Top executives’ school-tie connections and management forecast disclosure

Jianqiao Yu; Ting Luo

doi:10.1080/21697213.2020.1822025

Top executives’ school-tie connections and management forecast disclosure

Yu, Jianqiao; Luo, Ting 2020-04-02 00:00:00 CHINA JOURNAL OF ACCOUNTING STUDIES 2020, VOL. 8, NO. 2, 214–248 https://doi.org/10.1080/21697213.2020.1822025 ARTICLE Top executives’ school-tie connections and management forecast disclosure a b Jianqiao Yu and Ting Luo a b College of Management and Economics, Tianjin University, Tianjin, China; School of Economics and Management, Tsinghua University, Beijing, China ABSTRACT KEYWORDS School ties; management This paper investigates whether top executives’ dependence on forecasts; information social connections has an impact on the information environment environment; supply chain of listed companies. Specifically, this paper explores the role of school ties between firms’ and suppliers’ top executives on man- agement earnings forecasts, an important channel of public infor- mation disclosure. We find a negative relation between a firm’s school ties with its suppliers and the likelihood/frequency to issue management forecasts, indicating that top executives’ school ties in a way substitute management forecasts and become the informa- tion channel along the supply chain. Further, we find the associa- tion is stronger when the firm faces higher proprietary cost or operational uncertainty, but the association becomes weaker when suppliers have bargaining advantage over the firm. Finally, we find the decrease in management forecast disclosure driven by school-tie connections weakens the access of firm-specific informa- tion for external information users, which may put individual inves- tors in a more vulnerable position. 1. Introduction China has been characterised and well-known for the prevalence of ‘guanxi’ (connections). Existing evidence largely documents that social ties have played a positive effect on China’s economic development, such as mitigating firms’ financial constraints (Allen et al., 2005), improving outward investment (Pan et al., 2009), encouraging knowledge sharing and innovation (Gao et al., 2008). However, as the level of economic development increases, the issue of unbalanced wealth distribution has gradually emerged, and the main reason for wealth inequality is the inequality in information possession (Yang et al., 2017). As such, whether social ties have caused ‘information gap’ between different economic agents has become an important question. Recent studies find that social-tie connected parties could earn significantly abnormal returns, indicating an information advantage attained from social ties (Cohen et al., 2008; He et al., 2014; Shen et al., 2015). CONTACT Ting Luo luot@sem.tsinghua.edu.cn School of Economics and Management, Tsinghua University, Beijing, 100084, China This article has been republished with minor changes. These changes do not impact the academic content of the article. Paper accepted by Guliang Tang. It’s pointed out in the ‘Report to the Nineteenth National Congress’ that the contradiction between the people’s growing needs for a better life and unbalanced development has become a main concern in China’s society. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CHINA JOURNAL OF ACCOUNTING STUDIES 215 However, little research has directly looked into the effect of social ties on corporate information environment. In this study, we investigate whether top executives’ reliance on social connections has an impact on the information environment of listed companies. Specifically, our paper explores whether the presence of school ties between firms’ and suppliers’ top executives affects management earnings forecasts, an important channel of public information disclosure. School ties have been documented to be an important form of social connections. First, the same educational background can facilitate personal communication and pro- vide connected executives better access to their partners’ private information (Cohen et al., 2008, 2010; Guan et al., 2016). School ties help foster mutual trust in the business world, which makes personal communication more frequent and more efficient, thus reducing the asymmetry of information between connected parties (Massa & Simonov, 2011). Second, school ties can help discourage non-cooperative and opportunistic beha- viour within the network, because the value of social networks often disciplines indivi- duals in the same network to be faithful to each other (Elster, 1989; Uzzi, 1996). This can mitigate the concern of ‘cheap talk’ and enhance the credibility of personal communica- tion between connected parties. We use supply chain as a setting in analysing and understanding the effect of top executives’ school-tie connections on corporate information environment. A supplier’s business success is closely tied to the earnings prospects and financial health of its major customers. If customers fall into financial distress or even go out of business, they are likely to default on contractual obligations and the suppliers will incur large cost due to decreased future sales, loss of expected gains from relationship-specific investments and unrecover- able trade credits. Therefore, suppliers have high information demand for their customers’ earnings prospects (Raman & Shahrur, 2008). To mitigate information asymmetry and the ensuing concerns of opportunistic behaviour, a customer usually needs a credible way to disclose earnings prospects to its suppliers. Specifically, a customer can choose between public channels (e.g. management forecast disclosure) or credible internal channels (e.g. communication via school ties). The presence of school ties between suppliers’ and custo- mers’ executives are likely to affect the choice between public channels and internal channels, thus making supply chain a relevant setting for our research question. Our empirical analysis is based on a sample of Chinese public firms that are reported by at least one public firm as their major customer during 2006–2015. A firm is identified as with a school tie when at least one top executive graduated from the same school as the executives of its suppliers. The baseline analysis shows firms that have school ties with suppliers have a lower likelihood and frequency to issue management earnings forecasts, indicating that top executives’ school ties in a way substitute management forecasts and become the information channel along the supply chain. The result is robust to a PSM approach, a change analysis and a Heckman two-stage analysis. In addition, we find that In our main tests, we examine the relation between school ties and customers’ management forecasts, rather than suppliers’ management forecasts, because the dataset is about the disclosure of firms’ major customers rather than suppliers. The customers are ‘key customers’ of the suppliers, but we have no guarantee that the suppliers are ‘key suppliers’ of the customers. Actually, the reported customers are much larger than their suppliers and economically more important to the suppliers than vice versa, presumably enhancing the suppliers’ information demand on customers’ earnings prospects and financial condition. Therefore, it is a more powerful setting to focus on customers’ management earnings forecasts in examining the effect of school ties. In robustness tests, we also examine the effect of school ties on suppliers’ management forecasts. 216 J. YU AND T. LUO the association is stronger when the firm faces higher proprietary cost or operational uncertainty, but the association becomes weaker when suppliers have bargaining advan- tage over the firm. Besides, we find school ties decrease the voluntariness and timeliness of management forecasts. Finally, we show that the school-tie-associated decrease in management forecast disclosure weakens the access of firm-specific information for external information users, leading to higher stock return synchronicity and analyst forecast dispersion. Taken together, the results of our study suggest that top executives’ reliance on social connections deteriorates information environment quality. Our study contributes to the literature in several ways. First, this paper contributes to a growing body of literature on social connections. Whether social connections have caused ‘information gap’ between different economic agents is of great interest to regulators, investors and researchers. Although prior studies have identified the effects of commercial ties and political connections on corporate disclosure (Ren & Wang, 2018; Gao & Wang, 2015), limited research has considered the effect of personal connections of corporate executives. Our study fills this gap by documenting the relation between top executives’ school ties and disclosure of management earnings forecasts. The results suggest that top executives’ dependence on social connections deteriorates quality of corporate information environment, which may put individual investors in a more vulner- able position. This paper sheds new light on the role of social connections in the market and provides an answer to the issue of ‘information gap’. Second, prior studies show that social connections can affect the quality of firms’ financial reporting. For example, Guan et al. (2016) document a negative relation between a firm’s school ties with auditors and audit quality. Our study, by documenting the relation between social connections and management earnings forecasts, can enrich this line of literature. Third, this paper also provides insights for regulators who are concerned with fair disclosure. The results indicate that the prevalence of social connections has an adverse effect on fair information disclosure and may put individual investors in an information disadvantage, thus providing reference value for regulatory agencies to further improve the quality of corporate disclosure, protect the interests of small investors and create a more transparent market. The remainder of the paper proceeds as follows. Section 2 reviews the related literature and develops hypotheses. Section 3 describes the sample and data, and presents research design. Section 4 presents the main empirical results. Section 5 provides additional analyses and Section 6 concludes the study. 2. Literature review and hypothesis development A major customer’s earnings prospects are highly value-relevant for a supplier firm, because a major customer is an important source of its supplier’s current and future sales, thus the current and future earnings and cash flows (Olsen & Dietrich, 1985; Pandit et al., 2011). A supplier may also invest in large amounts of assets that are specific to one particular customer, which have little value in alternative use (Williamson, 1985). Besides, suppliers often provide large trade credits for their major customers. If customers experi- ence financial distress or even go out of business, they are likely to default on contractual obligations and their suppliers will incur high cost due to loss of the expected gains from the relationship-specific investments and unrecoverable trade credits. Thus, a supplier CHINA JOURNAL OF ACCOUNTING STUDIES 217 usually has great information demand for its major customers’ earnings prospects and financial condition. On the other hand, a major customer also has incentives to disclose such information to their suppliers in order to attract more relationship-specific invest- ments and trade credits (Raman & Shahrur, 2008). Though both parties are motivated to share information about earnings prospects, prior research suggests that self-interested trading parties often have incentives to hide some private information to maximise their own benefits. Information asymmetry increases the difficulty for a supplier to determine whether a customer has the intention or ability to fulfill the contractual obligations. Besides, many relationship-specific invest- ments are not perfectly contractible due to its nature of uncertainty, making it further difficult for a supplier to get protection from its customers’ opportunistic behaviour (Klein et al., 1978). As a result, suppliers may under-invest in specific assets, tighten trade credits or increase price to compensate for their risk, which cause transaction cost for both trading parties (X. Chen et al., 2015; Raman & Shahrur, 2008; Tang et al., 2017). A customer can choose public disclosure or more credible internal channels to enhance suppliers’ assurance of its future earnings prospects and the ability of fulfiling contractual obligations. Regarding public disclosure, Cao et al. (2013) find that firms tend to issue management earnings forecasts to communicate their earnings prospects with supply chain partners. As long as customers issue management forecasts publicly, these forecasts are likely to be consistent with the forecasts privately communicated with suppliers, because any inconsistency is likely to arouse suppliers’ concerns about the credibility of customers’ management, which is harmful for maintaining a long-lasting trading relationship (Feng & Li, 2014). Therefore, a firm can use public earnings forecasts to enhance suppliers’ confidence in its future earnings prospects, thus mitigating infor- mation asymmetry along the supply chain and reducing transaction cost. However, issuing public earnings forecasts can incur considerable cost. Once a forecast is disclosed publicly, not only supply chain partners can receive the information, but other stakeholders including competitors will be informed. Prior studies indicate leaking earn- ings prospects to competitors may put a firm at a disadvantage in the competition (Wang, 2007). Besides, if the forecasted earnings turn out to be inaccurate, managers have to bear high litigation cost and reputation loss, and the firm’s market value will be negatively affected (Kasznik & Lev, 1995). Therefore, when other channels could increase the cred- ibility of private communication, a firm are likely to use the alternative channels instead of public disclosure to avoid the disclosure cost. We argue that school ties of corporate executives between suppliers and customers can substitute management forecasts and work as such an alternative information chan- nel along the supply chain. First, school ties can facilitate information transfer and provide connected executives better access to their partners’ private information (Cohen et al., 2008, 2010). Two individuals could get to know each other by attending the same university or meeting at alumni association, and thus build a stable and trusting bond. Further, sharing the same educational background could allow persons to interact with common values of the university, which also helps to foster mutual trust in the business world (Massa & Simonov, 2011). Prior studies indicate mutual trust could make personal communication between connected executives more frequent and more effective (Bhowmik & Rogers, 1970; Granovetter, 2005), thus reducing the asymmetry of informa- tion between supply chain partners. Besides, compared with public disclosure, a firm can 218 J. YU AND T. LUO choose to share earnings prospects to a specific supplier, thereby avoiding the disclosure cost of public earnings forecasts. Second, and more importantly, school ties could make private communication more credible. Prior research suggests that school ties can help discourage non-cooperative and opportunistic behaviour in supply chain relationships (Luo & Yu, 2019). The value of social networks often encourages individuals in the same network to be faithful to each other, and individuals face high reputation cost if they conduct opportunistic acts (Elster, 1989; Uzzi, 1996). The damage to reputation would make it difficult for them to conduct business with other individuals within the same network in the future, which has a discipline effect on ‘cheap talk’ between connected individuals. Thus, private commu- nication between school-tie-associated partners are inherently more credible, thereby reducing the need to enhance the credibility of communication through public disclosure. In sum, when a firm’s executives have school ties with suppliers’ executives, the firm is more likely to rely on internal channels to communicate with suppliers, leading to a decrease in public disclosure of management earnings forecasts. This could take the form of omitting all forecasts during the year or forecasting less frequently. Our first hypothesis is stated formally as follows: H1a: Ceteris paribus, firms whose executives have school ties with their suppliers’ execu- tives would have a lower likelihood of issuing management forecasts than other firms. H1b: Ceteris paribus, firms whose executives have school ties with their suppliers’ execu- tives would have a lower frequency of issuing management forecasts than other firms. We then consider the situations that can influence the effect of school ties on manage- ment forecast disclosure. We first identify the circumstances under which managers are more concerned about the cost of public disclosure. The first concern is proprietary cost. Leaking proprietary information, including earnings prospects, to competitors would put firms at a disadvantage in the competition. Prior studies have documented that proprie- tary cost is an important deterrent to management forecast disclosure (Verrecchia, 1983; Verrecchia & Weber, 2006). For example, Wang (2007) find that R&D expenditures, as a proxy for proprietary cost, negatively impact the decision to issue public earnings fore- casts. Thus, we expect the firms that have higher proprietary cost are more motivated to use school ties as the information channel so that to protect proprietary information from competitors. By contrast, in the case with lower proprietary cost, firms would have less incentives to rely on school ties. Therefore, we expect school ties are more likely asso- ciated with the likelihood/frequency of issuing management forecasts when customers’ proprietary cost is higher. Our second hypothesis is stated as follows: An implicit premise of our hypotheses is that the information communicated by public disclosure and school ties is homogenous. First, information sharing by both channels aims to enhance the credibility of private communication and help suppliers better evaluate customers’ intention or ability to fulfill the contractual obligations, thereby reducing the transaction cost. Thus, though public disclosure and communication via school ties have different forms, the goal is the same. Second, existing literature supports that managers can use both public disclosure and internal channels to share information about a firm’s earnings prospects (Feng & Li, 2014). CHINA JOURNAL OF ACCOUNTING STUDIES 219 H2: Ceteris paribus, the relation between school ties with suppliers and the likelihood/ frequency of issuing management forecasts, as documented in H1, is stronger when customers’ proprietary cost is higher. A second concern of issuing a public earnings forecast is managerial reputation cost if the forecast turns out to be inaccurate. Managers have strong incentives to forecast accurately in order to signal managerial quality (Trueman, 1986) and increase the cred- ibility of future guidance (Hutton & Stocken, 2009). On the other hand, issuing inaccurate forecasts can lead to loss of managerial reputation and even litigation (Kasznik & Lev, 1995). Besides, firms who fall below one’s own forecasts would incur large drops in the market value. Therefore, issuing inaccurate forecasts can be very costly; accordingly, Feng and Koch (2010) find managers who fail to meet one’s own forecasts tend to reduce the provision of public earnings forecasts in the future. As such, we expect when firms have higher operational uncertainty, thus facing more difficulty in providing accurate forecasts, they are more likely to use school ties as the information channel to avoid reputation loss caused by forecast error. We predict the relation between school ties and the likelihood/ frequency to issue management forecasts is stronger when firms’ operational uncertainty is higher. Our third hypothesis is stated as follows: H3: Ceteris paribus, the relation between school ties with suppliers and the likelihood/ frequency of issuing management forecasts, as documented in H1, is stronger when customers’ operational uncertainty is higher. Third, a firm’s incentives to share information with suppliers depend on its bargaining position vis-à-vis its suppliers. When a firm has relatively high dependence on its suppliers and large switching cost, its suppliers usually have higher bargaining power over the firm (Porter, 1998; Xue et al., 2018). In such scenarios, reducing information asymmetry along the supply chain becomes more important, and a firm would make more efforts to satisfy suppliers’ information need. Consistent with this view, Hui et al. (2012) find when supply chain partners have relative bargaining advantage over a firm, the importance of trading relationships leads the firm to report more conservatively. Thus, we expect when suppliers have relatively high bargaining power, a firm would use more than one information channel (e.g. both public disclosure and school ties) to enhance suppliers’ confidence in its earnings prospects, thus weakening the substitution effect of school ties on manage- ment forecast disclosure. Therefore, we predict that school ties are less likely associated with the likelihood/frequency to issue management forecasts when suppliers’ bargaining power is higher. Our fourth hypothesis is stated as follows: H4: Ceteris paribus, the relation between school ties with suppliers and the likelihood/ frequency of issuing management forecasts, as documented in H1, is weaker when suppliers’ bargaining power is higher. 