Merchant Guild Culture and MD&A Disclosure Quality: Transparency versus Reticence

Merchant Guild Culture and MD&A Disclosure Quality: Transparency versus Reticence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Merchant Guild Culture and MD&A Disclosure Quality: Transparency versus Reticence Zixuan Zhuang, Qianyi Zhang, Zhongrong Xu, Haozhou Yin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8821313/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This research investigate how merchant guild culture (MGC) rooted in imperial China continues to shape the disclosure practices in contemporary firms. Drawing on a set of competing theoretical hypotheses, we find that firms more deeply embedded in MGC tend to disclose MD&A reports of lower informational quality, as evidenced by higher year-to-year (vertical) textual similarity. We further explore cross-sectional tests, moderating effects, and the capital market’s response to such disclosure practices. Our evidence suggests that boilerplate MD&A disclosure represents a deliberate, strategic response by managers to mitigate competitive risks and safeguard stakeholder interests, rather than a manifestation of opportunistic concealment. Business and commerce/Business and management Social science/Business and management Business and commerce/Finance Social science/Finance MD&A disclosure Merchant guild culture Informal institutions Social norms 1 Introduction High-quality disclosure constitutes the bedrock of efficient capital markets. In environments characterized by incomplete information flows, firms enhance market efficiency by supplying investors with credible and timely information. The Management Discussion and Analysis (MD&A) section, a central component of listed firms’ financial reports, plays a pivotal role in transmitting decision-useful information to investors (Bryan, 1997 ; Cole & Jones, 2004 ). Through narratives about current financial performance, major events, and forward-looking statements, MD&A reduces information asymmetry in a transparent and accessible manner, offering investors essential insights for evaluating firm value and risk. Since the mandatory introduction of MD&A reporting, boilerplate disclosure—where annual reports are strikingly similar to those of the previous year—has remained widespread (Brown & Wu, 2011). China is no exception. Critics contend that such disclosures embody insufficient informational content (Brown & Wu, 2011). Prior studies reveal that MD&A reports lacking substantive content invite regulatory scrutiny, trigger adverse market reactions (Qian & Zhu, 2020 ; Zhao et al., 2019 ), impair capital market efficiency, and heighten stock price crash risk (Meng et al., 2017 ). Yet, a critical question remains unresolved: should boilerplate disclosure be condemned as an unethical practice warranting strict regulation? Evidence from Lu et al. ( 2023 ) suggests otherwise. They document that, in China, information demand is jointly shaped by a broad set of non-arm length stakeholders rather than solely by external investors, highlighting a stakeholder-oriented governance model that contrasts sharply with the shareholder-centric system in the U.S. Inspired by Lu et al. ( 2023 ) and the new institutional economics literature, we argue that examining the institutional origins of MD&A disclosure provides a promising lens of inquiry. Economic development unfolds through the interplay between formal and informal institutions. Informal institutions, being spontaneous, functional, enduring, and deeply rooted, often exert greater influence than formal rules (North, 1990 ; Williamson, 2000 ). They leave a profound imprint on corporate governance, accounting practices, and financial outcomes (see Habib et al., 2023 ; Leventis et al., 2024 ). Merchant guild culture (MGC), as a quintessential informal institution, embodies region-specific norms and values forged through centuries of commercial activity. Its enduring legacy continues to shape China’s economic trajectory, fostering robust growth, entrepreneurial dynamism, and social capital accumulation. Theoretically, we posit that MGC shapes corporate disclosure through two distinct mechanisms. On the one hand, its cultural core of integrity may dampen managers’ incentives to conceal information, thereby enhancing the information content of MD&A. On the other hand, its stakeholder-oriented values and the doctrine of moderation may encourage managers to pursue a more conservative disclosure strategy. In practice, disclosure decisions require managers to strike a delicate balance between safeguarding proprietary information and maintaining transparency—excessive disclosure could undermine the interests of key stakeholders, such as shareholders and employees, thereby creating tension with MGC’s underlying values. Using panel data from Chinese non-financial listed firms over the period 2010–2023, we find empirical support for the latter mechanism: firms more deeply influenced by MGC tend to provide MD&A disclosures with lower informational content. We further explore cross-sectional tests, moderating effects, and economic outcomes on capital market. Taken together, our evidence grounded in Chinese traditional culture suggests that boilerplate MD&A texts are not simply the product of managerial obfuscation but rather a strategic disclosure practice aimed at protecting stakeholder interests. This aligns with Lu et al. ( 2023 )’s argument that managerial behavior in China is collectively shaped by stakeholders. Our findings, therefore, challenge the prevailing view that template-style disclosure constitutes an unethical practice requiring strict regulatory intervention. This study makes three key contributions. First, we enrich the literature on the role of MGC in shaping corporate behavior. Prior research has predominantly explored its influence on charitable giving (Kanagaretnam et al., 2019 ), financing costs and structures (Wang, 2022; Xiu et al., 2023 ; Weng et al., 2023 ), cash holdings (Wang et al., 2024), and innovation (Wu and Wan, 2024 ). In contrast, we focus specifically on the informational content of MD&A disclosures, highlighting how MGC affects a core component of firms’ financial reporting. Our goal is to shed light on the protection of both current and prospective investors’ interests. Particularly relevant to our study is Zhang et al. ( 2025 ), who document that managers influenced by MGC tend to respond evasively in earnings calls so as not to reveal sensitive business information—an intentional strategy to safeguard existing investors. By quantifying formal MD&A texts, we extend Zhang et al. ( 2025 )’s insights from oral managerial disclosure and provide robust complementary evidence. Second, we contribute to the literature on the determinants of MD&A information content by incorporating a cultural perspective. Existing studies suggest that MD&A disclosure quality—commonly proxied by vertical textual similarity—is linked to CEOs’ prior secretary experience (Li et al., 2021 ), managerial earnings manipulation (Wang et al., 2023 ), corporate innovation strategies (Zhang et al., 2023 ), auditor turnover and the reporting of key audit matters (Ge et al., 2020 ; Xie & Li, 2025 ), as well as the influence of well-connected institutional investors and independent directors (Lu, 2022; Zhu & Ge, 2025 ). Yet, research on regional culture and other informal institutions as determinants of disclosure remains scarce. Accordingly, we not only broaden the scope of inquiry into MGC’s influence but also provide a fresh theoretical lens for examining how cultural underpinnings shape corporate disclosure practices. Third, we shed light on the logic behind conservative disclosure practices in competitive market settings. Prior research on innovation disclosure suggests that when firms face intense product-market rivalry, they strategically scale back the amount of information disclosed in order to safeguard innovation outcomes and preserve competitive advantages (Cao et al., 2018 ; Zhang et al., 2023 ; Kankanhalli et al., 2024 ). Such withholding strategies also send discernible risk signals to external stakeholders (Oh et al., 2024 ). Building on MGC—an informal institution consistently shown to enhance corporate governance and accounting outputs—we suggest that limited incremental disclosure should not be equated with deliberate concealment; rather, it reflects a rational managerial choice aimed at navigating competition and protecting proprietary information. The remainder of the paper unfolds as follows. Section 2 introduces the theoretical framework and lays out our hypotheses. Section 3 details the empirical research design. Sections 4 and 5 present the main findings from hypothesis testing as well as a series of additional analyses, including heterogeneity across firms, moderating mechanisms, and examinations of economic consequences. Section 6 offers concluding remarks. 