found that fund managers held more shares of companies in their hometowns. However, the return is not higher. The reason for this phenomenon is found in another theory to explain home bias; that is, capital market participants may make decisions based on identification with their hometown. Kranton (2000, 2002) proposed the concept of identity economics, defining identity as the self-perception associated with a specific social category. After introducing identity factors, individual utility depends on three factors, which are one's own behaviour, the behaviour of others in the group and self-identity. This means that to maximise individual utility, in addition to one's own self-behaviour, other people influence individuals and their self-identity in the group.
Subsequently, a large number of articles have confirmed that this theory can explain many economic phenomena. For example, Li and Xu (2016) found that identity is one of the deep-seated factors affecting regional economic development. Specifically, the political resources of Central Committee members owned by the provinces help local governments achieve more expedient economic growth and achieve higher investment in fixed assets and bank loans (Shih, 2004). More financial transfer payments are received in hometowns of minister-level officials in China, and economic growth is also more rapid (Fan et al., 2016). In addition to regional economic growth, micro-enterprise behaviour is also influenced by executives' hometown preferences. For example, Cao et al. (2018) found that listed companies will set up more subsidiaries in CEOs' hometowns. Lai et al. (2020) revealed that local CEOs are unlikely to cut R&D spending by exceeding analysts' forecasts or avoiding revenue decline.
In summary, the above research presents a theoretical basis of the influence of home bias on economic behaviour that includes characteristics of information advantage and identity. However, in China, home bias is manifested as hometown identity when it also affects regional economic development and corporate behaviour. For example, the home bias of government officials and enterprise executives is reflected in giving back to their hometowns more, and information advantage is less apparent. This paper conducts a theoretical analysis from perspectives of information advantage and hometown identity and proposes research hypotheses.