220 J. YU AND T. LUO 3. Research design 3.1. Sample and data Our sample consists of firms listed in Shanghai and Shenzhen stock exchanges that are reported by at least one public firm as their major customer over the period from 2006 to 2015. Chinese public firms are encouraged to voluntarily disclose their top 5 customers in their annual reports. We refer to the CSMAR database to retrieve names of major customers. We then search online the information of a customer based on the disclosed name and determine whether the customer is publicly listed. We retain only customers that are listed firms in order to obtain their financial data for empirical analyses. Consistent with prior literature (Ellis et al., 2012), if a reported customer is a subsidiary of a public firm, we classify the customer as a public firm and use its parent firm’s data in the analyses. We exclude the firms when a firm and its supplier are the subsidiaries of the same public firm. We also drop the observations without required data in our main analyses. The final sample consists of 1,443 observations. Each observation represents a customer-year. The sample selection process is summarised in Table 1. Top executives include chairman of the board, CEO, CFO, COO, CTO and CMO. We retrieve the educational background information of top executives, both of the sample firms and of their suppliers, from CSMAR and WIND databases. For executives whose educational information is not available from CSMAR or WIND, we manually search the information online. Given that many universities in China have merged and renamed, and overtime the renamed universities have earned more recognition by alumni, we use the renamed universities in identifying school ties. For example, Zhejiang University and Hangzhou University merged to form the new Zhejiang University, thus the executives who graduated from Hangzhou University are regarded as alumni of Zhejiang University. We obtain management earnings forecasts from RESSET database, and all other data is from CSMAR or RESSET. 3.2. Model specification To examine the effect of a firm’s school ties with its suppliers on the likelihood and frequency to issue management forecasts, we estimate the following logistic regression model (1) and ordinary least squared (OLS) model (2), respectively. Guide ¼ γ þ γ Alumni þ γ Controls þ industry fixed effects i;t i;t i;t 1 0 1 n þ year fixed effects þ ε (1) i;t Freq ¼ δ þ δ Alumni þ δ Controls þ industry fixed effects i;t 0 1 i;t n i;t 1 þ year fixed effects þ ε (2) i;t A firm may be reported by multiple public firms as their major customer; thus, one customer may correspond to more than one supplier in a year. In such cases, we weight each supplier’s data with the supplier’s sales to the customer and then aggregate supplier data for each customer-year. In robustness tests, we redefine top executives as including chairman of the board, CEO and CFO. The results are unchanged. CHINA JOURNAL OF ACCOUNTING STUDIES 221 Table 1. Sample selection. Selection Criteria Observations Firms listed in Shanghai and Shenzhen stock exchanges that are reported by at least one public firm as 2,256 their major customer during 2006–2015 Less: Firms that are subsidiaries of the same public firm as their suppliers (178) Firms without required data in the main empirical analyses (635) Final sample 1,443 where i and t are firm and year indicators, respectively. In model (1), the dependent variable is the likelihood of issuing a management forecast (Guide), which equals 1 if the firm issued at least one management forecast during the year, and zero otherwise. In model (2), the dependent variable is the frequency of management forecasts (Freq), defined as the natural logarithm of one plus the total number of management forecasts issued during a year. We set Freq to be zero if the firm doesn’t issue any forecasts. Our independent variable of interest is Alumni. Following Cohen et al. (2010), Guan et al. (2016), and Gu et al. (2019), we classify two executives as having a school tie if they have attended the same university for either undergraduate or graduate degrees, without requiring them to attend the university for the same period, the same campus or the same major. Alumni represents one of the two alternative measures: (1) An indicator variable for the presence of school ties (Alumni1), defined as equal to 1 if the firm and its suppliers are connected by at least one school tie during a year, and zero otherwise; (2) School-tie connections measured as a continuous variable (Alumni2), which equals the total number of school ties that the firm has with its suppliers (T. Chen et al., 2017). When a firm corresponds to more than one supplier in a year, in our main tests we weight each supplier’s data with the supplier’s sales to the firm and then aggregate supplier data for each firm- year. According to H1, we predict the coefficient on Alumni (γ /δ ) to be positive. 1 1 We include a set of firm characteristics and its suppliers’ characteristics to control for other factors that may affect management forecast disclosure. Regarding firm character- istics, we include size (Size), age (Age), operating performance (ROA), stock performance (Return) and growth opportunity (MB, Growth), because larger, better performing and fast- growing firms are usually more likely to provide management forecasts (Core, 2001; Penman, 1980). We also include industry concentration (HHI), defined as Herfindahl– Hirschman Index of the industry which the firm belongs to, because Verrecchia (1983) finds protecting proprietary information from competitors is an important deterrent to the disclosure of management forecasts. Besides, Kasznik (1999) finds managers manip- ulate earnings to beat or meet earlier forecasts; thus, we include a firm’s ability to manage earnings as reflected in its discretionary accruals (DACC). We further include stock return volatility (MRETVOL) and a loss indicator (Loss) to control for the effect of operational uncertainty on management forecast disclosure (Brown, 2001). As for supplier characteristics, we follow Cao et al. (2013) to include the importance of the trading relationship to suppliers (Importance), calculated as a supplier’s sales to the firm divided by the supplier’s total revenue. Besides, we include suppliers’ total sales (Sup Sales), profitability (Sup ROA) and growth opportunity (Sup MB). When suppliers have In untabulated tests, we aggregate suppliers’ data by taking equal-weighted average instead of sales-weighted average and the results remain consistent. 222 J. YU AND T. LUO larger sales, better performance and more growth opportunities, they potentially have greater impact on customers’ disclosure. We further include suppliers’ industry mean of relationship-specific investments (Sup RSI) and trade credits (Sup TC) to control for the effect of suppliers’ provision of investments and trade credits on customers’ management forecasts. Finally, we include industry and year fixed effects. To mitigate the effect of outliers, continuous variables are winsorised at 1% and 99%. All variables are defined in detail in Table 2. 4. Empirical results 4.1. Descriptive statistics Table 3 reports descriptive statistics for the variables that are used in our main analysis. Among our sample firms, 53.6% issue at least one management earnings forecast during a year (Guide); on average, a firm issues 1.44 management forecasts (Freq). For the mea- sures of school-tie connections, mean of Alumni1 and Alumni2 are 3.9% and 6.6%, respectively. Specifically, among the full sample of 1,443 firm-years, 121 firm-years, have executives connected with suppliers’ executives by educational experience. For these 121 firm-years, there are 262 pairs of executives identified as have a school tie. Panel A of Table 4 presents the top 10 universities with the most pairs of connected executives between supply chain partners. The top 1 is Tsinghua University, followed by Peking University and Shanghai Jiao Tong University. These three universities account for 13.74%, 7.63%, and 7.63%, respectively, of the 262 pairs of connected executives. Panel B reports the top 20 universities from which most of the executives graduated. Following Guan et al. (2016), we sort all universities that appear in our sample into percentile ranks based on the total number of supplier executives and customer executives that graduated from these universities, denoted as R(SCH ) and R(SCH ), respectively. Then for each sup cus university, we define R(SCH) as the average of those two ranks. A higher R(SCH) means that the university educates more executives, and thus its graduates are more likely to have supplier-customer school ties. Panel B reports the top 20 universities with the highest R(SCH) scores. When firms’ executives graduated from one of these universities, they have higher chance of being connected with suppliers’ executives because such universities have a wider social network. Consistent with this notion, the universities whose R(SCH) values are ranked as top 10 in Panel B largely overlap the universities with the most pairs of observed connected executives in Panel A. 4.2. Hypotheses testing Table 5 reports the logistic estimation results of model (1). The p-values are two-sided and are based on standard errors adjusted for firm-level clustering. The first two columns include only Alumni1 or Alumni2 as the independent variable, and the last two columns report the estimation results of the full model. Consistent with H1a, the coefficients on We use logged number of Freq in the regressions. The number of pairs of connected executives, 262, is larger than the number of firm-years, 121, because for one single firm-year, there could be more than one pair of connected executives. We use R(SCH) as a predictor in our PSM selection model to control the ex ante probability of having a supplier-customer school tie. CHINA JOURNAL OF ACCOUNTING STUDIES 223 Table 2. Variable definition. Variable Definition Guide Equal to 1 if the firm issued at least one management earnings forecast during the year, and zero otherwise; Freq Natural logarithm of one plus the number of management earnings forecasts issued during the year; Freq equals zero if the firm doesn’t issue any forecasts; Alumni1 A dummy variable that equals 1 if the firm and its suppliers are connected by at least one school tie during a year (i.e. there exist two executives who attended the same university for either undergraduate or graduate degrees), and zero otherwise; Alumni2 The total number of school ties that the firm has with its suppliers; Size Natural logarithm of total assets in previous year; Age Natural logarithm of one plus the number of years that the firm has been listed; ROA Net income divided by total assets in previous year; Return Market-adjusted stock returns in previous year; MB The market-to-book ratio in previous year; Growth Annual percentage of sales growth in previous year; HHI Herfindahl-Hirschman Index in previous year of the industry which the firm belongs to, calculated by squaring the market share of each firm in the industry and adding all firms up; DACC The discretionary accruals in previous year, calculated as the absolute value of residual estimated from modified Jones model (Dechow et al., 1995); MRETVOL The standard deviation of monthly stock returns in previous year; Loss A dummy variable that equals one if earnings in previous year is negative, and zero otherwise; Importance The importance of the trading relationship to suppliers, calculated as the percentage of a supplier’s sales to the firm; Sup Sales Suppliers’ natural logarithm of total sales in previous year; Sup ROA Suppliers’ net income divided by total assets in previous year; Sup MB Suppliers’ market-to-book ratio in previous year; Sup RSI Suppliers’ industry mean of R&D expenditures scaled by beginning total sales in previous year ; Sup TC Suppliers’ industry mean of accounts receivables scaled by beginning total sales in previous year. Milgrom and Roberts (1992) point out that R&D expenditures are usually specific to supplier-customer relationships; for example, software companies often invest in R&D to develop software products designed for a particular partner. Besides, Armour and Teece (1980) and Levy (1985) further suggest that research-intensive firms tend to capture environments that require specialised inputs and relationship-specific assets are prevalent in. Therefore, we follow prior literature to measure relationship-specific investments using R&D expenditures (Allen & Phillips, 2000; X. Chen et al., 2015; Raman & Shahrur, 2008). 224 J. YU AND T. LUO Table 3. Descriptive statistics. Mean STD Min 25% Median 75% Max Guide 0.536 0.499 0.000 0.000 1.000 1.000 1.000 Freq 1.439 1.722 0.000 0.000 1.000 3.000 7.000 Alumni1 0.039 0.168 0.000 0.000 0.000 0.000 1.000 Alumni2 0.066 0.331 0.000 0.000 0.000 0.000 4.000 Size 23.462 1.913 19.638 22.129 23.087 24.648 30.089 Age 2.382 0.550 1.099 2.079 2.565 2.833 3.219 ROA 0.040 0.047 −0.142 0.012 0.034 0.065 0.211 Return 0.085 0.457 −0.750 −0.186 −0.010 0.239 2.022 MB 2.684 2.129 0.508 1.245 2.020 3.353 11.273 Growth 0.220 0.684 −0.576 −0.056 0.064 0.265 4.436 HHI 0.062 0.101 0.011 0.014 0.016 0.063 0.477 DACC 0.059 0.059 0.001 0.019 0.041 0.077 0.329 MRETVOL 0.094 0.044 0.026 0.063 0.084 0.114 0.272 Loss 0.124 0.330 0.000 0.000 0.000 0.000 1.000 Importance 6.272 5.888 0.440 2.800 4.527 7.480 36.050 Sup Sales 20.526 3.004 4.377 19.741 20.869 22.036 24.831 Sup ROA 0.041 0.061 −0.295 0.016 0.040 0.068 0.206 Sup MB 3.439 2.623 0.324 1.752 2.700 4.239 13.625 Sup RSI 0.033 0.033 0.000 0.010 0.026 0.046 0.158 Sup TC 0.229 0.134 0.030 0.118 0.201 0.319 0.797 CHINA JOURNAL OF ACCOUNTING STUDIES 225 华中科技大学华中科技大学中央党校北京理工大学上海交通大学中国石油大学中国人民大学西安交通大学中南大学中南大学东北大学中山大学山东大学清华大学清华大学浙江大学厦门大学武汉大学武汉大学北京大学北京大学复旦大学复旦大学同济大学上海交通大学中欧国际工商学院哈尔滨工业大学北京科技大学北京理工大学 Table 4. Distribution of universities. Panel A: Top 10 universities with the most pairs of connected executives between supply chain partners Rank University Ratio (%) 1 Tsinghua University ( ) 13.74 2 Peking University ( ) 7.63 2 Shanghai Jiao Tong University ( ) 7.63 4 Central South University ( ) 6.49 5 Xiamen University ( ) 3.82 5 Huazhong University of Science and Technology ( ) 3.82 5 Beijing Institute of Technology ( ) 3.82 8 Wuhan University ( ) 3.44 8 Fudan University ( ) 3.44 8 China University of Petroleum ( ) 3.44 Panel B: Top 20 universities that graduated most corporate executives Rank University R(SCH ) (%) R(SCH ) (%) R(SCH) (%) sup cus 1 Tsinghua University ( ) 100.00 100.00 100.00 2 Peking University ( ) 99.78 99.72 99.75 3 Shanghai Jiao Tong University 98.01 99.43 98.72 ( ) 4 China Europe International Business School 98.45 98.86 98.66 ( ) 5 Wuhan University ( ) 98.90 97.72 98.31 6 Xi’an Jiao Tong University ( ) 98.23 98.01 98.12 7 Huazhong University of Science and Technology ( ) 99.34 96.01 97.67 8 Central South University ( ) 97.35 97.15 97.25 9 Fudan University ( ) 97.79 96.58 97.19 10 Party School of the Central Committee of C.P.C. ( ) 95.81 96.87 96.34 11 Tongji University ( ) 94.04 98.58 96.31 12 Zhejiang University ( ) 98.45 94.02 96.24 13 Harbin Institute of Technology 95.81 95.16 95.48 ( ) 14 Sun Yat-sen University ( ) 97.57 92.31 94.94 15 University of Science and Technology Beijing 93.60 95.73 94.66 ( ) 16 Northeastern University ( ) 96.47 92.59 94.53 17 Renmin University of China ( ) 95.58 93.45 94.52 18 Shandong University ( ) 90.51 97.44 93.97 19 Beijing Institute of Technology 95.14 92.02 93.58 ( ) 20 Shanghai University of Finance and Economics (上海财经大学 ) 96.69 90.31 93.50 226 J. YU AND T. LUO Table 5. School ties with suppliers and the likelihood of issuing management forecasts. Dependent variable: Guide (1) (2) (3) (4) Alumni1 −0.643** −0.845** (0.044) (0.028) Alumni2 −0.344** −0.495** (0.045) (0.015) Size −0.524*** −0.522*** (0.000) (0.000) Age −0.538*** −0.541*** (0.000) (0.000) ROA −3.952** −4.090** (0.032) (0.027) Return 0.260 0.247 (0.121) (0.138) MB 0.059 0.063 (0.128) (0.104) Growth 0.021 0.016 (0.876) (0.906) HHI −0.316 −0.345 (0.926) (0.919) DACC 3.803*** 3.791*** (0.001) (0.001) MRETVOL 1.204 1.271 (0.536) (0.514) Loss 0.581** 0.567** (0.018) (0.021) Importance −0.015 −0.016 (0.163) (0.153) Sup Sales 0.026 0.026 (0.254) (0.261) Sup ROA 0.721 0.724 (0.506) (0.503) Sup MB 0.002 0.001 (0.598) (0.794) Sup RSI −3.398 −3.311 (0.259) (0.272) Sup TC 0.961 0.910 (0.138) (0.160) Constant 0.171*** 0.168*** 24.841*** 24.548*** (0.002) (0.002) (0.000) (0.000) Industry fixed effects No No Yes Yes Year fixed effects No No Yes Yes Observations 1,443 1,443 1,443 1,443 Pseudo R 0.002 0.002 0.213 0.213 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Alumni1 and Alumn2 are significantly negative at the 5% level in all columns. The results suggest a firm’s school ties with its suppliers reduce the likelihood to issue management forecasts, indicating top executives’ school ties in a way substitute management forecasts and become the information channel along the supply chain. In terms of economic significance, a one-standard-deviation increase in Alumni1 decreases the likelihood of issuing management forecasts by 3.5%. This result suggests the effect of school ties on management forecast disclosure is economically significant. Table 6 reports the OLS estimation results of model (2). The first and last two columns report the estimation results without and with control variables, respectively. Consistent with H1b, we find a significant and negative coefficient on Alumni1 and Alumn2. These CHINA JOURNAL OF ACCOUNTING STUDIES 227 Table 6. School ties with suppliers and the frequency of issuing management forecasts. Dependent variable: Freq (1) (2) (3) (4) Alumni1 −0.195* −0.174** (0.056) (0.050) Alumni2 −0.092* −0.084* (0.074) (0.076) Size −0.150*** −0.150*** (0.000) (0.000) Age −0.275*** −0.275*** (0.000) (0.000) ROA −0.958** −0.980** (0.032) (0.029) Return 0.088** 0.086** (0.040) (0.046) MB 0.013 0.013* (0.108) (0.093) Growth 0.013 0.011 (0.687) (0.733) HHI 0.625 0.614 (0.476) (0.483) DACC 0.867*** 0.871*** (0.002) (0.002) MRETVOL −0.014 −0.004 (0.976) (0.993) Loss 0.169*** 0.167*** (0.008) (0.009) Importance −0.003 −0.003 (0.361) (0.349) Sup Sales 0.002 0.002 (0.725) (0.727) Sup ROA 0.221 0.224 (0.404) (0.397) Sup MB −0.0004 −0.001 (0.504) (0.463) Sup RSI −0.535 −0.505 (0.410) (0.439) Sup TC 0.151 0.140 (0.362) (0.399) Constant 1.242*** 1.242*** 4.894*** 4.886*** (0.005) (0.005) (0.000) (0.000) Industry fixed effects No No Yes Yes Year fixed effects No No Yes Yes Observations 1,443 1,443 1,443 1,443 Adj. R 0.001 0.001 0.290 0.290 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. findings are consistent with our expectation that school ties with suppliers reduce the frequency of management forecasts. Further, the more school ties a firm has with its suppliers, the less frequently it issues management forecasts. The results again support that social connections substitute management forecasts and become the information channel along the supply chain. H2 hypothesises that the effect of school-tie connections on management forecast disclosure is stronger when firms face higher proprietary cost. Following Wang (2007), we measure proprietary cost using R&D expenditures scaled by beginning total sales (Proprietary Cost). Higher R&D expenditures usually indicate higher proprietary cost. We add interaction terms, Alumni1× Proprietary Cost and Alumni2× Proprietary Cost, to model (1) 228 J. YU AND T. LUO Table 7. School-tie connections, proprietary cost and management forecast disclosure. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni1 −0.154 0.007 (0.741) (0.947) Alumni1× Proprietary Cost −24.479* −7.883*** (0.056) (0.008) Alumni2 −0.106 0.010 (0.643) (0.839) Alumni2× Proprietary Cost −15.320** −4.437*** (0.018) (0.000) Proprietary Cost −3.365 −3.116 0.137 0.176 (0.448) (0.486) (0.894) (0.864) Size −0.528*** −0.523*** −0.150*** −0.149*** (0.000) (0.000) (0.000) (0.000) Age −0.536*** −0.534*** −0.271*** −0.270*** (0.000) (0.000) (0.000) (0.000) ROA −3.637* −3.763** −0.894** −0.910** (0.054) (0.047) (0.046) (0.043) Return 0.226 0.218 0.082* 0.081* (0.180) (0.193) (0.055) (0.059) MB 0.061 0.063 0.012 0.012 (0.120) (0.109) (0.131) (0.123) Growth 0.026 0.015 0.014 0.011 (0.843) (0.909) (0.661) (0.724) HHI −0.094 −0.100 0.638 0.636 (0.978) (0.977) (0.466) (0.467) DACC 3.825*** 3.825*** 0.866*** 0.870*** (0.001) (0.001) (0.002) (0.002) MRETVOL 1.433 1.524 0.046 0.071 (0.464) (0.435) (0.921) (0.880) Loss 0.553** 0.539** 0.164*** 0.161** (0.024) (0.028) (0.010) (0.011) Importance −0.015 −0.016 −0.003 −0.003 (0.159) (0.153) (0.362) (0.357) Sup Sales 0.027 0.027 0.002 0.002 (0.230) (0.245) (0.656) (0.700) Sup ROA 0.849 0.863 0.237 0.237 (0.431) (0.423) (0.369) (0.369) Sup MB 0.001 0.0003 −0.001 −0.001 (0.677) (0.942) (0.444) (0.383) Sup RSI −2.708 −2.583 −0.438 −0.416 (0.376) (0.400) (0.501) (0.522) Sup TC 0.926 0.894 0.144 0.137 (0.157) (0.171) (0.387) (0.412) Constant 24.801*** 25.463*** 4.856*** 4.839*** (0.000) (0.000) (0.000) (0.000) Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.215 0.216 0.292 0.293 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. and (2). The results are shown in Table 7. We find the coefficients on Alumni1× Proprietary Cost and Alumni2× Proprietary Cost are both significantly negative, which supports H2. H3 predicts when firms have higher operational uncertainty, school ties have a more pronounced impact on the likelihood and frequency to issue management forecasts. We measure operational uncertainty using the standard deviation of monthly stock returns in a specific year (STD_MRET). Prior literature finds that higher volatility in a firm’s stock return CHINA JOURNAL OF ACCOUNTING STUDIES 229 Table 8. School-tie connections, operational uncertainty and management forecast disclosure. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni1 0.733 0.133 (0.351) (0.465) Alumni1× STD_MRET −16.266** −3.180** (0.022) (0.041) Alumni2 0.462 0.121 (0.334) (0.259) Alumni2× STD_MRET −10.763** −2.393** (0.048) (0.039) STD_MRET 3.369** 3.243* 0.260 0.251 (0.045) (0.053) (0.501) (0.516) Size −0.515*** −0.511*** −0.149*** −0.149*** (0.000) (0.000) (0.000) (0.000) Age −0.543*** −0.542*** −0.276*** −0.275*** (0.000) (0.000) (0.000) (0.000) ROA −3.745** −3.998** −0.938** −0.985** (0.042) (0.030) (0.034) (0.027) Return 0.299* 0.291* 0.089** 0.087** (0.054) (0.059) (0.018) (0.020) MB 0.059 0.063 0.012 0.013* (0.127) (0.102) (0.122) (0.098) Growth 0.008 0.004 0.011 0.009 (0.950) (0.974) (0.730) (0.765) HHI −0.058 −0.043 0.617 0.618 (0.986) (0.990) (0.480) (0.479) DACC 3.929*** 3.913*** 0.873*** 0.876*** (0.001) (0.001) (0.002) (0.002) Loss 0.601** 0.588** 0.170*** 0.168*** (0.015) (0.017) (0.007) (0.008) Importance −0.016 −0.016 −0.003 −0.003 (0.153) (0.145) (0.360) (0.353) Sup Sales 0.026 0.025 0.002 0.001 (0.257) (0.281) (0.744) (0.798) Sup ROA 0.732 0.760 0.225 0.225 (0.503) (0.485) (0.395) (0.396) Sup MB 0.001 −0.001 −0.001 −0.001 (0.702) (0.879) (0.471) (0.356) Sup RSI −3.304 −3.083 −0.546 −0.520 (0.277) (0.311) (0.401) (0.423) Sup TC 0.887 0.842 0.147 0.136 (0.177) (0.199) (0.378) (0.413) Constant 24.473*** 24.431*** 4.854*** 4.848*** (0.000) (0.000) (0.000) (0.000) Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.215 0.216 0.291 0.291 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. reflects higher volatility in operating performance, and thus higher operational uncertainty (Bulan, 2005; Leahy & Whited, 1996). We add interaction terms, Alumni1× STD_MRET and Alumni2× STD_MRET, to model (1) and (2). The results in Table 8 report a significant and negative coefficient on both Alumni1× STD_MRET and Alumni2× STD_MRET, supporting H3. STD_MRET and the control variable of MRETVOL measure a firm’s operational uncertainty in year t and t-1, respectively. We replace MRETVOL with STD_MRET as a control to avoid multicollinearity. 230 J. YU AND T. LUO H4 predicts that the relation between school ties and management forecast disclosure is less pronounced when suppliers’ bargaining power is higher. Following prior studies (Hui et al., 2012; Raman & Shahrur, 2008), we measure suppliers’ bargaining power using a supplier’s market value relative to the firm (Sup Bargain). Higher Sup Bargain indicates suppliers have higher bargaining advan- tage over the firm, which may push the firm to communicate with suppliers in multiple ways, thereby weakening the substitution effect of school ties on manage- ment forecasts. We add interaction terms, Alumni1× Sup Bargain and Alumni2× Sup Bargain, to model (1) and (2). The results in Table 9 report a significant and positive coefficient on both Alumni1× Sup Bargain and Alumni2× Sup Bargain, supporting H4. 4.3. PSM method In the previous section, the results suggest school-tie-associated firms are likely to rely on social ties to communicate with suppliers, resulting in a lower likelihood and frequency of issuing management forecasts. It is possible that the negative relation between school ties and management forecast disclosure is driven by some endogeneity issues. For example, firms that are more reluctant to issue earnings forecasts tend to conduct business with school-tie- associated suppliers, which may yield a reverse causal relationship between Alumni and Guide or Freq. To mitigate this concern and other concerns on spurious omitted variables, we employ a PSM method to test the relation between school ties and management forecast disclosure. To construct the matched sample, we first estimate a selection model for the presence of school ties with supplier firms, with a dummy variable of Tie as the dependent variable. Tie equals 1 if the firm is connected with at least one supplier by school ties, and zero otherwise. We follow Guan et al. (2016) to start with all the independent variables in Section 3.2. Besides, we assign each executive the R(SCH) score of the university she graduated from, take the sum of R(SCH) score of all executives of a firm, and denote it as P P RðSCHÞ. We include RðSCHÞ in the selection model, considering that a firm with a higher γ score is more likely to have school ties with its suppliers, because the executives of these firms tend to have a well-connected alumni network. In addition, we also add a set of city dummies where the universities with the top 20 R(SCH) scores are located. Firms headquartered in these cities are more likely to have school ties with suppliers because the people graduating from the top 20 universities are likely to work in these cities. As in Augurzky and Schmidt (2001), we do not keep insignificant covariates in the model. We adopt the backward selection approach to arrive at the final selection model, which contains predictors with statistical significance of at least 10% (Guan et al., 2016). The estimation result of PSM selection model is reported in Column (1) of Panel A, Table 10. As expected, RðSCHÞ is a strong predictor for the probability of having school ties with supplier firms. In addition, we find firms size (Size), profitability (ROA), industry concentration (HHI) and suppliers’ relationship-specific investments (Sup RSI) significantly impact the presence of school ties in the supply chain. For each firm that has school ties with suppliers (treatment firm), we match it with a firm without school-tie connections (control firm) that has the closest propensity score as the treatment firm. We are able to identify 121 pairs of observations. Column (2) of Panel A reports the comparison of mean values of all the relevant covariates between the treatment and control firms, and there is no significant difference between the two groups. Then, we re- CHINA JOURNAL OF ACCOUNTING STUDIES 231 Table 9. School-tie connections, suppliers’ bargaining power and management forecast disclosure. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni1 −2.468*** −0.286*** (0.003) (0.003) Alumni1× Sup Bargain 2.454** 0.074*** (0.020) (0.005) Alumni2 −1.640*** −0.171*** (0.001) (0.001) Alumni2× Sup Bargain 1.568*** 0.064*** (0.008) (0.000) Sup Bargain −0.015 −0.015 −0.006 −0.006 (0.627) (0.633) (0.484) (0.496) Size −0.511*** −0.507*** −0.152*** −0.151*** (0.000) (0.000) (0.000) (0.000) Age −0.541*** −0.539*** −0.274*** −0.275*** (0.000) (0.000) (0.000) (0.000) ROA −4.041** −4.166** −1.000** −0.971** (0.030) (0.026) (0.025) (0.030) Return 0.270 0.262 0.091** 0.089** (0.112) (0.120) (0.034) (0.036) MB 0.060 0.061 0.012 0.011 (0.128) (0.120) (0.122) (0.150) Growth 0.002 0.003 0.012 0.009 (0.986) (0.981) (0.712) (0.781) HHI −0.258 −0.154 0.636 0.641 (0.939) (0.963) (0.468) (0.465) DACC 3.807*** 3.789*** 0.859*** 0.869*** (0.001) (0.001) (0.002) (0.002) MRETVOL 1.247 1.326 −0.025 −0.004 (0.525) (0.500) (0.957) (0.993) Loss 0.549** 0.542** 0.160** 0.159** (0.026) (0.028) (0.012) (0.012) Importance −0.016 −0.016 −0.003 −0.003 (0.146) (0.142) (0.335) (0.330) Sup Sales 0.028 0.026 0.002 0.002 (0.233) (0.252) (0.740) (0.755) Sup ROA 0.691 0.700 0.191 0.206 (0.530) (0.524) (0.472) (0.438) Sup MB 0.001 −0.001 −0.001 −0.001 (0.735) (0.894) (0.481) (0.414) Sup RSI −3.173 −2.934 −0.516 −0.409 (0.298) (0.336) (0.429) (0.533) Sup TC 1.017 0.985 0.154 0.143 (0.126) (0.137) (0.358) (0.392) Constant 25.567*** 26.234*** 4.946*** 4.923*** (0.000) (0.000) (0.000) (0.000) Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.217 0.218 0.292 0.293 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. estimate model (1) and (2) using the matched sample. As reported in Panel B of Table 10, the variables of interest, Alumni1 and Alumni2, are still significantly negative, again supporting H1. Table 10 to 16 only present the estimation results for testing H1 due to limited space. In untabulated tests, we test our hypotheses H2, H3 and H4, and the results remain consistent. 232 J. YU AND T. LUO Table 10. The results of PSM method. Panel A: First-stage regression (1) Selection model (2) The covariate means of the matched sample coefficient p-value Treatment firms Control firms p-value of the differences ∑R(SCH) 0.189*** 0.000 2.517 2.064 0.265 Size 0.162*** 0.001 24.757 24.588 0.562 ROA −2.143* 0.099 0.035 0.028 0.193 HHI −1.528* 0.091 0.084 0.062 0.138 Sup RSI 0.850* 0.052 0.039 0.041 0.666 Constant −8.780 0.947 City fixed effects Yes Industry fixed effects Yes Year fixed effects Yes Observations 1,443 Pseudo R 0.243 Panel B: Second-stage regression Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni1 −1.183** −0.182* (0.039) (0.073) Alumni2 −0.807** −0.086* (0.033) (0.094) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 242 242 242 242 2 2 Pseudo R /Adj. R 0.466 0.472 0.531 0.530 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. 4.4. Change analysis To mitigate the concern that some unobservable time-invariant firm attributes may drive our results, we perform a change analysis. Specifically, we regress changes in the like- lihood and frequency of issuing management forecasts on changes in school ties, con- trolling the changes of control variables. The dependent variables are ΔGuide and ΔFreq, measured as the difference of Guide and Freq from year t-1 to t, respectively. The independent variables of interest are ΔAlumni1 and ΔAlumni2. ΔAlumni1 equals 1 if the firm changes from not having a school tie with suppliers in year t-1 to having a tie in year t, −1 if the firm changes from having a school tie with suppliers in year t-1 to not having a tie in year t, and zero for no changes in school ties. ΔAlumni2 equals the difference in the number of school ties a firm has with its suppliers from year t-1 to t. The sample size drops to 746 observations because of missing values in calculating the changes. The results of the change analysis are reported in Table 11. As shown, ΔAlumni1 and ΔAlumni2 are significantly negative in all columns, indicating that an increase in school ties with suppliers reduces the likelihood and frequency of issuing management forecasts, further supporting H1. We don’t include Age in the change analysis due to no variance in taking differences in firm age from year t-1 to t. CHINA JOURNAL OF ACCOUNTING STUDIES 233 Table 11. The results of change analysis. Dependent variable:ΔGuide Dependent variable:ΔFreq (1) (2) (3) (4) ΔAlumni1 −0.352** −0.061* (0.029) (0.064) ΔAlumni2 −1.259** −0.166* (0.034) (0.084) ΔControl variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 746 746 746 746 2 2 Pseudo R /Adj. R 0.074 0.076 0.048 0.048 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Table 12. The results of Heckman two-stage test. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni1 −0.661* −0.136* (0.078) (0.090) Alumni2 −0.433** −0.072* (0.028) (0.063) IMR 0.747** 0.735** 0.150* 0.151* (0.016) (0.018) (0.077) (0.080) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,305 1,305 1,305 1,305 2 2 Pseudo R /Adj. R 0.228 0.229 0.292 0.292 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. 4.5. Heckman two-stage test To further account for the possibility that endogeneity is caused by unobservable factors, we follow Guan et al. (2016) to perform a Heckman two-stage test. In the first stage, we run the same propensity score model specification (but using a probit model), with Tie as the dependent variable. In the second stage, we re-estimate model (1) and (2), including inverse mills ratio (IMR) calculated from the first stage. The results, as shown in Table 12, remain unchanged after we control for IMR. 4.6. Robustness checks To ensure the robustness of our results, we conduct some robustness checks. Firstly, we include a batch of additional control variables in model (1) and (2) to mitigate the concerns that those factors may drive our results: (1) We add top management team’s average age (Exe Age), tenure (Exe Tenure) and female ratio (Exe Female), because prior studies indicate characteristics of management team may impact corporate disclosure (Lin & Yang, 2019; Srinidhi et al., 2011) (2) To control for the effect of financial reporting quality on manage- ment forecasts, we add auditor choice (Auditor) and audit opinion (Opinion). Specifically, Auditor equals 1 if a firm hires a Big 10 auditor, and zero otherwise; Opinion equals 1 if a firm receives modified audit opinions, and zero otherwise; (3) To control the effect of corporate governance on management forecast disclosure (Ajinkya et al., 2005), we include board size 234 J. YU AND T. LUO Table 13. Additional control variables. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni1 −0.815** −0.167* (0.032) (0.064) Alumni2 −0.480** −0.080* (0.018) (0.095) Exe Age −0.030* −0.031* −0.008** −0.008** (0.074) (0.071) (0.045) (0.045) Exe Tenure −0.068* −0.068* −0.013* −0.013 (0.052) (0.054) (0.098) (0.102) Exe Female −0.005 −0.017 0.003 0.003 (0.990) (0.966) (0.974) (0.972) Auditor −0.571*** −0.569*** −0.097*** −0.096*** (0.000) (0.000) (0.008) (0.008) Opinion −0.786 −0.782 −0.137 −0.136 (0.131) (0.134) (0.276) (0.280) Board Size −0.639* −0.646* −0.238*** −0.240*** (0.093) (0.089) (0.010) (0.009) Independence −2.046 −2.089 −0.568* −0.578* (0.125) (0.116) (0.068) (0.063) Dual 0.054 0.057 0.073 0.074 (0.778) (0.767) (0.123) (0.118) RD −2.807 −2.779 0.198 0.181 (0.378) (0.381) (0.785) (0.802) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,428 1,428 1,428 1,428 2 2 Pseudo R /Adj. R 0.227 0.227 0.299 0.299 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. (Board Size), board independence (Independence) and CEO duality (Dual); (4) We add a firm’s R&D expenditures scaled by beginning total sales (RD) to control for the effect of proprietary cost. The results, as presented in Table 13, remain unchanged. Secondly, we check whether our main results are robust to alternative measures of school ties. First, in previous tests, top executives include chairman of the board, CEO, CFO, COO, CTO and CMO in defining school ties. Considering the influence of different executive ranks on corporate disclosure, we redefine school-tie connections (Alumni3) as equal to 1 if school ties come from the most powerful executives, including chairman of the board, CEO and CFO, and zero otherwise. The results are presented in column (1) and (3) of Table 14, with Guide and Freq Table 14. Alternative measures for school ties. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni3 −0.830** −0.171* (0.035) (0.060) Alumni4 −2.560* −0.420* (0.074) (0.094) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.212 0.212 0.290 0.289 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. CHINA JOURNAL OF ACCOUNTING STUDIES 235 as the dependent variables, respectively. The results remain unchanged. Second, to control the effect of the size of top management team on forming school ties, we redefine school-tie connections (Alumni4) as equal to the number of school ties that the firm has with its suppliers, scaled by the number of pairs of firm’s executives and suppliers’ executives. As reported in column (2) and (4) of Table 14, the results again remain consistent. Thirdly, prior studies find students tend to start their career in the place where the universities they graduated from are located. Thus, supply chain partners in closer distance are more likely to form school ties. To mitigate the concern that geographic proximity between the firm and its suppliers may drive our results, we conduct the following robustness tests. First, we add a control variable, Same Place, to model (1) and (2). Same Place equals 1 if the firm’s and its suppliers’ headquarters are located in the same province, and zero otherwise. The results, as presented in Panel A of Table 15, suggest Alumni1 and Alumni2 are still significantly negative after controlling for Same Place. Second, we exclude the firms when a firm and at least one of its suppliers are located in the same province and re-estimate the regressions. The results are reported in Panel B of Table 15 and remain unchanged. Lastly, we test the effect of school ties on supplier firms’ management forecast disclosure. We use supplier firms’ likelihood (Sup Guide) and frequency (Sup Freq) of issuing management forecasts as the dependent variables. Specifically, Sup Guide equals 1 if a supplier firm issued at least one management earnings forecast during a year, and zero otherwise. Sup Freq is defined as natural logarithm of one plus the total number of management forecasts issued by a supplier firm; Sup Freq equals zero if the supplier doesn’t issue any forecasts. The independent variables of interest are Alumni1 and Table 15. Controlling geographic proximity. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Panel A: Add a control variable for geographic proximity Alumni1 −0.881** −0.168* (0.024) (0.062) Alumni2 −0.517** −0.080* (0.013) (0.092) Same Place 0.087 0.095 −0.015 −0.016 (0.604) (0.571) (0.730) (0.711) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.213 0.213 0.289 0.289 Panel B: Exclude the firms located in the same province as their suppliers Alumni1 −1.205* −0.275* (0.073) (0.076) Alumni2 −1.197** −0.189** (0.040) (0.011) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 953 953 953 953 2 2 Pseudo R /Adj. R 0.233 0.234 0.292 0.292 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. 236 J. YU AND T. LUO Table 16. School-tie connections and suppliers’ management forecast disclosure. Dependent variable: Sup Guide Dependent variable: Sup Freq (1) (2) (3) (4) Alumni1 −0.538* −0.116* (0.056) (0.090) Alumni2 −0.370*** −0.086** (0.006) (0.020) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,841 1,841 1,841 1,841 2 2 Pseudo R /Adj. R 0.232 0.233 0.302 0.303 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Alumni2. As for control variables, we include suppliers’ size, age, profitability, stock performance, sales growth, market-to-book ratio, industry concentration, discretionary accruals, stock return volatility and a loss indicator. We also include the importance of the trading relationship to the supplier. At last, we include customers’ size, profitability, growth opportunity, relationship-specific investments and trade credits. We include industry and year fixed effects. When a supplier firm corresponds to more than one customer in a year, we weight each customer’s data with its percentage of sales and then aggregate customer data for each supplier-year. The analysis is based on 1,841 supplier-year observations. The results are presented in Table 16. We find that Alumni1 and Alumni2 are significantly negative in all columns, suggesting school ties between supply chain partners decrease suppliers’ provision of management forecasts. Taken together with previous results, we show that school ties in a way substitute management forecasts and become the information channel along the supply chain. 