CEOs' hometown connections and access to trade credit: Evidence from China

Journal Pre-proof CEOs' hometown connections and access to trade credit: Evidence from China

Dongmin Kong, Yue Pan, Gary Gang Tian, Pengdong Zhang PII:

S0929-1199(20)30018-3

DOI:

https://doi.org/10.1016/j.jcorpfin.2020.101574

Reference:

CORFIN 101574

To appear in:

Journal of Corporate Finance

Received date:

5 February 2019

Revised date:

26 November 2019

Accepted date:

8 January 2020

Please cite this article as: D. Kong, Y. Pan, G.G. Tian, et al., CEOs' hometown connections and access to trade credit: Evidence from China, Journal of Corporate Finance(2020), https://doi.org/10.1016/j.jcorpfin.2020.101574

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© 2020 Published by Elsevier.

Journal Pre-proof

CEOs’ Hometown Connections and Access to Trade Credit: Evidence from China 1 a

b

Dongmin Kong [email protected], Yue Pan [email protected], Gary Gang Tian [email protected], Pengdong Zhangd [email protected] a

c

Huazhong University of Science and Technology, China b Xiamen University, China c Macquarie University, Australia d

Xiamen University, China

*

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Corresponding author.

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Abstract:

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In this study, we investigate how informal institutions, namely, chief executive officers’ hometown connections with suppliers, impact firms’ access to trade credit. Using unique data

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manually collected from China, we find that hometown connections significantly increase

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access to trade credit. The hometown effect is more pronounced for non-state-owned firms, firms in provinces with poorly developed financial institutions, and firms whose chief

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executive officers come from hometowns with a strong merchant guild culture or hold an

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important position in the hometown’s chamber of commerce. We suggest two plausible

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channels for the hometown effect: information and social trust. Overall, this study contributes to the literature by documenting how hometown connections help firms to obtain external financing in emerging markets.

Keywords: Hometown connections, Trade credit, CEOs, Suppliers

1

We greatly appreciate the helpful comments and suggestions from Qianwei Ying, Xueyong Zhang, Weihua Zhang and

seminar participants at East China University of Science and Technology, Huazhong University of Science and Technology, Guangxi University, Xiamen University, Zhongnan University of Economics and Law, Beijing Technology and Business University and Shanghai Jiaotong University. We also gratefully acknowledge financial support from the National Natural Science Foundation of China (Grant No. 71772178, 71772155, 71572158). All errors are our own. 1

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1. Introduction The question of whether and how trade credit is influenced by informal institutions, especially social connections, is far from fully understood, even though numerous studies have examined why firms receive or grant trade credit from the perspectives of product or firm characteristics (e.g., Lee and Stowe, 1993; Petersen and Rajan, 1997). In this study, we propose and test a

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novel determinant of firms’ likelihood of receiving trade credit from suppliers —homophily,

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that is, the sociological principle that individuals tend to associate and interact with others who

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have similar backgrounds and characteristics, such as ethnicity, age, gender, and education

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(e.g., McPherson et al., 2001; Granovetter, 2005; Stolper and Walter, 2018).2

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The pervasiveness of homophily means that cultural, behavioral, genetic, or material information that flows through networks tends to be localized (McPherson et al., 2001).

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Common places of origin play an important role in individual growth and create contexts in

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which homophilous relations form, breeding hometown connections between individuals.

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Although the literature has documented well the impacts of hometown connections on stock investment (e.g., Coval and Moskowitz, 2010) and resource allocation of governments (Knight, 2008; Cohen et al., 2011; Hodler and Raschky, 2014), surprisingly little is known about their economic consequences at the firm level. We conjecture that hometown connections should play an important role in opaque and high-risk markets, such as the supply of trade credit, the practice of buying now and paying later, in which both suppliers and 2

Previous studies attribute social connections to a sense of familiarity and trust between individuals of similar

backgrounds and document the important effects of social connections, for examp le, in the context of boards (Cohen et al., 2012; Engelberg et al., 2013; Ishii and Xuan, 2014), mutual fund performance (Cohen et al., 2008), securities analyst recommendations (Cohen et al., 2010), bank financing (Engelberg et al., 2012; Lin et al., 2013), and venture capital markets (Hegde and Tu mlinson, 2014). However, there is a lack specific ev idence for the impact of homophily on corporate behavior. 2

Journal Pre-proof customers face severe information asymmetries and moral hazard (Wu et al., 2014). The hometown effect should be more pronounced in emerging markets that have poorly developed formal financial systems, such as bank lending and bond markets (Liu et al., 2016), and thus, borrowers rely heavily on trade credit as an alternative source of financing (Lee and Stowe, 1993; Cull et al., 2009). In this study, we shed light on whether and how access to trade credit is influenced by

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homophily by examining the relationship between the hometown connections of chief

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executive officers (CEOs) and firms’ access to trade credit. Granovetter (2005) argues that

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social connections can serve as information channels and influence economic decisions.

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Connections based on homophily facilitate the selection of business partners because of better access to superior information within social networks, and shared norms and discourse may

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improve coordination and monitoring among socially close individuals after these individuals

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form a partnership (Hegde and Tumlinson, 2014).

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We consider that CEOs’ hometown connections with suppliers, as a basic source of social ties, are relevant to firms’ access to trade credit for two reasons. First, the exchange of information facilitated by connections between customers and suppliers can mitigate concerns over uncertainty when partners form a business relationship (Uzzi, 1996; Uzzi and Lancaster, 2003). Second, the CEO’s prior exchange experience, including that with suppliers, increase the

likelihood

of subsequent

interorganizational exchange by enhancing

mutual

trustworthiness and reliability (Barden and Mitchell, 2007), which in turn alleviates concerns over opportunism that cannot be fully addressed contractually (Uzzi, 1997). Therefore, we hypothesize that CEOs’ hometown connections with suppliers help firms to access trade credit. 3

Journal Pre-proof To test our hypothesis, we analyze data collected from Chinese firms listed on both the Shanghai and Shenzhen securities exchanges for the sample period from 2007 to 2016. The institutional environment of the Chinese market provides the ideal setting in which to investigate this question for the following reasons. First, hometown connections (laoxiang in Chinese) play a central role in guanxi, the culture of favor exchange in Chinese society (Fisman et al., 2018). As Chen and Chen (2004) observe, hometown connections are among the most

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common and distinctive bases on which guanxi is built. As members of a relationship-centered

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society strongly influenced by Confucianism, the Chinese often define themselves in relation

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to others and view hometown connections as an essential foundation of their self -identity

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(Jacobs, 1982; Yang, 1994). Such depersonalized affection among those who share a hometown resulting from the process of social categorization and identification (Turner et al.,

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1987; Tsui and Farh, 1997) facilitates communication in common dialects, checking credit

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worthiness through hometown networks, and the formation of a coherent identity (Pan et al.,

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2018). Therefore, it is important to examine whether CEOs’ hometown connections, as an important type of social tie, influence firms’ ability to obtain trade credit. We expect that CEOs’ hometown connections should play an important role in helping firms to access trade credit, because such connections enable firms to build trust and mitigate information asymmetry between firms and suppliers. Second, Chinese firms mainly support their growth through informal financial channels that rely largely on implicit contractual relations (Ge and Qiu, 2007). Thus, trade credit, as the most important form of informal financing, plays an even more important role in China than in developed economies. China provides a unique setting for us to test how firms in a country 4

Journal Pre-proof with a poorly developed financial sector fund potential growth opportunity. Third, China’s weak legal system provides insufficient protection for creditors and puts suppliers of trade credit at high risk of default (Peng and Luo, 2000; Liu and Tian, 2012). Therefore, it is particularly important to explore ways in which Chinese firms can reduce credit risks and obtain trade credit. Allen et al. (2005) point out that China is an important counterexample to findings in the literature on law, institutions, finance, and growth because of

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the coexistence of its weak legal system and rapidly growing economy. The authors conjecture

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that alternative governance mechanisms, such as those based on reputation and relationships,

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must support growth, especially in the private sector. Therefore, China is an ideal setting for

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studying the role of informal institutions as a substitute for a good legal environment (Karlan, 2005).

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We identify the suppliers registered in CEOs’ hometown province (hereafter, “hometown

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suppliers”) and sum the shares of inputs that the listed firms purchased from these hometown

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suppliers to construct the variable HOMEPUR. For example, if two of the top five suppliers are registered in CEOs’ hometown province and the listed firm purchases 14% and 10% of all goods and services in the observed fiscal year from these two hometown suppliers, respectively, then HOMEPUR takes a value of 0.24 (=14%+10%). In our baseline regression, HOMEPUR is used to explain listed firms’ trade credit. The empirical results show that CEOs’ hometown connections help firms to access trade credit. Specifically, we find that a 1 standard deviation increase of share purchased from hometown suppliers results in a 0.494% increase of the trade credit ratio (equivalent to about 14.2 million RMB in trade credit). By investigating cross-sectional variation based on features of Chinese institutions, we 5

Journal Pre-proof confirm that the effect of CEOs’ hometown connections on firms’ access to trade credit is greater in firms that are not state-owned and in firms located in provinces with poorly developed financial institutions. We further find that the hometown effect is more pronounced in regions with stronger informal institutions, such as a strong merchant guild culture in CEOs’ hometown, and in firms whose CEOs hold an important position in the hometown chamber of commerce.

