CHINA JOURNAL OF ACCOUNTING STUDIES 2019, VOL. 7, NO. 1, 93–118 https://doi.org/10.1080/21697213.2019.1630179 ARTICLE Management language experience, cultural integration and the performance of mergers and acquisitions a b c c Lu Li , Tusheng Xiao , Yuqian He and Xueding Wang a b School of Economics and Finance, Shanghai International Studies University, Shanghai, China; School of Accountancy, Central University of Finance and Economics, Beijing, China; School of Accountancy, Shanghai University of Finance and Economics, Shanghai, China ABSTRACT KEYWORDS Linguistic distance; M&A Institutional economists believe that language, as an important performance; culture; informal institution, has a signiﬁcant impact on economic activ- informal institution ities. However, the study of language on corporate behavior is still relatively insuﬃcient. Our paper examines the inﬂuence of linguis- tic distance between the acquirer and the target ﬁrm on M&A performance in China, from 2000 to 2012. The results indicate that the linguistic distance between the acquirer and the target ﬁrm signiﬁcantly reduces the performance of M&As. Furthermore, we suggest that the inﬂuence of language diﬀerences on the perfor- mance of M&As is mainly driven by the cultural eﬀect rather than the communication eﬀect. In addition, we ﬁnd that the relation- ship between language diﬀerences and M&A performance is aﬀected not only by manager characteristics, types of mergers and corporate ownership structures, but also by regional develop- ment. This paper provides a new way to examine management’s participation in M&A activities from the perspective of language. 1. Introduction Social institutions, as an aggregation of various rules, are the basis for the eﬀective operation of economic mechanisms. Social institutions include not only formal institu- tions such as laws, regulations and supervisions, but also informal ones such as social culture, customs and practices. Informal and formal institutions promote the evolution of society jointly (Grief, 1994; North, 1990; Weber, 1958). China is an emerging economy in a transition period, which is diﬀerent from developed countries. It is deeply inﬂuenced by traditional culture based on Confucianism. On the one hand, Chinese enterprises are located in a relationship-based society (Chen & Xie, 2012) in which the unique social and organizational contexts make it diﬃcult to explain all economic phenomena with the help of formal institutions alone. On the other hand, although formal institutions in China have achieved great progress, they are still imperfect and play a limited role in the country’s corporate governance and economic activities. Therefore, informal institutions CONTACT Tusheng Xiao firstname.lastname@example.org School of Accountancy, Central University of Finance and Economics, Beijing, China Paper accepted by Donghua Chen. This article has been republished with minor changes. These changes do not impact the academic content of the article. © 2019 Accounting Society of China 94 L. LI ET AL. are of great and unique importance in China. (Allen, Qian, & Qian, 2005; Chen, Hu, Liang, & Xin, 2013). Societal culture is an important part of informal institutions. It is stable because of the long-term formation process in history. Throughout history, the interaction among culture, society, politics and the economy has attracted much attention (Stulz & Williamson, 2003). Chinese culture is complex due to diﬀerences in the country’s geographical environment, its history and the economic development status among regions. Multi-level cultural diﬀerences inevitably aﬀect social and economic activities. However, due to the complicated constituent elements of culture, it is diﬃcult to systematically and comprehensively investigate regional cultural diﬀerences in China (Zhao, Li, & Sun, 2015). Based on the research of Alvesson and Kärreman (2000) we use language as the proxy to investigate the inﬂuence of social culture on economic activities. This perspective has three main desirable features. First, language, as an important component of social culture and a channel to deliver cultural meanings, can reﬂect the characteristics of language users. Hence, language diﬀerences can be used to measure cultural diﬀerences (Luo, 2009). Second, language has an economic attribute. As the most frequently used tool in economic exchanges, language has a communicative function and human capital attribute at the individual level (Zhang, 2008), and plays an important role in economic activities. Third, the diversity of Chinese language provides an ideal setting. China is vast in scale and has a very long history. These features form a unique geographical and diverse linguistic phenomenon that makes it possible to study language diﬀerences within one country (Ramsey, 1987). Over the past two decades, the capital market has gradually become an important allocation channel of resources in China’s market economy. Firms can be seen as a combination of various stakeholders, including shareholders, managers, employees, suppliers, customers, governments and so on. These stakeholders compete ﬁercely for resources and control. Mergers and acquisitions (M&As), as a major investment activity of ﬁrms, often involve various kinds of participants and contain a high degree of uncertainty. The initiator, leader and decision-maker of an M&A event is the acquirer’s CEO, the core of corporate management, who determines the probability of a - successful M&A (Huang & Sheng, 2013). Previous literature has studied the inﬂuence of CEOs’ personality traits, such as age, education, career experience and political connections (Chen, Jiang, & Lu, 2013; Hambrick & Mason, 1984; Jensen, 1986; Masulis, Wang, & Xie, 2007). However, it remains unknown whether language, as one of the personal characteristics of a CEO as well as an important medium for transaction negotiations, has an impact on corporate M&A activities. Using 516 unrelated M&As between Chinese public ﬁrms and private ﬁrms from 2000 to 2012, we examine the impact of linguistic distance between the acquirer and the target ﬁrm on M&A performance. Theoretical analysis suggests that language may become the linguistic capital of managers, acquirers and target ﬁrms in that people with similar language backgrounds tend to share a common culture, as well as ideas and ways of doing things, which helps to reduce diﬀerences in thought patterns and negotiation strategies. Hence, the negative impact of cultural diﬀerences after a merger is alleviated, while the competitiveness of enterprises in cross-regional eco- nomic activities is enhanced and the performance of M&As is improved. In addition, CHINA JOURNAL OF ACCOUNTING STUDIES 95 language, as an important representative of regional culture, can promote interpersonal identity, and become a link between managers who rely on a hometown voice (Dai, Xiao, & Pan, 2016). A similar language background can become a natural link between the acquirer and the target ﬁrm, making it easier to mobilize resource allocations, promote transactional negotiation and improve the performance of M&As. According nd to the classiﬁcation of the Language Atlas of China (2 Edition), we construct a linguistic distance proxy. Our empirical study shows that the greater the linguistic distance between the acquirer and the target ﬁrm, the worse the performance of M&As will be. Further analysis shows that the inﬂuence of linguistic diﬀerences on the performance of M&As is driven mainly by the cultural eﬀect rather than the communication eﬀect. In addition, we also ﬁnd that the inﬂuence of language diﬀerences on the performance of M&As is aﬀected not only by manager’s characteristics, types of M&As and the corporate ownership structure, but also by regional development. Our study makes three main contributions to the literature. First, it is helpful in understanding how informal institutions (i.e., languages) aﬀect economic activity in emerging and transitional countries (Chen et al., 2013; Liu, Xu, & Xiao, 2015). Although language is a basic social institution, institutional economics has not paid enough attention to language, either in the study of the institution itself or in the process of exploring the impact of institution on economic performance (Zhang, 2008). Second, our study contributes to recent literature on linguistic economics by examining the relation- ship between language and corporate M&As (Chang, Hong, Tiedens, & Zhao, 2015; Dai et al., 2016; Rubinstein, 2004; Wesson, 2009; Zhang, 2008). Third, our study contributes to the large body of research that examines the role of corporate managers’ personal characteristics in M&A activities, and ﬁnds that the language background of the acquirer’s manager aﬀects the performance of M&A (Huang & Sheng, 2013; Jensen, 1986). The rest of the paper is organized as follows. Section 2 discusses the related literature and the institutional background, and develops our hypotheses. Section 3 explains our sample and research design. Section 4 presents the main results. Finally, Section 5 concludes. 