Research hypotheses
As noted, the two effects of home bias on executives can affect M&As' performance from two different directions. From the perspective of executives' access to information, there is a natural barrier to information acquisition in inter-regional M&As, and the information asymmetry between listed companies and acquired companies is relatively high. Whether the purchaser is able to accurately assess the true value of the acquisition target depends on the degree of mutual information asymmetry. Compared with other regions, executives are more familiar with the economic, cultural and social environment of their hometowns and can also obtain relevant information on M&As' subject matter through a variety of private channels. For example, they can easily speak with friends in their hometown, local media and distributors and suppliers in the industry, and these actors trust them more and will deliver more authentic information to the executives (Coval & Moskowitz, 1999). In other words, the executives of listed companies can more accurately access word-of-mouth of targets, the financial status of distributors and suppliers and other useful information for valuation. Therefore, from the perspective of information acquisition, compared with other M&As, the degree of information asymmetry between listed companies and executives' hometown enterprises is lower.
The information advantage can affect long-term performance following M&As by influencing the valuation of listed companies to the purchasers. From the perspective of valuation, compared with other M&As, purchasers can make full use of established social networks to help listed companies obtain incremental information that is useful for valuation. This can help purchasers assess the true value of the acquired enterprise more accurately (Fuller et al., 2002) and merge the executives' hometown enterprise at a more reasonable cost. Accurate pricing can save cash or other assets for listed companies while also reducing the risk of goodwill impairment following high-premium M&As, both of which can have a significant impact on post-merger performance. In addition, following M&A, the enterprises of both sides are faced with adjustments of employment policies and other considerations, and the effect of these adjustments directly influences postmerger performance. Schweiger & Goulet (2000) reviewed the problems of post-merger integration, asserting that if both parties fail to smoothly integrate and form a common corporate culture, this may lead to estrangement and opposition between employees, and even integration failure, which will ultimately affect performance. Executives are more familiar with the economy and informal system, including customs, culture and other relevant knowledge, of their hometowns, which can alleviate challenges of integration following the merger with positive effects for both parties' enterprises. In summary, if the acquired enterprise is located in the hometown of the executives of the listed company, post-merger performance is expected to be superior.
However, from the perspective of home bias identity, such mergers may also have a negative impact on long-term performance. In China, hometown identity is a highly representative traditional culture and an important informal system. Chinese people generally love their hometowns. For example, in ancient times, people always returned home in their twilight years. Existing literature also demonstrates that entrepreneurs have a strong sense of identification with their hometowns, which has an important explanatory power with regard to economic phenomena (Li & Wu, 2016).
According to identity economics, individual utility function includes three aspects of individual behaviour, the behaviour of other individuals in the group and self-identity (Akerlof & Kranton, 2000). From this perspective, the executives of listed companies choose hometown enterprises as targets, not based on the maximisation of the interests of listed companies but on maximising their own utility. This is because executives' identity has the binding effect of public opinion, and executives' choice of M&As of enterprises in their hometown can help relatives and friends in their hometown solve employment problems and earnestly safeguard the interests of the hometown itself. Senior executives' personal deeds will be widely publicised and discussed in their hometowns, and they can establish a good personal reputation. In contrast, if executives do not seek benefits for their hometown and contribute to their hometown, they will appear impersonal, and they will not have a good reputation in their hometown; that is, other people in their hometown will not give a high evaluation to executives, and their selfworth identity will not be satisfied. For example, Lai et al. (2020) found that when executives work in enterprises near their hometown, they will not exhibit short-sighted investment behaviours. Specific to M&As, to obtain higher individual utility, executives will more strongly consider hometown enterprises when choosing targets to support their hometowns' economic development. Moreover, they may be more generous in terms of the M&A price, paying a higher premium in exchange for positive comments regarding the executives from the hometown group. In other words, executives' acquisition of hometown enterprises is not only to maximise the interests of listed companies. Compared with M&As based on maximising listed companies' interests, the performance of M&As-in-Hometown may be worse.
A third perspective suggests that when a hometown enterprise is the best choice among many alternative M&A targets, it can maximise both executives' personal utility and the interests of listed companies, achieving a win-win situation. However, if the integration effect following an M&A is considered, even if the hometown enterprise is the best target, the executives' identity in hometown may still have a negative impact on performance. The logic of this view is that to improve management and operation efficiency, listed companies may need to adjust employment policies, and the executives' hometown identity can restrict the listed companies' integration decisions. For example, listed companies may not over-reduce employees or may even over-hire local employees to prevent hometown people's criticism of executives' personal reputations. Therefore, it may be difficult for the executives of listed companies to effectively integrate the enterprises of both sides after returning to their hometowns for M&As to achieve synergy, ultimately reducing the long-term performance following M&As.
Based on the above analysis, the following competitive research hypotheses are proposed: Hypothesis 1a: Compared with other M&As, the long-term performance of M&As-in-Hometown is better.
Hypothesis 1b: Compared with other M&As, the long-term performance of M&As-in-Hometown is worse.

Data and model
Inter-regional M&As initiated by listed companies from 2005 to 2016 are selected as samples. M&As in the sample are defined as inter-regional when the registered provinces of listed companies are inconsistent with those of the acquired parties. To make the samples more accurately meet the research needs, the samples were cleaned according to the following standards: (1) Eliminate unsuccessful M&As; (2) Eliminate related M&As within enterprise groups; (3) Excluding listed companies in the financial and insurance industries; (4) To avoid the interference of the same repeated observation on the research, only the first merger event will be reserved when the same purchaser conducts multiple M&As with the same target; (5) Eliminate M&As for which the province where the target company is located cannot be accurately identified. Finally, 1460 samples were obtained, among which, the number of samples of M&As-in-Hometown was 141. M&A data, financial statement data and stock price data of listed companies is retrieved from the China Stock Market & Accounting Research Database (CSMAR). As for executives' birthplace data, a combination of data from the Chinese Research Data Services Platform (CNRDS) and CSMAR is used. Goodwill impairment data comes from the Wind database. To reduce the influence of potential outliers, winsorising was conducted on all continuous variables at the level of 1% and 99% annually. To verify the hypotheses, the regression model used in this paper is as follows. Table 1 presents the variables' definitions.