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To provide direct evidence to support our finding that CEOs’ hometown connections help

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firms to access trade credit, we further hypothesize that hometown connections enhance

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transparency and social trust, which in turn increases the probability of obtaining trade credit

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from the supplier. Then, we investigate whether the relationship between hometown connections and trade credit varies between firms with different levels of information opacity

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and firms located in regions with different levels of social trust. Using high information opacity

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and low level of social trust as measures for high credit risk faced by suppliers, we find that the

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positive relationship between CEOs’ hometown connections and trade credit is strengthened when suppliers face high credit risk. We use several approaches to address potential endogeneity issues. First, to control the variation of characteristics of CEOs’ hometown provinces, we add fixed effects at different dimensions: CEO’s hometown province, CEO’s hometown province×year, and CEO’s hometown province×year×listed firm industry. Second, we find that our conclusion is robust using two-stage least square (2SLS) instrumental variable estimation. The two instrumental variables selected are rice acreage in 1978 in the provinces where the listed firms are located (hereafter, “listed firm provinces”) and the number of dialects in that province. We document 6

Journal Pre-proof that less rice farming and more dialects in the listed firm province generate weaker social trust, which makes CEOs more likely to find trustworthy suppliers in their hometown. Third, we construct two propensity score matching–difference in differences (PSM-DID) models for two situations: existing connections broken and new connections emerged after the CEO changed. The results support our hypothesis that new connections emerged increase trade credit to listed firms while existing connections broken reduce firms’ access to credit. Finally, we identify the

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supply role of trade credit by investigating the relationship between hometown connections

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and trade credit during and after the global financial crisis (GFC; Love et al., 2010; Liu et al.,

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2016) and industry crisis (Yonker, 2017), when the overall credit risk is higher and information

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asymmetry between suppliers and receivers of trade credit is greater. Moreover, we provide evidence that our results are robust to alternative measures of key

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variables. Furthermore, our conclusion holds when we exclude firms hiring local CEOs to rule

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out the alternative explanation of geographical distance. Limiting the sample to the five largest

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disclosed counterparties of accounts payable, we verify our argument at the supplier level. The results support the role of CEOs’ hometown connections in firms’ access to trade credit. Our study contributes to the literature in the following ways. First, we contribute to the literature on the role of social connections in corporate financial decisions. Previous studies provide substantial evidence that social connections (from school, work, etc.) have an important influence on corporate financing in terms of bank loans (Engelberg et al., 2012; Lin et al., 2013), venture capital (Hegde and Tumlinson, 2014), and the angels market (Venugopal, 2017), but there is a lack of evidence for whether such connections influence the supply of trade credit. This is the first study in the literature to examine this issue from the perspective of 7

Journal Pre-proof hometown connections between CEOs and suppliers from their hometowns, which is an important type of social tie. We document a significant positive influence of hometown connections. Second, our study contributes to the literature on firms’ access to trade credit. Unlike financial variables and product or firm characteristics (Lee and Stowe, 1993; Petersen and Rajan, 1997; Nilsen, 2002; Mateut et al., 2006; Cunat, 2006; Giannetti et al., 2011), very few

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personal characteristics of managers, other than professional connections (Liu et al., 2016),

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have been investigated in terms of their influence on trade credit. Our study provides new

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evidence and enriches this literature.

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Third, only very recently have a few studies emerged to show how firm-to-firm trade network facilitates the formation of supplier–customer linkage (Bernard and Moxnes 2018;

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Bernard et al., 2019a, 2019b; Carvalho and Tahbaz-Salehi, 2019). 3 Our study, by focusing on

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hometown connections between suppliers and customers, complements this literature. We

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create and test the hypothesis that hometown connections enhance transparency and social trust, which in turn increases the probability of accessing trade credit from suppliers. Our confirmed results suggest that network effects, such as “hometown connections”, reduce search costs and improve the probability and quality of matching. Finally, this study helps to explain the contradiction between China’s rapid economic growth and its poorly developed legal and financial systems, first proposed by Allen et al.

3

For examp le, using large datasets from a credit reporting agency in Japan identifying firms’ customers and

suppliers, Bernard et al. (2019b) show that lower search and outsourcing costs lead firms to search more and find better suppliers, which in turn drives down marg inal costs. Bernard et al. (2019a) further docu ment that larger firm size can be derived fro m high production capability, more or better buyers and su ppliers, and/or better matches between buyer and suppliers using datasets of buyer–supplier relationships in Belgium. 8

Journal Pre-proof (2005), by showing how hometown connections, as a type of informal institution, ease corporate financial constraints. Our findings point to a plausible financing mechanism of firms in emerging economies whose financial markets lack development. Meanwhile, our results also help to explain the ubiquity of Lao Xiang Hui (hometown associations) and Hui Guan (guild halls) based on place of origin among migrant communities within China and the global Chinese diaspora.

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The rest of this paper is organized as follows. Section 2 discusses related studies and

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develops our hypotheses. Section 3 describes the sample selection and research design. The

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2. Hypothesis development

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empirical results and analysis are presented in Section 4. Section 5 concludes.

Information asymmetry and moral hazard are the main causes of credit rationing and high costs

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in financial markets (e.g., Jaffee and Russell, 1976; Stiglitz and Weiss, 1981). When providing

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trade credit, suppliers neither charge interest nor require collateral from customers, and thus,

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expose themselves to high credit risks that result in great loss when a customer defaults (Wu et al., 2014). Hometown connections, however, could help to spread information and build trust, thereby reducing uncertainty and opportunism in the business relationship. Specifically, we expect that CEOs’ hometown connections with suppliers reduce the credit risk faced by suppliers and help firms to access more trade credit via the following mechanisms. First, CEOs’ hometown connections with suppliers could alleviate information asymmetry between customers and suppliers, thereby mitigating uncertainty in the business relationship (Uzzi, 1996; Uzzi and Lancaster, 2003). Chinese entrepreneurs prefer to recr uit and do business with those from their own hometowns (Redding, 1990), and corporate executives are widely connected through hometown ties (Hamilton, 1996; Douw, 1999). Information on 9

Journal Pre-proof customer firms would be transmitted by people with these ties, which could directly stimulate the flow of information between customers and suppliers. Furthermore, Podolny (1994) argues that decision makers tend to take information cost into consideration if the cost is high and serious information asymmetry exists. The spread of information through the hometown network facilitates the verification of such information and enables suppliers to monitor customers, which may result in more provision of trade credit. Second, CEOs’ hometown connections with suppliers could help to improve their

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reputation and interpersonal trust (Barden and Mitchell, 2007), alleviating concerns about opportunism that cannot be fully addressed contractually (Uzzi, 1997). Guiso et al. (2004) find

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that the amount of trade credit accessible to customers depends largely on suppliers’ trust. In

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addition, hometown connections could cultivate a favorable impression (Chen and Chen, 2004)

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and cause suppliers to regard customers’ behavior more favorably (Uzzi, 1996). Thus, commitments to repay by customer firms whose CEOs have hometown connections with

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suppliers would be regarded as reliable and trustworthy. However, if CEOs were to renege on

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their commitment to suppliers with whom they were connected, such as by failing to pay trade

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credit at maturity, their reputations in the network would be damaged (Hwang, 1987; Yang, 1994). Therefore, just as Corwin and Schultz (2005) suggest that bringing a friend to the table may alleviate moral hazard, we expect that CEOs’ hometown connections, as an important type of social tie, would help firms to access more trade credit. Based on this discussion, we propose the following hypothesis. H1. CEOs’ hometown connections with suppliers are positively associated with firms’ access to trade credit. If, as expected, CEOs’ hometown connections help firms to access trade credit by alleviating information asymmetry and improving social trust, one rational extension of this conjecture is that CEOs’ hometown connections should play a more important role when firms 10

Journal Pre-proof have greater need for trade credit and when more severe information asymmetry exists between suppliers and customers. Firms locating in an institutional environment with backward financial marketization rely more on informal financing, such as trade credit (Allen et al., 2005). This is especially true for non-state-owned firms, which lack an implicit guarantee from the government and generally have fewer assets and less information transparency. Thus, we hypothesize as follows. H2: There is a more pronounced positive relationship between CEOs’ hometown

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connections and firms’ access to trade credit in firms located in regions with poor developed financial market and non-SOEs.

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Based on these arguments, we further conjecture that CEOs’ hometown connections play a

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more important role in firms’ access to trade credit when it is easier to build social trust. With a

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weak legal system, social trust in Chinese society mainly stems from its informal institutions, such as the merchant guild and hometown chambers of commerce, which are widespread in

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China. First, the merchant guild culture in China is associated with honesty and justice, which

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played a role in relieving moral hazard in ancient Chinese commercial activities (Golas, 1977;

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Du et al., 2017). CEOs born into a strong merchant guild culture are more likely to earn the trust of and obtain help from hometown suppliers. Second, we expect that holding a position in a hometown chamber of commerce might be helpful for a CEO to establish a good reputation among hometown suppliers and might signal that corporate statements and decisions made by the CEOs are more trustworthy. Thus, we hypothesize as follows. H3: There is a more pronounced positive relationship between hometown connections and firms’ access to trade credit when the CEO’s hometown has a strong merchant guild culture or the CEO holds an important position in the hometown chamber of commerce.

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3. Data, variables, and methodology 3.1. Data Our sample includes all firms listed on both the Shanghai and Shenzhen securities exchanges for the sample period from 2007 to 2016. Our data come from three sources. First, we obtain financial data and data on firms’ top five suppliers from the China Stock Market & Accounting Research database, a widely used database in studies on China. Second, we manually collect

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information on the top five suppliers’ locations (registered provinces) from the National

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Enterprise Credit Information Publicity System (http://www.gsxt.gov.cn), by the names

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disclosed in the annual reports. Third, we manually identify the locations of CEOs from firms’ public announcements, firm-related news, and search engines (Google and Baidu).

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In identifying a person as a CEO, we follow Kato and Long’s (2006) criteria that (1) if the

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chairperson also works as the general manager, he or she is classified as the CEO and (2) if the chairperson is paid by the firm, he or she is classified as the CEO; otherwise, the general

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manager is regarded as the CEO.

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We exclude the following observations: suppliers whose names are undisclosed (e.g.,

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suppliers recognized only as “supplier one,” “supplier two,” etc.); suppliers that are overseas enterprises (including Hong Kong, Macao, and Taiwan); suppliers that are disclosed in ambiguous abbreviations; CEOs whose hometown provinces are not identified; firms listed in the financial industry (because of different accounting standards); and special treatment firms with financial troubles. To minimize the effect of outliers, we winsorize our sample at 1% on each continuous variable in each tail. The final sample consists of 3,472 firm-year observations from 2007 to 2016.