2. Literature review and theoretical analysis 2.1. Literature review A large body of research examines the relationship between formal institutions and economic activities (Chen, Zhang, & Li, 2008; La Porta, Lopez-De-Silanes, Shleifer, & Vishny, 1997; Shleifer & Vishny, 1986). Some scholars also study how economic activities are aﬀected by informal institutions, such as religious beliefs and culture. For example, Stulz and Williamson (2003) show that diﬀerences in a country’s principal religion aﬀect creditor rights. Riahi-Belkaoui (2004) investigates the relationship between earnings transparency across 24 countries and elements of religiosity. The empirical results of this study show that earnings transparency is signiﬁcantly positively related to the degree of church attendance. Chen et al. (2013) examine the impact of religious tradi- tions on corporate governance in China, and ﬁnd that ﬁrms in regions with stronger 96 L. LI ET AL. religious traditions are less likely to violate laws and stipulations from government, and are less likely to earnings management. Existing academic literature reveals the inﬂuence of culture on economic activities from the aspects of regional culture, corporate culture and cultural diﬀerences between the two parties of a merger or acquisition. For example, Newman and Stanley (1996) employ data come from 176 work units of a large U.S. based corporation operating in Europe and Asia, and examine the impact of the congruence between management practices and national culture on ﬁnancial performance. They ﬁnd that work unit ﬁnancial performance is higher when management practices in the work unit are congruent with the national culture. Uysal, Kedia, and Panchapagesan (2008) suggest that acquirer returns in local transactions are more than twice that in non-local transac- tions, and that higher returns to the local acquirer appear to be related to information advantages arising from geographical proximity. Weber and Drori (2011) propose that in addition to culture clashes, the organizational identiﬁcation of a merger has a direct eﬀect on acquirer attitudes and behaviors, thereby inﬂuencing the probability of success after a merger, which also contributes to the improvement of long-term performance. Based on 112 large cross-border acquisitions undertaken by U.S. ﬁrms from 1978 to 1990, Datta and Puia (1995) suggest that acquisitions characterized by high cultural distance were accompanied by lower wealth eﬀects for the acquiring ﬁrm’s share- holders. Ahern, Daminelli, and Fracassi (2015) investigate how three key dimensions of national culture (i.e., trust, hierarchy, and individualism) aﬀect merger volume and synergy gains. They ﬁnd strong evidence that the volume of cross-border mergers is lower when countries are more culturally distant. In addition, greater cultural distance in trust and individualism leads to lower combined announcement returns. Zhou and Li (2008) ﬁnd that a higher ﬁtting degree of organizational culture reduces target compa- nies employees’ resistance toward M&As, which creates higher value. Using the theory of organizational learning and the theory of institutions, Yan (2009) examines the impact of overseas M&As on the performance of Chinese enterprises, and ﬁnds that a shorter cultural distance improves the performance of overseas M&As. Wang and Kan (2014) ﬁnd that the corporate culture intensity of the acquirer is signiﬁcantly negatively correlated with the long-term performance of M&As, and the negative impact is more obvious when the cultural integration between the acquirer and the target ﬁrm is more challenging. Overall, the existing literature on culture provides an analytical framework and methodology for the study of the relationship between language and economic activ- ities. Based on the linguistic distance perspective, our study expands the previous literature from the following aspects. Firstly, unlike geographical distance, dialects evolve over the history of a region and are strongly related to characteristics of the geographical environment, which leads to the interaction between dialects and eco- nomic development. The research on dialects and M&As in this paper includes geogra- phical distance, while the related existing literature indirectly supports our study. Secondly, given the complexity of cultural composition, it is hard to systematically and comprehensively investigate cultural diﬀerences in the various regions in China (Zhao et al., 2015). Therefore, it is practical to select one perspective and conduct in-depth research. This paper takes linguistic distance as the perspective of cultural distance, which has an incremental contribution to existing literature on culture and M&As. CHINA JOURNAL OF ACCOUNTING STUDIES 97 Language, as an important tool and channel, plays a signiﬁcant role in cultural heritage. In the process of exploring the inﬂuence of informal institutions on economic activities, language has made a great contribution to institutional analysis. In addition, language can be regarded as a kind of basic social institution, since all institutions created by human beings are recorded through language. The specialized ﬁeld of language economics has emerged from research into the interaction between language and economic activities (Rubinstein, 2004; Wesson, 2009; Zhang, 2008). The existing literature has explored the inﬂuence of language from various perspectives, such as transaction costs (Tainer, 1988), international trade (Melitz, 2008), job opportunities (Kossoudji, 1988), and individual income (Pendakur & Pendakur, 1998). However, most of the previous research is based on cross-country analysis and as such, it is hard to rule out the interaction between language and formal institutions. Because China is a country with a diverse linguistic phenomenon, it is feasible to study the inﬂuence of language diﬀerences among regions on economic activities. For example, Li and Meng (2014) use Mandarin to measure communication costs and use dialects to capture cultural backgrounds in their analysis of how these two factors aﬀect Chinese labor migration. Their ﬁndings imply that there are intangible linguistic and cultural borders within a country that impede the transfer of labor across regions, and that people are more willing to move to culturally familiar environments with low communication costs. Liu et al. (2015) use the pairwise dialectal distance of 278 prefectures to explore the impacts of dialectal distance on the migration of labor. They suggest that identiﬁcation and the complementary eﬀects of dialectal distance will ﬁrst promote, then prevent, the migration of labor (i.e., an inversed ‘U-shaped’ pattern). Xu, Liu, and Xiao (2015) ﬁnd that dialect diversity has a signiﬁcant negative impact on economic growth. Dai et al. (2016) analyzed the inﬂuence mechanism of the consistent dialect spoken by the chairman of the board and the CEO to the interactive relationship between them. Their results show that if the chairman and CEO come from an area with the same dialect, the ﬁrm will have lower agency costs, which becomes even more signiﬁcant when the types of dialects are more ﬁnely classiﬁed. Huang and Liu (2017) take social trust as a channel for under- standing the eﬀect of dialect on economic performance, and empirically investigate the impact of dialect on social trust. They ﬁnd that dialect can be used as an identiﬁcation symbol of people’s place of origin, thereby enhancing each’s sense of identity, which impacts the formation of social trust. Chang et al. (2015) show that investors who live in linguistically diverse areas express more diverse opinions on stock message boards, and trade stocks more actively. In short, prior literature shows that language diﬀerences among regions in China are related to economic activities, which provides theoretical support for our investigation into the inﬂuence of language diﬀerences on corporate M&As. Our study is also related to literature on manager characteristics and corporate governance. Since the upper-echelons theory was proposed by Hambrick and Mason (1984), scholars have begun to analyze how managers’ behaviors and corporate invest- ment activities are aﬀected by the personal characteristics of managers. The conclusion that the characteristics of managers have information content has been further inspired and supported by empirical studies related to the form of investment and corporate M&A activities. For example, Jensen (1986) implies that CEOs who have a ﬁnancial background are more likely to undertake diversiﬁcation programs. Peng 98 L. LI ET AL. and Wei (2008) show that companies with female executives tend to prefer conservative investment strategies. Wu, Wu, and Zheng (2008) ﬁnd that overconﬁdence of the acquirer’s manager leads to successive declines in the performance of serial acquisitions and that the learning behavior of managers leads to the opposite. Huang and Sheng (2013) suggest that a CEO’s background has information content. A CEO, as the leader of the company’s strategic decision-making, has a signiﬁcant inﬂuence on corporate activ- ities (e.g., production and management activities) and corporate performance, and the CEO’s characteristics become more important when the