Dependent variables
Market performance is measured by buy-and-hold abnormal return (BHAR) for 12 months, 24 months and 36 months on the announcement date of merger and acquisition. Accounting performance (ΔROS) is the change value of operating margin, measured by total profit divided by operating income (ROS) minus the ROS of the year before the merger is completed.

Independent variables
The independent variables of this research are dummy variables. If the acquired enterprise is located in the province where the executives of listed companies were born, the Home variable takes 1; otherwise, it is 0. Different positions of executives have different influences on M&As. It is expected that executives with lower positions have less influence on important investment decisions than those at the level of Chairman or CEO; therefore, two explanatory variables are established. Home_cc is a dummy variable; if the acquired enterprise is located in the Proportion of the number of independent directors of listed companies to the total number of board members.

Province
Fixed effect of the province where the acquired company is located.

Industry
Fixed effects of listed companies' industries.

Year
Fixed effect of the year to which the sample belongs.
province where the Chairman or CEO of listed companies was born, the value is 1; otherwise, it is 0. If the acquired enterprise is located in the province where executives other than the Chairman or CEO were born, then Home_other takes the value 1; otherwise, it is 0.

Control variables
Previous literature is referenced (Pan & Yu, 2011;Tang & Chen, 2010;Xiao et al., 2018) to select the control variables of Size, Lev,Roa,Growth,Soe,Top1,Sep,CashPay,ComIND,Dual,Indir,Province Fixed effect,Industry Fixed effect,Year Fixed effect. (See Table 1 for variable definitions.) Table 2 presents the number of samples each year. The number of inter-regional M&As initiated by listed companies is rising year by year. In 2005, the number of inter-regional M&As was 18, with the largest number in 2015, reaching 309. The number of M&As-in-Hometown accounted for 9.7% of the total number of M&As, totalling 141 cases. For Home_cc = 1, the sample size is 76 and for Home_other = 1, the sample size is 65. On the whole, the proportion of the number of M&As-in-Hometown to the total number is evenly distributed and not concentrated in certain years. Table 3 presents the descriptive statistics of main regression variables. The mean value of BHAR 12 is 0.064, and the mean values of BHAR 24 and BHAR 36 are also positive, indicating that inter-regional M&As have generally elicited positive benefits for shareholders. Compared with the year prior to mergers, accounting performance in the year following mergers demonstrates an increasing trend, but accounting performance in the following two years was weak. The average value of Home is 0.097, indicating that the number of M&As-in-Hometown accounts for 9.7% of the total number of M&As. The mean value of Home_cc is 0.052. The mean value of Size is 21.914, the mean value of Lev is 0.426, the mean value of Roa is 0.044, and the mean value of Growth is 2.138. The number of inter-regional M&As initiated by state-owned enterprises accounted for 20.8%. The average shareholding ratio of the largest shareholder (Top1) is 32.9%. The mean value of Sep is 4.253. The proportion of M&As using cash payment is 79.5%, indicating that inter-regional M&As in China mainly uses cash payment rather than shares or other assets. The average value of ComIND is 0.492.  (1) is Home, with a coefficient of −0.119, which is significantly negative at the level of 5% (t = −2.47). The result demonstrates that, compared with other M&As, the long-term market performance of M&As-in-Hometown is poor. The result in Column (2) shows a regression coefficient of Home_cc of −0.184, which is significantly negative at the level of 1% (t = −2.90). The result in Column (3) shows that the regression coefficient of Home_other is not statistically significant. The result in Column (5) indicates that when BHAR 24 is taken as an independent variable, the coefficient of Home_cc is −0.230, which is significantly negative at the 5% level. The result in Column (6) shows that when BHAR 36 is taken as an independent variable, the coefficient of Home_cc is −0.289, which is significantly negative at the 5% level. On the whole, the results in Table 4 demonstrate that compared with other M&As, the long-term performance of M&As-in-Hometown is poorer, supporting H1b. The dependent variable in Table 5 is the change in sales profit margin (ΔROS). The dependent variable in Column (1) is ΔROS T (ΔROS T = ROS T − ROS T−1 ), and the coefficient of Home_cc is −0.060, which is significantly negative at the 5% level. The dependent variable in Column (2) is ΔROS T+1 (ΔROS T+1 = ROS T+1 − ROS T−1 ), and the coefficient of Home_cc is −0.048, which is significantly negative at 10%. The dependent variable in Column (3) is ΔROS T+2 (ΔROS T+2 = ROS T+2 − ROS T−1 ), and the coefficient of Home_cc is −0.055, which is significantly negative at 10%. In Column (4), accounting performance for three years is combined to investigate the overall difference in the performance. The coefficient of Home_cc is −0.058, which is significantly negative at the level of 1%. The regression result using accounting performance as a dependent variable also supports H1b.