2 Institutional background and hypothesis development 2.1 The Historical Roots of MGC in China and Its Contemporary Influence Merchant guilds were merchant groups formed through bonds of geography, kinship, and trade. Traditional Chinese business guilds originated during the Song Dynasty and flourished in the Ming and Qing Dynasties. Existing literature generally classifies them into the ten major guilds, including the Ningbo, Longyou, Guangdong, Shanxi, Huizhou, Shaanxi, Fujian, Jiangyou, Dongting, and Shandong guilds (Du et al., 2017 ; Zhang & Zhang, 1993 ). The rise of merchant guilds greatly facilitated economic prosperity at that time and, in turn, nurtured the formation of MGC. In ancient China, MGC was deeply rooted in Confucianism, reflecting both the inheritance and development of Confucian thought (Du et al., 2017 ). Confucianism emphasized the “Five Constant Virtues”—benevolence, righteousness, propriety, wisdom, and trustworthiness—with xin (trustworthiness) regarded as the cornerstone of social interaction and economic activity. Under the influence of Confucian values, MGC elevated honesty and trustworthiness as core principles (Zhang & Zhang, 1993 ). This value system encouraged guild members to uphold integrity and honor contracts, thereby enhancing their moral and ethical standards (Weng et al., 2023 ), and was continuously reinforced and transmitted through business practice. Over time, guild members developed behavioral norms centered on integrity, which not only standardized commercial practices but also supported the expansion and prosperity of business guilds. The cultural emphasis on honesty embedded in MGC was gradually accepted by a broader set of stakeholders—including customers, suppliers, shareholders, and creditors—helping to create a social atmosphere of trust and further reinforcing the transmission and evolution of MGC (Du et al., 2017 ; Wang & Hu, 2024 ; Zhang et al, 2025 ). Existing literature shows that MGC, through its unique ethical system, exerts a systematic influence on contemporary firms. Du et al. ( 2017 ) were the first to introduce MGC into the framework of regional culture and firm-level behavior. Their study suggests that the integrity values shaped by MGC help alleviate principal–agent conflicts, as reflected in lower agency costs and reduced cash holdings (Du et al., 2017 ; Wang et al., 2024). Managers guided by the principle of integrity are regarded as loyal guardians of public interest; accordingly, firms more deeply influenced by MGC can obtain lower-cost financing and greater financial support from supply chain alliances (Wang et al., 2022 ; Xiu et al., 2023 ; Weng et al., 2023 ). Social responsibility is another important quality emphasized by MGC. Firms influenced by MGC are more likely to engage in charitable giving and at a higher scale (Kanagaretnam et al., 2019 ). Managers influenced by MGC tend to provide vague disclosures during performance briefings in order to avoid revealing trade secrets that may undermine investor returns (Zhang et al., 2025 ). Moreover, studies on innovation indicate that MGC also drives firms’ strategic behavior (Wu & Wan, 2024 ). 2.2 The Impact of MGC on the Information Content of MD&A Disclosures According to embeddedness theory, individuals are inherently situated within social organizations and cannot act independently of their social context. Consequently, their behaviors are largely constrained by prevailing ideologies and social norms (Granovetter, 1985 ). We argue that the effect of MGC on the information content of MD&A disclosures is primarily channeled through individual ideologies. In particular, the core tenets of MGC—sincerity in interpersonal relations, integrity in business conduct, and the Confucian principle of moderation—subtly mold how corporate leaders and employees perceive themselves, organizations, and the broader society. On the one hand, the quality and content of corporate disclosures are shaped by multiple factors, with managerial incentives and the stability of corporate strategy being particularly critical. A primary reason for low information content lies in managers’ opportunistic motives. Driven by private interests, managers may deliberately withhold certain financial information, thereby reducing the informativeness of financial reports. Such asymmetry results in pronounced disparities in the quantity and quality of information accessible to different parties in a transaction. Investors often face restricted access to lower-quality information, which exacerbates information asymmetry. This imbalance undermines investor interests, reduces market efficiency, elevates transaction risks, and in extreme cases, may even trigger market failure. Therefore, from a managerial perspective, the core values of honesty and integrity espoused by MGC are of critical importance. Integrity, as a foundational principle of business, obliges managers to refrain from concealing information and to provide truthful and comprehensive disclosures. Such practices enhance disclosure quality, particularly by increasing the information content of MD&A. In addition, integrity underscores the firm’s responsibility to external stakeholders, encouraging managers to prioritize transparent communication with external investors, including potential investors. As a key channel of such communication, MD&A disclosure becomes more effective when enriched with substantive information, thereby alleviating information asymmetry, attracting outside investors, and safeguarding stakeholder interests. Second, from the perspective of corporate strategy, when a firm’s strategic orientation is stable and its operations remain steady, its fundamentals typically show little variation. Yet, as an informal institution rooted in Confucian philosophy, MGC strengthens social trust by embedding relational networks and Confucian business ethics into commercial practices. The guild-based elements of MGC promote corporate innovation by alleviating financing constraints and curbing earnings management (Wu and Wan, 2024 ). Such innovations can generate significant internal developments, which in turn necessitate strategic adjustments. Under these circumstances, firms are more likely to enrich the content of their MD&A disclosures, thereby enhancing informativeness and offering stakeholders more decision-relevant insights. H1a : Ceteris paribus, firms with greater MGC exposure are expected to disclose MD&A reports with higher information content. On the other hand, MGC places strong emphasis on protecting the interests of stakeholders. Prior studies show that business communities deeply influenced by MGC often display greater tendencies toward philanthropic giving once they accumulate wealth, reflecting the Confucian virtue of benevolence (Kanagaretnam et al., 2019 ). This cultural trait may further reinforce managers’ inclination to avoid disclosure risks. While MD&A disclosures provide valuable information to investors, they also carry the potential threat of revealing proprietary or commercially sensitive information. A considerable body of literature suggests that the doctrine of the mean ( zhongyong ) represents a fundamental principle by which Chinese individuals approach problem-solving and decision-making (e.g., Ip, 2009 ; Lam, 2003 ; Ning et al., 2021 ). As a central tenet of Confucian philosophy, zhongyong stresses moderation and the search for balance between extremes (Ames and Hall, 2001 ; Yang et al., 2016 ). This cognitive and behavioral orientation has profound implications for business practices and is deeply embedded in MGC. Under the influence of this doctrine, Chinese corporate managers often avoid extreme behaviors in decision-making, preferring a balanced approach that simultaneously protects firm interests and mitigates risks. Applied to information disclosure, managers shaped by MGC are less likely to engage in extreme transparency. Instead, they may adopt moderate disclosure strategies, limiting the information content of MD&A reports to maintain balance between satisfying investors’ informational needs and safeguarding corporate secrets. Moreover, because MGC underscores the importance of protecting stakeholders, managers are particularly sensitive to the potential negative consequences of information leakage, such as the erosion of competitive advantages and potential harm to shareholders, employees, and other stakeholders. Consequently, firms influenced by MGC may strategically reduce the amount of substantive information in MD&A disclosures to mitigate corporate risks. H1b : Ceteris paribus, firms with greater MGC exposure are expected to disclose MD&A reports with Lower information content. 3 Research design 3.1 Data resource We construct our sample using annual data of firms listed on the Shanghai and Shenzhen A-share markets from 2010 to 2023. After excluding firms in the financial sector, observations with missing values, and applying 1% winsorization at both tails, we obtain a final sample of 22,504 firm-year observations. The data are primarily obtained from various sub-databases of CSMAR (China Stock Market & Accounting Research Database). Specifically, the information content of MD&A reports is sourced from the “Management Discussion and Analysis Sentiment Analysis” module in the Research Database of Business Distress of Chinese Listed Firms, which employ the Latent Semantic Indexing (LSI) method to compute the cosine similarity between a firm’s current-year MD&A text and its prior-year MD&A text, thereby capturing the degree of textual similarity across time. The list of merchant guild origins is based on historical records documented in Zhang and Zhang ( 1993 ), while Xiu et al. ( 2023 ) provide a more detailed enumeration from which we retrieve the corresponding geographical coordinates of each guild origin. 