3.2. Variables 3.2.1. CEOs’ hometown connections 12

Journal Pre-proof To create the key independent variables of hometown connections, we first identify suppliers registered in CEOs’ hometown provinces and then sum up the shares that the listed firms purchase from these hometown suppliers for each specific firm-year. The following example illustrates the process. Assume listed firm XYZ is headquartered in Beijing and its CEO was born in Zhejiang province. First, we collect all the information available for suppliers, that is, the top five suppliers, from XYZ’s annual report in a given fiscal year, say 2016, including the name (e.g.,

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suppliers A, B, C, D, and E) and the supplier shares purchased (amount that XYZ spent on purchases from suppliers A, B, C, D, and E over the total purchase from all suppliers in 2016,

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e.g., 14%, 12%, 10%, 8%, and 6%, respectively). Second, we identify suppliers’ registration

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province by matching the names of suppliers A, B, C, D, and E with the National Enterprise

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Credit Information Publicity System (http://www.gsxt.gov.cn). Third, we identify “hometown suppliers” if the registration province of the supplier is the same as the CEO’s hometown,

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Zhejiang province. A dummy variable HOMECON created at the firm level takes the value of 1

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if there is at least one supplier registered in Zhejiang province. Finally, we sum up the shares

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purchased from hometown suppliers and construct the continuous variable HOMEPUR (e.g., suppliers A and C are registered in Zhejiang province while suppliers B, D, and E are not; then, the value of HOMEPUR is 14%+10%=24%). We use the continuous variable HOMEPUR, which contains more information than HOMECON, as the key independent variable in our baseline tests and we adopt the dummy variable HOMECON as the alternative measurement of CEOs’ hometown connections to test the robustness of our results.

3.2.2. Trade credit Following existing studies, such as Petersen and Rajan (1997), Fisman and Love (2003), 13

Journal Pre-proof Giannetti et al. (2011), Wu et al. (2014), and Liu et al. (2016), we define our key dependent variable, firms’ access to trade credit (CREDIT), as total accounts payable divided by total assets. We also measure trade credit in three other ways for robustness checks. First, we create the variable L_CREDIT, or total accounts payable divided by total liabilities, following Fisman and Love (2003). Second, following Liu et al. (2016), we use the net amount of accounts payable and accounts receivable divided by total assets (NET_CREDIT). Third, based on Ge

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and Qiu (2007), we divide accounts payable into transaction and financing components and use the financing component only (accounts payable with terms more than 1 year), scaled by total

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assets (FIN_CREDIT), as an alternative measure of trade credit.

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3.2.3. Control variables

For consistency with the literature, we follow Ge and Qiu (2007), Wu et al. (2014), and

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Liu et al. (2016) and introduce control variables for capturing other determinants related to

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trade credit. In addition to variables at the firm level, we control the characteristics of CEOs

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(e.g., gender, age, education, and tenure), suppliers (e.g., registered capital and age), and transaction characteristics (e.g., purchased shares of listed firms from all top five suppliers, local suppliers, and overseas enterprises). Detailed definitions of all variables used in this study are reported in Appendix A.

3.3. Descriptive analysis and univariate tests Table 1 presents the descriptive statistics. Summary statistics for our main regression variables are reported in panel A. On average, Chinese listed firms receive trade credit worth 8.86% of total assets and 23.64% of total liabilities, which are consistent with Wu et al. (2014) and Liu et al. (2016). When using the net value of accounts payable and receivable to measure 14

Journal Pre-proof the trade credit, it is worth -1.62% of total assets. The proportion of accounts payable with terms more than 1 year is 0.82%, scaled by total assets. Other main results show that 71.77% of our sample firms have at least one supplier registered in CEOs’ hometown province, and the average share purchased from hometown suppliers is 16.08%. Panel B of Table 1 reports results of the univariate tests, in which we compare the characteristics at the level of the firm, CEO, and supplier between firms with and without hometown connections. Our results show that firms with CEO hometown connections use

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trade credit worth 9.2% of assets, whereas firms without such connections use trade credit worth only 8.1% of assets; this difference (1.1 percentage points) is statistically significant at

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the 1% level. The results hold when we repeat the univariate tests for other dependent variables

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(L_CREDIT, NET_CREDIT, and FIN_CREDIT). These results confirm our hypothesis that

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CEOs’ hometown connections help firms to access trade credit. Moreover, on the one hand, firms with CEOs’ hometown connections have a longer history, more fixed assets, and more

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operating cash, comprise a larger percentage of state-owned enterprises, and usually purchase

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from smaller suppliers, suggesting greater bargaining power against counterparties and less

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likelihood of the CEO seeking help from hometown suppliers. On the other hand, connected firms have lower earnings before interest and tax (EBIT) and return on assets (ROA) and less research and development (R&D) expenditure, are located in regions with poorly developed financial markets and insufficient social trust, and are managed by CEOs with shorter tenure and from hometowns with a strong merchant guild culture, suggesting stronger motivation for the CEO to seek help from hometown suppliers. Since the mean and median of these variables significantly differ between these two groups, we control these variables in our regressions. [Insert Table 1 here]

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Journal Pre-proof 3.4. Regression models To examine the effect of CEOs’ hometown connections on firms’ access to trade credit, we use the following baseline regression model: CREDITi,t = β0 + β1 ×HOMEPUR i,t + β2 ×Xi,t + Firm and Year Dummies + ε

(1)

where the dependent variable (CREDIT) is firms’ access to trade credit measured by total accounts payable divided by total assets. The independent variable HOMEPUR is the sum of the shares purchased from hometown firms among the top five suppliers. X is a vector of

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control variables defined in Appendix A. Because trade credit is generally short term, trade credit at the year-end is mainly affected by the major suppliers in the current year, and thus, we

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use all independent variables for the current year, consistent with Liu et al. (2016). 4 Firm and

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year dummies are included in all regression models to control for firm- and year-specific

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effects. We add additional variables to Eq. (1), the baseline model, to interact with our key independent variable (HOMEPUR) to investigate how the relationship between trade credit

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and CEOs’ hometown connections is moderated by other factors.

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4. Empirical results and analysis 4.1. The effect of CEOs’ hometown connections on firms’ access to trade credit We first examine whether CEOs’ hometown connections influence firms’ access to trade credit and report the baseline regression results in Table 2. Column 1 of Table 2 includes only the dependent variable CREDIT and the independent variable HOMEPUR, whereas column 2 adds the control variables at the listed firm level and the listed firm and year dummies. Columns 3 and 4 further add the characteristics of CEO, supplier, and transaction. The results show that CEOs’ hometown connections with suppliers

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We also use lagged independent variables in the robustness tests and find that our results are robust. To save

space, we do not report the results in this paper but they are available upon request. 16

Journal Pre-proof significantly increase firms’ access to trade credit, as the estimated coefficient of HOMEPUR is 0.0505 in column 1 and changes to 0.0236 in column 4 with the addition of the control variables and fixed effects, remaining statistically significant at the 1% level. These results indicate that CEOs’ hometown connections help firms to access trade credit. In particular, we find that a 1 standard deviation increase of share purchased from hometown suppliers results in a 0.494% increase of the trade credit ratio (equivalent to about 14.2 million RMB in trade credit).5 The baseline results support hypothesis 1.

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The coefficients of the control variables are consistent with our expectations. For example, the coefficient of SIZE is negative and significant, which indicates that larger firms

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receive less trade credit (Ge and Qiu, 2007). The coefficients of fixed assets (PPE) and

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profitability (ROA) are both positive and significant, reflecting that greater solvency and lower

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risk of default are associated with obtaining more trade credit. The estimated coefficient of financial leverage (LEV) and operation cash (CASH) are both positive and significant, which is

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consistent with Wu et al. (2014) and Liu et al. (2016). CEOs who are highly educated

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(CEOEDU) have more access to formal financing resources and use less trade credit. The

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significantly negative relationship between the concentration of procurement (TOPFIVESUP) and firms’ access to trade credit means that the more bargaining power and negotiation advantage suppliers have, the less trade credit firms can access. The same holds for suppliers’ founding years (SUPAGE).

[Insert Table 2 here]

4.2. Cross-sectional tests based on features of China’s institutions In this subsection, we examine China’s unique formal institutions (e.g., state ownership and 5

The coefficient of HOMEPUR is 0.0236 (column 4 of Table 2). Table 4 shows that the standard deviation for HOMEPUR is

0.2094 in Table 1. Hence, a 1 standard deviation increase in share purchased from hometown suppliers yields a 0.494% increase in the trade credit ratio. As the mean value of total assets is 2,867 million RM B (=e^(21.7767)), a 1 standard deviation increase in HOMEPUR leads to 14.2 million RM B more trade credit (=2867*0.494%). 17

Journal Pre-proof financial marketization) and informal settings (e.g., merchant guild culture and hometown chambers of commerce) to provide further evidence to support our main argument about the effect of CEOs’ hometown connections on firms’ access to trade credit.

4.2.1. Influence of ownership structure We first investigate the difference in CEOs’ hometown connections in state -owned and non-state-owned enterprises. We construct a new dummy variable NON-SOE, which takes the

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value of 1 if a firm is not state owned and 0 otherwise. Then, we interact the variable with HOMEPUR in Eq. (1). The result is shown in column 1 of Table 3. The estimated coefficient of

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the interaction term HOMEPUR×NON-SOE is positive and significant at the 1% confidence

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level, which confirms our expectation that the effect of CEOs’ hometown connections on trade

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credit is more pronounced in non-state-owned firms, supporting hypothesis 2. In terms of economic significance, compared to state-owned enterprises, the trade credit for

al

non-state-owned enterprises increases by 0.555% (equivalent to about 15.9 million RMB trade

rn

credit) when the firms’ share purchased from hometown suppliers increased by 1 standard

Jo u

deviation.

4.2.2. Influence of regional financial marketization We next examine the effect of regional financial development on the relationship established in the baseline regression. In particular, we use the marketization index of financial industry compiled by Fan and Wang (2016) to measure the financial development in provinces in which the listed firms are located. The level of financial marketization is lower in provinces in which financial markets are not well developed and credit rationing is severe. The dummy variable MARKET equals 1 if financial marketization is higher than the mean value in a specific year and 0 otherwise. 18

Journal Pre-proof Column

2

of Table

3

reports

the

estimated

result.

The

coefficient

of

HOMEPUR×MARKET is negative and statistically significant at the 1% level, which confirms the arguments of Guiso et al. (2004) and Carlin et al. (2009) that in areas with poor developed financial systems, informal institutional arrangements (e.g., hometown connections, which act as alternative governance mechanisms) play a more important role in accessing external financing. This result supports hypothesis 2. From the perspective of economic significance, compared to firms located in regions with well-developed financial markets,

oo

f

listed firms located in regions with poorly developed financial markets receive 15.3 million RMB more trade credit (equivalent to a 0.532% increase of the trade credit ratio) with a 1

e-

pr

standard deviation increase of share purchased from hometown suppliers.