Regression results
Considering that market performance includes all the information in the market (for example, accounting performance, competitive position, and other relevant considerations), it can more comprehensively reflect M&As' performance. Therefore, robustness tests and further analyses take market performance (BHAR 12 ) as the dependent variable.

Short-term performance
The dependent variable in Column (1) of Table 6 is CAR, and the window period is [0, 1]. The estimated period of normal stock return is three days to three months before the announcement date. The coefficient of Home_cc is −0.025, which is significantly negative at the 5% level (t = −1.97). The sample size is less than 1460 because if the stock returns are missing on the day of the merger announcement and the day after the merger The numbers in the parentheses are t-values. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.
announcement, such samples were deleted. The latter two columns adopt similar sample screening methods. The dependent variable in Column (2) is CAR [0, 2]. The coefficient of Home_cc is −0.030, which is significantly negative at the 5% level (t = −2.18). The dependent variable in Column (3) is CAR [0, 3]. The coefficient of Home_cc is −0.047, which is significantly negative at the level of 5% (t = −2.26). These results indicate that the short-term performance of M&As-in-Hometown is significantly lower than other M&As, also supporting H1b.

False information from hometown
Theoretically, the results of this paper may have an alternative explanation that people in executives' hometowns misled executives. Specifically, to attract external funds, the stakeholders in a hometown send false information to the executives of listed companies, which lead to worse long-term performance. However, this alternative explanation cannot be in an equilibrium state; that is, listed companies cannot be misled many times. The numbers in the parentheses are t-values. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.
To assess the influence of this alternative explanation, the first acquired sample from each province is deleted, and the remaining sample is called sample A. The listed companies in each province as buyers for the first time were deleted, and the remaining sample is called sample B. Secondly, the trust scores of each province are measured using the questionnaire survey data of the 'China Entrepreneur Survey System' from 2000. The higher the score, the more trustworthy the enterprises in the region are; that is, it is less likely to mislead the executives. Specifically, according to the score of the province where the acquired enterprise is located, the sample group with a high trust level is higher than the median, and the sample group with a low trust level is lower than or equal to the median.
The regression results in Columns (1) and (2) of Table 7 demonstrate that the long-term performance of M&As-in-Hometown remains lower. The regression results in Columns (3) and (4) indicate that the degree of trust in the province where the merged enterprise is located has no effect on the results. The results in Table 7 can, to a certain extent, exclude the alternative explanation that executives are misled. The numbers in the parentheses are t-values. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.