capital market is more mature. Overall, the existing literature provides strong evidence that the characteristics of management aﬀect corporate M&A activities, which oﬀer insights on whether the language backgrounds of managers aﬀect corporate M&A activities. Prior literature on cultural and linguistic diﬀerences has at least two limitations. First, most of the previous research examined the inﬂuence of inter-country rather than intra- country language diﬀerences on economic activities. Although research on China has grown recently, few researchers have focused on how dialects aﬀect the behaviors of microenterprises. Second, because measurement of individual cultures is challenging, most studies have analyzed the whole company without consideration of decision- making at the level of the individual. Language has both human capital attributes and communication functions. Therefore, investigating the language users, at the micro level, can improve understanding of the impact of language diﬀerences. Importantly, China’s diverse linguistic environment provides an opportunity for a feasible examina- tion into the inﬂuence of language diﬀerences among regions. Unlike prior literature, our study examines the relationship between management’s linguistic background and corporate M&A activities based on the individual-level linguistic background within a single country, which provides insights into how management’s individual character- istics inﬂuence M&A activities. 2.2. Institutional background and theoretical analysis China is a multi-ethnic and multi-lingual country. According to statistics from the nd Language Atlas of China (2 Edition), Chinese dialects are divided into nine groups: Mandarin, Jin Dialect, Komese, Huetseu Dialect, Min Chinese, Goetian (i.e., Wu-Chinese), Xiang Dialect, Cantonese and Hakka. Each dialect group can be further divided into several sub-dialects. For example, Mandarin can be divided into Beijing Mandarin, Northeastern Mandarin, Jilu Mandarin, Central Plains Mandarin, Jianghuai Mandarin, Jiaoliao Mandarin, Lanyin Mandarin and Southwestern Mandarin. The division of these dialect areas does not completely coincide with the division of China’s administrative districts, because formation of the dialect areas has been driven by each’s long-term comprehensive social and historical evolution, the constraints of the diﬀerent geogra- phical environments and the characteristics of language. Therefore, it is common to hear diﬀerent dialects spoken within the same administrative region, while diﬀerent admin- istrative regions have common dialects. To some extent, dialects are important repre- sentatives of regional culture. Language, as the most frequently used tool in economic exchanges, has a communicative function and plays an important role in economic activities. Dustmann and Soest (2001) point out that language diﬀerences increase transaction CHINA JOURNAL OF ACCOUNTING STUDIES 99 costs. These transaction costs include not only the direct costs of translation, but also indirect costs that result from meaning loss and information leakage in the translation process, as well as distrust due to misunderstandings that often result from inaccu- rate translations. The language diﬀerences in China are mainly reﬂected in the coexistence of regional dialects. Dialects not only hinder communication, but also represent unique cultures. Popularization of Mandarin in China may have reduced translation costs, but the diﬀerent pronunciations, vocabulary and grammar among the dialects continue to cause communication problems. The promotion of Mandarin throughout the country has not eliminated dialects, but has formed a bilingual phenomenon in which Mandarin and dialects are used in parallel. For example, according to the 2000 Chinese Language and Literature Survey, 86.38 percent of Chinese people still used dialects, exceeding the proportion of Mandarin (53.06%). In addition, Shi and Luo (2011) points out that the interaction and common value between a speaker and a listener are important to Chinese-style cognition. Chinese people pay attention to the asymmetry of language forms and meanings, therefore regional dialects play a signiﬁcant role in daily communication. In a typical M&A transaction, the manager is the initiator and executor of the M&A decision. This manager is committed to building external connections and establishing social network channels for information transmission, in order to reduce information asymmetry between the acquirer and the target ﬁrm (Chen et al., 2013; Chen & Xie, 2012). The personality traits of managers play an important role during this process. Based on the diversity of language and culture in various regions of China, we suggest that the language diﬀerences between the acquirer and the target ﬁrm inﬂuence the M&A activities through the following mechanisms. Firstly, language is a kind of human capital that can be regarded as the accumulation of management’s intellectual capital (Zhang, 2008). According to Hayek’s(1945)deﬁni- tion of speciﬁc knowledge, most of corporate management’s knowledge should be speciﬁc knowledge. One of the obvious features of speciﬁc knowledge is that it is diﬃcult to transfer; it is attached to individuals and forms proprietary human capital. Prior literature shows that language, as an important dimension of human capital, produces economic beneﬁts (Pendakur & Pendakur, 1998). From the perspective of a manager’s personal characteristics, language knowledge can be regarded as important personal background information, which will aﬀect the formation of individual thinking, reasoning and negotiation strategies. Acquirers and target ﬁrms with similar language backgrounds tend to share a common culture, as well as ideas and ways of doing things, which help narrow emotional distance and reduce diﬀerences in thinking patterns and negotiation strategies. These are conducive to the reduction of communication and coordination costs, and the alleviation of conﬂicts in management systems and human resources after a merger (Lu & Hu, 2014). In addition, managers generate spillover eﬀects within the merged company because human capital can help to improve resource integration and ease employee resistance (Zhou & Li, 2008). Therefore, we suggest that corporate M&As are more likely to succeed and produce positive performance when the acquirers and the target ﬁrms have similar language backgrounds. Secondly, as a representation of regional culture, language is an essential dimension of identity, which aﬀects trust and communication. The ‘sequence pattern’ in Chinese society enables people to identify insiders and outsiders according to the closeness of 100 L. LI ET AL. their relationships (Fei, 1985). Sharing a common dialect is usually regarded as a sense of regional belonging and identity (Li & Meng, 2014). Mcpherson, Smith-Lovin, and Cook (2001) point out that people with similar characteristics are more likely to interact with each other compared with nonsimilar individual, because the commonalities narrow the psychological distance, which makes communication easier. In daily life, interaction between people often results from homophily social space, including the same com- munity, identity, language, etc. In corporate M&A activities, language is used to not only record transactions, but also to communicate and exchange information. Language plays an important role in investment decision-making and information sharing among corporate managers, which aﬀects the transaction costs of M&As. According to the social identity theory, having a similar culture helps to establish and maintain social identities among individuals. Therefore, acquirers and target ﬁrms with similar language backgrounds are more likely to achieve social identity. Social identity can not only narrow the emotional distance and cultural distance between the acquirer and the target ﬁrm, but can also help to form a language connection, which may become an important channel for information exchange (Chen et al., 2013). In addition, language may also become a link among managers who rely on a hometown voice (Dai et al., 2016). A similar language background can become a natural link between acquirers and their target ﬁrms, making it easier to mobilize resource allocations, promote transac- tional negotiations and improve the performance of M&As. On the contrary, languages with large diﬀerences may become barriers and have the opposite eﬀects, thus increas- ing transaction costs. It is worth noting that language diﬀerences may also lead to the improvement of M&A performance. First, language aﬀects the cognition, communication and interac- tion of individuals. Communication among individuals with diﬀerent cultural back- grounds and ways of thinking helps to foster innovative ideas and practices (Pan, Xiao, & Dai, 2017). Similarly, the communication and mobility of individuals from diﬀerent language backgrounds can promote the spillover of knowledge and skills among M&A ﬁrms, which will lead to the complementarity of productive capacities and the improvement of workforce skills (Ottaviano & Peri, 2006). Second, cultural inclusiveness