Pr

4.2.3. Influence of the merchant guild culture

The merchant guild was widespread in China’s Ming and Qing dynasties as well as

al

medieval European countries, and is regarded as the beginning of contemporary enterprise (Du

rn

et al., 2017). There are 10 well-known merchant guilds in the Ming and Qing dynasties (1368–

Jo u

1911), namely, Jin, Hui, Yue, Min, Yong, Longyou, Dongting, Lu, Jiangyou, and Shan 6 (Du et al., 2017). We construct a dummy GUILD, which equals 1 if CEOs’ hometowns are located in the districts of these guilds, and add the intersection of GUILD and HOMEPUR into the baseline regression. The positive sign for the coefficient of HOMEPUR×GUILD shown in column 3 of Table 3 supports hypothesis 3, indicating that the stronger the merchant guild culture in the CEO’s hometown, the more trade credit is accessed by firms through CEOs’ hometown connections. Specifically, firms with CEOs from hometowns with a merchant guild culture have 15 million RMB more trade credit (0.524% of the trade credit ratio) than do firms whose CEOs’ 6

They are 晋, 徽, 粤, 闽, 涌, 龙游, 洞庭, 鲁, 江右, and 陕 in Chinese, respectively. 19

Journal Pre-proof hometowns do not have a merchant guild culture when there is a 1 standard deviation increase of share purchased from hometown suppliers.

4.2.4. Influence of professional connections We manually collect the data of CEOs’ positions in hometown chambers of commerce (including branches where the listed firms are located). The dummy CHAMBER equals 1 if the CEO holds a director position in the hometown chamber of commerce and 0 otherwise. An

oo

f

intersection item of CHAMBER and HOMEPUR is added to Eq. (1).

The results in column 4 of Table 3 show that the estimated coefficient of

pr

HOMEPUR×CHAMBER is positive and statistically significant at the 1% level. This suggests

e-

that CEOs’ positions in hometown chambers of commerce strengthen the effect of their

Pr

hometown connections on firms’ access to trade credit, which supports hypothesis 3. In terms of economic significance, firms whose CEOs hold a position in the chamber of commerce

al

obtain 0.4% more trade credit ratio (equivalent to about 11.5 million RMB) than do firms

rn

whose CEOs without a position in the hometown cha mber of commerce when firms increase

Jo u

share purchased from hometown suppliers by 1 standard deviation. [Insert Table 3 here]

4.3. Potential channels Thus far, we have presented evidence that CEOs’ hometown connections play an important role in helping firms to access trade credit. We argue that the hometown connections work by mitigating information asymmetry and building trust, thereby reducing the credit risk faced by hometown suppliers. In this subsection, we provide more direct evidence to support this argument.

20

Journal Pre-proof 4.3.1. Information opacity Following our discussion in Section 2, we investigate the effect of CEOs’ hometown connections on trade credit in firms with varying levels of information opacity. Referring to Hutton et al. (2009), we use the sum of the absolute values of accrued earnings management for the previous 3 years to calculate the information opacity of listed firms. The dummy OPAQUE equals 1 if information opacity is higher than the mean value and 0 otherwise. The results, reported in column 1 of Table 4, show that the coeffic ient of

oo

f

HOMEPUR×OPAQUE is positive and statistically significant at the 5% level, which indicates that CEOs’ hometown connections are more important for firms with high information opacity;

pr

that is, high information opacity increases the effect of CEOs’ ho metown connections on firms’

Pr

e-

access to trade credit.

4.3.2. Social trust

al

We next investigate how the relationship between CEOs’ hometown connections and

rn

firms’ access to trade credit is influenced by social trust in the listed firm province. We use

Jo u

results from the 2013 China General Social Survey, jointly conducted by the Hong Kong University of Science and Technology and the People’s University of China, as a measure of regional social trust to interact with our key variable of CEOs’ hometown connections. This measure is widely used in studies in the Chinese setting, such as Wu et al. (2014). The dummy TRUST equals 1 if the score for trust is higher than the mean value and 0 otherwise. The estimated results, reported in column 2 of Table 4, show that the coefficient of the interaction term HOMEPUR×TRUST is negative and statistically significant at the 5% level, which indicates a substitution effect of CEOs’ hometown connections and social trust in the listed firm province. The result suggests that CEOs’ hometown connections play a more important role when social trust in the listed firm province is weak. 21

Journal Pre-proof Overall, we find direct evidence that mitigating information asymmetry and building trust are two potential channels through which CEOs’ hometown connections help firms to access trade credit. [Insert Table 4 here]

4.4. Endogeneity It is possible that CEOs’ hometown connections with suppliers are not exogenous, which

oo

f

means that our results may be influenced by potential endogeneity, especially from omitted variables. For example, it is possible that CEOs’ hometown connections and their impact on

e-

hometowns, or other unobserved variables.

pr

trade credit may also be driven by economic growth, the industrial characteristics of CEOs’

Pr

In this subsection, we address the endogeneity issues using various approaches. We first control the fixed effects at different dimensions and use the 2SLS model with instrumental

al

variables to investigate omitted variables. Then, we build a PSM-DID model using the settings

rn

of CEO turnover as a supplementary analysis. Finally, to identify the supply role of trade credit,

Jo u

we compare the effect of hometown connections on trade credit during and after crises.

4.4.1. Fixed effects at different dimensions One concern about our conclusion is that firms with and without hometown connections vary in CEOs’ hometown-specific characteristics, which means that our findings may be due to these different specific characteristics rather than hometown connections. Therefore, we control the fixed effects at the level of CEOs’ hometown province as well as the interaction term between CEOs’ hometown province and year dummy. The results are reported in columns 1 and 2 of Table 5. The positive and statistically significant coefficients of HOMEPUR, which are consistent with the baseline results, support our hypothesis. 22

Journal Pre-proof Meanwhile, the industrial characteristics embedded in the economic development of CEOs’ hometown provinces also may determine the connections between the listed firms and CEOs’ hometown suppliers. To address the variance of different industries’ development stages in CEOs’ hometown province, we add the fixed effects of CEO’s hometown province×year×listed firm industry (CEOPROV×YEAR×INDUS) into the baseline regression. As shown in column 3 of Table 5, the result still holds.

oo

f

[Insert Table 5 here]

4.4.2. 2SLS model with instrumental variables

pr

To further address the endogeneity issue, we conduct two-stage instrumental variable

e-

estimation. There are two instrumental variables used in our study: rice acreage in 1978 and

Pr

number of dialects, which are both for the listed firm province. The underlying logic of these two instrumental variables is that, when the social trust in the listed firm province is weak,

al

CEOs are more likely to find trustworthy suppliers in their hometown provinces by

rn

establishing connections with suppliers in their hometowns. First, a history of farming rice

Jo u

makes cultures more interdependent (Talhelm et al., 2014) owing to the cooperation required to build a rice irrigation system, thereby generating a higher level of social trust. Therefore, if rice acreage is lower in the listed firm province, firms tend to find suppliers in their CEO’s hometown provinces. Second, a large number of dialects in the listed firm province decreases the level of trust, because people prefer to trust those who speak the same dialect (Tajfel, 1982). The reason why we use the measurements of social trust in the listed firm province, instead of CEO hometown province, is that the trade credit granted by hometown suppliers is directly related to the interpersonal trust in CEOs’ hometown, which is one of the channels in our hypothesis. Furthermore, as for the exclusion restriction condition of instrumental variables, rice acreage in 1978 consists of historical data while the number of dialects is also shaped by 23

Journal Pre-proof history, and thus, neither directly affected firms’ access to trade credit during our sample period from 2007 to 2016. We create the following two instrumental variables: RICE is the per capita rice acreage in 1978 for each listed firm province (Talhelm et al., 2014); DIANUM is the number of the dialects, which is collected from the Chinese Language Atlas: Chinese Direct Volume (Chinese Academy of Social Sciences, 2012), divided by the population for the listed firm province. We first add the two instrumental variables into the regression separately. The first-stage

oo

f

regression results are reported in columns 1 and 3 of Table 6. The negative coefficient of RICE in column 1 means that more cooperation in farming creates a higher level of trust within the

pr

listed firm province, thereby reducing trade between listed firms and hometown suppliers. The

e-

coefficient of DIANUM in column 3 is statistically significantly positive, suggesting that more

Pr

dialects decrease social trust in the listed firm province and encourage CEOs to purchase more from hometown suppliers. The second-stage regression results, reported in columns 2 and 4 of

al

Table 6, further confirm that our baseline results hold when we address potential endogeneity

rn

using the two-stage approach. In columns 5 and 6 of Table 6, we add both instrumental

Jo u

variables in the regression and find that the results are robust. In fact, finding a truly exogenous variable that is also correlated with the independent variable is a difficult task. As it is impossible to empirically test the exclusion restriction condition, we qualitatively judge the effectiveness of our IVs with the me thod recommended by Larcker and Rusticus (2010). According to their study, the correlation between an independent variable and IV are the critical determinants of whether IV estimators are 2 preferred to OLS estimators 7 . The 𝑅𝑥𝑧 for the first stage in column 5 is 0.7265, which means 2 that the correlation between IV and residual ( 𝑅𝑧𝑢 ) can be no more than 72.65% of the 7

2 2 2 The conclusion of their study is that once 𝑅𝑧𝑢 < 𝑅𝑥𝑧 𝑅𝑥𝑢 , the IV estimators are preferred to OLS estimators even

2 2 if the IV is “semi-exogenous”. While it is impossible to calculate the 𝑅𝑧𝑢 and 𝑅𝑥𝑢 because the real residual is

unknown (we only have the estimated residual), we can only do a qualitative analysis to evaluate the effectiveness of IV. 24

Journal Pre-proof 2 correlation between independent variable and residual (𝑅𝑥𝑢 ) for the IV estimation results to be

statistically preferred to the OLS results. Since the reason to tackle the endogeneity issue is that there is severe correlation between independent variable and residual (meaning high value of 2 𝑅𝑥𝑢 ), a value of 0.7265 might ensure to obtain better IV estimators.