Benefit exchange
The results may also have alternative explanations in terms of benefit exchange; that is, executives are more likely to have interests in their birthplace. For example, listed companies acquire enterprises in the hometown of executives at a high premium, which is actually a political contribution in exchange for the subsequent benefit of the company or executives in the province. For example, listed companies may get more government subsidies and tax incentives, or executives may get personal honours given by their hometown after M&As (such as NPC deputies or CPPCC members). To assess the influence of this alternative explanation, variables are constructed referencing the articles of Dai et al. (2014)and Li & Wang (2019). ΔSubs is the ratio of government subsidies to operating income of listed companies following M&As minus the value in the year prior to M&As 4 and ΔTaxr refers to the actual tax rate of listed companies following merger minus the actual tax rate of the previous year. 5 Table 8 presents the regression results with ΔSubs and ΔTaxr as dependent variables. The coefficient of Home_cc is not statistically significant, indicating that compared with other M&As, after returning home, the listed companies have neither received more government subsidies nor more tax benefits. In addition, the personal benefits of executives following M&As have been analysed from the perspective that executives serve as NPC deputies or CPPCC members in their hometowns. A manual review of executives' resumes provided by CSMAR revealed no executives at the level of Chairman or CEO who did not serve prior to mergers but served as deputies to the National People's Congress or CPPCC members in their hometown following mergers. Therefore, no evidence is found that the behaviour of exchanging interests can explain the results of this paper.

Historical connections
Executives choosing to acquire enterprises in their hometowns may be due to historical contacts between listed companies and executives' hometowns and not because of executives' home bias. For example, the company has more business in the region or has better personal relationships with local officials. Such existing historical contacts may The numbers in the parentheses are t-values. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.
be the origin of more executives coming from the province. Moreover, when listed companies are looking for targets, the stakeholders in executives' hometowns can actively advocate their intention through historical contacts and promote the completion of M&As. However, the hometown enterprise may not be the best target for listed companies' M&As, but listed companies choose the hometown enterprise as the target of M&A based on historical relationships, eventually leading to poor performance. The method of Cao et al. (2018) is used to rule out this alternative explanation, to control whether the birthplace of the former executive of the company is consistent with the province where the acquired enterprise is located. If so, the value of Havehome is 1; otherwise, it is 0. If Havehome is not significantly related to performance, the alternative explanation of historical relationships can be excluded. Column (1) of Table 9 presents the results of adding Havehome as the control variable. The coefficient of Havehome is not statistically significant.

Strategic aggressiveness
The acquisition of enterprises in executives' hometowns may not be an exogenous event but could be largely determined by the listed companies' strategy. Aggressive companies may be more inclined to merge and expand; thus, a higher probability of M&As in The numbers in the parentheses are t-values. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively. The numbers in the parentheses are t-values. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively. executives' hometowns. Moreover, other M&A enterprises of the company in that year may also affect the company's performance. In short, strategic aggressiveness may simultaneously affect the long-term performance of the company and the probability of acquiring hometown enterprises. Enterprises' strategic aggressiveness is examined according to the number of M&As to assess its influence. Listed companies are grouped according to the total number of M&As from one year before the merger to one year after the merger (a total of three years). If the number is higher than the median sample, it is defined as a sample with high strategic aggressiveness; otherwise, it is defined as a sample with low strategic aggressiveness. If this alternative explanation is valid, the sample group should only exhibit high strategic aggressiveness. The results in Columns (2) and (3) of Table 9 support the conclusions of this paper for both groups of samples. This test eliminates the influence of strategic aggressiveness.

Degree of economic development
There may also be alternative explanations for differences in economic development.
Listed companies in economically developed regions merge with companies in underdeveloped regions, which will naturally incur some negative cost factors. To assess this alternative explanation, the Real GDP per capita of the company's registered location is assumed as the standard to measure whether the regional economy is developed. If the per capita GDP of the registered locale of a listed company is higher than the per capita GDP of the region where the acquired company is located, then this sample is called Sample C. If the per capita GDP is lower, the sample is called sample D. If this alternative explanation holds, then samples of companies in economically developed regions would only be shown to merge with companies in underdeveloped regions. Columns (4) and (5) of Table 9 present the results of the regression by grouping according to the degree of economic development. No significant difference in the coefficient of Home_cc between the two groups is evident. The result excludes the alternative explanation of the degree of economic development.