can improve economic performance. Successful M&As among companies with diﬀerent language backgrounds can promote culture inclusiveness, which contri- butes to the improvement of corporate performance. However, the existing literature shows that greater cultural diﬀerences lead to more diﬃcult merger integration (Datta, 1991). The degree of integration after a merger directly aﬀects the realization of synergies, which is essential to value creation (Zhou & Li, 2008). Therefore, linguistic distance can lead to improved M&A performance, although the diﬃculty of merger integration may make it tough to apply. Based on the above institutional background and theoretical analysis, we form the following hypothesis: Hypothesis: The greater the language diﬀerence between the management of the acquirer and the target, the worse the M&As performance will be. CHINA JOURNAL OF ACCOUNTING STUDIES 101 3. Data and methodology 3.1. Sample selection This paper analyzes the M&As announced by Chinese listed companies from 2000 to 1 2 2012. We exclude deals in which the acquirer and the target ﬁrms are related parties. Following Lehn and Zhao (2006) and Masulis et al. (2007), we apply the following criteria to reﬁne our sample: (1) the deal value of the merger must have been at least 1 million Yuan to ensure that we include all of the M&As that represent large investments by acquiring ﬁrms; (2) the acquirer must have controlled less than 50 percent of the target ﬁrm prior to the M&A announcement and more than 50 percent after; (3)all of the targets are private ﬁrms that have no ownership relationship with the acquirer; (4) the acquire ﬁrms in the ﬁnancial industry are excluded; (5) the status of the M&A has been completed; and (6) M&As with incomplete ﬁnancial data are excluded. These criteria result in our ﬁnal sample of 516 M&As. In order to reduce the eﬀect of potential outliers, st th we winsorize all of the continuous variables at the 1 and 99 percentiles. The M&A data are from WIND database, supplemented by the China Stock Market and Accounting Research (CSMAR) database and the RESSET database. Both the stock price and the ﬁnancial data of the listed companies are obtained from the CSMAR database. We collect the marketization index from the report of Fan, Wang, and Zhu nd (2011). We ﬁrst classify regional dialects according to the Language Atlas of China (2 Edition) compiled by the Chinese Academy of Social Sciences, and manually collect the resume information of the acquirer’s manager including their birthplace and historical workplace. Then, we manually construct the language distance index between the acquirer and the target ﬁrm. 3.2. Deﬁnition of main variables 3.2.1. M&A performance (Perf) Our research focuses on the short-term market reaction around the announcement date of M&As, because the fundamentals of corporations may change signiﬁcantly after the merger or acquisition, and because the external macroeconomic environment also aﬀects performance. Following Lehn and Zhao (2006), we employ the cumulative abnormal returns (CAR) for acquiring ﬁrms during the two trading days before and the two trading days after the announcements (i.e., [−2, +2]). Market model parameters are estimated over a period of 240 through 41 (i.e., [−240, −41]) trading days preceding the announcement date for each M&A. Prior to 2000, most of the M&A announcement or completion dates are missing. Therefore, we choose 2000 as the beginning year of our sample. We choose 2012 as the end year of our sample because it usually takes one to two years from the ﬁrst announcement date of the merger or acquisition to the completion date. Once the acquirer and target ﬁrm are related parties, the M&A decisions are mainly driven by ownership rather than economic or institutional factors (Han & Tang, 2017). The Language Atlas of China is an atlas about the distribution of language use in various regions in China. It was compiled by the Institute of Linguistics of the Chinese Academy of Social Sciences and the Australian Academy of Humanities. The dialect survey and data analysis research began in 1983 and was completed in 1987. The atlas, which is based on the comprehensive linguistic survey, classiﬁes Chinese dialects according to the evolution principles of ancient phonological characters. It has become the standard for Chinese dialect academic research because of its scientiﬁc classiﬁcations. In 2012, the Institute of Linguistics of the Chinese Academy of Social Sciences compiled the nd Language Atlas of China (2 Edition), which includes progress and achievements over the past 20 years. 102 L. LI ET AL. 3.2.2. Linguistic distance (LD) Unlike the existing literature, which mostly regards a CEO as the representative of corporate management, Song (2006) suggests that the chairman of the board of directors usually plays a more signiﬁcant role in the decision-making of listed companies in China, which means that he or she is the actual head of the company. Therefore, this paper generally regards the chairman as the representative of management (i.e., the manager). An exception is that if the chairman is not full-time or does not receive the highest salary from the company, we regard the CEO as management’s representative (i.e., the manager). To construct the linguistic distance index, we employ the following three steps. nd Firstly, we classify the regional languages in China. The Language Atlas of China (2 Edition) proposes a ﬁve-level division method for Chinese dialects (i.e., point, small piece, piece, area and large area). Speciﬁcally, Point refers to the dialect point. In general, the Atlas selects one dialect point in a county and two or more dialect points in areas with complex dialects. Several points form a small dialect piece, several small pieces form a dialect piece, several dialect pieces form a dialect area and several dialect areas form a large dialect area. Dialects in the same small piece, piece, area or large area all have obvious similarities. Secondly, we identify the language background of the manager. If the birthplace of the manager is disclosed in the resume, we choose the dialect of the birthplace as the language background, and choose the dialect of the workplace before the announce- ment date otherwise. Since language acquisition generally requires a long period of time, when there are multiple workplaces we identify the language background of the manager based on the workplace with the longest cumulative working years. Finally, we calculate the linguistic distance. Similar to Liu et al. (2015), we classify the regional languages according to the ﬁve-level division method proposed in the nd Language Atlas of China (2 Edition), and match the language category of the acquirer with the target ﬁrm. Speciﬁcally, LD equals zero if they belong to the same dialect point, one if they belong to the same small dialect piece but diﬀerent dialect points, two if they belong to the same dialect piece but diﬀerent small dialect pieces, three if they belong to the same dialect area but diﬀerent dialect pieces, four if they belong to the same large dialect area but diﬀerent dialect areas, and ﬁve if they belong to diﬀerent large dialect areas. A larger value of LD indicates greater language distance. 3.3. Model speciﬁcation To examine whether the language diﬀerences between the acquirer and the target ﬁrm aﬀect the performance of M&As, we estimate the following ordinary least square (OLS) model: Perf ¼ α þ α LD þ θ Distance þ κ X þ ε (1) 0 1 We control the geographic distance (Distance) between the acquirer and the target ﬁrm, which equals the natural logarithm of one plus the physical distance (in kilometers) of the two cities in which the acquirer and the target ﬁrm are located. Liu et al. (2015) suggest that the segmentation caused by geographical factors is likely to increase CHINA JOURNAL OF ACCOUNTING STUDIES 103 linguistic distance. Therefore, the relationship between language diﬀerences and M&A performance is aﬀected by geographic distance. X is the vector of the control variables, including the manager’s characteristics, the deal characteristics, the acquiring ﬁrm’s characteristics, corporate governance and the regional characteristics. (a) Manager characteristics. Education equals from one to ﬁve for the manager’s highest degree is technical secondary school or below, junior college, undergraduate, master, and doctor, respectively. Age equals the natural logarithm of the age of the manager. IndExp equals one if the manager has experience in the industry of the target ﬁrm, and zero otherwise. (b) Deal characteristics. DealSize is the relative size of the target ﬁrm to the acquiring ﬁrm, which is equal to the amount of the deal scaled by the market value of the acquiring ﬁrm. PayStock equals one if the deal is a stock or stock and cash deal, and zero otherwise. Diversify equals one if the acquirer and the target ﬁrm do not belong to the same industry, and zero otherwise. (c) Acquiring ﬁrm characteristics. Size equals the natural logarithm of total assets. Leverage equals the total liabilities divided by total assets. B/M is the book-to-market ratio, measured as the book value of the acquiring ﬁrm divided by the market value. SOE is the ownership indicator, which equals one if the acquiring ﬁrm is state-owned, and zero otherwise. (d) Corporate governance proxies. Following Lehn and Zhao (2006), we select the following ﬁve indicators: (1) Dual, which equals one if the chairman of the company also serves as the CEO, and zero otherwise; (2) Board is the board size, which equals the natural logarithm of the number of board directors at company; (3) Ind_Board is the independent directors ratio, which equals the proportion of the number of independent directors to the total number of directors; (4) H5 is the ownership concentration, which equals the percentage of shares held by the top ﬁve shareholders; and (5) Insider is the management ownership, which equals the percentage of shares held by senior executives. (e) Regional characteristics. PerGDP measures regional economic development, which equals the natural logarithm of GDP per capita in the region where the acquiring ﬁrms are located. Marketization is the marketization index formulated by Fan et al. (2011). Finally, we include industry and year ﬁxed eﬀects in all of our regressions. We predict that the coeﬃcient on LD is signiﬁcantly negative, suggesting that a greater language diﬀerence between the acquirer and target ﬁrm is related to poorer M&A performance. 