[Insert Table 6 here]

4.4.3. PSM-DID model with settings of CEO turnover

oo

f

Since our measurement of the connection between listed firms and suppliers is based on CEOs’ hometowns, the turnover of CEOs naturally changes the connections, which provide an

pr

ideal setting to deal with the endogeneity issues. 8 CEO turnover may have two opposite

e-

influences on hometown connections: a new CEO may weaken or break connections

Pr

established by the former CEO; and a new CEO may bring a brand-new hometown connection. We distinguish these two situations when testing the effects of CEO turnover.

al

We first identify firms that experienced a CEO change in our sample, keeping those whose

rn

hometown connections changed after the CEO changed and separating them into two

Jo u

categories. For the first category, there is no hometown connection before the CEO changed, which means the share purchased from hometown suppliers is 0 (HOMECON=0 and HOMEPUR=0). However, hometown connections emerged after the new CEO took over (HOMECON=1 and HOMEPUR>0). For the second category, hometown connections were broken after the CEO changed. These two categories are regarded as the treated groups in their respective regressions and a dummy TREAT taking the value of 1 is assigned to them. The other new dummy POST equals 1 for observations after the CEO changed and 0 for ones before the CEO changed. Using the propensity score matching method, we provide one controlled sample without changed connections to each treated observation, matched from the same industry as

8

We appreciate an anonymous reviewer’s suggestion for addressing the endogeneity issues using this setting. 25

Journal Pre-proof well as the same year with the control variables of the baseline regression. We construct difference-in-difference models using each category and their matched control group. We are interested in the coefficient of the intersection term TREAT×POST. As shown in Table 7, the coefficient for the regression with hometown connections that emerged after the CEO changed is statistically significantly positive, while that for the regression with connections that were broken after the CEO changed is statistically significantly negative. These results support our expectation that new hometown connections bring more trade credit

oo

f

to listed firms while a break in hometown connections reduces their access to credit. However, the results should be taken cautiously because the samples we used are not continuous due to

pr

the disclosure issue we discussed in the part of sample selection9 .

Pr

e-

[Insert Table 7 here]

4.4.4. Identification of the supply role of trade credit

al

Since the observed trade credit obtained by the firms demonstrates an equilibrium

rn

generated between the listed firms and their suppliers, a positive relationship between CEOs’

Jo u

hometown connections and firms’ access to trade credit might not sufficiently support our argument that CEOs help their firms to access trade credit by their connections with suppliers. To address this issue by focusing on the supply side of trade credit, we analyze the situation in which customers face tight financial constraints during financial crises, following the design of Love et al. (2010). Specifically, we compare the difference of the relationship between CEOs’ hometown connections with firms’ access to trade credit during and after crises. Two scenarios with financial constraint are adopted: the GFC and industry cr ises. We create two dummy variables to capture their influences. First, a dummy FINCRISIS to measure the GFC equals 1 if sample observations are from the years 2008 or 2009 and 0 for sample 9

We exclude observations with suppliers whose names are undisclosed or disclosed in ambiguous abbreviations,

as well as observations with CEOs whose hometown provinces are not identified. 26

Journal Pre-proof observations for 2010 and 2011, following Liu et al. (2016). Second, a dummy INDCRISIS to measure the industry crises equals 1 if the median annual revenue growth rate of the industry is less than 0 or the median annual stock yield is less than –20% and 0 otherwise (Yonker, 2017). Then, we add these two dummy variables into our baseline regression model and interact them with CEOs’ hometown connections. The results in Table 8 show that the coefficients of the two interaction terms HOMEPUR×FINCRISIS and HOMEPUR×INDCRISIS are both positive and statistically

oo

f

significant, which indicates that the effect of hometown connections on trade credit is strengthened when the listed firms experience financial constraints and have greater demand

pr

for trade credit. The results support our expectation that hometown suppliers grant mo re trade

e-

credit to listed firms when the firms experience more severe financial constraints.

4.5. Robustness tests

al

Pr

[Insert Table 8 here]

rn

In this subsection, we use alternative measures of our dependent and independent variables,

conclusion.

Jo u

change our sample range, and use supplier-level data to ensure the robustness of our

4.5.1. Alternative measures of dependent and independent variables Instead of the continuous variable HOMEPUR, we use a dummy HOMECON to directly measure whether there is at least one firm from the CEO’s hometown province listed as one of the top five suppliers. We test the robustness of our conclusion with this dummy variable and report the result in column 1 of Table 9. As for the dependent variable, there are three other measures frequently used in the existing literature. We construct three new variables for the robustness tests: (1) L_CREDIT 27

Journal Pre-proof equals the total accounts payables divided by total liabilities (Fisman and Love, 2003); (2) NET_CREDIT equals the net amount of accounts payable and receivable divided by total assets (Liu et al., 2016); and (3) FIN_CREDIT focuses on the financing of trade credit and equals accounts payable with terms more than 1 year divided by total assets (Ge and Qiu, 2007). The results are shown in columns 2 to 4 of Table 9. All coefficients of HOMECON and HOMEPUR in Table 9 are statistically significantly positive when the alternative measures are used, which confirms the robustness of our

oo

f

conclusion.

e-

4.5.2. Exclude firms hiring local CEOs

pr

[Insert Table 9 here]

Pr

Our findings may be affected by geographical distance, especially when a relationship can be built simply owing to the convenience of communication. To exclude this alternative

al

explanation, we include the shares purchased from suppliers registered in the listed firm

rn

province (LOCALSUP) in all the proceeding tests.

Jo u

To address this issue, we exclude the samples hiring local CEOs. It is much easier for firms whose CEOs are born in the same location to access trade credit from hometown suppliers (in this case, hometown suppliers are also the local suppliers), owing to the close geographical proximity and/or CEOs’ connection with suppliers. Therefore, the results using the samples hiring non-local CEOs may provide stronger evidence for the effect of CEOs’ hometown connections, even if there is selection bias when excluding the firms hiring local CEOs. Columns 1 and 2 in Table 10 illustrate the regression results for sample firms with non-local CEOs. Different measurements of hometown connections are adopted separately. The positive effect of CEOs’ hometown connections on firms’ access to trade credit still holds. 28

Journal Pre-proof

4.5.3. Using supplier-level data While it would be better to use the data at the supplier level to verify our argument, listed firms are not required to disclose and, in most cases, do not disclose information about the name of a supplier of specific trade credit. However, the five largest counterparties of accounts payable provide an opportunity to test the robustness of our hypothesis. It should be noted that the top five suppliers are ranked according to the firms’ purchased amount during the fiscal

oo

f

year, but they are not necessarily the five largest counterparties of accounts payable. We construct a new dummy TOPFIVEAP, which takes the value of 1 if a supplier from the

pr

top five suppliers is one of the five largest counterparties of accounts payable. The variable

e-

HOMECON here represents whether the supplier is registered in the CEO’s hometown

Pr

province. We test the effect of TOPFIVEAP on HOMECON and report the result in column 3 of Table 10. The coefficient of HOMECON is significantly positive, suggesting that the

al

probability of top five suppliers becoming top five trade credit suppliers (i.e., top recipients of

rn

accounts payable) is higher when they are registered in CEOs’ hometown province.

Jo u

We further keep the sample with suppliers that are both a top five supplier of purchased amount and a top five recipient of accounts payable. A continuous variable APPER is constructed to measure the percentage of trade credit owed to the supplier over the total trade credit. A positive coefficient of HOMECON in column 4 of Table 10 indicates that suppliers from CEOs’ hometown provinces grant more trade credit to a listed firm. Our conclusion is robust using the supplier- level data. [Insert Table 10 here]

5. Conclusions This study examines the effect of CEOs’ hometown connections with suppliers on firms’ access 29

Journal Pre-proof to trade credit using a sample of Chinese listed firms. We find that CEOs’ hometown connections help firms to access trade credit. We further find that the hometown effect is more pronounced for firms that are not state-owned, firms that are located in provinces with poorly developed financial systems, and firms whose CEOs’ hometowns have a strong merchant guild culture or whose CEOs hold an important position in the hometown chamber of commerce. Moreover, CEOs’ hometown connections play a more important role in firms with higher information opacity and in firms located in regions with weaker social trust.

oo

f

Finally, the positive relationship between CEOs’ hometown connections with suppliers and access to trade credit is robust to several methods adopted to address endogeneity,

pr

including fixed effects at different dimensions, 2SLS with instrumental variables, and DID

e-

models with CEO turnover and crises. Moreover, the results hold when we replace measures of

Pr

key variables, exclude firms hiring local CEOs, and adopt supplier-level data. Our study provides evidence that in an emerging market such as China, whose financial

al

market is immature, credit rationing is severe, and legal protection is weak, firms make full use

rn

of CEOs’ hometown connections with suppliers to access more trade credit to alleviate

Jo u

financing constraints. The findings help to explain why China’s economy grew so rapidly over the past 4 decades despite China’s undeveloped formal institutions. Our results also provide implications of taking advantage of this unique informal institution for other emerging markets when promoting their economic development. However, there are some inevitable shortcomings in this study. For example, it would be valuable to clearly identify the hometown of the supplier’s owner; however, this information is not available in most current studies because a large percentage of suppliers are private firms. Moreover, further research is required on the subject of how firms establish connections with CEOs’ hometown suppliers.

30

Journal Pre-proof

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oo

f

Wu W, Firth M, Rui OM. Trust and the provision of trade credit. Journal of Banking & Finance, 2014, 39(1): 146–159.

pr

Yang MM. Gifts, Favors, and Banquets: The Art of Social Relationships in China. Cornell University Press,

e-

1994.

Yonker SE. Do managers give hometown labor an edge? The Review of Financial Studies, 2017, 30(10):

Jo u

rn

al

Pr

3581–3604.