Regression analysis with paired samples
Samples of Home_cc = 1 accounted for 5.2% of the total samples From the perspective of econometrics, the proportion of two types of samples does not need a fixed threshold for regression analysis, but when the proportion of a certain type of samples is low, the problem of large standard error may occur. The method of propensity score matching (PSM) is adopted to conduct sample matching of 1 with 1. 6 The sample size in Column (1) of Table 10 is 152 observations. The sample size of M&As-in-Hometown and other M&As each account for 50%. The coefficient of Home_cc remains significantly negative, which also supports H1b.

Change performance benchmark
In this section, the samples are divided into three categories: Sample E (M&As-in-Hometown, the sample size is 141), Sample F (other M&As, the sample size is 1319) and Sample G (M&As in the same province, the sample size is 1087). Among them, samples E and F constitute the total sample of the main test (1460). The variable Treat is designated to represent the type of M&As. When the value of Treat is 1, it indicates that the acquisition is in the same province; otherwise, it is 0. Samples E and G are selected as regression samples in Columns (2) and (3) of Table 10. Compared with M&As in the same province, the long-term performance of M&As-in-Hometown is worse. In Column (4), samples F and G were selected as regression samples, and the Treat coefficient was −0.000, which was not statistically significant.

Analysis after excluding samples with smaller M&A amounts
If the amount of a merger is minimal, the merger may not have a significant impact on the long-term performance of listed companies; therefore, samples with a small amount of M&A are eliminated to re-examine the research hypothesis. Specifically, the sample is sorted according to the purchaser's expenditure amount (Rank1) and the proportion of the purchaser's expenditure amount to total assets at the end of the period (Rank2). The samples ranked below 10% and 20% are defined as M&As with a small amount. Finally, the hypothesis is tested after eliminating mergers with smaller amounts. The results in Table 11 once again validate H1b after.

M&A premium
One of the main factors affecting post-merger performance in China is the substantial goodwill impairment that occurs following unreasonable premium acquisitions. The next analysis examines whether a high-premium mechanism might exist for home bias to affect postmerger performance. Premium refers to M&A premium, which is equal to the buyer's The numbers in the parentheses are t-values. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.
expenditure value (the value of cash or assets or stocks paid by the buyer) minus the assessed value of the transaction target, and then divided by the assessed value of the transaction target.
The dependent variable in Column (1) of Table 12 is Premium. The coefficient of Home_cc is 0.644, which is significantly positive at the level of 1% (t = 2.90). The dependent variables in Columns (2)-(4) are goodwill impairment loss divided by yearend net assets. Notably, China's Accounting Standards for Business Enterprises stipulated the independent presentation of goodwill on balance sheets in 2007. Therefore, this part assesses only the M&As from 2008 to 2016 as samples. The results in Columns (2)-(4) reveal that listed companies reported more goodwill impairment losses following M&Asin-Hometown.

Post-merger integration effect: Excess employees
Referencing Zeng & Chen (2006), the model below is used to measure excess employees.
Employee ¼α 0 þβ 1 Sizeþβ 2 Capitalþβ 3 Growthþ where Employee is the number of employees corresponding to every million yuan of total assets; Size is the natural logarithm of total assets; Capital is capital intensive, which is equal to the ratio of fixed assets to total assets; Growth is the sales growth rate; and Industry and Year are dummy variables. The residual value of the model regression reflects The numbers in the parentheses are t-values. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively. The numbers in the parentheses are t-values. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.
the degree of excess employees. If the degree of excess employees in the year of and year following the merger is greater than the year prior to the merger, the ExsEme value is 1; otherwise, it is 0. Table 13 presents the results with ExsEme as the dependent variable. The result in Column (1) shows that the coefficient of Home_cc is 0.502, which is significantly positive at the 10% level. The result in Column (2) shows that when the time window is extended to the year when the merger is completed and the following year, the coefficient of Home_cc is significantly positive at the 5% level. The result in Column (3) shows that when the time window extends to the second year following the completion of the merger, the coefficient of Home_cc is 0.385, which is also significantly positive at the 5% level.