3.2 Empirical model We estimate the following baseline regression model to test the effect of MGC on the information quality of MD&A disclosures: Similarity i,t = β 0 + β 1 MGC i,t + CVs i,t + FE + ε (1) where i indexes firms and t denotes years. Similarity measures the textual overlap between a firm’s MD&A in year t and that in year t-1 . MGC captures the degree of a firm’s exposure to MGC. CVs denote firm-level control variables drawn from financial characteristics, governance attributes, and MD&A textual properties. FE refers to industry and year fixed effects. A significantly negative (positive) coefficient on MGC provides support for Hypothesis H1a (H1b). 3.3 Variable measurement and Descriptive statistics 3.3.1 Merchant guild culture Following the literature on MGC (Du et al., 2017 ; Kanagaretnam et al., 2019 ; Wang et al., 2022 , 2024; Zhang et al., 2025 ), we construct a geographic proximity-based measure to capture the extent to which a firm is influenced by MGC. Specifically, we first collect the geographical coordinates of the origins of the ten historical merchant guilds and the headquarters locations of listed firms. We then calculate the spherical distance between each firm and all merchant guild origins using the formula adopted by Du et al. ( 2017 ). The minimum distance between a firm and the nearest merchant guild origin is selected to proxy for the firm’s exposure to MGC. A smaller distance indicates greater influence of MGC. For ease of interpretation, we multiply the distance by − 1 so that higher values consistently indicate stronger MGC influence. It is worth noting that there exists an alternative firm-level measure of MGC influence. Instead of the “nearest distance” approach, one may draw a circle with the firm’s headquarters as the center and count the number of merchant guild origins located within the circle. Regions with more origins are more likely to have cultivated and retained MGC (Kanagaretnam et al., 2019 ; Xiu et al., 2023 ). While this approach captures the potential overlapping effects of multiple guilds, the choice of radius remains ambiguous, and hence we only report it as a robustness test rather than as the main measure. 3.3.2 MD&A disclosure quality Consistent with prior research on MD&A disclosure quality (e.g., Li et al., 2021 ; Wang et al., 2023 ; Zhang et al., 2023 ; Xie and Li, 2025 ), we use the longitudinal similarity of MD&A texts (i.e., the cosine similarity between period t and period t–1 disclosures) as an inverse proxy for information content. A higher similarity score indicates greater overlap between the current and prior year’s MD&A reports, implying that the current MD&A provides fewer incremental disclosures. Economically, this corresponds to lower-quality disclosure. 3.3.3 Control variables In line with Du et al. ( 2017 ), Meng et al. ( 2017 ), and Zhang et al. ( 2025 ), we control for firm characteristics at three levels. (1) Financial characteristics: firm size ( SizeR ), financial leverage ( Lev ), profitability ( ROA ), and cash flow ( Cashflow ). (2) Corporate governance characteristics: ownership concentration ( Top ), Independence of the Board ( ID ), CEO duality ( Dual ), and state ownership ( SOE ). (3) MD&A textual characteristics: disclosure tone ( Posi ) and readability ( Word ). Detailed definitions of all variables are provided in Appendix 1. Appendix 2 reports the descriptive statistics of the variables defined above. The results are broadly consistent with those documented in prior studies. 4 Empirical result 4.1 Univariate analysis To provide preliminary evidence on the relationship between the level of MGC influence and the information content of MD&A, we divide the sample into two groups based on the industry–year median of MGC ( MGCdum ) and examine the mean differences in other variables across groups. Table 1 reports that firms with greater MGC exposure (i.e., those located closer to historical merchant-guild origins) exhibit significantly higher longitudinal similarity in MD&A disclosures—implying less incremental textual information—lending support to Hypothesis H1b In addition, we observe that firms with stronger exposure to MGC tend to be smaller in size, less leveraged, more profitable, and better positioned in terms of cash flows. These firms are also more likely to combine the roles of CEO and chairperson and are more likely to be non-state-owned enterprises. Furthermore, they display more negative sentiment and disclose longer MD&A reports. By contrast, ownership concentration and the proportion of independent directors do not differ systematically with MGC exposure. [Insert Table 1 here] 4.2 Baseline regressions Table 2 presents the hypothesis testing results based on the specification of Eq. (1). Column (1) reports the univariate regression controlling for fixed effects, whereas Columns (2) through (4) progressively add controls for financial attributes, corporate governance features, and MD&A disclosure characteristics. Column (4) thus corresponds to the full Eq.(1). Across all specifications, the coefficient on MGC remains significantly positive at the 1% level (0.003 with t=4.303; 0.003 with t=4.012; 0.002 with t=2.779; 0.003 with t=4.366; and 0.002 with t=2.614). Economically, these results imply that stronger exposure to MGC is associated with greater vertical similarity in MD&A disclosures, thereby reducing their incremental information content and ultimately reflecting lower-quality disclosure. This evidence supports H1b, indicating that firms influenced more heavily by MGC tend to disclose less incremental information in MD&A, consistent with Zhang et al. (2025). [Insert Table 2 here] 4.3 Robustness checks 4.3.1 Alternative Explanations A firm that operates in a steady manner without any new matters to report would naturally produce an MD&A that largely mirrors the previous year. Accordingly, the significant positive association between MGC and MD&A similarity might be interpreted differently: firms more deeply influenced by MGC may pursue inherently stable business models and strategies, thereby generating “no new information to disclose.” To test this possibility, we replace the dependent variable in Eq.(1) with the competitive strategy index of Hu et al. (2020) and the business strategy index of Bentley et al. (2013). Following Hu et al. (2020), we construct three measures: the proportion of differentiation-related words in MD&A reports ( Diff ), the number of such words scaled by the number of cost-leadership ( Diff_Cost ), and a binary variable indicating whether Diff_Cost exceeds the annual average ( Strategy_Diff ). Higher values of these measures indicate a stronger preference for differentiation strategies. The business strategy index of Bentley et al. (2013) ( Strategy_Bent ) captures strategic dynamics, with lower values reflecting greater stability. If the alternative explanation were valid, we would expect firms more influenced by MGC to avoid differentiation and to exhibit lower business strategy index values, consistent with a defensive posture. The results reported in Table 3. MGC loads significantly positively on the differentiation measures (0.014 with t=3.893; 0.032 with t=5.870; and 0.037 with t=4.109), while its coefficient on the business strategy index is insignificantly negative (-0.119 with t=-1.447). Taken together, these findings suggest that firms under stronger MGC influence are more inclined toward differentiation and not necessarily defensive strategies. This implies that such firms are likely to have new matters worth reporting. Hence, we find no evidence to support the alternative explanation. [Insert Table 3 here] 4.3.2 Propensity Score Matching (PSM) The geographic distribution of listed firms is not random. Historical MGC origins, with their accumulated commercial experience and pro-business environment, may increase the likelihood of firms going public or motivate them to establish headquarters nearby. To mitigate endogeneity from such sample selection, we implement a PSM procedure. In particular, we use the MGCdum from the univariate analysis as the treatment indicator, while including financial characteristics, corporate governance variables, and fixed effects from Eq.(1) as covariates. We then perform 1:1 nearest-neighbor matching to identify comparable control firms for those located closer to MGC origins. Column (1) of Table 4 presents the matched-sample results, which confirm that the significantly positive coefficient on MGC persists (0.003 with t = 2.381). 