35

Journal Pre-proof Table 1 Descriptive statistics. Panel A Max

3472

0.0886

0.0688

0.0032

0.3410

L_CREDIT

3472

0.2364

0.1692

0.0096

0.7707

NET_CREDIT

3472

-0.0162

0.0925

-0.2919

0.2517

FIN_CREDIT

3472

0.0082

0.0176

0.0000

0.1097

HOMEPUR

3472

0.1608

0.2094

0.0000

1.0000

HOMECON

3472

0.7177

0.4502

0.0000

1.0000

SIZE

3472

21.7767

1.1951

19.2952

25.2833

AGE

3472

2.6871

0.4263

1.3863

3.3673

PPE

3472

0.2278

0.1701

0.0017

0.7156

ROA

3472

0.0378

0.0546

-0.1883

0.2020

EBIT

3472

0.0558

0.0584

-0.1611

0.2529

CASH

3472

0.0402

0.0772

-0.2242

0.2563

LEV

3472

0.4323

0.2220

0.0398

0.9560

R&D

3472

0.0140

0.0166

0.0000

0.0846

SOE

3472

0.3589

0.4797

0.0000

1.0000

CEOSEX

3472

0.9554

0.2065

0.0000

1.0000

CEOAGE

3472

3.9461

0.1361

3.5553

4.2767

CEOEDU

3472

0.9113

0.2844

0.0000

1.0000

CEOTEN

3472

3.6881

3.1897

0.0000

13.0000

3472

7.4860

2.6607

2.2209

13.4945

3472

2.2257

0.5372

0.6931

3.4965

3472

0.3820

0.2285

0.0000

1.0000

LOCALSUP

3472

0.0676

0.1333

0.0000

0.9030

FOREIGN

3472

0.0225

0.0592

0.0000

0.3627

Pr al

rn

Jo u

TOPFIVESUP

oo

CREDIT

SUPAGE

Std. Dev.

f

Min

SUPCAP

Mean

pr

Obs

e-

Variable

Panel B Univariate tests HOMECON = 1 N

HOMECON = 0

Mean

N

Mean

Difference

Firm Characteristics CREDIT

2492

0.092

980

0.081

0.011***

L_CREDIT

2492

0.244

980

0.218

0.026***

36

Journal Pre-proof 2492

-0.01

980

-0.031

0.021***

FIN_CREDIT

2492

0.009

980

0.006

0.003***

SIZE

2492

21.762

980

21.813

-0.051

AGE

2492

2.699

980

2.657

0.042***

PPE

2492

0.238

980

0.203

0.034***

ROA

2492

0.036

980

0.042

-0.006***

EBIT

2492

0.054

980

0.06

-0.006**

CASH

2492

0.042

980

0.035

0.007**

LEV

2492

0.434

980

0.427

0.007

R&D

2492

0.013

980

0.016

-0.003***

SOE

2492

0.378

980

0.31

0.068***

TOPFIVESUP

2492

0.381

980

0.384

-0.002

MARKET

2492

0.541

980

0.669

-0.128***

TRUST

2492

0.227

980

0.25

-0.024***

980

0.956

-0.001

e-

pr

oo

f

NET_CREDIT

CEO Characteristics 2492

0.955

CEOAGE

2492

3.948

980

3.942

0.006

CEOEDU

2492

0.911

980

0.911

0.000

CEOTEN

2492

3.576

980

3.972

-0.396***

GUILD

2492

0.532

980

0.505

0.027***

rn

al

Pr

CEOSEX

Supplier Characteristics

SUPAGE

2492

Jo u

SUPCAP

2492

7.282

980

8.004

-0.722***

2.219

980

2.242

-0.022

37

Journal Pre-proof Table 2 Effect of CEOs’ hometown connections on firms’ access to trade credit. The dependent variable CREDIT is the ratio of total accounts payable to total assets; the independent variable HOMEPUR is the sum of the shares purchased from hometown firms among the top five suppliers. Detailed definitions of all variables are reported in Appendix A. The sample is trimmed at 1% on each continuous variable in each tail. T statistics are reported in parentheses below the coefficients. *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

0.0505 (8.80)

Jo u

rn

al

ROA

R&D

0.0173 (4.82) -0.0037** (-2.03) 0.0023 (0.20) 0.0282*** (3.28) 0.1385* (1.87) -0.0962 (-1.42) 0.0198** (2.07) 0.1321*** (19.91) 0.0838 (0.98)

e-

PPE

LEV

***

Pr

AGE

CASH

(4)

CREDIT ***

SIZE

EBIT

(3)

f

(2)

oo

(1)

pr

Dependent Variable HOMEPUR

CEOSEX CEOAGE CEOEDU CEOTEN SUPCAP SUPAGE 38

0.0170*** (4.73) -0.0036** (-2.00) 0.0028 (0.25) 0.0277*** (3.22) 0.1357* (1.83) -0.0928 (-1.37) 0.0197** (2.06) 0.1321*** (19.89) 0.0859 (1.01) -0.0027 (-0.42) 0.0033 (0.37) -0.0105** (-2.15) -0.0004 (-1.25)

0.0236*** (5.31) -0.0041** (-2.26) -0.0002 (-0.01) 0.0261*** (3.03) 0.1300* (1.75) -0.0872 (-1.29) 0.0171* (1.78) 0.1324*** (19.95) 0.0842 (0.99) -0.0017 (-0.26) 0.0019 (0.22) -0.0112** (-2.31) -0.0003 (-1.00) 0.0002 (0.57) -0.0030**

Journal Pre-proof

TOPFIVESUP LOCALSUP FOREIGN Y Y 0.836 3472

Y Y 0.836 3472

rn

al

Pr

e-

pr

oo

f

N N 0.023 3472

Jo u

FIRM FE YEAR FE Adj. R2 N

39

(-2.08) -0.0157*** (-3.36) -0.0001* (-1.69) 0.0003* (1.87) Y Y 0.837 3472

Journal Pre-proof Table 3 Effect of China’s unique institutional settings on hometown connections–trade credit relationship. The dependent variable CREDIT is the ratio of total accounts payable to total assets; the independent variable HOMEPUR is the sum of the shares purchased from hometown firms among the top five suppliers; the dummy NON-SOE equals 1 if the listed firm is privately owned; the dummy MARKET equals 1 if the degree of financial marketization is higher than the mean value; the dummy GUILD equals 1 if the CEO’s hometown province is among the top

oo

f

10 merchant guilds in the Ming and Qing dynasties; and the dummy CHAMBER equals 1 if the CEO holds a position in the hometown chamber of commerce. Detailed definitions of all

pr

variables are reported in Appendix A.

MARKET

GUILD

CREDIT 0.0407*** 0.0061 (5.81) (0.80)

HOMEPUR×CHAMBER SIZE AGE PPE ROA

-0.0039** (-2.16) -0.0002 (-0.02) 0.0262*** (3.06) 0.1288* (1.74)

0.0195*** (4.13)

-0.0094*** (-3.29) 0.0250*** (2.88)

HOMEPUR×GUILD CHAMBER

(4)

0.0248 (0.89) -0.0254*** (-3.14)

Jo u

HOMEPUR×MARKET

rn

al

HOMEPUR×NON-SOE

0.0078 (1.25) 0.0265*** (3.66)

(3)

Pr

Dependent Variable HOMEPUR

(2)

e-

(1)

-0.0044** (-2.39) 0.0016 (0.14) 0.0267*** (3.11) 0.1276* (1.72) 40

-0.0040** (-2.18) -0.0020 (-0.17) 0.0275*** (3.21) 0.1362* (1.84)

0.0001 (0.05) 0.0191*** (2.65) -0.0039** (-2.14) -0.0011 (-0.10) 0.0256*** (2.99) 0.1216 (1.64)

Journal Pre-proof

CEOTEN SUPCAP SUPAGE TOPFIVESUP LOCALSUP FOREIGN FIRM FE YEAR FE Adj. R2 N

f

-0.0925 (-1.37) 0.0177* (1.85) 0.1310*** (19.74) 0.0910 (1.07) -0.0019 (-0.29) 0.0041 (0.46) -0.0113** (-2.33) -0.0004 (-1.21) 0.0002 (0.75) -0.0031** (-2.15) -0.0129*** (-2.72) -0.0001** (-2.17) 0.0002 (1.56) Y Y 0.838 3472

oo

CEOEDU

pr

CEOAGE

e-

CEOSEX

-0.0840 (-1.24) 0.0168* (1.75) 0.1320*** (19.92) 0.0806 (0.95) -0.0001 (-0.02) 0.0036 (0.41) -0.0113** (-2.33) -0.0004 (-1.13) 0.0002 (0.61) -0.0030** (-2.03) -0.0167*** (-3.57) -0.0001 (-1.12) 0.0003* (1.94) Y Y 0.838 3472

Pr

R&D

al

LEV

rn

CASH

-0.0865 (-1.28) 0.0175* (1.83) 0.1323*** (19.99) 0.0799 (0.94) -0.0018 (-0.29) 0.0021 (0.23) -0.0108** (-2.23) -0.0003 (-0.89) 0.0002 (0.64) -0.0031** (-2.14) -0.0147*** (-3.15) -0.0001 (-1.56) 0.0003* (1.87) Y Y 0.838 3472

Jo u

EBIT

41

-0.0797 (-1.18) 0.0165* (1.72) 0.1314*** (19.81) 0.0811 (0.95) -0.0014 (-0.21) 0.0016 (0.18) -0.0112** (-2.30) -0.0003 (-0.83) 0.0002 (0.56) -0.0030** (-2.06) -0.0158*** (-3.40) -0.0001 (-1.39) 0.0003* (1.89) Y Y 0.838 3472

Journal Pre-proof Table 4 Results of mechanism tests. The dependent variable CREDIT is the ratio of total accounts payable to total assets; the independent variable HOMEPUR is the sum of the shares purchased from hometown firms among the top five suppliers; the dummy OPAQUE equals 1 if the information opacity of the listed firm is higher than the mean value; and the dummy TRUST equals 1 if the trust index of the province where the listed firms are located is higher than the mean value. Detailed definitions of all variables are reported in Appendix A.

oo

Dependent Variable HOMEPUR

pr

0.0152** (2.56) 0.0009 (0.48) 0.0135** (2.09)

TRUST

EBIT CASH LEV R&D CEOSEX CEOAGE

rn Jo u

ROA

al

HOMEPUR×TRUST

PPE

0.0286*** (5.79)

ePr

HOMEPUR×OPAQUE

AGE

(2)

CREDIT

OPAQUE

SIZE

f

(1)

-0.0043** (-2.33) 0.0028 (0.25) 0.0271*** (3.16) 0.1262* (1.71) -0.0853 (-1.26) 0.0174* (1.82) 0.1323*** (19.92) 0.0866 (1.02) -0.0011 (-0.17) 0.0033 42

0.0138 (0.35) -0.0193** (-2.32) -0.0042** (-2.27) 0.0001 (0.01) 0.0263*** (3.06) 0.1323* (1.79) -0.0886 (-1.31) 0.0168* (1.75) 0.1318*** (19.84) 0.0884 (1.04) -0.0009 (-0.14) 0.0029

(0.37) -0.0108** (-2.23) -0.0003 (-1.04) 0.0002 (0.61) -0.0030** (-2.04) -0.0155*** (-3.33) -0.0001* (-1.75) 0.0003* (1.92) Y Y 0.838 3472