Further subdivision of hometown: Province or city
One of the objects of this research is to determine whether effects occur in all cities in a hometown province or only in cities where the executives were born, which deserves further discussion. The result of poor performance following a merger is found to exist in the same city as well as different cities in the same province. Due to limited space, these results are not shown; however, they are available upon request.

Chairman or CEO
In examining whether there is any difference between Chairmen and the CEOs, the home bias of Chairmen and CEOs are both found to have an impact on M&A performance. Due to limited space, these results are not shown; however, they are available upon request.

Cross section analysis
Areas that are economically underdeveloped generally require external capital to support the growth of local economies. It is assumed in this study that in the samples with low market levels when listed companies acquire enterprises in executives' hometowns, the hometown personnel will benefit more, and listed company executives will acquire more praise, driving their personal utility higher. The empirical results show that poor performance following M&As-in-Hometown is only found in hometowns with low economic development levels. The numbers in the parentheses are z-values. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.
Second, if executives give back to their hometown to satisfy their personal utility at the expense of damaging the interests of state-owned shareholders, this will affect their political promotion and reputation considerably. The empirical results show that listed companies' poor performance following the acquisition of executives' hometown enterprises is evident in only non-state-owned listed companies.
Third, executives' motivations to give back to their hometown may vary with age. Younger executives face greater pressure for career development, and it is more likely that M&As are conducted based on listed companies' interests. The results find that only older executives follow this motivation. Due to limited space, these results are not shown; however, they are available upon request.

Moderating effect analysis
Our findings represent an agency problem within the scope of corporate governance. It has been demonstrated that Public Offering of Fund, analysts and media all have positive corporate governance effects. It is found that the higher the shareholding ratio of Public Offering of Fund, the more analysts following and the more media coverage, the smaller the negative impact of the home bias on M&A performance. Due to limited space, these results are not shown; however, they are available upon request.

Conclusions
In recent years, the M&A market has exponentially developed, but with the expansion of the scale, M&A under-performance has aroused heated discussion in professional, policy and academic circles. This paper investigates the long-term performance of M&As based on the informal system of home bias. Specifically, 1460 inter-regional M&As from 2005 to 2016 are used as samples, and the results indicate that the long-term performance of M&As-in-Hometown is poor compared with other M&As. Part of the reason is that when the acquire is located in executives' hometowns, the listed company pays a higher premium and in the following years, more impairment losses of goodwill are revealed. The integration efficiency following hometown M&As is low and is reflected in the higher degree of excess employees.
An important driving force of China's economic growth is derived from enterprises' production, investment and reproduction processes. As an important link, investment directly affects enterprises' future performance. M&As are an essential investment approach for Chinese enterprises at present and of substantial significance to China's continuous economic transformation. Based on the conclusions of this study, the influence of home bias on the efficiency of resource allocation in capital markets cannot be ignored, and this phenomenon becomes more obvious in areas with lower degrees of marketisation, so investors should be cautious regarding such M&As. In addition, proactive internal and external governance mechanisms can also alleviate the negative impact of home bias on M&A performance. Vigorously developing Public Offering of Fund and analyst industries, enhancing the media's coverage and broadening news dissemination to the capital market could also provide effective mitigation mechanisms.
Although this study found that the performance of M&As-in-Hometown is weaker compared with other M&As, from the descriptive statistics in Table 3, the long-term market performance following M&A remains positive. In other words, M&As back home still elicit positive market returns to investors. No simple, direct, positive or negative value judgment can be made regarding the effects of executives' home bias on M&As in their hometowns. In addition, the indicators of M&A performance and excess employees in this paper are measured using the data of the whole or consolidated statements of listed companies. Due to the availability of data, the performance and other indicators of the purchased enterprises alone cannot be assessed, which may be a limitation of this study. Readers are reminded to pay particular attention to the above two considerations.