4.3.3 Omitted Variables A potential concern for our baseline results is the possibility of omitted variables arising from auditor attributes and regional economic conditions. Prior research suggests that auditors, by virtue of their responsibility to verify firms’ annual reports, may influence the information content of MD&A (Ge et al., 2020). To account for this, we incorporate three auditor-specific controls: BigN accounting firms ( Big4 ), changes in auditor or audit firm ( Change ), and audit tenure (Tenure ). Column (2) of Table 4 shows that our main conclusion remains valid after including these controls (0.002 with t=2.205). Notably, and in contrast to Ge et al. (2020), we do not find significant evidence that auditor changes affect MD&A information content. To further address potential bias from regional economic conditions, we introduce additional fixed effects, including provincial fixed effects ( Pro ) and joint industry–year–province fixed effects ( Ind×Year×Pro ). As reported in Columns (3) and (4) of Table 4, while statistical significance weakens somewhat, the coefficient on MGC remains significantly positive (0.005 with t=1.940; 0.007 with t=2.303). [Insert Table 4 here] 4.3.4 Controlling for other regional /historical cultures Existing research on the effects of informal institutions on accounting outcomes seldom controls for alternative informal institutions, raising the risk of omitted variable bias (Leventis et al., 2024). To mitigate this concern, we augment the baseline specification in Eq. (1) by incorporating Confu and Clan , along with their interaction terms with MGC . Confu follows Du et al. (2015) and serves as a proxy for Confucian culture, while Clan is constructed at the city level, measured by the population share of the three largest surnames as an indicator of clan culture. Table 5 shows that the coefficients on Confucian and clan culture are insignificant; the interaction term MGC×Confu is likewise insignificant, whereas MGC×Clan is negative and significant at the 10% level. Crucially, even after controlling for these two informal institutions and their interaction effects, the coefficient on MGC remains significantly positive. This evidence indicates that our main results are robust when alternative regional historical cultures are taken into account. [Insert Table 5 here] 4.3.5 Alternative Measure of MGC As outlined in our variable definitions, an alternative proxy for MGC is the count of historical birthplace sites located within a given distance ( R ) from a firm’s headquarters. Table 6 presents regression results using cutoffs of 30, 50, 150, and 300 km. Across all specifications, the coefficients on R are significantly positive (0.001 with t=2.384, 1.828, 4.585, and 5.515, respectively), thereby providing consistent support for H1b. [Insert Table 6 here] 5 Further tests 5.1 Cross-sectional Tests: Business groups Relational transaction modes constitute a distinctive governance mechanism in China (Li, 2017). Within this framework, two micro-level firm attributes are particularly salient: business group affiliation and state ownership. In emerging markets, business groups often serve as substitutes for underdeveloped external institutions, such as weak capital markets or financing frictions (Poczter, 2018). During the mid-1980s, the Chinese government—aiming, among other things, to enhance SOE competitiveness under economic liberalization—adopted a series of policy initiatives to foster business group development (He et al., 2013). In subsequent decades, numerous private firms also evolved into business groups with multiple listed subsidiaries. By contrast, state ownership remains the most significant source of heterogeneity in Chinese firm studies, since managers of SOEs are typically appointed by the State-owned Assets Supervision and Administration Commission rather than recruited through market channels. Accordingly, SOE strategies, financial policies, and disclosure practices are less shaped by managerial discretion. In both contexts, managerial choices primarily accommodate the requirements of superior affiliates—including governmental directives—rather than being fully autonomous. As such, managerial orientations are less likely to be molded by broader social value norms. Based on this reasoning, we expect MD&A disclosure strategies of business group affiliates and SOEs to be largely unaffected by social environments such as MGC. The cross-sectional tests reported in Table 7 confirm this view: for firms within business groups or SOEs, the coefficient on MGC is statistically insignificant, while the main effect emerges more strongly and robustly among stand-alone firms and non-SOEs (0.004 with t=3.487; 0.003 with t=2.476). These findings align well with our expectations. [Insert Table 7 here] 5.2 Moderating effects: Independent director centrality and marketization Independent directors carry supervisory responsibilities for disclosure, particularly when serving on audit committees. Prior research indicates that the network centrality of independent directors is associated with the information richness of managerial reporting: directors who occupy more central network positions—signifying greater authority and influence—tend to enhance MD&A informativeness (Lu et al., 2022). Building on Lu et al. (2022), we incorporate independent director centrality ( IDNet ) into Eq.(1). Results reported in Column (1) of Table 8 show that the interaction term MGC×IDNet is insignificant and negative (-0.002 with t=-0.780). This suggests that managers are not intentionally lowering MD&A informativeness to conceal information; otherwise, authoritative independent directors would have been expected to mitigate the main effect. Furthermore, IDNet itself is significantly positive (0.003 with t=5.000), implying that firms with more centrally positioned independent directors exhibit greater longitudinal similarity in MD&A. This finding diverges from the evidence reported in Lu et al. (2022). The interplay between formal and informal institutions represents a core theme in cultural research at the firm level (Williamson, 2000; Du et al., 2017). Du et al. (2017) find that higher regional marketization attenuates the constraining role of MGC on agency costs. Following their approach, we introduce a provincial marketization index ( Market ) developed by Fan et al. (2011) into Eq.(1) to investigate how marketization moderates the relationship between MGC and MD&A informativeness. Results in Column (2) of Table 8 indicate that the interaction term MGC×Market is significantly positive (0.001 with t=3.492). This implies that firms located in regions with higher levels of marketization, when influenced by MGC, are more inclined to reduce MD&A informativeness. Such evidence is consistent with our theoretical argument that firms adopt this disclosure strategy to hedge against competitive risks. In sum, the analyses in this section furnish additional support for our main conclusions. Were the high-level vertical similarity of MD&A under MGC merely a manifestation of managerial information hoarding, one would anticipate the effect to be mitigated by director network centrality and regional marketization—through the internal monitoring role of influential independent directors and the external pressures of more developed markets. Yet, the evidence does not bear out this information-concealment hypothesis. 5.3 Moderating effect: Micro-level organizational culture The emergence of merchant guilds during the Ming and Qing dynasties was shaped by intensified market competition. To respond, ancient Chinese merchants increasingly relied on collective action, which fostered a cooperative ethos of mutual support and joint business ventures (Du et al., 2017; Wang et al., 2022; Zhang et al., 2025). Thus, the essence of MGC reflects a coexistence of cooperative and competitive values. These historically transmitted ideas have been internalized in modern firms as “micro-level cultures” ,namely organizational cultures. We posit that, because organizational culture is more closely tied to corporate behavior, it weakens the extent to which regional cultural contexts influence firms’ disclosure practices. Following Pan et al. (2019), we use measures of cooperative culture ( Cooperate ) and competitive culture ( Compete ) as moderating variables in Eq.(1). Results reported in Columns (3) and (4) of Table 8 show that the interaction terms MGC×Cooperate and MGC×Compete are significantly negative (-0.003 with t=-2.375; -0.005 with t=-2.973), while the main effect of MGC remains significantly positive (0.003 with t=3.540; 0.004 with t=3.861). These findings support our prediction: when firms exhibit clear value orientations and adhere to internally embedded organizational cultures, the influence of MGC on MD&A informativeness diminishes. [Insert Table 8 here] 5.4 Economic Consequences: Market Reactions to Low-Quality Disclosure Investors in capital markets consistently demand high-quality disclosure. Prior research shows that MD&A informativeness is negatively related to subsequent stock price crash risk (Meng et al., 2017). A central question, therefore, is whether market participants can discern managers’ strategic withholding of information designed to safeguard proprietary knowledge. Their reactions provide a valuable perspective on how such disclosure strategies are evaluated. Building on Di Giuli and Laux’s (2022) empirical framework, we develop following models to test market responses to MGC-driven low-informativeness disclosure: NCSKEW/ DUVOL/ SYN i,t+1 = β 0 + β 1 Similarity i,t +CVs i,t +FE+ ε i,t (2) NCSKEW/ DUVOL/ SYN i,t+1 = β 0 + β 1 Similarity_hat i,t +CVs i,t +FE+ ε i,t (3) First, we estimate Eq.(2) to test the direct market responses triggered by low-informativeness MD&A disclosure. The dependent variables capture market outcomes, namely stock price crash risk ( NCSKEW and DUVOL ) and stock price synchronicity ( SYN ), while the key explanatory variable mirrors the dependent variable of Model (1). Crash risk is computed following Kim et al. (2011), and synchronicity is measured following Durnev et al. (2003). Second, we estimate Eq.(3) to examine how the capital market interprets the low-informativeness MD&A disclosure of firms influenced by MGC. The difference between Eq.(3) and Eq.(2) lies in the explanatory variable. In Eq.(3), the explanatory variable ( Similarity_hat ) is the predicted value from Eq.