CEOEDU CEOTEN SUPCAP SUPAGE TOPFIVESUP LOCALSUP

oo

FOREIGN

f

Journal Pre-proof

Jo u

rn

al

Pr

e-

pr

FIRM FE YEAR FE Adj. R2 N

43

(0.32) -0.0111** (-2.27) -0.0003 (-0.94) 0.0002 (0.53) -0.0030** (-2.04) -0.0155*** (-3.31) -0.0001* (-1.79) 0.0003* (1.83) Y Y 0.837 3472

Journal Pre-proof Table 5 Fixed effects of different dimensions added to the baseline regression. The dependent variable CREDIT is the ratio of total accounts payable to total assets; and the independent variable HOMEPUR is the sum of the shares purchased from hometown firms among the top five suppliers. Columns 1 to 3 add the fixed effects of CEOs’ hometown province, CEOs’ hometown province × Year, and CEOs’ hometown province × listed firms’ industry × Year, respectively. Detailed definitions of all variables are reported in Appendix A.

pr

SIZE

e-

AGE

Pr

PPE ROA

R&D CEOSEX CEOAGE CEOEDU CEOTEN SUPCAP SUPAGE TOPFIVESUP

rn Jo u

LEV

al

EBIT CASH

0.0245*** (5.48) -0.0049*** (-2.67) -0.0027 (-0.24) 0.0296*** (3.42) 0.1221 (1.64) -0.0786 (-1.15) 0.0182* (1.89) 0.1328*** (19.91) 0.0802 (0.94) 0.0029 (0.44) 0.0042 (0.46) -0.0120** (-2.46) -0.0003 (-0.96) 0.0004 (1.28) -0.0029** (-1.96) -0.0135***

oo

Dependent Variable HOMEPUR

(2) CREDIT 0.0253*** (5.43) -0.0060*** (-3.01) -0.0030 (-0.25) 0.0204** (2.17) 0.0871 (1.12) -0.0556 (-0.78) 0.0233** (2.31) 0.1320*** (18.74) 0.1819** (2.01) 0.0021 (0.31) 0.0025 (0.27) -0.0114** (-2.23) -0.0002 (-0.66) 0.0003 (0.93) -0.0028* (-1.85) -0.0097*

f

(1)

44

(3) 0.0285*** (3.30) 0.0007 (0.15) 0.0057 (0.23) 0.0215 (1.09) -0.2981* (-1.82) 0.3440** (2.25) 0.0170 (0.93) 0.1001*** (7.22) 0.2195 (1.25) 0.0000 (.) -0.0362* (-1.84) -0.0140 (-1.37) -0.0003 (-0.43) -0.0005 (-0.80) -0.0065** (-2.25) -0.0059

Journal Pre-proof

LOCALSUP FOREIGN

(-1.95) -0.0001* (-1.76) 0.0003* (1.69) Y Y N Y N 0.843 3472

Jo u

rn

al

Pr

e-

pr

oo

f

FIRM FE YEAR FE CEOPROV FE CEOPROV×YEAR FE CEOPROV×YEAR×INDUS FE Adj. R2 N

(-2.88) -0.0001* (-1.77) 0.0003* (1.80) Y Y Y N N 0.840 3472

45

(-0.62) -0.0002 (-1.37) -0.0002 (-0.51) Y Y N N Y 0.862 3472

Journal Pre-proof Table 6 IV regression results. The dependent variable CREDIT is the ratio of total accounts payable to total assets; the independent variable HOMEPUR is the sum of the shares purchased from hometown firms among the top five suppliers; the instrumental variable RICE is the per capita rice planting area of the listed firms’ province of location in 1978; and the variable DIANUM is the number of dialects in the listed firms’ province of location divided by the population. Detailed definitions of all variables are reported in Appendix A.

ROA EBIT CASH LEV R&D CEOSEX CEOAGE CEOEDU

f

Pr

-0.0292** (-2.41) 0.0582 (0.82) 0.0743 (1.47) 0.6946** (2.05) -0.5630* (-1.85) 0.0340 (0.70) 0.0200 (0.57) 0.2292 (0.48) -0.0336 (-0.45) -0.0577 (-1.20) -0.0156 (-0.63)

al

rn

PPE

-0.0299** (-2.47) 0.0596 (0.84) 0.0786 (1.55) 0.6994** (2.05) -0.5717* (-1.87) 0.0342 (0.70) 0.0178 (0.51) 0.2676 (0.56) -0.0327 (-0.44) -0.0496 (-1.04) -0.0030 (-0.12)

0.1503*** (2.88) -0.0005 (-0.15) -0.0065 (-0.33) 0.0235* (1.69) 0.0193 (0.21) -0.0072 (-0.08) 0.0178 (1.33) 0.1250*** (10.24) 0.1027 (0.67) 0.0035 (0.27) 0.0114 (0.87) -0.0049 (-0.84)

Jo u

AGE

oo

2.7093*** (3.01)

HOMEPUR SIZE

pr

DIANUM

(3) (4) IV2: DIANUM HOMEPUR CREDIT

e-

Variable RICE

(1) (2) IV1: RICE HOMEPUR CREDIT -0.2742** (-2.58)

46

0.0803*** (3.14) -0.0024 (-0.67) -0.0057 (-0.32) 0.0240* (1.68) 0.0793 (0.96) -0.0497 (-0.63) 0.0176 (1.38) 0.1313*** (10.48) 0.0738 (0.53) 0.0003 (0.03) 0.0051 (0.44) -0.0105 (-1.50)

(5)

(6)

IV 1&2 HOMEPUR CREDIT -0.2051*** (-5.60) 1.5995*** (4.90) 0.1232*** (5.98) -0.0295** -0.0013 (-2.43) (-0.38) 0.0601 -0.0048 (0.85) (-0.26) 0.0773 0.0256* (1.52) (1.89) 0.6921** 0.0380 (2.03) (0.45) -0.5658* -0.0226 (-1.85) (-0.28) 0.0348 0.0187 (0.71) (1.45) 0.0173 0.1254*** (0.50) (10.42) 0.2640 0.1099 (0.55) (0.75) -0.0328 0.0026 (-0.44) (0.22) -0.0520 0.0100 (-1.08) (0.82) -0.0052 -0.0050 (-0.21) (-0.91)

Journal Pre-proof

TOPFIVESUP LOCALSUP FOREIGN FIRM FE YEAR FE N Weak IV F Stat.

-0.0059*** (-3.25) 0.0001 (0.06) -0.0087 (-1.11)

0.0000 (0.09) 0.0002 (0.59) -0.0030 (-1.52)

-0.0059*** (-3.31) 0.0001 (0.09) -0.0083 (-1.05)

0.0003 (0.60) 0.0002 (0.53) -0.0023 (-1.12)

0.1937*** (5.94) 0.0083*** (17.78)

-0.0431*** (-3.22) -0.0012*** (-2.63)

0.1948*** (5.97) 0.0083*** (17.81)

-0.0269*** (-3.02) -0.0006*** (-2.65)

0.1932*** (5.92) 0.0083*** (17.78)

-0.0378*** (-4.14) -0.0009*** (-5.01)

-0.0032*** 0.0006** (-3.82) (2.54) Y Y Y Y 3472 3472 25.78

-0.0032*** 0.0005** (-3.83) (2.57) Y Y Y Y 3472 3472 25.92

f

SUPAGE

0.0004 (0.80) 0.0002 (0.48) -0.0021 (-0.94)

oo

SUPCAP

-0.0060*** (-3.32) 0.0002 (0.12) -0.0082 (-1.04)

pr

CEOTEN

Jo u

rn

al

Pr

e-

Sargan’s P

47

-0.0033*** 0.0005*** (-3.84) (3.08) Y Y Y Y 3472 3472 129.97 0.3799

Journal Pre-proof Table 7 PSM-DID results using the settings of CEO turnover. The dependent variable CREDIT is the ratio of total accounts payable to total assets; the independent variable HOMEPUR is the sum of the shares purchased from hometown firms among the top five suppliers; TREAT is the group variable that takes a value of 1 if treated (CEO changed) and 0 for matched control samples; and POST is a dummy variable that takes the value 1 for observed years after CEO turnover. Detailed definitions of all variables are reported in Appendix A.

PPE ROA EBIT CASH LEV R&D CEOSEX CEOAGE CEOEDU CEOTEN

f

(2) Hometown Connections Broken After CEO Changed 0.0114 (1.36) -0.0281*** (-2.64) -0.0030 (-0.47) 0.1038*** (2.82) 0.0723** (2.09) 0.6830* (1.94) -0.5865* (-1.83) 0.0268 (0.71) 0.1933*** (6.92) -0.1633 (-0.75) 0.0033 (0.20) -0.0024*** (-3.73) 0.0142 (0.86) 0.0014 (1.24)

oo

pr

Pr

al

AGE

rn

SIZE

Jo u

TREAT×POST

e-

Dependent Variable: CREDIT POST

(1) Hometown Connections Emerged After CEO Changed -0.0220* (-1.81) 0.0396*** (2.67) -0.0008 (-0.11) 0.0746 (1.58) -0.0323 (-0.97) 0.4077 (1.64) -0.3980* (-1.77) 0.0258 (0.69) 0.1609*** (6.67) 0.2398 (0.49) 0.0257 (0.96) -0.0006 (-1.03) 0.0189 (0.64) -0.0012 (-1.08) 48

Journal Pre-proof

SUPAGE TOPFIVESUP LOCALSUP FOREIGN

Jo u

rn

al

Pr

e-

pr

FIRM FE YEAR FE Adj. R2 N

f

0.0003 (0.33) -0.0081 (-1.41) -0.0329** (-2.09) -0.0001 (-0.43) -0.0001 (-0.10) Y Y 0.823 428

oo

SUPCAP

49

0.0004 (0.46) -0.0006 (-0.12) -0.0199 (-1.26) 0.0000 (0.29) 0.0010* (1.69) Y Y 0.849 458

Journal Pre-proof Table 8 Effect of crises on the relationship between hometown connections and trade credit. This table identifies the supply role of hometown connections. The dependent variable CREDIT is the ratio of total accounts payable to total assets; the key independent variable HOMEPUR is the sum of the shares purchased from hometown firms among the top five suppliers; the dummy FINCRISIS equals 1 for samples in the years 2008 and 2009 and 0 for samples in the years 2010 and 2011; and the dummy INDCRISIS equals 1 if the median annual revenue growth rate of the industry is less than 0 or the median annual stock yield is less than –

pr

oo

f

20%. Detailed definitions of all variables are reported in Appendix A.