4. Empirical results 4.1. Descriptive statistics Table 1 presents the sample selection process and the distribution of the sample. M&A decisions are driven mainly by ownership if the acquirer and the target are related parties, we restrict the ﬁnal sample to include only the non-related M&As. Our untabu- lated results show that the average deal size is 180 million Yuan, accounting for about ten percent of the market value of the acquiring ﬁrms. The number of M&As in China increased signiﬁcantly since 2007, while the number of mergers in other years is relatively uniform. 104 L. LI ET AL. Table 1. Sample selection. (1) (2) (3) Excluding deals the value of Excluding the Excluding which is less than 1 million Yuan, aﬃliated M&As acquirer in (4) (5) Initial and the ownership does not meet and the acquirers the ﬁnancial Excluding failed or Final Year sample the requirements are private ﬁrms industry uncompleted M&As sample 2000 628 76 17 17 16 9 2001 1,258 197 60 60 58 22 2002 1,567 254 25 25 23 17 2003 1,970 293 30 30 30 20 2004 3,431 511 32 32 29 21 2005 2,823 394 33 32 31 25 2006 3,125 417 30 30 29 17 2007 4,323 675 62 61 59 35 2008 5,231 733 92 88 80 59 2009 4,840 863 75 75 72 44 2010 5,540 1,188 108 108 99 78 2011 5,735 1,319 122 121 117 89 2012 1,873 880 106 104 99 80 Total 42,344 7,800 792 783 742 516 nd According to statistics from the Language Atlas of China (2 Edition), Chinese dialects are divided into ten groups, namely Mandarin (including Beijing Mandarin, Northeastern Mandarin, Jilu Mandarin, Central Plains Mandarin, Jianghuai Mandarin, Jiaoliao Mandarin, Lanyin Mandarin and Southwestern Mandarin), the Jin Dialect, Komese, Huetseu Dialect, Min Chinese, Goetian (i.e., Wu-Chinese), Xiang Dialect, Cantonese, Hakka and other dialects (i.e., Pinghua and minority dialects). Panel A in Table 2 presents the distribution of language backgrounds of the acquirer and the target ﬁrm. Prior literature suggests Table 2. Language distribution. Panel A: Language background distribution of the acquirer and the target Language background of the acquirer Language background of the target Type of languages N Percent N Percent Mandarin 256 49.61% 239 46.32% -Beijing Mandarin 72 13.95% 54 10.47% -Northeastern Mandarin 15 2.91% 22 4.26% -Jilu Mandarin 19 3.68% 22 4.26% -Central Plains Mandarin 24 4.65% 27 5.23% -Jianghuai Mandarin 37 7.17% 27 5.23% -Jiaoliao Mandarin 18 3.49% 13 2.52% -Lanyin Mandarin 7 1.36% 8 1.55% -Southwestern Mandarin 64 12.40% 66 12.79% Jin Dialect 7 1.36% 10 1.94% Komese 7 1.36% 3 0.58% Huetseu Dialect 0 0.00% 1 0.19% Min Chinese 31 6.01% 23 4.46% Goetian (Wu-Chinese) 129 25.00% 142 27.52% Xiang Dialect 18 3.49% 15 2.91% Cantonese 66 12.79% 72 13.95% Hakka 2 0.39% 6 1.16% Other Dialects 0 0.00% 5 0.97% Full sample 516 100.0% 516 100.0% Panel B: Linguistic distance distribution Linguistic distance LD = 0 LD = 1 LD = 2 LD = 3 LD = 4 LD = 5 N 237 53 61 62 36 67 Percent 45.93% 10.27% 11.82% 12.02% 6.98% 12.98% CHINA JOURNAL OF ACCOUNTING STUDIES 105 that Mandarin is the most widely spoken language in China (i.e., about 70 percent of the total population), of which about one-third of the Mandarin speakers are Southwestern Mandarin. Consistent with these results, our ﬁnal sample shows that nearly half of the M&As have the Mandarin background, and about one-third of them have a Southwestern Mandarin background. The most commonly used languages are Goetian, Cantonese, Min Chinese and Beijing Mandarin, whose users are located in the Yangtze River Delta, Pearl River Delta, and Bohai Rim economic circles, respectively. These areas are in the most developed regions of China, which suggests that M&As may be closely related to the level of regional economic development. Panel B in Table 2 reports the frequency of linguistic distance. We ﬁnd that LD =0is the most common (45.93%), which indicates that the acquirers’ managers are more likely to be involved in M&As that match their own language backgrounds. On the one hand, the same language background can alleviate the principal-agent problem, and reduce the cost of information collection as well as information asymmetry in the process of M&As. On the other hand, it also suggests that M&As are more likely to occur between ﬁrms that are closer to each other (i.e., local preferences), as long geographic distance mergers may increase the cost of information collection and result in poor performance (Pan & Yu, 2011). The percentage of other linguistic distances is relatively uniform, except for the cases where LD =4. Table 3 presents the descriptive statistics for the main variables. The mean and median of CAR [−2, +2] are 0.011 and 0.004, respectively, which suggest that the market reaction of M&As is positive. Linguistic distance (LD) exhibits a large variation, with a mean of 1.628, and a standard deviation of 1.837. The most common educational background of acquirers’ managers is undergraduate, the average age is about 50, and approximately 60 percent of them have relevant industry experience. The average deal size is about 7 percent of the market value of the acquiring ﬁrms, 41.5 percent of the M&As are diversiﬁed, and most of the deals are paid for in cash. Besides, most of the acquiring ﬁrms’ characteristics are likely to follow a normal distribution. Table 3. Summary statistics. Variables Mean Std. 25% Median 75% CAR [−2, +2] 0.011 0.069 −0.028 0.004 0.033 LD 1.628 1.837 0.000 1.000 3.000 Distance 2.042 1.349 0.000 2.620 3.120 Education 3.440 0.865 3.000 4.000 4.000 Age 49.98 7.267 45.00 49.00 55.00 IndExp 0.636 0.482 0.000 1.000 1.000 DealSize 0.071 0.327 0.004 0.012 0.034 PayStock 0.021 0.145 0.000 0.000 0.000 Diversify 0.415 0.493 0.000 0.000 1.000 Size 21.53 1.270 20.66 21.36 22.21 Leverage 0.505 0.836 0.315 0.502 0.620 B/M 0.691 0.256 0.502 0.694 0.883 SOE 0.434 0.496 0.000 0.000 1.000 Dual 0.194 0.396 0.000 0.000 0.000 Board 9.171 2.063 8.000 9.000 9.000 Ind_Board 0.335 0.103 0.333 0.333 0.375 H5 0.501 0.172 0.365 0.498 0.630 Insider 0.238 1.484 0.000 0.000 0.000 PerGDP 4.151 0.399 3.919 4.185 4.443 Marketization 6.779 1.345 5.700 7.040 8.320 106 L. LI ET AL. Table 4. Univariate analysis of linguistic distance and M&A performance. (1) (2) (3) (4) (5) (6) (7) Statistics Full Sample LD = 0 LD = 1 LD = 2 LD = 3 LD = 4 LD = 5 N 516 237 53 61 62 36 67 Mean 0.011*** 0.015*** 0.019* 0.007 0.025*** 0.004 −0.015 [p-value] [0.001] [0.002] [0.053] [0.257] [0.005] [0.764] [0.106] Median 0.004** 0.005** 0.006 0.003 0.015** 0.005 −0.006 [p-value] [0.013] [0.019] [0.192] [0.391] [0.019] [0.863] [0.137] ***, ** Notes: and * represent signiﬁcance at the 1%, 5% and 10% levels, respectively. Table 4 presents our univariate tests. In column (2), the CAR [−2, +2] is positive and statistically signiﬁcant when the acquirer and the target ﬁrm have the same linguistic background (i.e., LD = 0). In columns (3) to (7), the performance of M&As decline as the linguistic distance increases, and the worst M&A performance is −0.015 when there is a greatest linguistic distance (i.e., LD = 5). Together, these results provide preliminary evidence to support our hypothesis. 