(1), which isolates the portion of MD&A informativeness attributable to MGC. Results reported in Columns (1), (3), and (5) of Table 9 indicate that Similarity is significantly positively associated with stock price crash risk (0.350 with t=2.609; 0.258 with t=2.873) and stock price synchronicity (0.079 with t=2.460). This suggests that boilerplate MD&A disclosures trigger adverse market responses—greater crash risk and stronger synchronicity—because investors cannot obtain decision-useful information from them. In contrast, Columns (2), (4), and (6) reveal that Similarity_hat no longer exhibits significant negative effects on future stock price crash risk (-10.962 with t=-1.394; 0.199 with t=0.038) and is in fact negatively related to synchronicity (-7.773 with t=-4.167). These results imply that investors are capable of recognizing managers’ strategic non-disclosure of incremental information. [Insert Table 9 here] 6 Conclusion and Discussion The MGC, with its dual emphasis on integrity and stakeholder protection, shapes corporate non-financial disclosure behavior in two opposing ways. On one hand, its value of integrity encourages managers to disclose information openly and honestly. On the other hand, in seeking to mitigate competitive risks and safeguard investor interests, responsible managers may be reluctant to provide full disclosure for fear of revealing trade secrets. Consequently, managers often adopt a cautious, even reticent, approach to disclosure. Drawing on annual data from Chinese non-financial listed firms from 2010 to 2023, we find consistent empirical evidence that stronger exposure to MGC leads managers to limit the information conveyed in their MD&A reports, resulting in more boilerplate disclosures. These results remain robust across a series of robustness checks. We then conducted several additional analyses. First, taking into account China’s distinctive relational transaction environment and firm-level heterogeneity, we find that the main effect is particularly pronounced for fully listed firms and non–SOEs. Second, we examine the moderating roles of independent director centrality, regional marketization, and firm-level organizational culture. Our findings show that independent director centrality has little influence on the main relationship, whereas the effect intensifies in regions with higher levels of marketization but is attenuated in firms with strong organizational cultures. Finally, we explore the market consequences of low-information-content MD&A disclosures shaped by MGC. While templated disclosures typically provoke adverse outcomes—such as heightened stock price crash risk and stronger price synchronicity—we find that when such disclosures are made by firms more deeply embedded in MGC, they do not elicit negative reactions. In conclusion, our evidence indicates that templated disclosure should not be viewed as an unethical practice warranting strict regulatory intervention. This study carries both theoretical and practical implications. On the theoretical front, it advances the literature on MGC and contemporary corporate behavior, extends the framework of cultural economics, and offers a fresh lens through which to understand how cultural forces shape firms’ disclosure practices. Practically, our results inform investors’ decision-making by shedding light on the logic behind ambiguous disclosure practices. When confronted with two equally conservative or opaque MD&A reports, investors should scrutinize the broader institutional setting and cultural context of the firm. In contrast to companies operating in highly marketized regions with transparent governance, firms more deeply embedded in MGC may disclose less due to the influence of informal governance mechanisms. Thus, investors are advised to incorporate cultural background, governance structure, and local marketization levels into their evaluations to make better-informed decisions and enhance market efficiency. Beyond investors, our evidence also carries important implications for regulators. Firms deeply shaped by MGC often resort to ambiguous MD&A disclosure as a means of safeguarding investor interests. Yet such behavior does not provoke negative market reactions; rather, it may stimulate investment by aligning with culturally conditioned investor expectations. This finding resonates with the argument that mandating extensive nonfinancial disclosures does not necessarily serve investors well. Prior studies further suggest that compulsory disclosure standards may constrain managers’ ability to convey valuable signals. Against this backdrop, regulators should take cultural contingencies into account, adopting differentiated supervisory approaches toward firms strongly influenced by MGC. Such policies should safeguard the quality of essential disclosures while allowing sufficient flexibility to avoid the pitfalls of one-size-fits-all regulation. Likewise, assessments of disclosure practices should resist oversimplification. Users of accounting information are encouraged to adopt a nuanced perspective: while excessive disclosure can undermine investor welfare, limited ambiguity may be reasonable in certain contexts. For firms whose disclosure is shaped by MGC, the goal should be to both regulate and respect cultural diversity, striking an optimal balance between tradition and modernity to improve market efficiency. Declarations Competing interests The author declares no competing interests. Ethical approval This article does not contain any studies with human participants performed by any of the authors. Informed consent The manuscript is approved by all authors for publication. Author Contribution Z.Z. curated the data, validated the research findings, and created visualizations. Q.Z. provided necessary resources, developed software tools, and assisted with data collection. Z.X. supervised the project, critically reviewed and edited the manuscript, and managed project administration. H.Y. developed the study concept, designed the methodology, and wrote the original draft. All authors have read and approved the final version of the manuscript. Data Availability The data generated during and/or analysed during the current study are available from the corresponding author on reasonable request. The raw data used in our study were obtained from the China Stock Market & Accounting Research Database (CSMAR, [https://data.csmar.com/](https:/data.csmar.com) ). The essential procedures for processing the raw data and relevant references have been documented in the manuscript. The dataset and do-file used for the empirical analysis tables in the manuscript are available at the following URL: [https://pan.baidu.com/s/1bpBFPLw04mSE-VdRNvRqhQ?pwd=t4kd] References Ames, R. T., Hall, D. L. 2001. Focusing the familiar: A translation and philosophical interpretation of the Zhongyong. United States, Honolulu: University of Hawaii Press. Bentley, K. A., Omer, T. C., Sharp, N. Y. 2013. Business Strategy, Financial Reporting Irregularities, and Audit Effort. 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Mean Similarity 11186 0.943 11318 0.941 3.951*** SizeR 11186 21.86 11318 21.99 -6.620*** Lev 11186 0.422 11318 0.444 -8.838*** ROA 11186 0.0450 11318 0.0410 6.756*** Cashflow 11186 0.0570 11318 0.0540 3.033*** Top 11186 0.359 11318 0.357 0.999 ID 11186 0.374 11318 0.375 -0.861 Dual 11186 0.270 11318 0.221 8.543*** SOE 11186 0.355 11318 0.499 -22.087*** Posi 11186 0.282 11318 0.290 -5.121*** Word 11186 4.417 11318 4.409 3.017*** Notes： MGCdum is a binary variable defined by partitioning MGC at the industry–year median, taking the value of 1 when MGC exceeds the median and 0 otherwise. Table 1 provides a preliminary overview of the correlations among key variables.。*, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. TABLE 2 Baseline regressions. (1) (2) (3) (4) (5) Dependent variable= Similarity MGC 0.003*** 0.003*** 0.002*** 0.003*** 0.002*** (4.303) (4.012) (2.779) (4.366) (2.614) SizeR -0.001*** -0.000 (-3.917) (-1.614) Lev -0.010*** -0.011*** (-5.390) (-6.015) ROA 0.003 -0.010 (0.437) (-1.510) Cashflow -0.004 -0.004 (-0.799) (-0.902) Top -0.001 -0.002 (-0.705) (-1.218) ID 0.005 0.005 (1.063) (1.125) Dual 0.002*** 0.002*** (3.806) (3.429) SOE -0.005*** -0.005*** (-8.980) (-8.448) Posi 0.013*** 0.015*** (5.214) (5.845) Word -0.009*** -0.012*** (-6.162) (-8.002) Constant 0.943*** 0.966*** 0.943*** 0.980*** 1.007*** (3,166.712) (219.283) (535.957) (143.887) (118.399) FE Yes Yes Yes Yes Yes N 22,504 22,504 22,504 22,504 22,504 ADJ-R 2 0.287 0.291 0.291 0.290 0.298 F 18.515 26.791 29.184 30.886 29.751 Notes: Table 2 reports the stepwise regression results of Eq. (1). Across all specifications, the coefficient on MGC remains significantly positive, consistent with the prediction of H1b. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. TABLE 3 Alternative Explanations (1) (2) (3) (4) Dependent variable= Diff Diff_Cost Strategy_Diff Strategy_Bent MGC 0.014*** 0.032*** 0.037*** -0.119 (3.893) (5.870) (4.109) (-1.447) CVs & FE YES YES YES YES Observations 22,453 22,446 22,490 20,980 r2_a 0.423 0.338 0.192 0.163 F 352.513 275.733 299.206 360.461 Notes: Table 3 presents the robustness tests described in Section 4.3.1, which exclude a competing explanation for the main effect—namely, that firms simply had no new information to disclose. In these models, the dependent variable in Eq. (1) is replaced by strategy-related proxies. Specifically, Diff , Diff_Cost , and Strategy_Diff are constructed following Hu et al. (2020)’s strategic competition index, while Strategy_Bent is based on Bentley et al. (2013)’s business strategy index. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. TABLE 4 PSM and Omitted Variables (1) (2) (3) (4) Dependent variable= Similarity MGC 0.003** 0.002** 0.005* 0.007** (2.381) (2.205) (1.940) (2.303) Big4 0.004*** (3.646) Change -0.000 (-0.978) Tenure 0.002*** (4.461) CVs & FE YES YES YES YES Pro NO NO YES NO Ind × Year × Pro NO NO NO YES Observations 12,002 22,493 22,504 21,063 r2_a 0.294 0.299 0.300 0.302 F 12.927 26.613 25.782 22.353 Notes: Table 4 reports the analyses in Sections 4.3.2 and 4.3.3, which address potential endogeneity stemming from sample selection bias and omitted variable concerns. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. TABLE 5 Controlling for other regional /historical cultures (1) (2) (3) (4) Dependent variable= Similarity MGC 0.003** 0.004*** 0.002* 0.002** (2.335) (2.797) (1.928) (2.167) Confu -0.004 -0.003 (-1.062) (-0.674) MGC × Confu 0.004 (1.551) Clan 0.001 (0.200) Clan -0.004 (-0.774) MGC × Clan -0.007* (-1.911) Constant 1.011*** 1.007*** 1.014*** 1.013*** (106.466) (118.101) (113.208) (113.774) CVs & FE YES YES YES YES Observations 22,504 22,504 20,230 20,230 r2_a 0.298 0.298 0.300 0.300 F 27.442 25.374 26.245 24.335 Notes: Table 5 presents the test corresponding to Section 4.3.4. Following Leventis et al. (2024), who highlight the importance of controlling for other informal institutions beyond the variable of interest in studies of informal institutions and accounting outcomes, we additionally include the interaction terms between MGC and other regional cultures. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. TABLE 6 Alternative Measure of MGC (1) (2) (3) (4) R =30km R =50km R =150km R =300km Dependent variable= Similarity R 0.001** 0.001* 0.001*** 0.001*** (2.384) (1.828) (4.585) (5.515) CVs & FE YES YES YES YES Observations 22,504 22,504 22,504 22,504 r2_a 0.298 0.298 0.298 0.299 F 29.583 29.384 30.693 31.334 Notes: Table 6 reports the tests in Section 4.3.5, where alternative measures of MGC are employed, and the conclusions remain robust. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. TABLE 7 Cross-sectional Tests: Business groups (1) (2) (3) (4) Group =1 Group =0 SOE =1 SOE =0 Dependent variable= Similarity MGC -0.001 0.004*** 0.001 0.003** (-0.776) (3.487) (0.695) (2.476) CVs & FE Yes Yes Yes Yes N 7,731 14,503 9,614 12,890 R-squared 0.290 0.305 0.298 0.288 F 5.563 21.748 4.982 17.545 Notes: Table 7 presents the cross-sectional tests in Section 5.1 regarding business groups. Group indicates whether a sample firm belongs to a group with multiple listed entities, while SOE denotes whether controlling ownership lies with state-owned capital. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. TABLE 8 Moderating effects: Independent director centrality, marketization and Micro-level organizational culture (1) (2) (3) (4) Dependent variable= Similarity MGC 0.002** 0.006*** 0.003*** 0.004*** (2.323) (4.357) (3.540) (3.861) IDNet 0.003*** (5.000) MGC × IDNet -0.002 (-0.780) Market -0.000 (-1.226) MGC × Market 0.001*** (3.492) Cooperate 0.003*** (6.616) MGC × Cooperate -0.003** (-2.375) Compete 0.007*** (10.932) MGC × Compete -0.005*** (-2.973) CVs & FE Yes Yes Yes Yes Observations 21,957 22,058 22,504 22,504 r2_a 0.298 0.296 0.300 0.304 F 26.561 25.148 29.157 34.894 Notes: Table 8 reports the moderation tests in Sections 5.2 and 5.3. We find that director network centrality does not alter the main effect, regional marketization strengthens it, and micro-level organizational culture weakens it. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. TABLE 9 Economic Consequences: Market Reactions to Low-Quality Disclosure (1) (2) (3) (4) (5) (6) NCSKEW t+1 DUVOL t+1 SYN t+1 Similarity 0.350*** 0.258*** 0.079** (2.609) (2.873) (2.460) Similarity_hat -10.962 0.199 -7.773*** (-1.394) (0.038) (-4.167) CVs & FE Yes Yes Yes Yes Yes Yes Observations 19,771 19,771 19,771 19,771 19,771 19,771 r2_a 0.029 0.029 0.032 0.032 0.371 0.371 F 10.729 10.230 9.909 9.102 189.325 190.736 Notes: Table 9 presents the tests in Section 5.4, which investigate how investors perceive seemingly low-quality MD&A disclosures. The results indicate that boilerplate MD&A narratives generally provoke adverse market responses, including elevated crash risk and greater synchronicity. However, when such disclosures originate from firms more strongly shaped by MGC, they do not generate such reactions. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively. Additional Declarations No competing interests reported. Supplementary Files APPENDIX1Definitionofvariables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8821313","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":618746446,"identity":"a0adbd76-59b9-40af-8754-d9c1f0006b21","order_by":0,"name":"Zixuan Zhuang","email":"","orcid":"","institution":"Suqian University","correspondingAuthor":false,"prefix":"","firstName":"Zixuan","middleName":"","lastName":"Zhuang","suffix":""},{"id":618746447,"identity":"a44e1dea-dbbb-472a-a2b6-7f555a867de2","order_by":1,"name":"Qianyi Zhang","email":"","orcid":"","institution":"Suqian University","correspondingAuthor":false,"prefix":"","firstName":"Qianyi","middleName":"","lastName":"Zhang","suffix":""},{"id":618746448,"identity":"4b6543c5-6a73-46b6-81d5-e68da6bdd995","order_by":2,"name":"Zhongrong Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYPCC/3L8zMyHH5CihdlYsp0tzYAkLYkG53kUJIhSa3D+7MPHBb/YEowP8zAYMNTYRBPUItlw3Nh4Zh9Pntlh3gMPGI6l5TYQ0sLP2MYmzdsjUWx2mC/BgLHhMGEtbMxsIC0GiZubeQwkiNLCzwbUwvMjIXEDM7FaJHvYmI15Gw4YSxwGBnICMX4xOH+M8THPnwNy/P2HDz/4UGNDWAsYMLZBGQlEKQeDP8QrHQWjYBSMghEIAEyDOAfutn6MAAAAAElFTkSuQmCC","orcid":"","institution":"Suqian University","correspondingAuthor":true,"prefix":"","firstName":"Zhongrong","middleName":"","lastName":"Xu","suffix":""},{"id":618746449,"identity":"8b327340-27f2-49f2-bd0c-176648560298","order_by":3,"name":"Haozhou Yin","email":"","orcid":"","institution":"Chinese Academy of Fiscal Science","correspondingAuthor":false,"prefix":"","firstName":"Haozhou","middleName":"","lastName":"Yin","suffix":""}],"badges":[],"createdAt":"2026-02-08 11:56:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8821313/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8821313/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106994311,"identity":"732c8787-acf0-45d3-a806-32d2770ae5e3","added_by":"auto","created_at":"2026-04-15 15:07:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1612588,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8821313/v1/a0ab0edd-22f7-44ba-a309-03c0913101e6.pdf"},{"id":106638316,"identity":"1ad3d43c-cbde-40c5-b48e-d6f624805b20","added_by":"auto","created_at":"2026-04-10 17:13:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19476,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIX1Definitionofvariables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8821313/v1/2ee080a77e010fd03cbef724.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Merchant Guild Culture and MD\u0026A Disclosure Quality: Transparency versus Reticence","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eHigh-quality disclosure constitutes the bedrock of efficient capital markets. In environments characterized by incomplete information flows, firms enhance market efficiency by supplying investors with credible and timely information. The Management Discussion and Analysis (MD\u0026amp;A) section, a central component of listed firms\u0026rsquo; financial reports, plays a pivotal role in transmitting decision-useful information to investors (Bryan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Cole \u0026amp; Jones, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Through narratives about current financial performance, major events, and forward-looking statements, MD\u0026amp;A reduces information asymmetry in a transparent and accessible manner, offering investors essential insights for evaluating firm value and risk.\u003c/p\u003e \u003cp\u003eSince the mandatory introduction of MD\u0026amp;A reporting, boilerplate disclosure\u0026mdash;where annual reports are strikingly similar to those of the previous year\u0026mdash;has remained widespread (Brown \u0026amp; Wu, 2011). China is no exception. Critics contend that such disclosures embody insufficient informational content (Brown \u0026amp; Wu, 2011). Prior studies reveal that MD\u0026amp;A reports lacking substantive content invite regulatory scrutiny, trigger adverse market reactions (Qian \u0026amp; Zhu, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), impair capital market efficiency, and heighten stock price crash risk (Meng et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Yet, a critical question remains unresolved: should boilerplate disclosure be condemned as an unethical practice warranting strict regulation? Evidence from Lu et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) suggests otherwise. They document that, in China, information demand is jointly shaped by a broad set of non-arm length stakeholders rather than solely by external investors, highlighting a stakeholder-oriented governance model that contrasts sharply with the shareholder-centric system in the U.S.