(2) Industrial Crisis

0.0173 (1.18) 0.0389** (2.50)

0.0198*** (4.25)

Pr

e-

Dependent Variable: CREDIT HOMEPUR

(1) Global Financial Crisis

INDCRISIS

AGE PPE ROA EBIT CASH LEV R&D

Jo u

SIZE

rn

HOMEPUR×INDCRISIS

al

HOMEPUR×FINCRISIS

-0.0051 (-0.81) 0.0743*** (2.87) -0.0006 (-0.02) 0.2180 (0.88) -0.1938 (-0.85) 0.0387 (1.56) 0.1476*** (7.57) -0.0301 (-0.15) 50

0.0008 (0.36) 0.0192*** (2.73) -0.0039** (-2.13) 0.0001 (0.01) 0.0263*** (3.06) 0.1334* (1.80) -0.0898 (-1.33) 0.0171* (1.79) 0.1327*** (20.03) 0.0811 (0.95)

Journal Pre-proof 0.0356 -0.0021 (1.49) (-0.33) CEOAGE 0.0819** 0.0009 (2.26) (0.11) CEOEDU -0.0197 -0.0112** (-0.88) (-2.30) CEOTEN -0.0018 -0.0003 (-1.20) (-0.99) SUPCAP -0.0002 0.0002 (-0.21) (0.57) SUPAGE -0.0026 -0.0027* (-0.56) (-1.86) TOPFIVESUP 0.0112 -0.0156*** (0.72) (-3.35) LOCALSUP -0.0004* -0.0001* (-1.86) (-1.69) FOREIGN 0.0001 0.0003* (0.15) (1.86) FIRM FE Y Y YEAR FE Y Y 2 Adj. R 0.842 0.838 a N 624 3472 a Note: Only samples from the years 2008 to 2011 are included in the regression of column 1 of

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CEOSEX

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this table. We compare the relationship between hometown connection and trade credit during

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the GFC period (2008 and 2009) and the post-GFC period (2010 and 2011).

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Journal Pre-proof Table 9 Results for other metrics for the dependent and independent variables. The variables CREDIT and L_CREDIT are the ratios of total accounts payable over total assets and total liabilities respectively; the variable NET_CREDIT measures the net amount of accounts payable and receivable to total assets; and the variable FIN_CREDIT is the accounts payable with terms more than 1 year over total assets. The independent variable HOMEPUR is the sum of the shares purchased from hometown firms among the top five suppliers; and the other independent variable HOMECON is a dummy variable that equals 1 if any suppliers are

in Appendix A.

HOMEPUR

PPE ROA EBIT CASH LEV R&D CEOSEX CEOAGE CEOEDU

(4) FIN_CREDIT

0.0129** (2.14) 0.0147*** (5.93) -0.0056 (-0.37) 0.0422*** (3.62) 0.3567*** (3.55) -0.4281*** (-4.66) 0.1312*** (10.08) 0.0652*** (7.24) -0.4269*** (-3.70) 0.0077 (0.87) -0.0033 (-0.27) -0.0136**

0.0071*** (3.84) -0.0013* (-1.65) 0.0087* (1.84) 0.0105*** (2.92) -0.0120 (-0.39) 0.0118 (0.42) 0.0003 (0.07) 0.0124*** (4.48) -0.0240 (-0.67) 0.0153*** (5.65) -0.0027 (-0.72) 0.0011

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0.0549*** (4.35) -0.0205*** (-3.95) -0.0777** (-2.42) 0.0465* (1.91) 0.7417*** (3.53) -0.6711*** (-3.49) -0.0185 (-0.68) -0.2575*** (-13.69) -0.1349 (-0.56) 0.0028 (0.15) 0.0163 (0.65) -0.0172

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AGE

-0.0047** (-2.56) 0.0018 (0.16) 0.0276*** (3.20) 0.1388* (1.87) -0.0930 (-1.37) 0.0179* (1.86) 0.1334*** (20.00) 0.0962 (1.12) -0.0018 (-0.28) 0.0011 (0.12) -0.0121**

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0.0054*** (2.72)

(2) L_CREDIT

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Dependent Variable HOMECON

(1) CREDIT

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registered in the CEO’s hometown province. Detailed definitions of all variables are reported

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Journal Pre-proof

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(-2.06) 0.0002 (0.46) -0.0003 (-0.73) -0.0025 (-1.26) 0.0030 (0.48) -0.0001 (-0.90) 0.0001 (0.33) Y Y 0.835 3472

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FIRM FE YEAR FE Adj. R2 N

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SUPCAP

(-1.25) -0.0004 (-0.47) 0.0001 (0.08) -0.0006 (-0.15) -0.0169 (-1.28) -0.0003* (-1.85) 0.0009** (2.03) Y Y 0.785 3472

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CEOTEN

(-2.46) -0.0004 (-1.23) 0.0003 (0.98) -0.0031** (-2.13) -0.0109** (-2.36) 0.0001 (1.43) 0.0002 (1.51) Y Y 0.836 3472

(0.56) -0.0000 (-0.33) -0.0001 (-0.56) 0.0003 (0.57) 0.0013 (0.68) -0.0001** (-2.27) -0.0000 (-0.46) Y Y 0.586 3472

Journal Pre-proof Table 10 Results of other robustness tests. In columns (1) and (2), the variable CREDIT is total accounts payable scaled by total assets; HOMEPUR is the sum of the shares purchased from hometown firms among the top five suppliers; and HOMECON is a dummy that equals 1 if any suppliers registered in CEO’s hometown province. In columns (3) and (4), TOP5AP equals 1 if the supplier is also one of the top five accounts payable counterparties disclosed; APPER measures the percentage of the accounts payable to the supplier over the total accounts payable of the listed firm; and

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HOMECON in the supplier-level setting takes the value of 1 if the supplier is registered in the CEO’s hometown province. Detailed definitions of all variables are reported in Appendix A.

AGE PPE ROA EBIT CASH LEV R&D CEOSEX CEOAGE CEOEDU

(3) (4) Supplier-Level Tests TOPFIVEAP APPER

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Variable HOMEPUR

(1) (2) Drop Local Suppliers CREDIT CREDIT 0.0517*** (5.35) 0.0109** (2.42) -0.0047 -0.0046 (-1.45) (-1.40) -0.0460 -0.0445 (-1.40) (-1.33) 0.0190 0.0261 (1.06) (1.44) 0.0141 0.0234 (0.11) (0.18) 0.0237 0.0130 (0.21) (0.11) 0.0224 0.0248 (1.25) (1.36) 0.1385*** 0.1404*** (11.13) (11.07) -0.1113 -0.0217 (-0.72) (-0.14) 0.0048 -0.0014 (0.38) (-0.11) 0.0030 -0.0018 (0.15) (-0.09) -0.0123 -0.0132 54

0.0444*** (2.98) 0.0054 (0.45) 0.0697*** (10.80) 0.0462 (0.82) 0.3150 (0.62) -0.2930 (-0.63) 0.0580 (0.94) 0.0595 (1.34) 0.9541* (1.68) -0.0029 (-0.06) -0.0157 (-0.26) -0.0187

0.0582*** (3.47) -0.0730*** (-5.76) 0.0023 (0.33) -0.1372** (-2.15) -0.1649 (-0.35) 0.2436 (0.56) -0.0248 (-0.49) 0.1534*** (3.10) -2.1276*** (-2.94) 0.0578 (0.63) 0.0023 (0.04) 0.0376

Journal Pre-proof

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(-0.66) (-0.70) (-0.54) (1.02) CEOTEN 0.0002 0.0000 -0.0028 -0.0027 (0.20) (0.03) (-1.28) (-1.36) SUPCAP 0.0005 0.0005 0.0217*** -0.0012 (0.85) (0.79) (6.51) (-0.32) SUPAGE -0.0040 -0.0033 -0.0015 0.0219 (-1.22) (-0.99) (-0.12) (1.64) TOPFIVESUP -0.0230** -0.0149 (-2.33) (-1.50) FOREIGN 0.0004 0.0003 (1.48) (0.99) FIRM FE Y Y Y Y YEAR FE Y Y Y Y 2 Adj. R 0.821 0.814 0.244 0.670 a N 1079 1079 10290 1556b Notes: a The number of observations in column 3 (10290) is larger than that in the baseline supplier for each firm-year observation.

In the regression of column 4, we require the top five suppliers to also be the top five

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regression (3472), because the observations are supplier-level data and there is more than 1

recipients of AP, thereby reducing the observation number. In other words, only suppliers that are both top five suppliers in terms of purchased amount and top five recipients of accounts

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payable are retained in the column 4 regression.

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Journal Pre-proof Appendix A. Definitions of variables Variable Detailed definition Dependent variables CREDIT Ratio of accounts payable over total assets L_CREDIT Ratio of accounts payable over total liabilities NET_CREDIT Net amount of accounts payable and receivable over total assets FIN_CREDIT Accounts payable with terms more than 1 year over total assets

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Key independent variables HOMEPUR Share purchased from CEOs’ hometown firms among top five suppliers HOMECON Equals 1 if any suppliers registered in CEO’s hometown province

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Control variables Customer/Listed Firm Characteristics SIZE Natural logarithm of total assets AGE Natural logarithm of the observed year minus the founding year plus one PPE Total fixed assets over total assets ROA Total net earnings over total assets EBIT Total earnings before tax and interest over total assets CASH Total operating cash flows over total assets LEV Total liabilities over total assets R&D Total research and development expenditures over total assets

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CEO Characteristics CEOSEX Takes the value of 1 if CEO is male, 0 otherwise CEOAGE Natural logarithm of CEOs’ age CEOEDU Takes the value of 1 if CEO has a college degree or above. CEOTEN Difference of the observed year and the CEO’s first employment year Supplier Characteristics SUPCAP Natural logarithm of the average suppliers’ registered capital SUPAGE Natural logarithm of the mean of the observed year minus founding year Transaction Characteristics TOPFIVESUP Sum of shares purchased from all top five suppliers LOCALSUP Sum of shares purchased from local firms among top five suppliers FOREIGN Sum of shares purchased from foreign firms among suppliers

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Journal Pre-proof Highlights: CEO’s hometown connections increase access to trade credit.

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The effect is more pronounced for non-SOEs, firms in poor regions. Plausible channels are information and social trust.

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