4.2. Main results Table 5 presents the results of whether the linguistic distance between the acquirer and the target ﬁrm aﬀects M&A performance. In column (1), the coeﬃcient on LD is −0.005 and is statistically signiﬁcant at the 1% level. As an alternative approach, we employ the dummy variable LD_D to measure linguistic distance. LD_D equals zero if the acquirer and the target ﬁrms have the same dialect point, and one otherwise. Consistent with our prediction, in column (2), the coeﬃcient on LD_D is negative and statistically signiﬁcant. Hence, our inferences are unchanged with this dummy measure of linguistic distance. The economic magnitude of the results is gauged from the eﬀect of linguistic distance on M&A performance. Based on the coeﬃcient estimates in Table 5, column (1), the CAR [−2, +2] is lower 2.5 percent (= −0.005 × 5) when the linguistic distance is the largest (i.e., LD = 5), relative to the M&As with the same language background. Based on a U.S. M&A sample, Masulis et al. (2007) ﬁnd that the eﬀect of the GIM antitakeover index is 0.54 percent over the [−2, +2] window. Thus, the impact of linguistic distance on M&A performance is similar to the anti-takeover provisions in the United States, which are both economically and statistically signiﬁcant. It is worth noting that we use the longest workplace to substitute for the missing birthplace in our main analysis, which may lead to a measurement error of the inde- pendent variable LD. To address this measurement error concern, we employ the approach used by Dai et al. (2016) and construct a dummy variable (Miss) to indicate the missing birthplace information. Miss equals one if the birthplace of the acquire is missing, and zero otherwise. In Table 5, we add a dummy variable Miss to Model (1) in column (3), and exclude the sample with missing birthplace information in column (4). The results show that the magnitude of the coeﬃcient on LD is larger than that reported in column (1), which is consistent with a stronger eﬀect of linguistic distance for these more accurate measurements. CHINA JOURNAL OF ACCOUNTING STUDIES 107 Table 5. Inﬂuence of linguistic distance on M&A performance. Dependent variable: (1) (2) (3) (4) CAR [−2, +2] Baseline model Alternative measure (LD_D) Controlling Miss Exclude the Miss sample LD −0.005*** −0.006*** −0.007*** (0.007) (0.002) (0.006) LD_D −0.014* (0.052) Miss 0.003 (0.683) Distance 0.001 −0.0003 0.002 0.004 (0.626) (0.906) (0.488) (0.291) Education 0.005 0.005 0.006 0.009* (0.164) (0.194) (0.143) (0.068) Age −0.0001 −0.00001 −0.00003 0.0003 (0.913) (0.991) (0.944) (0.663) IndExp −0.003 −0.003 −0.003 −0.012 (0.648) (0.653) (0.660) (0.133) DealSize 0.034* 0.033* 0.034* 0.055** (0.082) (0.094) (0.081) (0.037) PayStock 0.109*** 0.103** 0.110*** 0.117** (0.001) (0.003) (0.001) (0.016) Diversify −0.005 −0.006 −0.005 −0.011 (0.455) (0.406) (0.448) (0.169) Size −0.001 −0.002 −0.001 −0.001 (0.625) (0.517) (0.683) (0.762) Leverage −0.014*** −0.013*** −0.014*** 0.004 (0.000) (0.000) (0.000) (0.870) B/M 0.029 0.026 0.029 0.007 (0.141) (0.183) (0.143) (0.785) SOE 0.001 0.001 0.001 −0.002 (0.877) (0.848) (0.929) (0.815) 0.008 Dual −0.005 −0.005 −0.004 (0.568) (0.498) (0.596) (0.411) Board 0.002 0.002 0.002 0.002 (0.224) (0.203) (0.213) (0.250) Ind_Board −0.012 −0.013 −0.012 −0.012 (0.826) (0.819) (0.825) (0.866) H5 −0.050** −0.050** −0.049** −0.010 (0.018) (0.020) (0.019) (0.671) Insider −0.002 −0.002 −0.002 −0.0002 (0.237) (0.235) (0.229) (0.935) PerGDP −0.013 −0.012 −0.014 −0.024 (0.446) (0.511) (0.439) (0.267) Marketization 0.002 0.001 0.002 0.001 (0.566) (0.716) (0.564) (0.812) Constant 0.085 0.080 0.078 0.088 (0.334) (0.375) (0.378) (0.434) Industry YES YES YES YES Year YES YES YES YES N 516 516 516 328 0.139 0.134 0.143 0.207 Adj-R ***, ** Notes: and * represent signiﬁcance at the 1%, 5% and 10% levels, respectively. The p-value shown in parentheses is adjusted for clustering by ﬁrm (Petersen, 2009). 4.3. Endogeneity issues Although the language background of the merger is exogenous, the linguistic distance between the acquirer and the target ﬁrm may be endogenous, because asymmetric information and agency costs may aﬀect managers’ M&A decisions in the choice of targets. According to Liu et al. (2015) and Dai et al. (2016), we take the relief degree of 108 L. LI ET AL. land surface (RDLS) of the acquirer as an instrumental variable for the following two reasons. Firstly, classiﬁcation of dialect is closely related to geographical regions, and the complexity of land surface directly aﬀects the diversity of languages. The more complex the RDLS is, the more mountains and rivers there will be, the more obvious regional independence and population segmentation are, and the more diverse the regional dialects will be in the long run (Liu et al., 2015). In other words, even if the geographical area is the same, the language diversity of each region varies with the complexity of the land surface, thus the RDLS directly aﬀects linguistic distance LD. Secondly, the RDLS is unrelated to M&As performance as its natural geographical attribute. Following Feng, Tang, and Yang (2007) and Feng, Yang, and You (2014), we match the RDLS data with the manager’s birthplace, and calculate the RDLS of the M&As. Then, we employ a 2SLS regression. The F-statistic of the correlation test between the instru- mental variables and endogenous variables is 12.8, which suggests that we can reject the null hypothesis of weak instrumental variables (i.e., the RDLS is a valid instrumental variable). Table 6 presents our instrumental regression results. The ﬁrst-stage regression results in column (1) show that the coeﬃcient on RDLS is positive and statistically signiﬁcant, indicating that the instrumental variable is reasonable. Column (2) shows the regression results of the second-stage, and the coeﬃcient of LD is consistent with our prediction. These results support the conclusion that after controlling for endogene- ity issues, the increase of linguistic distance reduces M&A performance. 4.4. Distinguishing the cultural eﬀect and the communication eﬀect Language has a cultural identity function and also serves as the intermediary of inter- personal communication. Therefore, linguistic distance may reduce cultural identity (i.e., the cultural eﬀect) or hinder language communication (i.e., the communication eﬀect), both of which may aﬀect M&A activities. To further distinguish between the two diﬀerent eﬀects of language, we conduct two additional analyses. Table 6. Instrumental regression. (1) (2) First-stage Second-stage LD −0.048*** (0.005) RDLS 0.469*** (0.000) Controls YES YES Industry YES YES Year YES YES N 516 516 Adj-R 0.382 0.102 ***, ** Notes: and * represent signiﬁcance at the 1%, 5% and 10% levels, respectively. The p-value shown in parentheses is adjusted for clustering by ﬁrm (Petersen, 2009). We use the same set of control vari- ables as those reported in Table 5. However, for the sake of brevity they are not tabulated in this table. Dai et al. (2016) suggest that the cultural eﬀect refers to the inﬂuence of mistrust and conﬂict arising from cultural diﬀerences related to language, while the communication eﬀect refers to the eﬀect of language barriers on communication. CHINA JOURNAL OF ACCOUNTING STUDIES 109 First, we control the diﬀerences in the Mandarin level. Although popularization of Mandarin has unblocked many physical communication channels that reduced commu- nication barriers, the diﬀerences between regional cultures related to language back- grounds are diﬃcult to eliminate in a short time period (Gao & Long, 2016). According to Liu et al. (2015), if linguistic distance plays a major role in M&A performance because it hinders language communication (i.e., the communication eﬀect), the inﬂuence of linguistic distance will be weakened after controlling the diﬀerent Mandarin levels. On the contrary, if linguistic distance plays a major role in M&A performance because it reduces cultural identity (i.e., the cultural eﬀect), the inﬂuence of linguistic distance will remain unchanged after controlling the diﬀerences in Mandarin levels. Speciﬁcally, we hand collect the Mandarin level data of each province from A Survey of Chinese Language Usage (2006), and match this data with the birthplace of the acquiring manager and the location of the target ﬁrm. Then, we construct the diﬀerence in Mandarin level proxy (Mandarin). Column (1) of Table 7 presents the results control- ling Mandarin. Our inferences remain unchanged, indicating that the inﬂuence of LD on the performance of M&As is driven mainly by the cultural eﬀect rather than the com- munication eﬀect. Besides, the coeﬃcient on Mandarin is negative but insigniﬁcant, which is consistent with the ﬁnding that a large diﬀerence in the Mandarin level will hinder communication, thus reducing the performance of M&As. Second, we divide the sample into a Mandarin area and a non-Mandarin area. In the North of China, language diﬀerences are small and the dialect is predominantly Mandarin. In contrast, in the South of China, because of its undulating topography, there are more diversiﬁed dialects that lead to communication diﬃculties. According to Dai et al. (2016), if the communication eﬀect of language plays a major role, it can be expected that southern dialects will have a greater impact than northern dialects on the performance of M&As in China. On the contrary, if the cultural eﬀect of language plays a major role, we expect no diﬀerence between the inﬂuence of southern and northern dialects on the performance of M&As. Table 7. Distinguishing the cultural eﬀect and the communication eﬀect. Dependent variable: (1) (2) CAR [−2, +2] Controlling Mandarin Controlling Guanhua LD −0.005*** −0.005** (0.008) (0.020) Mandarin −0.016 (0.496) Guanhua 0.008 (0.411) LD×Guanhua −0.0004 (0.951) Controls YES YES Industry YES YES Year YES YES N 516 516 Adj-R 0.140 0.142 ***, ** Notes: and * represent signiﬁcance at the 1%, 5% and 10% levels, respectively. The p-value shown in parentheses is adjusted for clustering by ﬁrm (Petersen, 2009). We use the same set of control variables as those reported in Table 5. However, for the sake of brevity they are not tabulated in this table. 