\u003c/p\u003e \u003cp\u003eInspired by Lu et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the new institutional economics literature, we argue that examining the institutional origins of MD\u0026amp;A disclosure provides a promising lens of inquiry. Economic development unfolds through the interplay between formal and informal institutions. Informal institutions, being spontaneous, functional, enduring, and deeply rooted, often exert greater influence than formal rules (North, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Williamson, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). They leave a profound imprint on corporate governance, accounting practices, and financial outcomes (see Habib et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Leventis et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Merchant guild culture (MGC), as a quintessential informal institution, embodies region-specific norms and values forged through centuries of commercial activity. Its enduring legacy continues to shape China\u0026rsquo;s economic trajectory, fostering robust growth, entrepreneurial dynamism, and social capital accumulation.\u003c/p\u003e \u003cp\u003eTheoretically, we posit that MGC shapes corporate disclosure through two distinct mechanisms. On the one hand, its cultural core of integrity may dampen managers\u0026rsquo; incentives to conceal information, thereby enhancing the information content of MD\u0026amp;A. On the other hand, its stakeholder-oriented values and the doctrine of moderation may encourage managers to pursue a more conservative disclosure strategy. In practice, disclosure decisions require managers to strike a delicate balance between safeguarding proprietary information and maintaining transparency\u0026mdash;excessive disclosure could undermine the interests of key stakeholders, such as shareholders and employees, thereby creating tension with MGC\u0026rsquo;s underlying values. Using panel data from Chinese non-financial listed firms over the period 2010\u0026ndash;2023, we find empirical support for the latter mechanism: firms more deeply influenced by MGC tend to provide MD\u0026amp;A disclosures with lower informational content. We further explore cross-sectional tests, moderating effects, and economic outcomes on capital market. Taken together, our evidence grounded in Chinese traditional culture suggests that boilerplate MD\u0026amp;A texts are not simply the product of managerial obfuscation but rather a strategic disclosure practice aimed at protecting stakeholder interests. This aligns with Lu et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u0026rsquo;s argument that managerial behavior in China is collectively shaped by stakeholders. Our findings, therefore, challenge the prevailing view that template-style disclosure constitutes an unethical practice requiring strict regulatory intervention.\u003c/p\u003e \u003cp\u003eThis study makes three key contributions. First, we enrich the literature on the role of MGC in shaping corporate behavior. Prior research has predominantly explored its influence on charitable giving (Kanagaretnam et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), financing costs and structures (Wang, 2022; Xiu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Weng et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), cash holdings (Wang et al., 2024), and innovation (Wu and Wan, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, we focus specifically on the informational content of MD\u0026amp;A disclosures, highlighting how MGC affects a core component of firms\u0026rsquo; financial reporting. Our goal is to shed light on the protection of both current and prospective investors\u0026rsquo; interests. Particularly relevant to our study is Zhang et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who document that managers influenced by MGC tend to respond evasively in earnings calls so as not to reveal sensitive business information\u0026mdash;an intentional strategy to safeguard existing investors. By quantifying formal MD\u0026amp;A texts, we extend Zhang et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u0026rsquo;s insights from oral managerial disclosure and provide robust complementary evidence.\u003c/p\u003e \u003cp\u003eSecond, we contribute to the literature on the determinants of MD\u0026amp;A information content by incorporating a cultural perspective. Existing studies suggest that MD\u0026amp;A disclosure quality\u0026mdash;commonly proxied by vertical textual similarity\u0026mdash;is linked to CEOs\u0026rsquo; prior secretary experience (Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), managerial earnings manipulation (Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), corporate innovation strategies (Zhang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), auditor turnover and the reporting of key audit matters (Ge et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xie \u0026amp; Li, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), as well as the influence of well-connected institutional investors and independent directors (Lu, 2022; Zhu \u0026amp; Ge, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Yet, research on regional culture and other informal institutions as determinants of disclosure remains scarce. Accordingly, we not only broaden the scope of inquiry into MGC\u0026rsquo;s influence but also provide a fresh theoretical lens for examining how cultural underpinnings shape corporate disclosure practices.\u003c/p\u003e \u003cp\u003eThird, we shed light on the logic behind conservative disclosure practices in competitive market settings. Prior research on innovation disclosure suggests that when firms face intense product-market rivalry, they strategically scale back the amount of information disclosed in order to safeguard innovation outcomes and preserve competitive advantages (Cao et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kankanhalli et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such withholding strategies also send discernible risk signals to external stakeholders (Oh et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Building on MGC\u0026mdash;an informal institution consistently shown to enhance corporate governance and accounting outputs\u0026mdash;we suggest that limited incremental disclosure should not be equated with deliberate concealment; rather, it reflects a rational managerial choice aimed at navigating competition and protecting proprietary information.\u003c/p\u003e \u003cp\u003eThe remainder of the paper unfolds as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e introduces the theoretical framework and lays out our hypotheses. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e details the empirical research design. Sections \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Sec21\" class=\"InternalRef\"\u003e5\u003c/span\u003e present the main findings from hypothesis testing as well as a series of additional analyses, including heterogeneity across firms, moderating mechanisms, and examinations of economic consequences. Section \u003cspan refid=\"Sec26\" class=\"InternalRef\"\u003e6\u003c/span\u003e offers concluding remarks.\u003c/p\u003e"},{"header":"2 Institutional background and hypothesis development","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The Historical Roots of MGC in China and Its Contemporary Influence\u003c/h2\u003e \u003cp\u003eMerchant guilds were merchant groups formed through bonds of geography, kinship, and trade. Traditional Chinese business guilds originated during the Song Dynasty and flourished in the Ming and Qing Dynasties. Existing literature generally classifies them into the ten major guilds, including the Ningbo, Longyou, Guangdong, Shanxi, Huizhou, Shaanxi, Fujian, Jiangyou, Dongting, and Shandong guilds (Du et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang \u0026amp; Zhang, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The rise of merchant guilds greatly facilitated economic prosperity at that time and, in turn, nurtured the formation of MGC.