5. Additional analyses 5.1. School-tie connections and management forecast characteristics 5.1.1. Forecast voluntariness and timeliness In this section, we discuss the effect of school ties on management forecast quality. We first examine whether school-tie connections impact the voluntaries and time- liness of management forecasts. Following Li et al. (2017), we classify a forecast as mandatory if the forecasted earnings are losses, turning profits from previous losses, large earnings increases and decreases (defined as earnings changes of at least 50% from the previous year). Voluntary equals 1 if a forecast doesn’t belong to the above four mandatory categories, and zero otherwise. We define the timeliness of manage- ment forecasts as the number of days between the earnings announcement date and the forecast release date (Horizon). Higher values of Horizon indicate more timely forecasts. The variables of interest are Alumni1 and Alumni2. We include the same set of control variables as in model (1) and (2) to control for their effect on forecast voluntariness and timeliness. The results are reported in Table 17. We find a In Table 17, the unit of analysis is a management forecast. Since a firm can issue more than one forecast in a year, the sample size increases to 2,076. CHINA JOURNAL OF ACCOUNTING STUDIES 237 Table 17. School-tie connections and the voluntariness/timeliness of issuing management forecasts. Panel A:The voluntariness of management forecasts Dependent variable: Voluntary (1) (2) Alumni1 −2.990** (0.016) Alumni2 −2.903** (0.027) Control variables Yes Yes Industry fixed effects Yes Yes Year fixed effects Yes Yes Observations 2,076 2,076 Pseudo R 0.195 0.198 Panel B:The timeliness of management forecasts Dependent variable: Horizon (1) (2) Alumni1 −9.436 (0.170) Alumni2 −7.249** (0.024) Control variables Yes Yes Industry fixed effects Yes Yes Year fixed effects Yes Yes Observations 2,076 2,076 Adj.R 0.268 0.270 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. significant negative relation between a firm’s school ties with suppliers and the voluntariness and timeliness to issue management forecasts. Taken together with the results on forecast likelihood and frequency, we show that top executives’ reliance on school ties reduces the overall willingness to publicly disclose earnings forecasts. 5.1.2. Quantitative management forecasts Compared with qualitative forecasts, prior studies suggest that quantitative earnings forecasts usually have greater information content, which can reduce the information asymmetry between managers and external information users to a greater extent (Hirst et al., 2008). Thus, many papers focus on only quantitative forecasts (Wang & Wang, 2012). In consistent with this notion, we independently test the effect of school ties on quantitative management forecasts. Specifically, we define Guide2 as equal to 1 if the firm issued at least one quantitative earnings forecast (i.e. a point, range or open-ended forecast) during a year, and zero otherwise. Freq2 is defined as natural logarithm of one plus the number of quantitative forecasts issued during a year. We replace Guide2 and Freq2 as the dependent variables in model (1) and (2), respectively. The results, as presented in Table 18, suggest firms that are connected by school ties with their suppliers have significantly lower likelihood and frequency to issue quantitative management forecasts, indicating school In untabulated tests, we also examine forecast precision, accuracy and bias. We find no significant difference in those quality characteristics between school-tie connected firms and unconnected firms. 238 J. YU AND T. LUO Table 18. Quantitative management forecasts. Dependent variable: Guide2 Dependent variable: Freq2 (1) (2) (3) (4) Alumni1 −0.917** −0.178** (0.016) (0.037) Alumni2 −0.487** −0.098** (0.018) (0.016) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.199 0.199 0.289 0.289 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. ties not only substitute simple descriptive forecasts, but quantitative forecasts which have higher information content. 5.1.3. Sub-sample analysis based on forecast news The effect of school ties on management forecasts may depend on forecast news. Prior literature indicates managers face higher litigation risk if they hold bad news (Skinner, 1994). In addition, compared with an inaccurate bad-news forecast, a firm’s market value would be more negatively affected when it issues a good-news forecast but the actual earnings fall below the forecast (Kasznik, 1999). Therefore, we expect that managers are more motivated to communicate by school ties when they have good news rather than bad news. We define Guide3 (Guide4) as equal to 1 if the firm issued at least one good- news (bad-news) forecast during a year, and zero otherwise; and Freq3 (Freq4) as natural logarithm of one plus the number of good-news (bad-news) forecasts issued during the year. We replace Guide3 (Guide4) and Freq3 (Freq4) as the dependent variables in model (1) and (2), respectively. The results based on good-news and bad-news subsample are reported in Panel A and Panel B of Table 19, respectively. Consistent with our expectation, coefficients on Alumni1 and Alumni2 are only significant in the good-news group, indicat- ing that the substitution effect of school ties is more salient for good-news management forecasts. 5.2. The effect of school-tie connections on the access of firm-specific information for external information users Our main tests document a negative relation between a firm’s school ties with suppliers and management forecast disclosure. Due to management forecasts’ influential role in reducing the information asymmetry between managers and external information users (Coller & Yohn, 1997), the decrease in management forecast disclosure would probably lead to a lower quality of information environment (Clement et al., 2003; Kitagawa & Okuda, 2016). In this section, we empirically test whether the school-tie-associated decrease in management forecast disclosure deteriorates information environment When the forecasted earnings exceed (fall short of) last-year actual earnings, the forecast is classified as a good-news (bad-news) forecast. We use the mid-point (endpoint) of a range (open-ended) forecast to define the forecasted earnings. We manually read forecast news when the forecast is a qualitative forecast. CHINA JOURNAL OF ACCOUNTING STUDIES 239 Table 19. Sub-sample analysis based on forecast news. Panel A: Good-news management forecasts Dependent variable: Guide3 Dependent variable: Freq3 (1) (2) (3) (4) Alumni1 −1.015*** −0.141* (0.006) (0.070) Alumni2 −0.622** −0.092** (0.021) (0.014) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.161 0.161 0.177 0.178 Panel B: Bad-news management forecasts Dependent variable: Guide4 Dependent variable: Freq4 (1) (2) (3) (4) Alumni1 −0.503 −0.055 (0.249) (0.473) Alumni2 −0.070 0.00003 (0.747) (0.999) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.193 0.192 0.195 0.195 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. quality by examining the extent to which external information users have access to firm- specific information. Specifically, we consider the access of firm-specific information by two important types of external information users: capital market investors and financial analyst. We use stock return synchronicity and analyst forecast dispersion to measure the level of investors’ and analysts’ access of firm-specific information, respectively. To calculate stock return syn- chronicity, we follow prior literature (Crawford et al., 2012; Li & Wang, 2016) to regress daily returns on the value-weighted market return and value-weighted industry return, as denoted in the following equation: RET ¼ γ þ γ Market þ γ Industry þ ε (3) i;t 0 1 i;t 2 i;t i;t In model (3), the dependent variable is firm i’s daily return for a specific date t (RET). Market (Industry) is value-weighted market (industry) return at date t. Industry is created using all firms in the same industry, with firm i’s daily return omitted. The model (3) is estimated 2 2 2 within firm and year. Then, we define synchronicity (SYNCH) as Ln (R /1- R ), where R is the coefficient of determination from the estimation. By construction, higher values of SYNCH indicate the firm’s stock returns reflect relatively less firm-specific information. We measure analyst forecast dispersion (DISP) using the standard deviation of analyst earnings forecasts for a firm, deflated by the absolute value of the mean forecast. Prior studies find that less provision of firm-specific information could enhance the divergence 240 J. YU AND T. LUO of beliefs among analysts, resulting in higher forecast dispersion (Byard et al., 2011; Lee et al., 2013). To examine the effect of school-tie connections on stock return synchronicity and analyst forecast dispersion, we run the following regressions, respectively: SYNCH ¼ α þ α Alumni þ α Alumni � MF þ α MF þ α Controls i;t 0 1 i;t 2 i;t i;t 3 i;t n i;t 1 þ industry fixed effectsþ year fixed effectsþ ε (4) i;t DISP ¼ β þ β Alumni þ Alumni � MF þ β MF þ β Controls i;t 0 1 i;t 2 i;t i;t 3 i;t n i;t 1 þ industry fixed effectsþ year fixed effectsþ ε (5) i;t where i and t are firm and year indicators, respectively. In model (4), the dependent variable is stock return synchronicity (SYNCH). Alumni is one of the two measures of school-tie connections, Alumni1 or Alumni2. MF is one of the two measures of management forecast disclosure, Guide or Freq. The variable of interest is Alumni×MF, which captures the conditional effect of school ties on the relation between management forecast disclosure and stock return synchronicity. Following Crawford et al. (2012) and Li and Wang (2016), the vector of controls includes firm size, age, profitability, growth opportunity, earnings volatility, audit quality, discre- tionary accruals, analysts following, institutional holdings, ownership property and concentration. In model (5), the dependent variable is analyst forecast dispersion (DISP). The variable of interest is also Alumni×MF. Following prior literature (Wang et al., 2017; Wang & Wang, 2012; Zhu et al., 2019), the vector of controls includes firm size, age, profitability, growth opportunity, financial leverage, earnings volatility, institutional holdings, analysts follow- ing, average correlation between stock returns and earnings in previous three years, forecast horizon, analysts’ forecast experience and update frequency. Finally, we include industry and year fixed effects in model (4) and (5). 16 17 The estimation results of model (4) and (5) are reported in Tables 20 and Tables 21, respectively. we find Alumni×MF is significantly positive in all columns, suggesting that the decrease in management forecast disclosure driven by school-tie connections weak- ens the access of firm-specific information for external information users, resulting in higher stock return synchronicity and analyst forecast dispersion. Overall, the results indicate top executives’ reliance on social connections deteriorates information environ- ment quality. 5.3. The ranks and strength of school ties We further provide evidence on whether the effect of school-tie connections on management forecasts varies across ranks of connected executives and strength of school ties. First, top-level executives potentially exhibit stronger effects than lower-level executives on corporate disclosure. Specifically, we classify school ties Following Crawford et al. (2012), we require that each firm-year has at least 50 observations to run model (3). This reduces the sample size to 1,364. Following Lee et al. (2013), we require that at least three analysts’ earnings forecasts to calculate DISP. This reduces the sample size to 1,033. CHINA JOURNAL OF ACCOUNTING STUDIES 241 Table 20. School-tie connections, management forecast disclosure and stock return synchronicity. Dependent variable: SYNCH (1) (2) (3) (4) Alumni1 −0.125 −0.126 (0.286) (0.278) Alumni2 −0.053 −0.051 (0.373) (0.392) Alumni1× Guide 0.232* (0.094) Alumni2× Guide 0.118* (0.064) Alumni1× Freq 0.181** (0.050) Alumni2× Freq 0.081* (0.061) Guide −0.030 −0.028 (0.345) (0.378) Freq −0.007 −0.006 (0.753) (0.813) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,364 1,364 1,364 1,364 Adj.R 0.475 0.475 0.475 0.475 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Table 21. School-tie connections, management forecast disclosure and analyst forecast dispersion. Dependent variable: DISP (1) (2) (3) (4) Alumni1 −0.091 −0.144 (0.510) (0.298) Alumni2 −0.048 −0.065 (0.417) (0.291) Alumni1× Guide 0.562** (0.034) Alumni2× Guide 0.435*** (0.006) Alumni1× Freq 0.582** (0.013) Alumni2× Freq 0.370*** (0.009) Guide −0.077 −0.079 (0.404) (0.387) Freq −0.084 −0.083 (0.260) (0.264) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,033 1,033 1,033 1,033 Adj. R 0.329 0.330 0.331 0.332 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. into three groups based on the ranks and positions of executives: (1) both con- nected executives are chairman of board and CEO in the firm and its suppliers; (2) one of the connected executive is chairman of board and CEO, and the other is lower-level executives; (3) both connected executives are lower-level executives. 242 J. YU AND T. LUO Table 22. The ranks and strength of school ties. Panel A: Ranks of school-tie connected executives Dependent variable: Guide Dependent variable: Freq Alumni Power −0.281** −0.061* (0.043) (0.065) Control variables Yes Yes Industry fixed effects Yes Yes Year fixed effects Yes Yes Observations 1,443 1,443 2 2 Pseudo R /Adj. R 0.213 0.291 Panel B: Strength of school ties Dependent variable: Guide Dependent variable: Freq Alumni Age −0.449** −0.090** (0.033) (0.048) Control variables Yes Yes Industry fixed effects Yes Yes Year fixed effects Yes Yes Observations 1,443 1,443 2 2 Pseudo R /Adj. R 0.212 0.290 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Alumni Power equals 3, 2 and 1 if the school ties come from the above three groups, respectively. We set Alumni Power to be zero for unconnected firms. We replace Alumni1/Alumni2 with Alumni Power in model (1) and (2), and re-run the regressions. As reported in Panel A of Table 22, we find a significant and negative coefficient on Alumni Power, suggesting that a firm is more likely to rely on school ties in communicating with suppliers when more powerful executives are connected. Second, we consider the effect of strength of ties on management forecast disclosure. Prior studies indicate longer interaction time would allow for more opportunities to develop stronger ties (Gu et al., 2019). Thus, alumni executives graduated during the same period are more likely to meet in the university and develop stronger bonding. We use executives’ age as a proxy for their graduation time because few firms disclose an exact graduation year of executives. Specifically, Alumni Age is defined as 2 (1) if the age difference of connected executives is less than (more than) one year. We set Alumni Age to be zero for unconnected firms. As reported in Panel B of Table 22, we find a significant and negative coefficient on Alumni Age, indicating a firm with stronger ties has a lower likelihood and frequency to issue management forecasts. 5.4. Alternative explanation: collusion between supply chain partners? Some studies find social ties may induce connected parities to collude with each other (Gu et al., 2019; Guan et al., 2016). Thus, a potential alternative explanation for our results If we only consider the ranks and positions of the connected executive in the sample firm (but not in its supplier firms), and redefine Alumni Power as equal to 2 if the connected executive is chairman of board or CEO, 1 if the connected executive is lower-level executives, and zero otherwise, the results are consistent. If we redefine Alumni Age as equal to 2 (1) if the age difference of connected executives is less than (more than) two years, the results remain unchanged. CHINA JOURNAL OF ACCOUNTING STUDIES 243 could be that managers reduce information disclosure to cover up its collusion acts with suppliers. To mitigate concerns of this alternative explanation, we further test whether school ties are associated with the consequences of possible collusion acts. Specifically, prior literature suggests supply chain collusion may lead to two consequences: On one hand, the firm and its supplies may cooperate to monopolise a certain industry to earn excess profits. Such being the case, we should be able to observe the school-tie-asso- ciated firms have abnormal higher profitability compared to unconnected firms. On the other hand, a firm may collude with its connected supplier to extract private benefits from the companies then run. Then, to cover up their opportunistic acts, the firm would not only reduce public disclosure, but often have a lower earnings quality (Tong & Cheng, 2007; Zheng, 2009). We estimate the following regressions to separately test the effect of school ties on firm’s abnormal profitability and earnings quality: AROA ¼ γ þ γ Alumni þ γ Controls þ industry fixed effects i;t i;t i;t 1 0 1 n þ year fixed effects þ ε (6) i;t EM ¼ δ þ δ Alumni þ δ Controls þ industry fixed effects i;t 0 1 i;t n i;t 1 þ year fixed effects þ ε (7) i;t where i and t are firm and year indicators, respectively. In model (6), the dependent variable is abnormal profitability (AROA), which equals ROA (earnings divided by total assets) minus matched firm’s ROA, where the matched firm is the firm in the same 3-digit industry with closest ROA in the beginning of year (Eberhart et al., 2004). The variables of interest are Alumni1 and Alumni2. Following Patatoukas (2012), the control variables include firm size, age, sales growth, return-on-assets, book-to-market ratio, financial leverage and stock performance. In model (7), the dependent variable is earnings quality (EM) captured by discretionary accruals, calculated as the absolute value of residual estimated from modified Jones model (Dechow et al., 1995). The variables of interest are Alumni1 and Alumni2. Following Guan et al. (2016), the control variables include firm size, age, sales growth, return-on-assets, book- to-market ratio, financial leverage, operating cash flows, loss indicator, audit quality, B-share or H-share issuance, ownership property and provincial market index. The estimation results of model (6) and (7) are reported in Panel A and Panel B of Table 23, respectively. For both abnormal profitability and earnings quality, the coefficients on Alumni1 and Alumni2 are not significant, indicating school ties don’t impact a firm’s excess profitability or earnings quality. Therefore, our results are not in support of the collusion argument. 6. Conclusion In this paper, we examine whether top executives’ dependence on social connections has an impact on the information environment of listed companies. Using Chinese data, we find a negative relation between a firm’s school ties with supply chain partners and the likelihood and frequency to issue management forecasts, indicating that top executives’ school ties in a way substitute management forecasts and become the information channel along the supply chain. Further, we find the relation is stronger when the firm 244 J. YU AND T. LUO Table 23. Alternative explanation: collusion between supply chain partners? Panel A: School-tie connections and abnormal profitability Dependent variable: AROA (1) (2) Alumni1 0.006 (0.351) Alumni2 0.006 (0.191) Control variables Yes Yes Industry fixed effects Yes Yes Year fixed effects Yes Yes Observations 1,201 1,201 Adj. R 0.332 0.314 Panel B: School-tie connections and earnings quality Dependent variable: EM (1) (2) Alumni1 −0.001 (0.828) Alumni2 −0.002 (0.553) Control variables Yes Yes Industry fixed effects Yes Yes Year fixed effects Yes Yes Observations 1,400 1,400 Adj. R 0.078 0.078 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. has higher proprietary cost or operational uncertainty; but the relation is less pronounced when suppliers have bargaining advantage over the firm. Besides, we find school ties decrease the voluntariness and timeliness of management forecasts. Finally, we find the school-tie associated decrease in management forecast disclosure weakens the access of firm-specific information for external information users, resulting in higher stock synchro- nicity and analyst forecast dispersion. Overall, the findings suggest top executives’ reli- ance on social connections deteriorates information environment quality, which may put individual investors in a more vulnerable position. This study extends the role of social connections in the area of corporate information environment and provides valuable implications for regulators, investors, and researchers. Acknowledgments We appreciate the helpful comments from editors and reviewers. The authors acknowledge the financial support from the National Natural Science Foundation of China (71672097, 71902134); China Scholarship Council (201906255039); “Beiyang Scholar” Independent Innovation Program of Tianjin University (2020XRG-0087). Disclosure statement No potential conflict of interest was reported by the authors. CHINA JOURNAL OF ACCOUNTING STUDIES 245 References Ajinkya, B.B., Bhojraj, S., & Sengupta, P. (2005). 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Publisher: Taylor & Francis
Copyright: © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
ISSN: 2169-7221
eISSN: 2169-7213
DOI: 10.1080/21697213.2020.1822025
Publisher site: See Article on Publisher Site

Abstract