110 L. LI ET AL. Speciﬁcally, we divide our sample into Mandarin and non-Mandarin subsamples according to the types of dialects. It is worth noting that Southwestern Mandarin belongs to the southern dialect, and is classiﬁed as a non-Mandarin subsample. Guanhua equals one if both the acquirer and the target ﬁrm are Mandarin, and zero otherwise. Column (2) of Table 7 presents the results. The coeﬃcient on LD is negative and statistically signiﬁcant, while the coeﬃcient on the interaction term LD×Guanhua is insigniﬁcant, suggesting that communication between dialects does not directly aﬀect the relationship between linguistic distance and M&A performance. We interpret this as evidence that the impact of linguistic distance on the performance of M&As is due to the cultural eﬀect rather than the communication eﬀect. In addition, we also employ those sub-samples in which the acquirer is Mandarin or both the acquirer and the target ﬁrm are Mandarin, respectively. Our untabulated results show that the coeﬃcients on LD are negative and statistically signiﬁcant, indicating that the cultural eﬀect of the language exists even in a Mandarin area where the dialect communication is smooth. Together, the results shown in Table 7 suggest that language diﬀerences between the acquirer and the target ﬁrm, which reduce the performance of M&As are mainly driven by the cultural eﬀect rather than the communication eﬀect. 4.5. Supplementary analyses In this section, we further examine the inﬂuence of the manager characteristics of the acquirer, the industry characteristics of the target ﬁrm, the ownership structure of the company, and regional development on the relationship between linguistic distance and the performance of M&As. First, manager characteristics of the acquiring ﬁrms. Previous studies show that the characteristics of managers have information content (Hambrick & Mason, 1984; Huang & Sheng, 2013). Table 8 presents the results. In Panel A, we divide our sample into two groups according to the manager’s education. The managers of the acquiring ﬁrms who have a master degree or above are classiﬁed as a ‘highly educated’ group, and ‘low educated’ group otherwise. Columns (1) and (2) show that the negative impact of language diﬀerences is more pronounced in the ‘low educated’ group rather than in the ‘highly educated’ group. One explanation is that managers with higher levels of Table 8. Impact of the manager characteristics. Panel A: Panel B: Panel C: By education By age By industry experience Dependent variable: (1) (2) (3) (4) (5) (6) CAR [−2, +2] Low educated Highly educated Younger Elder Inexperienced Experienced LD −0.005* −0.004 −0.008*** −0.002 −0.009*** −0.004 (0.063) (0.127) (0.008) (0.314) (0.006) (0.181) Controls YES YES YES YES YES YES Industry YES YES YES YES YES YES Year YES YES YES YES YES YES N 244 272 244 272 188 328 Adj-R 0.180 0.194 0.248 0.222 0.175 0.216 ***, ** Notes: and * represent signiﬁcance at the 1%, 5% and 10% levels, respectively. The p-value shown in parentheses is adjusted for clustering by ﬁrm (Petersen, 2009). We use the same set of control variables as those reported in Table 5. However, for the sake of brevity they are not tabulated in this table. CHINA JOURNAL OF ACCOUNTING STUDIES 111 education have accumulated more intellectual capital, and thus rely more on formal institutions such as contracts rather than informal institutions such as language in business. On the other hand, it is unlikely that language is an obstacle in communication as Mandarin is mainly used in daily work throughout China, and higher levels of education weaken the negative impact of language diﬀerences in M&A activities. In Panel B of Table 8, we divide our sample into two groups according to the manager’s age, which is classiﬁed as ‘elder’ when the age of the manager of the acquiring ﬁrms is higher than the median value of the sample, and ‘younger’ otherwise. Columns (3) and (4) show that the negative impact of language diﬀerences is more pronounced in the ‘younger’ group rather than in the ‘elder’ group. A possible reason is that elder managers have richer experiences and a deeper understanding of regional cultures with diﬀerent language backgrounds, which is helpful in carrying out business activities in diﬀerent environments that depend less on the language background. Although a similar language background can make up for a younger manager’s lack of experience, it also means that a larger diﬀerence in the language background of M&As will bring about more an apparent negative impact on M&A activities. Panel C of Table 8, we divide the sample into two groups according to the manager’s industry experience. Managers of the acquiring ﬁrms who possess the industry experi- ence of the target ﬁrms are classiﬁed as ‘experienced’, and ‘inexperienced’ otherwise. Columns (5) and (6) show that the negative impact of linguistic diﬀerences is more pronounced when managers lack the industry experience of the target ﬁrms, suggesting that a proximal language background can make up for a manager’s lack of relevant industry experience. Second, industry characteristics of the target ﬁrms. There are two advantages to choosing target ﬁrms in the same industry as the acquiring ﬁrms. On the one hand, the acquirer can have a more accurate valuation of the target ﬁrms and avoid paying excessive premiums. On the other hand, it is conducive to better resource integration after the merger or acquisition, which leads to better M&A performance (Zhou & Li, 2008). On the contrary, the information asymmetry problem in diversiﬁed mergers is more serious, and the cultural integration between the acquirer and the target ﬁrms will be more diﬃcult (Faccio & Masulis, 2005). Therefore, language, as an important part of culture, may play diﬀerent roles in diﬀerent types of M&As. Table 9 presents the results. We ﬁnd that the coeﬃcient on LD is negative but insigniﬁcant in horizontal M&As, while it is negative and Table 9. Impact of the types of M&As. Dependent variable: (1) (2) CAR [−2, +2] Diversiﬁed M&As Horizontal M&As LD −0.010*** −0.0004 (0.002) (0.867) Controls YES YES Industry YES YES Year YES YES N 214 302 Adj-R 0.195 0.241 ***, ** Notes: and * represent signiﬁcance at the 1%, 5% and 10% levels, respectively. The p-value shown in parentheses is adjusted for clustering by ﬁrm (Petersen, 2009). We use the same set of control variables as those reported in Table 5. However, for the sake of brevity they are not tabulated in this table. 112 L. LI ET AL. statistically signiﬁcant in diversiﬁed M&As. These results suggest that language plays an important role in M&As activities, especially in diversiﬁed M&As where information asym- metry is serious. Third, ownership structure. The motivation behind M&A and the resource integration after M&As vary greatly according to the ownership structure (Xu, Li, & Rui, 2018), the role of language background may also be diﬀerent. Table 10 presents our results. We ﬁnd that the coeﬃcients on LD are signiﬁcantly negative in private ﬁrms but not signiﬁcant in state-owned ﬁrms. One possible explanation is that state-owned enter- prises are more aﬀected by institutional factors such as government intervention in M&A activities (Pan, Xia, & Yu, 2008), which weakens the role of language background. Finally, regional development. The inﬂuence of language background on economic activities in China may diﬀer due to variations in regional economic development (Xu et al., 2015). In developed areas, economic transactions depend more on formal institu- tions, thus language plays a weaker role in economic activities. If more than half years the per capita GDP of a particular province (excluding Hong Kong, Macao and Taiwan) exceeds the national per capita GDP during the period 2000–2012, the region is classiﬁed as a ‘developed region’ which includes a total of 10 regions, and a ‘less developed region’ which includes 21 regions, otherwise. The results in Table 11 show that the coeﬃcient on LD is negative but insigniﬁcant in a developed region, while it is negative and statistically signiﬁcant in a less developed region. Table 10. Impact of the ownership structure. Dependent variable: (1) (2) CAR [−2, +2] Private ﬁrms State-owned ﬁrms LD −0.008*** −0.002 (0.004) (0.462) Controls YES YES Industry YES YES Year YES YES N 292 224 Adj-R 0.217 0.160 ***, ** Notes: and * represent signiﬁcance at the 1%, 5% and 10% levels, respectively. The p-value shown in parentheses is adjusted for clustering by ﬁrm (Petersen, 2009). We use the same set of control variables as those reported in Table 5. However, for the sake of brevity they are not tabulated in this table. Table 11. Impact of the regional development. Dependent variable: (1) (2) CAR [−2, +2] Less developed region Developed region LD −0.008** −0.003 (0.032) (0.190) Controls YES YES Industry YES YES Year YES YES N 200 316 Adj-R 0.204 0.160 ***, ** Notes: and * represent signiﬁcance at the 1%, 5% and 10% levels, respectively. The p-value shown in parentheses is adjusted for clustering by ﬁrm (Petersen, 2009). We use the same set of control variables as those reported in Table 5. However, for the sake of brevity they are not tabulated in this table. CHINA JOURNAL OF ACCOUNTING STUDIES 113 4.6. Robustness checks In this section, we conduct a series of robustness checks. 4.6.1. Alternative deﬁnitions of linguistic distance We use the longest workplace to substitute for the missing birthplace data in our main analysis, which may lead to a measurement error of linguistic distance. To address this measurement error concern, we conduct two additional analyses. First, we calculate the linguistic distance between the manager’s previous workplaces, as revealed in the resume and the target ﬁrms, and then employ the minimum or average of those linguistic distances to measure language diﬀerence, respectively. Second, because a manager’s education stage (especially the university stage) is a critical formative period, we also replace the birthplace of a manager with places of education and then recalculate the linguistic distance. Our inferences remain unchanged with these alternative deﬁnitions. 4.6.2. Alternative representatives of management Unlike the existing literature which mostly regards a CEO as the representative of corporate management, it is unknown whether the chairman or the CEO plays a more critical role in the decision-making of the listed companies in China (Song, 2006). As a robustness check, we also choose the chairman or the CEO as management’s repre- sentative, respectively. We ﬁnd that the results are qualitatively the same as our main results. 4.6.3. Alternative deﬁnitions of M&A performance We repeat our analysis with four alternative deﬁnitions of M&A performance. First, CARs are calculated with other event windows, such as [−1, +1], [−3, +3] and [−5, +5]. Second, the abnormal returns are estimated by the market-adjusted model. Third, we use the value-weighted return to measure the market return. Fourth, the ﬁnancial performance of M&As. More speciﬁcally, we use the changes of industry-adjusted ROA (ROE) in the current year or three-year period after the merger event year to measure the ﬁnancial performance of M&As, respectively. Our untabulated results show that the coeﬃcient on LD is negative and statistically signiﬁcant at the 1% or 5% levels, and that our inferences are unchanged. 4.6.4. The inﬂuence of geographic distance We acknowledge that the relationship between linguistic distance and M&A perfor- mance can be partly explained by geographic distance. To ensure that our results are not purely driven by geographic distance, we conduct two additional analyses. First, we estimate the model after partitioning the sample by the geographic distance. If our main results are driven primarily by the geographic distance, we expect the results to disappear (or be much weaker) in the near geographical distance sample where the linguistic distance is smaller. Second, we estimate the model after partitioning the sample based on whether the acquirer and the target ﬁrms are located in the same province. Similarly, if our main results are driven primarily by the geographic distance, we expect the results to disappear (or be much weaker) in the same province sample 114 L. LI ET AL. where the linguistic distance is smaller. However, our untabulated results show that the coeﬃcients on LD are signiﬁcantly negative regardless of the geographical distance and whether the acquirer and the target ﬁrm come from the same province or not. Overall, this evidence further supports our ﬁnding that the negative associations between linguistic distance and the performance of M&As do not appear to be driven by geographic distance. 4.6.5. The inﬂuence of linguistic distance on the pre- and during-merger Our previous analysis focuses on the inﬂuence of linguistic distance on ﬁrm performance after a merger or acquisition. To provide more direct evidence, we further examine the inﬂuence of linguistic distance on the pre- and during-merger. First, for the pre-merger period, we investigate whether linguistic distance aﬀects the probability of a - successful M&A. We hand collect all of the M&As in our sample, and deﬁne Success as equal one if the merger is successful, and zero otherwise. Our untabulated results show that the coeﬃcient on LD is signiﬁcantly negative, suggesting that linguistic diﬀerences signiﬁcantly reduce the probability of successful M&As, and thus increase the risk of failure. Second, for the during-merger period, we examine the inﬂuence of linguistic distance on the integration of M&As from the perspectives of manager turnover, excess employees, and analysts following, respectively. Speciﬁcally, Turnover equals one if the chairman or CEO turnover after the merger, and zero otherwise. △ExcEmp measures the change of excess employees before and after the merger, where excess employees are estimated according to the model developed by Zeng and Chen (2006). △AFNum measures the change of analysts following before and after the merger, where analysts following is the number of analysts following the acquiring ﬁrms. Our untabulated results show that greater linguistic distance leads to an increase in manager turnover, a larger number of excess employees and a lower number of analysts following. All these evidence support that linguistic distance between the acquirer and the target ﬁrm increases the diﬃculty of M&A integration. For simplicity, we do not tabulate the results of the robustness checks. But they are available upon request. 5. Conclusions and limitations Informal institutions play an important role in China, due to the country’s unique social, cultural and economic development. Geographical diﬀerences, social culture and histor- ical environment have led to great language diﬀerences among the diﬀerent regions, which aﬀect corporate governance as a kind of informal institution. In this paper, we investigate the inﬂuence of linguistic distance between the acquirer and the target on M&A performance, using Chinese public companies from 2000 to 2012. Based on the diversity of language in China, we ﬁnd that the linguistic distance between the acquirer and the target ﬁrm signiﬁcantly reduces the performance of M&As. Though further tests, we show that the inﬂuence of linguistic diﬀerences on the performance of M&As is driven mainly by the cultural eﬀect rather than the communication eﬀect. In addition, our cross-sectional analysis shows that the inﬂuence of linguistic diﬀerences on We thank an anonymous reviewer for this comment. CHINA JOURNAL OF ACCOUNTING STUDIES 115 the performance of M&As is not only aﬀected by manager characteristics, types of M&As and ownership structure, but it is also aﬀected by regional development. Our evidence suggests that language, as an important informal institution, plays a complementary role to formal institutions. Three limitations of our study are worth mentioning. First, we focus on the inﬂuence of linguistic diﬀerences on the performance of M&As without distinguishing among the types of dialects. Second, we classify regional dialects according to the Language Atlas of nd China (2 Edition). Although the classiﬁcation method is authoritative, the classiﬁcation of dialects in some areas remains controversial. Third, we use the longest workplace to substitute for missing birthplace data in our main analysis, which may lead to measure- ment errors. We caution the reader that the reliability of our conclusions still needs to be further veriﬁed when the data are more complete. Acknowledgments We appreciate helpful comments from two anonymous reviewers, Donghua Chen (Associate editor), Hanwen Chen (Joint editor), and Jigao Zhu (our discussants). Lu Li acknowledges the ﬁnancial support from the Fundamental Research Funds for the Central Universities (No. 2018114037). Tusheng Xiao acknowledges the ﬁnancial support from the National Natural Science Foundation of China (No. 71402197), the Young Elite Teacher Project (No. QYP1803) and the Program for Innovation Research in Central University of Finance and Economics. Tusheng Xiao also thanks China’s Management Accounting Research and Development Center. Jingyin Zhou, Lun Jiang, Ying Zhang, Bing Hua and Shao Zhao provided research assistance. Disclosure statement No potential conﬂict of interest was reported by the authors. References Ahern, K., Daminelli, D., & Fracassi, C. (2015). Lost in translation? The eﬀect of cultural values on mergers around the world. Journal of Financial Economics, 117(1), 165–189. Allen, F., Qian, J., & Qian, M. (2005). Law, ﬁnance, and economic growth in China. Journal of Financial Economics, 77(1), 57–116. Alvesson, M., & Kärreman, D. (2000). Varieties of discourse: On the study of organizations through discourse analysis. Human Relations, 53(9), 1125–1149. 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China Journal of Accounting Studies
– Taylor & Francis
Published: Jan 2, 2019
Keywords: Linguistic distance; M&A performance; culture; informal institution