CHINA JOURNAL OF ACCOUNTING STUDIES 2020, VOL. 8, NO. 2, 214–248 https://doi.org/10.1080/21697213.2020.1822025 ARTICLE Top executives’ school-tie connections and management forecast disclosure a b Jianqiao Yu and Ting Luo a b College of Management and Economics, Tianjin University, Tianjin, China; School of Economics and Management, Tsinghua University, Beijing, China ABSTRACT KEYWORDS School ties; management This paper investigates whether top executives’ dependence on forecasts; information social connections has an impact on the information environment environment; supply chain of listed companies. Specifically, this paper explores the role of school ties between firms’ and suppliers’ top executives on man- agement earnings forecasts, an important channel of public infor- mation disclosure. We find a negative relation between a firm’s school ties with its suppliers and the likelihood/frequency to issue management forecasts, indicating that top executives’ school ties in a way substitute management forecasts and become the informa- tion channel along the supply chain. Further, we find the associa- tion is stronger when the firm faces higher proprietary cost or operational uncertainty, but the association becomes weaker when suppliers have bargaining advantage over the firm. Finally, we find the decrease in management forecast disclosure driven by school-tie connections weakens the access of firm-specific informa- tion for external information users, which may put individual inves- tors in a more vulnerable position. 1. Introduction China has been characterised and well-known for the prevalence of ‘guanxi’ (connections). Existing evidence largely documents that social ties have played a positive effect on China’s economic development, such as mitigating firms’ financial constraints (Allen et al., 2005), improving outward investment (Pan et al., 2009), encouraging knowledge sharing and innovation (Gao et al., 2008). However, as the level of economic development increases, the issue of unbalanced wealth distribution has gradually emerged, and the main reason for wealth inequality is the inequality in information possession (Yang et al., 2017). As such, whether social ties have caused ‘information gap’ between different economic agents has become an important question. Recent studies find that social-tie connected parties could earn significantly abnormal returns, indicating an information advantage attained from social ties (Cohen et al., 2008; He et al., 2014; Shen et al., 2015). CONTACT Ting Luo luot@sem.tsinghua.edu.cn School of Economics and Management, Tsinghua University, Beijing, 100084, China This article has been republished with minor changes. These changes do not impact the academic content of the article. Paper accepted by Guliang Tang. It’s pointed out in the ‘Report to the Nineteenth National Congress’ that the contradiction between the people’s growing needs for a better life and unbalanced development has become a main concern in China’s society. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CHINA JOURNAL OF ACCOUNTING STUDIES 215 However, little research has directly looked into the effect of social ties on corporate information environment. In this study, we investigate whether top executives’ reliance on social connections has an impact on the information environment of listed companies. Specifically, our paper explores whether the presence of school ties between firms’ and suppliers’ top executives affects management earnings forecasts, an important channel of public information disclosure. School ties have been documented to be an important form of social connections. First, the same educational background can facilitate personal communication and pro- vide connected executives better access to their partners’ private information (Cohen et al., 2008, 2010; Guan et al., 2016). School ties help foster mutual trust in the business world, which makes personal communication more frequent and more efficient, thus reducing the asymmetry of information between connected parties (Massa & Simonov, 2011). Second, school ties can help discourage non-cooperative and opportunistic beha- viour within the network, because the value of social networks often disciplines indivi- duals in the same network to be faithful to each other (Elster, 1989; Uzzi, 1996). This can mitigate the concern of ‘cheap talk’ and enhance the credibility of personal communica- tion between connected parties. We use supply chain as a setting in analysing and understanding the effect of top executives’ school-tie connections on corporate information environment. A supplier’s business success is closely tied to the earnings prospects and financial health of its major customers. If customers fall into financial distress or even go out of business, they are likely to default on contractual obligations and the suppliers will incur large cost due to decreased future sales, loss of expected gains from relationship-specific investments and unrecover- able trade credits. Therefore, suppliers have high information demand for their customers’ earnings prospects (Raman & Shahrur, 2008). To mitigate information asymmetry and the ensuing concerns of opportunistic behaviour, a customer usually needs a credible way to disclose earnings prospects to its suppliers. Specifically, a customer can choose between public channels (e.g. management forecast disclosure) or credible internal channels (e.g. communication via school ties). The presence of school ties between suppliers’ and custo- mers’ executives are likely to affect the choice between public channels and internal channels, thus making supply chain a relevant setting for our research question. Our empirical analysis is based on a sample of Chinese public firms that are reported by at least one public firm as their major customer during 2006–2015. A firm is identified as with a school tie when at least one top executive graduated from the same school as the executives of its suppliers. The baseline analysis shows firms that have school ties with suppliers have a lower likelihood and frequency to issue management earnings forecasts, indicating that top executives’ school ties in a way substitute management forecasts and become the information channel along the supply chain. The result is robust to a PSM approach, a change analysis and a Heckman two-stage analysis. In addition, we find that In our main tests, we examine the relation between school ties and customers’ management forecasts, rather than suppliers’ management forecasts, because the dataset is about the disclosure of firms’ major customers rather than suppliers. The customers are ‘key customers’ of the suppliers, but we have no guarantee that the suppliers are ‘key suppliers’ of the customers. Actually, the reported customers are much larger than their suppliers and economically more important to the suppliers than vice versa, presumably enhancing the suppliers’ information demand on customers’ earnings prospects and financial condition. Therefore, it is a more powerful setting to focus on customers’ management earnings forecasts in examining the effect of school ties. In robustness tests, we also examine the effect of school ties on suppliers’ management forecasts. 216 J. YU AND T. LUO the association is stronger when the firm faces higher proprietary cost or operational uncertainty, but the association becomes weaker when suppliers have bargaining advan- tage over the firm. Besides, we find school ties decrease the voluntariness and timeliness of management forecasts. Finally, we show that the school-tie-associated decrease in management forecast disclosure weakens the access of firm-specific information for external information users, leading to higher stock return synchronicity and analyst forecast dispersion. Taken together, the results of our study suggest that top executives’ reliance on social connections deteriorates information environment quality. Our study contributes to the literature in several ways. First, this paper contributes to a growing body of literature on social connections. Whether social connections have caused ‘information gap’ between different economic agents is of great interest to regulators, investors and researchers. Although prior studies have identified the effects of commercial ties and political connections on corporate disclosure (Ren & Wang, 2018; Gao & Wang, 2015), limited research has considered the effect of personal connections of corporate executives. Our study fills this gap by documenting the relation between top executives’ school ties and disclosure of management earnings forecasts. The results suggest that top executives’ dependence on social connections deteriorates quality of corporate information environment, which may put individual investors in a more vulner- able position. This paper sheds new light on the role of social connections in the market and provides an answer to the issue of ‘information gap’. Second, prior studies show that social connections can affect the quality of firms’ financial reporting. For example, Guan et al. (2016) document a negative relation between a firm’s school ties with auditors and audit quality. Our study, by documenting the relation between social connections and management earnings forecasts, can enrich this line of literature. Third, this paper also provides insights for regulators who are concerned with fair disclosure. The results indicate that the prevalence of social connections has an adverse effect on fair information disclosure and may put individual investors in an information disadvantage, thus providing reference value for regulatory agencies to further improve the quality of corporate disclosure, protect the interests of small investors and create a more transparent market. The remainder of the paper proceeds as follows. Section 2 reviews the related literature and develops hypotheses. Section 3 describes the sample and data, and presents research design. Section 4 presents the main empirical results. Section 5 provides additional analyses and Section 6 concludes the study. 2. Literature review and hypothesis development A major customer’s earnings prospects are highly value-relevant for a supplier firm, because a major customer is an important source of its supplier’s current and future sales, thus the current and future earnings and cash flows (Olsen & Dietrich, 1985; Pandit et al., 2011). A supplier may also invest in large amounts of assets that are specific to one particular customer, which have little value in alternative use (Williamson, 1985). Besides, suppliers often provide large trade credits for their major customers. If customers experi- ence financial distress or even go out of business, they are likely to default on contractual obligations and their suppliers will incur high cost due to loss of the expected gains from the relationship-specific investments and unrecoverable trade credits. Thus, a supplier CHINA JOURNAL OF ACCOUNTING STUDIES 217 usually has great information demand for its major customers’ earnings prospects and financial condition. On the other hand, a major customer also has incentives to disclose such information to their suppliers in order to attract more relationship-specific invest- ments and trade credits (Raman & Shahrur, 2008). Though both parties are motivated to share information about earnings prospects, prior research suggests that self-interested trading parties often have incentives to hide some private information to maximise their own benefits. Information asymmetry increases the difficulty for a supplier to determine whether a customer has the intention or ability to fulfill the contractual obligations. Besides, many relationship-specific invest- ments are not perfectly contractible due to its nature of uncertainty, making it further difficult for a supplier to get protection from its customers’ opportunistic behaviour (Klein et al., 1978). As a result, suppliers may under-invest in specific assets, tighten trade credits or increase price to compensate for their risk, which cause transaction cost for both trading parties (X. Chen et al., 2015; Raman & Shahrur, 2008; Tang et al., 2017). A customer can choose public disclosure or more credible internal channels to enhance suppliers’ assurance of its future earnings prospects and the ability of fulfiling contractual obligations. Regarding public disclosure, Cao et al. (2013) find that firms tend to issue management earnings forecasts to communicate their earnings prospects with supply chain partners. As long as customers issue management forecasts publicly, these forecasts are likely to be consistent with the forecasts privately communicated with suppliers, because any inconsistency is likely to arouse suppliers’ concerns about the credibility of customers’ management, which is harmful for maintaining a long-lasting trading relationship (Feng & Li, 2014). Therefore, a firm can use public earnings forecasts to enhance suppliers’ confidence in its future earnings prospects, thus mitigating infor- mation asymmetry along the supply chain and reducing transaction cost. However, issuing public earnings forecasts can incur considerable cost. Once a forecast is disclosed publicly, not only supply chain partners can receive the information, but other stakeholders including competitors will be informed. Prior studies indicate leaking earn- ings prospects to competitors may put a firm at a disadvantage in the competition (Wang, 2007). Besides, if the forecasted earnings turn out to be inaccurate, managers have to bear high litigation cost and reputation loss, and the firm’s market value will be negatively affected (Kasznik & Lev, 1995). Therefore, when other channels could increase the cred- ibility of private communication, a firm are likely to use the alternative channels instead of public disclosure to avoid the disclosure cost. We argue that school ties of corporate executives between suppliers and customers can substitute management forecasts and work as such an alternative information chan- nel along the supply chain. First, school ties can facilitate information transfer and provide connected executives better access to their partners’ private information (Cohen et al., 2008, 2010). Two individuals could get to know each other by attending the same university or meeting at alumni association, and thus build a stable and trusting bond. Further, sharing the same educational background could allow persons to interact with common values of the university, which also helps to foster mutual trust in the business world (Massa & Simonov, 2011). Prior studies indicate mutual trust could make personal communication between connected executives more frequent and more effective (Bhowmik & Rogers, 1970; Granovetter, 2005), thus reducing the asymmetry of informa- tion between supply chain partners. Besides, compared with public disclosure, a firm can 218 J. YU AND T. LUO choose to share earnings prospects to a specific supplier, thereby avoiding the disclosure cost of public earnings forecasts. Second, and more importantly, school ties could make private communication more credible. Prior research suggests that school ties can help discourage non-cooperative and opportunistic behaviour in supply chain relationships (Luo & Yu, 2019). The value of social networks often encourages individuals in the same network to be faithful to each other, and individuals face high reputation cost if they conduct opportunistic acts (Elster, 1989; Uzzi, 1996). The damage to reputation would make it difficult for them to conduct business with other individuals within the same network in the future, which has a discipline effect on ‘cheap talk’ between connected individuals. Thus, private commu- nication between school-tie-associated partners are inherently more credible, thereby reducing the need to enhance the credibility of communication through public disclosure. In sum, when a firm’s executives have school ties with suppliers’ executives, the firm is more likely to rely on internal channels to communicate with suppliers, leading to a decrease in public disclosure of management earnings forecasts. This could take the form of omitting all forecasts during the year or forecasting less frequently. Our first hypothesis is stated formally as follows: H1a: Ceteris paribus, firms whose executives have school ties with their suppliers’ execu- tives would have a lower likelihood of issuing management forecasts than other firms. H1b: Ceteris paribus, firms whose executives have school ties with their suppliers’ execu- tives would have a lower frequency of issuing management forecasts than other firms. We then consider the situations that can influence the effect of school ties on manage- ment forecast disclosure. We first identify the circumstances under which managers are more concerned about the cost of public disclosure. The first concern is proprietary cost. Leaking proprietary information, including earnings prospects, to competitors would put firms at a disadvantage in the competition. Prior studies have documented that proprie- tary cost is an important deterrent to management forecast disclosure (Verrecchia, 1983; Verrecchia & Weber, 2006). For example, Wang (2007) find that R&D expenditures, as a proxy for proprietary cost, negatively impact the decision to issue public earnings fore- casts. Thus, we expect the firms that have higher proprietary cost are more motivated to use school ties as the information channel so that to protect proprietary information from competitors. By contrast, in the case with lower proprietary cost, firms would have less incentives to rely on school ties. Therefore, we expect school ties are more likely asso- ciated with the likelihood/frequency of issuing management forecasts when customers’ proprietary cost is higher. Our second hypothesis is stated as follows: An implicit premise of our hypotheses is that the information communicated by public disclosure and school ties is homogenous. First, information sharing by both channels aims to enhance the credibility of private communication and help suppliers better evaluate customers’ intention or ability to fulfill the contractual obligations, thereby reducing the transaction cost. Thus, though public disclosure and communication via school ties have different forms, the goal is the same. Second, existing literature supports that managers can use both public disclosure and internal channels to share information about a firm’s earnings prospects (Feng & Li, 2014). CHINA JOURNAL OF ACCOUNTING STUDIES 219 H2: Ceteris paribus, the relation between school ties with suppliers and the likelihood/ frequency of issuing management forecasts, as documented in H1, is stronger when customers’ proprietary cost is higher. A second concern of issuing a public earnings forecast is managerial reputation cost if the forecast turns out to be inaccurate. Managers have strong incentives to forecast accurately in order to signal managerial quality (Trueman, 1986) and increase the cred- ibility of future guidance (Hutton & Stocken, 2009). On the other hand, issuing inaccurate forecasts can lead to loss of managerial reputation and even litigation (Kasznik & Lev, 1995). Besides, firms who fall below one’s own forecasts would incur large drops in the market value. Therefore, issuing inaccurate forecasts can be very costly; accordingly, Feng and Koch (2010) find managers who fail to meet one’s own forecasts tend to reduce the provision of public earnings forecasts in the future. As such, we expect when firms have higher operational uncertainty, thus facing more difficulty in providing accurate forecasts, they are more likely to use school ties as the information channel to avoid reputation loss caused by forecast error. We predict the relation between school ties and the likelihood/ frequency to issue management forecasts is stronger when firms’ operational uncertainty is higher. Our third hypothesis is stated as follows: H3: Ceteris paribus, the relation between school ties with suppliers and the likelihood/ frequency of issuing management forecasts, as documented in H1, is stronger when customers’ operational uncertainty is higher. Third, a firm’s incentives to share information with suppliers depend on its bargaining position vis-à-vis its suppliers. When a firm has relatively high dependence on its suppliers and large switching cost, its suppliers usually have higher bargaining power over the firm (Porter, 1998; Xue et al., 2018). In such scenarios, reducing information asymmetry along the supply chain becomes more important, and a firm would make more efforts to satisfy suppliers’ information need. Consistent with this view, Hui et al. (2012) find when supply chain partners have relative bargaining advantage over a firm, the importance of trading relationships leads the firm to report more conservatively. Thus, we expect when suppliers have relatively high bargaining power, a firm would use more than one information channel (e.g. both public disclosure and school ties) to enhance suppliers’ confidence in its earnings prospects, thus weakening the substitution effect of school ties on manage- ment forecast disclosure. Therefore, we predict that school ties are less likely associated with the likelihood/frequency to issue management forecasts when suppliers’ bargaining power is higher. Our fourth hypothesis is stated as follows: H4: Ceteris paribus, the relation between school ties with suppliers and the likelihood/ frequency of issuing management forecasts, as documented in H1, is weaker when suppliers’ bargaining power is higher. 220 J. YU AND T. LUO 3. Research design 3.1. Sample and data Our sample consists of firms listed in Shanghai and Shenzhen stock exchanges that are reported by at least one public firm as their major customer over the period from 2006 to 2015. Chinese public firms are encouraged to voluntarily disclose their top 5 customers in their annual reports. We refer to the CSMAR database to retrieve names of major customers. We then search online the information of a customer based on the disclosed name and determine whether the customer is publicly listed. We retain only customers that are listed firms in order to obtain their financial data for empirical analyses. Consistent with prior literature (Ellis et al., 2012), if a reported customer is a subsidiary of a public firm, we classify the customer as a public firm and use its parent firm’s data in the analyses. We exclude the firms when a firm and its supplier are the subsidiaries of the same public firm. We also drop the observations without required data in our main analyses. The final sample consists of 1,443 observations. Each observation represents a customer-year. The sample selection process is summarised in Table 1. Top executives include chairman of the board, CEO, CFO, COO, CTO and CMO. We retrieve the educational background information of top executives, both of the sample firms and of their suppliers, from CSMAR and WIND databases. For executives whose educational information is not available from CSMAR or WIND, we manually search the information online. Given that many universities in China have merged and renamed, and overtime the renamed universities have earned more recognition by alumni, we use the renamed universities in identifying school ties. For example, Zhejiang University and Hangzhou University merged to form the new Zhejiang University, thus the executives who graduated from Hangzhou University are regarded as alumni of Zhejiang University. We obtain management earnings forecasts from RESSET database, and all other data is from CSMAR or RESSET. 3.2. Model specification To examine the effect of a firm’s school ties with its suppliers on the likelihood and frequency to issue management forecasts, we estimate the following logistic regression model (1) and ordinary least squared (OLS) model (2), respectively. Guide ¼ γ þ γ Alumni þ γ Controls þ industry fixed effects i;t i;t i;t 1 0 1 n þ year fixed effects þ ε (1) i;t Freq ¼ δ þ δ Alumni þ δ Controls þ industry fixed effects i;t 0 1 i;t n i;t 1 þ year fixed effects þ ε (2) i;t A firm may be reported by multiple public firms as their major customer; thus, one customer may correspond to more than one supplier in a year. In such cases, we weight each supplier’s data with the supplier’s sales to the customer and then aggregate supplier data for each customer-year. In robustness tests, we redefine top executives as including chairman of the board, CEO and CFO. The results are unchanged. CHINA JOURNAL OF ACCOUNTING STUDIES 221 Table 1. Sample selection. Selection Criteria Observations Firms listed in Shanghai and Shenzhen stock exchanges that are reported by at least one public firm as 2,256 their major customer during 2006–2015 Less: Firms that are subsidiaries of the same public firm as their suppliers (178) Firms without required data in the main empirical analyses (635) Final sample 1,443 where i and t are firm and year indicators, respectively. In model (1), the dependent variable is the likelihood of issuing a management forecast (Guide), which equals 1 if the firm issued at least one management forecast during the year, and zero otherwise. In model (2), the dependent variable is the frequency of management forecasts (Freq), defined as the natural logarithm of one plus the total number of management forecasts issued during a year. We set Freq to be zero if the firm doesn’t issue any forecasts. Our independent variable of interest is Alumni. Following Cohen et al. (2010), Guan et al. (2016), and Gu et al. (2019), we classify two executives as having a school tie if they have attended the same university for either undergraduate or graduate degrees, without requiring them to attend the university for the same period, the same campus or the same major. Alumni represents one of the two alternative measures: (1) An indicator variable for the presence of school ties (Alumni1), defined as equal to 1 if the firm and its suppliers are connected by at least one school tie during a year, and zero otherwise; (2) School-tie connections measured as a continuous variable (Alumni2), which equals the total number of school ties that the firm has with its suppliers (T. Chen et al., 2017). When a firm corresponds to more than one supplier in a year, in our main tests we weight each supplier’s data with the supplier’s sales to the firm and then aggregate supplier data for each firm- year. According to H1, we predict the coefficient on Alumni (γ /δ ) to be positive. 1 1 We include a set of firm characteristics and its suppliers’ characteristics to control for other factors that may affect management forecast disclosure. Regarding firm character- istics, we include size (Size), age (Age), operating performance (ROA), stock performance (Return) and growth opportunity (MB, Growth), because larger, better performing and fast- growing firms are usually more likely to provide management forecasts (Core, 2001; Penman, 1980). We also include industry concentration (HHI), defined as Herfindahl– Hirschman Index of the industry which the firm belongs to, because Verrecchia (1983) finds protecting proprietary information from competitors is an important deterrent to the disclosure of management forecasts. Besides, Kasznik (1999) finds managers manip- ulate earnings to beat or meet earlier forecasts; thus, we include a firm’s ability to manage earnings as reflected in its discretionary accruals (DACC). We further include stock return volatility (MRETVOL) and a loss indicator (Loss) to control for the effect of operational uncertainty on management forecast disclosure (Brown, 2001). As for supplier characteristics, we follow Cao et al. (2013) to include the importance of the trading relationship to suppliers (Importance), calculated as a supplier’s sales to the firm divided by the supplier’s total revenue. Besides, we include suppliers’ total sales (Sup Sales), profitability (Sup ROA) and growth opportunity (Sup MB). When suppliers have In untabulated tests, we aggregate suppliers’ data by taking equal-weighted average instead of sales-weighted average and the results remain consistent. 222 J. YU AND T. LUO larger sales, better performance and more growth opportunities, they potentially have greater impact on customers’ disclosure. We further include suppliers’ industry mean of relationship-specific investments (Sup RSI) and trade credits (Sup TC) to control for the effect of suppliers’ provision of investments and trade credits on customers’ management forecasts. Finally, we include industry and year fixed effects. To mitigate the effect of outliers, continuous variables are winsorised at 1% and 99%. All variables are defined in detail in Table 2. 4. Empirical results 4.1. Descriptive statistics Table 3 reports descriptive statistics for the variables that are used in our main analysis. Among our sample firms, 53.6% issue at least one management earnings forecast during a year (Guide); on average, a firm issues 1.44 management forecasts (Freq). For the mea- sures of school-tie connections, mean of Alumni1 and Alumni2 are 3.9% and 6.6%, respectively. Specifically, among the full sample of 1,443 firm-years, 121 firm-years, have executives connected with suppliers’ executives by educational experience. For these 121 firm-years, there are 262 pairs of executives identified as have a school tie. Panel A of Table 4 presents the top 10 universities with the most pairs of connected executives between supply chain partners. The top 1 is Tsinghua University, followed by Peking University and Shanghai Jiao Tong University. These three universities account for 13.74%, 7.63%, and 7.63%, respectively, of the 262 pairs of connected executives. Panel B reports the top 20 universities from which most of the executives graduated. Following Guan et al. (2016), we sort all universities that appear in our sample into percentile ranks based on the total number of supplier executives and customer executives that graduated from these universities, denoted as R(SCH ) and R(SCH ), respectively. Then for each sup cus university, we define R(SCH) as the average of those two ranks. A higher R(SCH) means that the university educates more executives, and thus its graduates are more likely to have supplier-customer school ties. Panel B reports the top 20 universities with the highest R(SCH) scores. When firms’ executives graduated from one of these universities, they have higher chance of being connected with suppliers’ executives because such universities have a wider social network. Consistent with this notion, the universities whose R(SCH) values are ranked as top 10 in Panel B largely overlap the universities with the most pairs of observed connected executives in Panel A. 4.2. Hypotheses testing Table 5 reports the logistic estimation results of model (1). The p-values are two-sided and are based on standard errors adjusted for firm-level clustering. The first two columns include only Alumni1 or Alumni2 as the independent variable, and the last two columns report the estimation results of the full model. Consistent with H1a, the coefficients on We use logged number of Freq in the regressions. The number of pairs of connected executives, 262, is larger than the number of firm-years, 121, because for one single firm-year, there could be more than one pair of connected executives. We use R(SCH) as a predictor in our PSM selection model to control the ex ante probability of having a supplier-customer school tie. CHINA JOURNAL OF ACCOUNTING STUDIES 223 Table 2. Variable definition. Variable Definition Guide Equal to 1 if the firm issued at least one management earnings forecast during the year, and zero otherwise; Freq Natural logarithm of one plus the number of management earnings forecasts issued during the year; Freq equals zero if the firm doesn’t issue any forecasts; Alumni1 A dummy variable that equals 1 if the firm and its suppliers are connected by at least one school tie during a year (i.e. there exist two executives who attended the same university for either undergraduate or graduate degrees), and zero otherwise; Alumni2 The total number of school ties that the firm has with its suppliers; Size Natural logarithm of total assets in previous year; Age Natural logarithm of one plus the number of years that the firm has been listed; ROA Net income divided by total assets in previous year; Return Market-adjusted stock returns in previous year; MB The market-to-book ratio in previous year; Growth Annual percentage of sales growth in previous year; HHI Herfindahl-Hirschman Index in previous year of the industry which the firm belongs to, calculated by squaring the market share of each firm in the industry and adding all firms up; DACC The discretionary accruals in previous year, calculated as the absolute value of residual estimated from modified Jones model (Dechow et al., 1995); MRETVOL The standard deviation of monthly stock returns in previous year; Loss A dummy variable that equals one if earnings in previous year is negative, and zero otherwise; Importance The importance of the trading relationship to suppliers, calculated as the percentage of a supplier’s sales to the firm; Sup Sales Suppliers’ natural logarithm of total sales in previous year; Sup ROA Suppliers’ net income divided by total assets in previous year; Sup MB Suppliers’ market-to-book ratio in previous year; Sup RSI Suppliers’ industry mean of R&D expenditures scaled by beginning total sales in previous year ; Sup TC Suppliers’ industry mean of accounts receivables scaled by beginning total sales in previous year. Milgrom and Roberts (1992) point out that R&D expenditures are usually specific to supplier-customer relationships; for example, software companies often invest in R&D to develop software products designed for a particular partner. Besides, Armour and Teece (1980) and Levy (1985) further suggest that research-intensive firms tend to capture environments that require specialised inputs and relationship-specific assets are prevalent in. Therefore, we follow prior literature to measure relationship-specific investments using R&D expenditures (Allen & Phillips, 2000; X. Chen et al., 2015; Raman & Shahrur, 2008). 224 J. YU AND T. LUO Table 3. Descriptive statistics. Mean STD Min 25% Median 75% Max Guide 0.536 0.499 0.000 0.000 1.000 1.000 1.000 Freq 1.439 1.722 0.000 0.000 1.000 3.000 7.000 Alumni1 0.039 0.168 0.000 0.000 0.000 0.000 1.000 Alumni2 0.066 0.331 0.000 0.000 0.000 0.000 4.000 Size 23.462 1.913 19.638 22.129 23.087 24.648 30.089 Age 2.382 0.550 1.099 2.079 2.565 2.833 3.219 ROA 0.040 0.047 −0.142 0.012 0.034 0.065 0.211 Return 0.085 0.457 −0.750 −0.186 −0.010 0.239 2.022 MB 2.684 2.129 0.508 1.245 2.020 3.353 11.273 Growth 0.220 0.684 −0.576 −0.056 0.064 0.265 4.436 HHI 0.062 0.101 0.011 0.014 0.016 0.063 0.477 DACC 0.059 0.059 0.001 0.019 0.041 0.077 0.329 MRETVOL 0.094 0.044 0.026 0.063 0.084 0.114 0.272 Loss 0.124 0.330 0.000 0.000 0.000 0.000 1.000 Importance 6.272 5.888 0.440 2.800 4.527 7.480 36.050 Sup Sales 20.526 3.004 4.377 19.741 20.869 22.036 24.831 Sup ROA 0.041 0.061 −0.295 0.016 0.040 0.068 0.206 Sup MB 3.439 2.623 0.324 1.752 2.700 4.239 13.625 Sup RSI 0.033 0.033 0.000 0.010 0.026 0.046 0.158 Sup TC 0.229 0.134 0.030 0.118 0.201 0.319 0.797 CHINA JOURNAL OF ACCOUNTING STUDIES 225 华中科技大学华中科技大学中央党校北京理工大学上海交通大学中国石油大学中国人民大学西安交通大学中南大学中南大学东北大学中山大学山东大学清华大学清华大学浙江大学厦门大学武汉大学武汉大学北京大学北京大学复旦大学复旦大学同济大学上海交通大学中欧国际工商学院哈尔滨工业大学北京科技大学北京理工大学 Table 4. Distribution of universities. Panel A: Top 10 universities with the most pairs of connected executives between supply chain partners Rank University Ratio (%) 1 Tsinghua University ( ) 13.74 2 Peking University ( ) 7.63 2 Shanghai Jiao Tong University ( ) 7.63 4 Central South University ( ) 6.49 5 Xiamen University ( ) 3.82 5 Huazhong University of Science and Technology ( ) 3.82 5 Beijing Institute of Technology ( ) 3.82 8 Wuhan University ( ) 3.44 8 Fudan University ( ) 3.44 8 China University of Petroleum ( ) 3.44 Panel B: Top 20 universities that graduated most corporate executives Rank University R(SCH ) (%) R(SCH ) (%) R(SCH) (%) sup cus 1 Tsinghua University ( ) 100.00 100.00 100.00 2 Peking University ( ) 99.78 99.72 99.75 3 Shanghai Jiao Tong University 98.01 99.43 98.72 ( ) 4 China Europe International Business School 98.45 98.86 98.66 ( ) 5 Wuhan University ( ) 98.90 97.72 98.31 6 Xi’an Jiao Tong University ( ) 98.23 98.01 98.12 7 Huazhong University of Science and Technology ( ) 99.34 96.01 97.67 8 Central South University ( ) 97.35 97.15 97.25 9 Fudan University ( ) 97.79 96.58 97.19 10 Party School of the Central Committee of C.P.C. ( ) 95.81 96.87 96.34 11 Tongji University ( ) 94.04 98.58 96.31 12 Zhejiang University ( ) 98.45 94.02 96.24 13 Harbin Institute of Technology 95.81 95.16 95.48 ( ) 14 Sun Yat-sen University ( ) 97.57 92.31 94.94 15 University of Science and Technology Beijing 93.60 95.73 94.66 ( ) 16 Northeastern University ( ) 96.47 92.59 94.53 17 Renmin University of China ( ) 95.58 93.45 94.52 18 Shandong University ( ) 90.51 97.44 93.97 19 Beijing Institute of Technology 95.14 92.02 93.58 ( ) 20 Shanghai University of Finance and Economics (上海财经大学 ) 96.69 90.31 93.50 226 J. YU AND T. LUO Table 5. School ties with suppliers and the likelihood of issuing management forecasts. Dependent variable: Guide (1) (2) (3) (4) Alumni1 −0.643** −0.845** (0.044) (0.028) Alumni2 −0.344** −0.495** (0.045) (0.015) Size −0.524*** −0.522*** (0.000) (0.000) Age −0.538*** −0.541*** (0.000) (0.000) ROA −3.952** −4.090** (0.032) (0.027) Return 0.260 0.247 (0.121) (0.138) MB 0.059 0.063 (0.128) (0.104) Growth 0.021 0.016 (0.876) (0.906) HHI −0.316 −0.345 (0.926) (0.919) DACC 3.803*** 3.791*** (0.001) (0.001) MRETVOL 1.204 1.271 (0.536) (0.514) Loss 0.581** 0.567** (0.018) (0.021) Importance −0.015 −0.016 (0.163) (0.153) Sup Sales 0.026 0.026 (0.254) (0.261) Sup ROA 0.721 0.724 (0.506) (0.503) Sup MB 0.002 0.001 (0.598) (0.794) Sup RSI −3.398 −3.311 (0.259) (0.272) Sup TC 0.961 0.910 (0.138) (0.160) Constant 0.171*** 0.168*** 24.841*** 24.548*** (0.002) (0.002) (0.000) (0.000) Industry fixed effects No No Yes Yes Year fixed effects No No Yes Yes Observations 1,443 1,443 1,443 1,443 Pseudo R 0.002 0.002 0.213 0.213 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Alumni1 and Alumn2 are significantly negative at the 5% level in all columns. The results suggest a firm’s school ties with its suppliers reduce the likelihood to issue management forecasts, indicating top executives’ school ties in a way substitute management forecasts and become the information channel along the supply chain. In terms of economic significance, a one-standard-deviation increase in Alumni1 decreases the likelihood of issuing management forecasts by 3.5%. This result suggests the effect of school ties on management forecast disclosure is economically significant. Table 6 reports the OLS estimation results of model (2). The first and last two columns report the estimation results without and with control variables, respectively. Consistent with H1b, we find a significant and negative coefficient on Alumni1 and Alumn2. These CHINA JOURNAL OF ACCOUNTING STUDIES 227 Table 6. School ties with suppliers and the frequency of issuing management forecasts. Dependent variable: Freq (1) (2) (3) (4) Alumni1 −0.195* −0.174** (0.056) (0.050) Alumni2 −0.092* −0.084* (0.074) (0.076) Size −0.150*** −0.150*** (0.000) (0.000) Age −0.275*** −0.275*** (0.000) (0.000) ROA −0.958** −0.980** (0.032) (0.029) Return 0.088** 0.086** (0.040) (0.046) MB 0.013 0.013* (0.108) (0.093) Growth 0.013 0.011 (0.687) (0.733) HHI 0.625 0.614 (0.476) (0.483) DACC 0.867*** 0.871*** (0.002) (0.002) MRETVOL −0.014 −0.004 (0.976) (0.993) Loss 0.169*** 0.167*** (0.008) (0.009) Importance −0.003 −0.003 (0.361) (0.349) Sup Sales 0.002 0.002 (0.725) (0.727) Sup ROA 0.221 0.224 (0.404) (0.397) Sup MB −0.0004 −0.001 (0.504) (0.463) Sup RSI −0.535 −0.505 (0.410) (0.439) Sup TC 0.151 0.140 (0.362) (0.399) Constant 1.242*** 1.242*** 4.894*** 4.886*** (0.005) (0.005) (0.000) (0.000) Industry fixed effects No No Yes Yes Year fixed effects No No Yes Yes Observations 1,443 1,443 1,443 1,443 Adj. R 0.001 0.001 0.290 0.290 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. findings are consistent with our expectation that school ties with suppliers reduce the frequency of management forecasts. Further, the more school ties a firm has with its suppliers, the less frequently it issues management forecasts. The results again support that social connections substitute management forecasts and become the information channel along the supply chain. H2 hypothesises that the effect of school-tie connections on management forecast disclosure is stronger when firms face higher proprietary cost. Following Wang (2007), we measure proprietary cost using R&D expenditures scaled by beginning total sales (Proprietary Cost). Higher R&D expenditures usually indicate higher proprietary cost. We add interaction terms, Alumni1× Proprietary Cost and Alumni2× Proprietary Cost, to model (1) 228 J. YU AND T. LUO Table 7. School-tie connections, proprietary cost and management forecast disclosure. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni1 −0.154 0.007 (0.741) (0.947) Alumni1× Proprietary Cost −24.479* −7.883*** (0.056) (0.008) Alumni2 −0.106 0.010 (0.643) (0.839) Alumni2× Proprietary Cost −15.320** −4.437*** (0.018) (0.000) Proprietary Cost −3.365 −3.116 0.137 0.176 (0.448) (0.486) (0.894) (0.864) Size −0.528*** −0.523*** −0.150*** −0.149*** (0.000) (0.000) (0.000) (0.000) Age −0.536*** −0.534*** −0.271*** −0.270*** (0.000) (0.000) (0.000) (0.000) ROA −3.637* −3.763** −0.894** −0.910** (0.054) (0.047) (0.046) (0.043) Return 0.226 0.218 0.082* 0.081* (0.180) (0.193) (0.055) (0.059) MB 0.061 0.063 0.012 0.012 (0.120) (0.109) (0.131) (0.123) Growth 0.026 0.015 0.014 0.011 (0.843) (0.909) (0.661) (0.724) HHI −0.094 −0.100 0.638 0.636 (0.978) (0.977) (0.466) (0.467) DACC 3.825*** 3.825*** 0.866*** 0.870*** (0.001) (0.001) (0.002) (0.002) MRETVOL 1.433 1.524 0.046 0.071 (0.464) (0.435) (0.921) (0.880) Loss 0.553** 0.539** 0.164*** 0.161** (0.024) (0.028) (0.010) (0.011) Importance −0.015 −0.016 −0.003 −0.003 (0.159) (0.153) (0.362) (0.357) Sup Sales 0.027 0.027 0.002 0.002 (0.230) (0.245) (0.656) (0.700) Sup ROA 0.849 0.863 0.237 0.237 (0.431) (0.423) (0.369) (0.369) Sup MB 0.001 0.0003 −0.001 −0.001 (0.677) (0.942) (0.444) (0.383) Sup RSI −2.708 −2.583 −0.438 −0.416 (0.376) (0.400) (0.501) (0.522) Sup TC 0.926 0.894 0.144 0.137 (0.157) (0.171) (0.387) (0.412) Constant 24.801*** 25.463*** 4.856*** 4.839*** (0.000) (0.000) (0.000) (0.000) Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.215 0.216 0.292 0.293 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. and (2). The results are shown in Table 7. We find the coefficients on Alumni1× Proprietary Cost and Alumni2× Proprietary Cost are both significantly negative, which supports H2. H3 predicts when firms have higher operational uncertainty, school ties have a more pronounced impact on the likelihood and frequency to issue management forecasts. We measure operational uncertainty using the standard deviation of monthly stock returns in a specific year (STD_MRET). Prior literature finds that higher volatility in a firm’s stock return CHINA JOURNAL OF ACCOUNTING STUDIES 229 Table 8. School-tie connections, operational uncertainty and management forecast disclosure. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni1 0.733 0.133 (0.351) (0.465) Alumni1× STD_MRET −16.266** −3.180** (0.022) (0.041) Alumni2 0.462 0.121 (0.334) (0.259) Alumni2× STD_MRET −10.763** −2.393** (0.048) (0.039) STD_MRET 3.369** 3.243* 0.260 0.251 (0.045) (0.053) (0.501) (0.516) Size −0.515*** −0.511*** −0.149*** −0.149*** (0.000) (0.000) (0.000) (0.000) Age −0.543*** −0.542*** −0.276*** −0.275*** (0.000) (0.000) (0.000) (0.000) ROA −3.745** −3.998** −0.938** −0.985** (0.042) (0.030) (0.034) (0.027) Return 0.299* 0.291* 0.089** 0.087** (0.054) (0.059) (0.018) (0.020) MB 0.059 0.063 0.012 0.013* (0.127) (0.102) (0.122) (0.098) Growth 0.008 0.004 0.011 0.009 (0.950) (0.974) (0.730) (0.765) HHI −0.058 −0.043 0.617 0.618 (0.986) (0.990) (0.480) (0.479) DACC 3.929*** 3.913*** 0.873*** 0.876*** (0.001) (0.001) (0.002) (0.002) Loss 0.601** 0.588** 0.170*** 0.168*** (0.015) (0.017) (0.007) (0.008) Importance −0.016 −0.016 −0.003 −0.003 (0.153) (0.145) (0.360) (0.353) Sup Sales 0.026 0.025 0.002 0.001 (0.257) (0.281) (0.744) (0.798) Sup ROA 0.732 0.760 0.225 0.225 (0.503) (0.485) (0.395) (0.396) Sup MB 0.001 −0.001 −0.001 −0.001 (0.702) (0.879) (0.471) (0.356) Sup RSI −3.304 −3.083 −0.546 −0.520 (0.277) (0.311) (0.401) (0.423) Sup TC 0.887 0.842 0.147 0.136 (0.177) (0.199) (0.378) (0.413) Constant 24.473*** 24.431*** 4.854*** 4.848*** (0.000) (0.000) (0.000) (0.000) Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.215 0.216 0.291 0.291 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. reflects higher volatility in operating performance, and thus higher operational uncertainty (Bulan, 2005; Leahy & Whited, 1996). We add interaction terms, Alumni1× STD_MRET and Alumni2× STD_MRET, to model (1) and (2). The results in Table 8 report a significant and negative coefficient on both Alumni1× STD_MRET and Alumni2× STD_MRET, supporting H3. STD_MRET and the control variable of MRETVOL measure a firm’s operational uncertainty in year t and t-1, respectively. We replace MRETVOL with STD_MRET as a control to avoid multicollinearity. 230 J. YU AND T. LUO H4 predicts that the relation between school ties and management forecast disclosure is less pronounced when suppliers’ bargaining power is higher. Following prior studies (Hui et al., 2012; Raman & Shahrur, 2008), we measure suppliers’ bargaining power using a supplier’s market value relative to the firm (Sup Bargain). Higher Sup Bargain indicates suppliers have higher bargaining advan- tage over the firm, which may push the firm to communicate with suppliers in multiple ways, thereby weakening the substitution effect of school ties on manage- ment forecasts. We add interaction terms, Alumni1× Sup Bargain and Alumni2× Sup Bargain, to model (1) and (2). The results in Table 9 report a significant and positive coefficient on both Alumni1× Sup Bargain and Alumni2× Sup Bargain, supporting H4. 4.3. PSM method In the previous section, the results suggest school-tie-associated firms are likely to rely on social ties to communicate with suppliers, resulting in a lower likelihood and frequency of issuing management forecasts. It is possible that the negative relation between school ties and management forecast disclosure is driven by some endogeneity issues. For example, firms that are more reluctant to issue earnings forecasts tend to conduct business with school-tie- associated suppliers, which may yield a reverse causal relationship between Alumni and Guide or Freq. To mitigate this concern and other concerns on spurious omitted variables, we employ a PSM method to test the relation between school ties and management forecast disclosure. To construct the matched sample, we first estimate a selection model for the presence of school ties with supplier firms, with a dummy variable of Tie as the dependent variable. Tie equals 1 if the firm is connected with at least one supplier by school ties, and zero otherwise. We follow Guan et al. (2016) to start with all the independent variables in Section 3.2. Besides, we assign each executive the R(SCH) score of the university she graduated from, take the sum of R(SCH) score of all executives of a firm, and denote it as P P RðSCHÞ. We include RðSCHÞ in the selection model, considering that a firm with a higher γ score is more likely to have school ties with its suppliers, because the executives of these firms tend to have a well-connected alumni network. In addition, we also add a set of city dummies where the universities with the top 20 R(SCH) scores are located. Firms headquartered in these cities are more likely to have school ties with suppliers because the people graduating from the top 20 universities are likely to work in these cities. As in Augurzky and Schmidt (2001), we do not keep insignificant covariates in the model. We adopt the backward selection approach to arrive at the final selection model, which contains predictors with statistical significance of at least 10% (Guan et al., 2016). The estimation result of PSM selection model is reported in Column (1) of Panel A, Table 10. As expected, RðSCHÞ is a strong predictor for the probability of having school ties with supplier firms. In addition, we find firms size (Size), profitability (ROA), industry concentration (HHI) and suppliers’ relationship-specific investments (Sup RSI) significantly impact the presence of school ties in the supply chain. For each firm that has school ties with suppliers (treatment firm), we match it with a firm without school-tie connections (control firm) that has the closest propensity score as the treatment firm. We are able to identify 121 pairs of observations. Column (2) of Panel A reports the comparison of mean values of all the relevant covariates between the treatment and control firms, and there is no significant difference between the two groups. Then, we re- CHINA JOURNAL OF ACCOUNTING STUDIES 231 Table 9. School-tie connections, suppliers’ bargaining power and management forecast disclosure. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni1 −2.468*** −0.286*** (0.003) (0.003) Alumni1× Sup Bargain 2.454** 0.074*** (0.020) (0.005) Alumni2 −1.640*** −0.171*** (0.001) (0.001) Alumni2× Sup Bargain 1.568*** 0.064*** (0.008) (0.000) Sup Bargain −0.015 −0.015 −0.006 −0.006 (0.627) (0.633) (0.484) (0.496) Size −0.511*** −0.507*** −0.152*** −0.151*** (0.000) (0.000) (0.000) (0.000) Age −0.541*** −0.539*** −0.274*** −0.275*** (0.000) (0.000) (0.000) (0.000) ROA −4.041** −4.166** −1.000** −0.971** (0.030) (0.026) (0.025) (0.030) Return 0.270 0.262 0.091** 0.089** (0.112) (0.120) (0.034) (0.036) MB 0.060 0.061 0.012 0.011 (0.128) (0.120) (0.122) (0.150) Growth 0.002 0.003 0.012 0.009 (0.986) (0.981) (0.712) (0.781) HHI −0.258 −0.154 0.636 0.641 (0.939) (0.963) (0.468) (0.465) DACC 3.807*** 3.789*** 0.859*** 0.869*** (0.001) (0.001) (0.002) (0.002) MRETVOL 1.247 1.326 −0.025 −0.004 (0.525) (0.500) (0.957) (0.993) Loss 0.549** 0.542** 0.160** 0.159** (0.026) (0.028) (0.012) (0.012) Importance −0.016 −0.016 −0.003 −0.003 (0.146) (0.142) (0.335) (0.330) Sup Sales 0.028 0.026 0.002 0.002 (0.233) (0.252) (0.740) (0.755) Sup ROA 0.691 0.700 0.191 0.206 (0.530) (0.524) (0.472) (0.438) Sup MB 0.001 −0.001 −0.001 −0.001 (0.735) (0.894) (0.481) (0.414) Sup RSI −3.173 −2.934 −0.516 −0.409 (0.298) (0.336) (0.429) (0.533) Sup TC 1.017 0.985 0.154 0.143 (0.126) (0.137) (0.358) (0.392) Constant 25.567*** 26.234*** 4.946*** 4.923*** (0.000) (0.000) (0.000) (0.000) Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.217 0.218 0.292 0.293 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. estimate model (1) and (2) using the matched sample. As reported in Panel B of Table 10, the variables of interest, Alumni1 and Alumni2, are still significantly negative, again supporting H1. Table 10 to 16 only present the estimation results for testing H1 due to limited space. In untabulated tests, we test our hypotheses H2, H3 and H4, and the results remain consistent. 232 J. YU AND T. LUO Table 10. The results of PSM method. Panel A: First-stage regression (1) Selection model (2) The covariate means of the matched sample coefficient p-value Treatment firms Control firms p-value of the differences ∑R(SCH) 0.189*** 0.000 2.517 2.064 0.265 Size 0.162*** 0.001 24.757 24.588 0.562 ROA −2.143* 0.099 0.035 0.028 0.193 HHI −1.528* 0.091 0.084 0.062 0.138 Sup RSI 0.850* 0.052 0.039 0.041 0.666 Constant −8.780 0.947 City fixed effects Yes Industry fixed effects Yes Year fixed effects Yes Observations 1,443 Pseudo R 0.243 Panel B: Second-stage regression Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni1 −1.183** −0.182* (0.039) (0.073) Alumni2 −0.807** −0.086* (0.033) (0.094) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 242 242 242 242 2 2 Pseudo R /Adj. R 0.466 0.472 0.531 0.530 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. 4.4. Change analysis To mitigate the concern that some unobservable time-invariant firm attributes may drive our results, we perform a change analysis. Specifically, we regress changes in the like- lihood and frequency of issuing management forecasts on changes in school ties, con- trolling the changes of control variables. The dependent variables are ΔGuide and ΔFreq, measured as the difference of Guide and Freq from year t-1 to t, respectively. The independent variables of interest are ΔAlumni1 and ΔAlumni2. ΔAlumni1 equals 1 if the firm changes from not having a school tie with suppliers in year t-1 to having a tie in year t, −1 if the firm changes from having a school tie with suppliers in year t-1 to not having a tie in year t, and zero for no changes in school ties. ΔAlumni2 equals the difference in the number of school ties a firm has with its suppliers from year t-1 to t. The sample size drops to 746 observations because of missing values in calculating the changes. The results of the change analysis are reported in Table 11. As shown, ΔAlumni1 and ΔAlumni2 are significantly negative in all columns, indicating that an increase in school ties with suppliers reduces the likelihood and frequency of issuing management forecasts, further supporting H1. We don’t include Age in the change analysis due to no variance in taking differences in firm age from year t-1 to t. CHINA JOURNAL OF ACCOUNTING STUDIES 233 Table 11. The results of change analysis. Dependent variable:ΔGuide Dependent variable:ΔFreq (1) (2) (3) (4) ΔAlumni1 −0.352** −0.061* (0.029) (0.064) ΔAlumni2 −1.259** −0.166* (0.034) (0.084) ΔControl variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 746 746 746 746 2 2 Pseudo R /Adj. R 0.074 0.076 0.048 0.048 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Table 12. The results of Heckman two-stage test. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni1 −0.661* −0.136* (0.078) (0.090) Alumni2 −0.433** −0.072* (0.028) (0.063) IMR 0.747** 0.735** 0.150* 0.151* (0.016) (0.018) (0.077) (0.080) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,305 1,305 1,305 1,305 2 2 Pseudo R /Adj. R 0.228 0.229 0.292 0.292 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. 4.5. Heckman two-stage test To further account for the possibility that endogeneity is caused by unobservable factors, we follow Guan et al. (2016) to perform a Heckman two-stage test. In the first stage, we run the same propensity score model specification (but using a probit model), with Tie as the dependent variable. In the second stage, we re-estimate model (1) and (2), including inverse mills ratio (IMR) calculated from the first stage. The results, as shown in Table 12, remain unchanged after we control for IMR. 4.6. Robustness checks To ensure the robustness of our results, we conduct some robustness checks. Firstly, we include a batch of additional control variables in model (1) and (2) to mitigate the concerns that those factors may drive our results: (1) We add top management team’s average age (Exe Age), tenure (Exe Tenure) and female ratio (Exe Female), because prior studies indicate characteristics of management team may impact corporate disclosure (Lin & Yang, 2019; Srinidhi et al., 2011) (2) To control for the effect of financial reporting quality on manage- ment forecasts, we add auditor choice (Auditor) and audit opinion (Opinion). Specifically, Auditor equals 1 if a firm hires a Big 10 auditor, and zero otherwise; Opinion equals 1 if a firm receives modified audit opinions, and zero otherwise; (3) To control the effect of corporate governance on management forecast disclosure (Ajinkya et al., 2005), we include board size 234 J. YU AND T. LUO Table 13. Additional control variables. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni1 −0.815** −0.167* (0.032) (0.064) Alumni2 −0.480** −0.080* (0.018) (0.095) Exe Age −0.030* −0.031* −0.008** −0.008** (0.074) (0.071) (0.045) (0.045) Exe Tenure −0.068* −0.068* −0.013* −0.013 (0.052) (0.054) (0.098) (0.102) Exe Female −0.005 −0.017 0.003 0.003 (0.990) (0.966) (0.974) (0.972) Auditor −0.571*** −0.569*** −0.097*** −0.096*** (0.000) (0.000) (0.008) (0.008) Opinion −0.786 −0.782 −0.137 −0.136 (0.131) (0.134) (0.276) (0.280) Board Size −0.639* −0.646* −0.238*** −0.240*** (0.093) (0.089) (0.010) (0.009) Independence −2.046 −2.089 −0.568* −0.578* (0.125) (0.116) (0.068) (0.063) Dual 0.054 0.057 0.073 0.074 (0.778) (0.767) (0.123) (0.118) RD −2.807 −2.779 0.198 0.181 (0.378) (0.381) (0.785) (0.802) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,428 1,428 1,428 1,428 2 2 Pseudo R /Adj. R 0.227 0.227 0.299 0.299 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. (Board Size), board independence (Independence) and CEO duality (Dual); (4) We add a firm’s R&D expenditures scaled by beginning total sales (RD) to control for the effect of proprietary cost. The results, as presented in Table 13, remain unchanged. Secondly, we check whether our main results are robust to alternative measures of school ties. First, in previous tests, top executives include chairman of the board, CEO, CFO, COO, CTO and CMO in defining school ties. Considering the influence of different executive ranks on corporate disclosure, we redefine school-tie connections (Alumni3) as equal to 1 if school ties come from the most powerful executives, including chairman of the board, CEO and CFO, and zero otherwise. The results are presented in column (1) and (3) of Table 14, with Guide and Freq Table 14. Alternative measures for school ties. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Alumni3 −0.830** −0.171* (0.035) (0.060) Alumni4 −2.560* −0.420* (0.074) (0.094) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.212 0.212 0.290 0.289 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. CHINA JOURNAL OF ACCOUNTING STUDIES 235 as the dependent variables, respectively. The results remain unchanged. Second, to control the effect of the size of top management team on forming school ties, we redefine school-tie connections (Alumni4) as equal to the number of school ties that the firm has with its suppliers, scaled by the number of pairs of firm’s executives and suppliers’ executives. As reported in column (2) and (4) of Table 14, the results again remain consistent. Thirdly, prior studies find students tend to start their career in the place where the universities they graduated from are located. Thus, supply chain partners in closer distance are more likely to form school ties. To mitigate the concern that geographic proximity between the firm and its suppliers may drive our results, we conduct the following robustness tests. First, we add a control variable, Same Place, to model (1) and (2). Same Place equals 1 if the firm’s and its suppliers’ headquarters are located in the same province, and zero otherwise. The results, as presented in Panel A of Table 15, suggest Alumni1 and Alumni2 are still significantly negative after controlling for Same Place. Second, we exclude the firms when a firm and at least one of its suppliers are located in the same province and re-estimate the regressions. The results are reported in Panel B of Table 15 and remain unchanged. Lastly, we test the effect of school ties on supplier firms’ management forecast disclosure. We use supplier firms’ likelihood (Sup Guide) and frequency (Sup Freq) of issuing management forecasts as the dependent variables. Specifically, Sup Guide equals 1 if a supplier firm issued at least one management earnings forecast during a year, and zero otherwise. Sup Freq is defined as natural logarithm of one plus the total number of management forecasts issued by a supplier firm; Sup Freq equals zero if the supplier doesn’t issue any forecasts. The independent variables of interest are Alumni1 and Table 15. Controlling geographic proximity. Dependent variable: Guide Dependent variable: Freq (1) (2) (3) (4) Panel A: Add a control variable for geographic proximity Alumni1 −0.881** −0.168* (0.024) (0.062) Alumni2 −0.517** −0.080* (0.013) (0.092) Same Place 0.087 0.095 −0.015 −0.016 (0.604) (0.571) (0.730) (0.711) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.213 0.213 0.289 0.289 Panel B: Exclude the firms located in the same province as their suppliers Alumni1 −1.205* −0.275* (0.073) (0.076) Alumni2 −1.197** −0.189** (0.040) (0.011) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 953 953 953 953 2 2 Pseudo R /Adj. R 0.233 0.234 0.292 0.292 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. 236 J. YU AND T. LUO Table 16. School-tie connections and suppliers’ management forecast disclosure. Dependent variable: Sup Guide Dependent variable: Sup Freq (1) (2) (3) (4) Alumni1 −0.538* −0.116* (0.056) (0.090) Alumni2 −0.370*** −0.086** (0.006) (0.020) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,841 1,841 1,841 1,841 2 2 Pseudo R /Adj. R 0.232 0.233 0.302 0.303 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Alumni2. As for control variables, we include suppliers’ size, age, profitability, stock performance, sales growth, market-to-book ratio, industry concentration, discretionary accruals, stock return volatility and a loss indicator. We also include the importance of the trading relationship to the supplier. At last, we include customers’ size, profitability, growth opportunity, relationship-specific investments and trade credits. We include industry and year fixed effects. When a supplier firm corresponds to more than one customer in a year, we weight each customer’s data with its percentage of sales and then aggregate customer data for each supplier-year. The analysis is based on 1,841 supplier-year observations. The results are presented in Table 16. We find that Alumni1 and Alumni2 are significantly negative in all columns, suggesting school ties between supply chain partners decrease suppliers’ provision of management forecasts. Taken together with previous results, we show that school ties in a way substitute management forecasts and become the information channel along the supply chain. 5. Additional analyses 5.1. School-tie connections and management forecast characteristics 5.1.1. Forecast voluntariness and timeliness In this section, we discuss the effect of school ties on management forecast quality. We first examine whether school-tie connections impact the voluntaries and time- liness of management forecasts. Following Li et al. (2017), we classify a forecast as mandatory if the forecasted earnings are losses, turning profits from previous losses, large earnings increases and decreases (defined as earnings changes of at least 50% from the previous year). Voluntary equals 1 if a forecast doesn’t belong to the above four mandatory categories, and zero otherwise. We define the timeliness of manage- ment forecasts as the number of days between the earnings announcement date and the forecast release date (Horizon). Higher values of Horizon indicate more timely forecasts. The variables of interest are Alumni1 and Alumni2. We include the same set of control variables as in model (1) and (2) to control for their effect on forecast voluntariness and timeliness. The results are reported in Table 17. We find a In Table 17, the unit of analysis is a management forecast. Since a firm can issue more than one forecast in a year, the sample size increases to 2,076. CHINA JOURNAL OF ACCOUNTING STUDIES 237 Table 17. School-tie connections and the voluntariness/timeliness of issuing management forecasts. Panel A:The voluntariness of management forecasts Dependent variable: Voluntary (1) (2) Alumni1 −2.990** (0.016) Alumni2 −2.903** (0.027) Control variables Yes Yes Industry fixed effects Yes Yes Year fixed effects Yes Yes Observations 2,076 2,076 Pseudo R 0.195 0.198 Panel B:The timeliness of management forecasts Dependent variable: Horizon (1) (2) Alumni1 −9.436 (0.170) Alumni2 −7.249** (0.024) Control variables Yes Yes Industry fixed effects Yes Yes Year fixed effects Yes Yes Observations 2,076 2,076 Adj.R 0.268 0.270 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. significant negative relation between a firm’s school ties with suppliers and the voluntariness and timeliness to issue management forecasts. Taken together with the results on forecast likelihood and frequency, we show that top executives’ reliance on school ties reduces the overall willingness to publicly disclose earnings forecasts. 5.1.2. Quantitative management forecasts Compared with qualitative forecasts, prior studies suggest that quantitative earnings forecasts usually have greater information content, which can reduce the information asymmetry between managers and external information users to a greater extent (Hirst et al., 2008). Thus, many papers focus on only quantitative forecasts (Wang & Wang, 2012). In consistent with this notion, we independently test the effect of school ties on quantitative management forecasts. Specifically, we define Guide2 as equal to 1 if the firm issued at least one quantitative earnings forecast (i.e. a point, range or open-ended forecast) during a year, and zero otherwise. Freq2 is defined as natural logarithm of one plus the number of quantitative forecasts issued during a year. We replace Guide2 and Freq2 as the dependent variables in model (1) and (2), respectively. The results, as presented in Table 18, suggest firms that are connected by school ties with their suppliers have significantly lower likelihood and frequency to issue quantitative management forecasts, indicating school In untabulated tests, we also examine forecast precision, accuracy and bias. We find no significant difference in those quality characteristics between school-tie connected firms and unconnected firms. 238 J. YU AND T. LUO Table 18. Quantitative management forecasts. Dependent variable: Guide2 Dependent variable: Freq2 (1) (2) (3) (4) Alumni1 −0.917** −0.178** (0.016) (0.037) Alumni2 −0.487** −0.098** (0.018) (0.016) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.199 0.199 0.289 0.289 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. ties not only substitute simple descriptive forecasts, but quantitative forecasts which have higher information content. 5.1.3. Sub-sample analysis based on forecast news The effect of school ties on management forecasts may depend on forecast news. Prior literature indicates managers face higher litigation risk if they hold bad news (Skinner, 1994). In addition, compared with an inaccurate bad-news forecast, a firm’s market value would be more negatively affected when it issues a good-news forecast but the actual earnings fall below the forecast (Kasznik, 1999). Therefore, we expect that managers are more motivated to communicate by school ties when they have good news rather than bad news. We define Guide3 (Guide4) as equal to 1 if the firm issued at least one good- news (bad-news) forecast during a year, and zero otherwise; and Freq3 (Freq4) as natural logarithm of one plus the number of good-news (bad-news) forecasts issued during the year. We replace Guide3 (Guide4) and Freq3 (Freq4) as the dependent variables in model (1) and (2), respectively. The results based on good-news and bad-news subsample are reported in Panel A and Panel B of Table 19, respectively. Consistent with our expectation, coefficients on Alumni1 and Alumni2 are only significant in the good-news group, indicat- ing that the substitution effect of school ties is more salient for good-news management forecasts. 5.2. The effect of school-tie connections on the access of firm-specific information for external information users Our main tests document a negative relation between a firm’s school ties with suppliers and management forecast disclosure. Due to management forecasts’ influential role in reducing the information asymmetry between managers and external information users (Coller & Yohn, 1997), the decrease in management forecast disclosure would probably lead to a lower quality of information environment (Clement et al., 2003; Kitagawa & Okuda, 2016). In this section, we empirically test whether the school-tie-associated decrease in management forecast disclosure deteriorates information environment When the forecasted earnings exceed (fall short of) last-year actual earnings, the forecast is classified as a good-news (bad-news) forecast. We use the mid-point (endpoint) of a range (open-ended) forecast to define the forecasted earnings. We manually read forecast news when the forecast is a qualitative forecast. CHINA JOURNAL OF ACCOUNTING STUDIES 239 Table 19. Sub-sample analysis based on forecast news. Panel A: Good-news management forecasts Dependent variable: Guide3 Dependent variable: Freq3 (1) (2) (3) (4) Alumni1 −1.015*** −0.141* (0.006) (0.070) Alumni2 −0.622** −0.092** (0.021) (0.014) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.161 0.161 0.177 0.178 Panel B: Bad-news management forecasts Dependent variable: Guide4 Dependent variable: Freq4 (1) (2) (3) (4) Alumni1 −0.503 −0.055 (0.249) (0.473) Alumni2 −0.070 0.00003 (0.747) (0.999) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,443 1,443 1,443 1,443 2 2 Pseudo R /Adj. R 0.193 0.192 0.195 0.195 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. quality by examining the extent to which external information users have access to firm- specific information. Specifically, we consider the access of firm-specific information by two important types of external information users: capital market investors and financial analyst. We use stock return synchronicity and analyst forecast dispersion to measure the level of investors’ and analysts’ access of firm-specific information, respectively. To calculate stock return syn- chronicity, we follow prior literature (Crawford et al., 2012; Li & Wang, 2016) to regress daily returns on the value-weighted market return and value-weighted industry return, as denoted in the following equation: RET ¼ γ þ γ Market þ γ Industry þ ε (3) i;t 0 1 i;t 2 i;t i;t In model (3), the dependent variable is firm i’s daily return for a specific date t (RET). Market (Industry) is value-weighted market (industry) return at date t. Industry is created using all firms in the same industry, with firm i’s daily return omitted. The model (3) is estimated 2 2 2 within firm and year. Then, we define synchronicity (SYNCH) as Ln (R /1- R ), where R is the coefficient of determination from the estimation. By construction, higher values of SYNCH indicate the firm’s stock returns reflect relatively less firm-specific information. We measure analyst forecast dispersion (DISP) using the standard deviation of analyst earnings forecasts for a firm, deflated by the absolute value of the mean forecast. Prior studies find that less provision of firm-specific information could enhance the divergence 240 J. YU AND T. LUO of beliefs among analysts, resulting in higher forecast dispersion (Byard et al., 2011; Lee et al., 2013). To examine the effect of school-tie connections on stock return synchronicity and analyst forecast dispersion, we run the following regressions, respectively: SYNCH ¼ α þ α Alumni þ α Alumni � MF þ α MF þ α Controls i;t 0 1 i;t 2 i;t i;t 3 i;t n i;t 1 þ industry fixed effectsþ year fixed effectsþ ε (4) i;t DISP ¼ β þ β Alumni þ Alumni � MF þ β MF þ β Controls i;t 0 1 i;t 2 i;t i;t 3 i;t n i;t 1 þ industry fixed effectsþ year fixed effectsþ ε (5) i;t where i and t are firm and year indicators, respectively. In model (4), the dependent variable is stock return synchronicity (SYNCH). Alumni is one of the two measures of school-tie connections, Alumni1 or Alumni2. MF is one of the two measures of management forecast disclosure, Guide or Freq. The variable of interest is Alumni×MF, which captures the conditional effect of school ties on the relation between management forecast disclosure and stock return synchronicity. Following Crawford et al. (2012) and Li and Wang (2016), the vector of controls includes firm size, age, profitability, growth opportunity, earnings volatility, audit quality, discre- tionary accruals, analysts following, institutional holdings, ownership property and concentration. In model (5), the dependent variable is analyst forecast dispersion (DISP). The variable of interest is also Alumni×MF. Following prior literature (Wang et al., 2017; Wang & Wang, 2012; Zhu et al., 2019), the vector of controls includes firm size, age, profitability, growth opportunity, financial leverage, earnings volatility, institutional holdings, analysts follow- ing, average correlation between stock returns and earnings in previous three years, forecast horizon, analysts’ forecast experience and update frequency. Finally, we include industry and year fixed effects in model (4) and (5). 16 17 The estimation results of model (4) and (5) are reported in Tables 20 and Tables 21, respectively. we find Alumni×MF is significantly positive in all columns, suggesting that the decrease in management forecast disclosure driven by school-tie connections weak- ens the access of firm-specific information for external information users, resulting in higher stock return synchronicity and analyst forecast dispersion. Overall, the results indicate top executives’ reliance on social connections deteriorates information environ- ment quality. 5.3. The ranks and strength of school ties We further provide evidence on whether the effect of school-tie connections on management forecasts varies across ranks of connected executives and strength of school ties. First, top-level executives potentially exhibit stronger effects than lower-level executives on corporate disclosure. Specifically, we classify school ties Following Crawford et al. (2012), we require that each firm-year has at least 50 observations to run model (3). This reduces the sample size to 1,364. Following Lee et al. (2013), we require that at least three analysts’ earnings forecasts to calculate DISP. This reduces the sample size to 1,033. CHINA JOURNAL OF ACCOUNTING STUDIES 241 Table 20. School-tie connections, management forecast disclosure and stock return synchronicity. Dependent variable: SYNCH (1) (2) (3) (4) Alumni1 −0.125 −0.126 (0.286) (0.278) Alumni2 −0.053 −0.051 (0.373) (0.392) Alumni1× Guide 0.232* (0.094) Alumni2× Guide 0.118* (0.064) Alumni1× Freq 0.181** (0.050) Alumni2× Freq 0.081* (0.061) Guide −0.030 −0.028 (0.345) (0.378) Freq −0.007 −0.006 (0.753) (0.813) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,364 1,364 1,364 1,364 Adj.R 0.475 0.475 0.475 0.475 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Table 21. School-tie connections, management forecast disclosure and analyst forecast dispersion. Dependent variable: DISP (1) (2) (3) (4) Alumni1 −0.091 −0.144 (0.510) (0.298) Alumni2 −0.048 −0.065 (0.417) (0.291) Alumni1× Guide 0.562** (0.034) Alumni2× Guide 0.435*** (0.006) Alumni1× Freq 0.582** (0.013) Alumni2× Freq 0.370*** (0.009) Guide −0.077 −0.079 (0.404) (0.387) Freq −0.084 −0.083 (0.260) (0.264) Control variables Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 1,033 1,033 1,033 1,033 Adj. R 0.329 0.330 0.331 0.332 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. into three groups based on the ranks and positions of executives: (1) both con- nected executives are chairman of board and CEO in the firm and its suppliers; (2) one of the connected executive is chairman of board and CEO, and the other is lower-level executives; (3) both connected executives are lower-level executives. 242 J. YU AND T. LUO Table 22. The ranks and strength of school ties. Panel A: Ranks of school-tie connected executives Dependent variable: Guide Dependent variable: Freq Alumni Power −0.281** −0.061* (0.043) (0.065) Control variables Yes Yes Industry fixed effects Yes Yes Year fixed effects Yes Yes Observations 1,443 1,443 2 2 Pseudo R /Adj. R 0.213 0.291 Panel B: Strength of school ties Dependent variable: Guide Dependent variable: Freq Alumni Age −0.449** −0.090** (0.033) (0.048) Control variables Yes Yes Industry fixed effects Yes Yes Year fixed effects Yes Yes Observations 1,443 1,443 2 2 Pseudo R /Adj. R 0.212 0.290 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Alumni Power equals 3, 2 and 1 if the school ties come from the above three groups, respectively. We set Alumni Power to be zero for unconnected firms. We replace Alumni1/Alumni2 with Alumni Power in model (1) and (2), and re-run the regressions. As reported in Panel A of Table 22, we find a significant and negative coefficient on Alumni Power, suggesting that a firm is more likely to rely on school ties in communicating with suppliers when more powerful executives are connected. Second, we consider the effect of strength of ties on management forecast disclosure. Prior studies indicate longer interaction time would allow for more opportunities to develop stronger ties (Gu et al., 2019). Thus, alumni executives graduated during the same period are more likely to meet in the university and develop stronger bonding. We use executives’ age as a proxy for their graduation time because few firms disclose an exact graduation year of executives. Specifically, Alumni Age is defined as 2 (1) if the age difference of connected executives is less than (more than) one year. We set Alumni Age to be zero for unconnected firms. As reported in Panel B of Table 22, we find a significant and negative coefficient on Alumni Age, indicating a firm with stronger ties has a lower likelihood and frequency to issue management forecasts. 5.4. Alternative explanation: collusion between supply chain partners? Some studies find social ties may induce connected parities to collude with each other (Gu et al., 2019; Guan et al., 2016). Thus, a potential alternative explanation for our results If we only consider the ranks and positions of the connected executive in the sample firm (but not in its supplier firms), and redefine Alumni Power as equal to 2 if the connected executive is chairman of board or CEO, 1 if the connected executive is lower-level executives, and zero otherwise, the results are consistent. If we redefine Alumni Age as equal to 2 (1) if the age difference of connected executives is less than (more than) two years, the results remain unchanged. CHINA JOURNAL OF ACCOUNTING STUDIES 243 could be that managers reduce information disclosure to cover up its collusion acts with suppliers. To mitigate concerns of this alternative explanation, we further test whether school ties are associated with the consequences of possible collusion acts. Specifically, prior literature suggests supply chain collusion may lead to two consequences: On one hand, the firm and its supplies may cooperate to monopolise a certain industry to earn excess profits. Such being the case, we should be able to observe the school-tie-asso- ciated firms have abnormal higher profitability compared to unconnected firms. On the other hand, a firm may collude with its connected supplier to extract private benefits from the companies then run. Then, to cover up their opportunistic acts, the firm would not only reduce public disclosure, but often have a lower earnings quality (Tong & Cheng, 2007; Zheng, 2009). We estimate the following regressions to separately test the effect of school ties on firm’s abnormal profitability and earnings quality: AROA ¼ γ þ γ Alumni þ γ Controls þ industry fixed effects i;t i;t i;t 1 0 1 n þ year fixed effects þ ε (6) i;t EM ¼ δ þ δ Alumni þ δ Controls þ industry fixed effects i;t 0 1 i;t n i;t 1 þ year fixed effects þ ε (7) i;t where i and t are firm and year indicators, respectively. In model (6), the dependent variable is abnormal profitability (AROA), which equals ROA (earnings divided by total assets) minus matched firm’s ROA, where the matched firm is the firm in the same 3-digit industry with closest ROA in the beginning of year (Eberhart et al., 2004). The variables of interest are Alumni1 and Alumni2. Following Patatoukas (2012), the control variables include firm size, age, sales growth, return-on-assets, book-to-market ratio, financial leverage and stock performance. In model (7), the dependent variable is earnings quality (EM) captured by discretionary accruals, calculated as the absolute value of residual estimated from modified Jones model (Dechow et al., 1995). The variables of interest are Alumni1 and Alumni2. Following Guan et al. (2016), the control variables include firm size, age, sales growth, return-on-assets, book- to-market ratio, financial leverage, operating cash flows, loss indicator, audit quality, B-share or H-share issuance, ownership property and provincial market index. The estimation results of model (6) and (7) are reported in Panel A and Panel B of Table 23, respectively. For both abnormal profitability and earnings quality, the coefficients on Alumni1 and Alumni2 are not significant, indicating school ties don’t impact a firm’s excess profitability or earnings quality. Therefore, our results are not in support of the collusion argument. 6. Conclusion In this paper, we examine whether top executives’ dependence on social connections has an impact on the information environment of listed companies. Using Chinese data, we find a negative relation between a firm’s school ties with supply chain partners and the likelihood and frequency to issue management forecasts, indicating that top executives’ school ties in a way substitute management forecasts and become the information channel along the supply chain. Further, we find the relation is stronger when the firm 244 J. YU AND T. LUO Table 23. Alternative explanation: collusion between supply chain partners? Panel A: School-tie connections and abnormal profitability Dependent variable: AROA (1) (2) Alumni1 0.006 (0.351) Alumni2 0.006 (0.191) Control variables Yes Yes Industry fixed effects Yes Yes Year fixed effects Yes Yes Observations 1,201 1,201 Adj. R 0.332 0.314 Panel B: School-tie connections and earnings quality Dependent variable: EM (1) (2) Alumni1 −0.001 (0.828) Alumni2 −0.002 (0.553) Control variables Yes Yes Industry fixed effects Yes Yes Year fixed effects Yes Yes Observations 1,400 1,400 Adj. R 0.078 0.078 The p-values are reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. has higher proprietary cost or operational uncertainty; but the relation is less pronounced when suppliers have bargaining advantage over the firm. Besides, we find school ties decrease the voluntariness and timeliness of management forecasts. Finally, we find the school-tie associated decrease in management forecast disclosure weakens the access of firm-specific information for external information users, resulting in higher stock synchro- nicity and analyst forecast dispersion. Overall, the findings suggest top executives’ reli- ance on social connections deteriorates information environment quality, which may put individual investors in a more vulnerable position. This study extends the role of social connections in the area of corporate information environment and provides valuable implications for regulators, investors, and researchers. Acknowledgments We appreciate the helpful comments from editors and reviewers. The authors acknowledge the financial support from the National Natural Science Foundation of China (71672097, 71902134); China Scholarship Council (201906255039); “Beiyang Scholar” Independent Innovation Program of Tianjin University (2020XRG-0087). Disclosure statement No potential conflict of interest was reported by the authors. CHINA JOURNAL OF ACCOUNTING STUDIES 245 References Ajinkya, B.B., Bhojraj, S., & Sengupta, P. (2005). 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Journal

China Journal of Accounting Studies – Taylor & Francis

Published: Apr 2, 2020

Keywords: School ties; management forecasts; information environment; supply chain

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Top executives’ school-tie connections and management forecast disclosure

Top executives’ school-tie connections and management forecast disclosure

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Top executives’ school-tie connections and management forecast disclosure

Top executives’ school-tie connections and management forecast disclosure

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