\u003c/p\u003e \u003cp\u003eIn ancient China, MGC was deeply rooted in Confucianism, reflecting both the inheritance and development of Confucian thought (Du et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Confucianism emphasized the \u0026ldquo;Five Constant Virtues\u0026rdquo;\u0026mdash;benevolence, righteousness, propriety, wisdom, and trustworthiness\u0026mdash;with \u003cem\u003exin\u003c/em\u003e (trustworthiness) regarded as the cornerstone of social interaction and economic activity. Under the influence of Confucian values, MGC elevated honesty and trustworthiness as core principles (Zhang \u0026amp; Zhang, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). This value system encouraged guild members to uphold integrity and honor contracts, thereby enhancing their moral and ethical standards (Weng et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and was continuously reinforced and transmitted through business practice. Over time, guild members developed behavioral norms centered on integrity, which not only standardized commercial practices but also supported the expansion and prosperity of business guilds. The cultural emphasis on honesty embedded in MGC was gradually accepted by a broader set of stakeholders\u0026mdash;including customers, suppliers, shareholders, and creditors\u0026mdash;helping to create a social atmosphere of trust and further reinforcing the transmission and evolution of MGC (Du et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang \u0026amp; Hu, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExisting literature shows that MGC, through its unique ethical system, exerts a systematic influence on contemporary firms. Du et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) were the first to introduce MGC into the framework of regional culture and firm-level behavior. Their study suggests that the integrity values shaped by MGC help alleviate principal\u0026ndash;agent conflicts, as reflected in lower agency costs and reduced cash holdings (Du et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al., 2024). Managers guided by the principle of integrity are regarded as loyal guardians of public interest; accordingly, firms more deeply influenced by MGC can obtain lower-cost financing and greater financial support from supply chain alliances (Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xiu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Weng et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Social responsibility is another important quality emphasized by MGC. Firms influenced by MGC are more likely to engage in charitable giving and at a higher scale (Kanagaretnam et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Managers influenced by MGC tend to provide vague disclosures during performance briefings in order to avoid revealing trade secrets that may undermine investor returns (Zhang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, studies on innovation indicate that MGC also drives firms\u0026rsquo; strategic behavior (Wu \u0026amp; Wan, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The Impact of MGC on the Information Content of MD\u0026amp;A Disclosures\u003c/h2\u003e \u003cp\u003eAccording to embeddedness theory, individuals are inherently situated within social organizations and cannot act independently of their social context. Consequently, their behaviors are largely constrained by prevailing ideologies and social norms (Granovetter, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). We argue that the effect of MGC on the information content of MD\u0026amp;A disclosures is primarily channeled through individual ideologies. In particular, the core tenets of MGC\u0026mdash;sincerity in interpersonal relations, integrity in business conduct, and the Confucian principle of moderation\u0026mdash;subtly mold how corporate leaders and employees perceive themselves, organizations, and the broader society.\u003c/p\u003e \u003cp\u003eOn the one hand, the quality and content of corporate disclosures are shaped by multiple factors, with managerial incentives and the stability of corporate strategy being particularly critical. A primary reason for low information content lies in managers\u0026rsquo; opportunistic motives. Driven by private interests, managers may deliberately withhold certain financial information, thereby reducing the informativeness of financial reports. Such asymmetry results in pronounced disparities in the quantity and quality of information accessible to different parties in a transaction. Investors often face restricted access to lower-quality information, which exacerbates information asymmetry. This imbalance undermines investor interests, reduces market efficiency, elevates transaction risks, and in extreme cases, may even trigger market failure. Therefore, from a managerial perspective, the core values of honesty and integrity espoused by MGC are of critical importance. Integrity, as a foundational principle of business, obliges managers to refrain from concealing information and to provide truthful and comprehensive disclosures. Such practices enhance disclosure quality, particularly by increasing the information content of MD\u0026amp;A.\u003c/p\u003e \u003cp\u003eIn addition, integrity underscores the firm\u0026rsquo;s responsibility to external stakeholders, encouraging managers to prioritize transparent communication with external investors, including potential investors. As a key channel of such communication, MD\u0026amp;A disclosure becomes more effective when enriched with substantive information, thereby alleviating information asymmetry, attracting outside investors, and safeguarding stakeholder interests.\u003c/p\u003e \u003cp\u003eSecond, from the perspective of corporate strategy, when a firm\u0026rsquo;s strategic orientation is stable and its operations remain steady, its fundamentals typically show little variation. Yet, as an informal institution rooted in Confucian philosophy, MGC strengthens social trust by embedding relational networks and Confucian business ethics into commercial practices. The guild-based elements of MGC promote corporate innovation by alleviating financing constraints and curbing earnings management (Wu and Wan, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such innovations can generate significant internal developments, which in turn necessitate strategic adjustments. Under these circumstances, firms are more likely to enrich the content of their MD\u0026amp;A disclosures, thereby enhancing informativeness and offering stakeholders more decision-relevant insights.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1a\u003c/b\u003e: \u003cb\u003eCeteris paribus, firms with greater MGC exposure are expected to disclose MD\u0026amp;A reports with higher information content.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOn the other hand, MGC places strong emphasis on protecting the interests of stakeholders. Prior studies show that business communities deeply influenced by MGC often display greater tendencies toward philanthropic giving once they accumulate wealth, reflecting the Confucian virtue of benevolence (Kanagaretnam et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This cultural trait may further reinforce managers\u0026rsquo; inclination to avoid disclosure risks. While MD\u0026amp;A disclosures provide valuable information to investors, they also carry the potential threat of revealing proprietary or commercially sensitive information.\u003c/p\u003e \u003cp\u003eA considerable body of literature suggests that the doctrine of the mean (\u003cem\u003ezhongyong\u003c/em\u003e) represents a fundamental principle by which Chinese individuals approach problem-solving and decision-making (e.g., Ip, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lam, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ning et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a central tenet of Confucian philosophy, \u003cem\u003ezhongyong\u003c/em\u003e stresses moderation and the search for balance between extremes (Ames and Hall, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This cognitive and behavioral orientation has profound implications for business practices and is deeply embedded in MGC. Under the influence of this doctrine, Chinese corporate managers often avoid extreme behaviors in decision-making, preferring a balanced approach that simultaneously protects firm interests and mitigates risks.\u003c/p\u003e \u003cp\u003eApplied to information disclosure, managers shaped by MGC are less likely to engage in extreme transparency. Instead, they may adopt moderate disclosure strategies, limiting the information content of MD\u0026amp;A reports to maintain balance between satisfying investors\u0026rsquo; informational needs and safeguarding corporate secrets. Moreover, because MGC underscores the importance of protecting stakeholders, managers are particularly sensitive to the potential negative consequences of information leakage, such as the erosion of competitive advantages and potential harm to shareholders, employees, and other stakeholders. Consequently, firms influenced by MGC may strategically reduce the amount of substantive information in MD\u0026amp;A disclosures to mitigate corporate risks.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1b\u003c/b\u003e: \u003cb\u003eCeteris paribus, firms with greater MGC exposure are expected to disclose MD\u0026amp;A reports with Lower information content.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Research design","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data resource\u003c/h2\u003e \u003cp\u003eWe construct our sample using annual data of firms listed on the Shanghai and Shenzhen A-share markets from 2010 to 2023. After excluding firms in the financial sector, observations with missing values, and applying 1% winsorization at both tails, we obtain a final sample of 22,504 firm-year observations.\u003c/p\u003e \u003cp\u003eThe data are primarily obtained from various sub-databases of CSMAR (China Stock Market \u0026amp; Accounting Research Database). Specifically, the information content of MD\u0026amp;A reports is sourced from the \u0026ldquo;Management Discussion and Analysis Sentiment Analysis\u0026rdquo; module in the Research Database of Business Distress of Chinese Listed Firms, which employ the Latent Semantic Indexing (LSI) method to compute the cosine similarity between a firm\u0026rsquo;s current-year MD\u0026amp;A text and its prior-year MD\u0026amp;A text, thereby capturing the degree of textual similarity across time. The list of merchant guild origins is based on historical records documented in Zhang and Zhang (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), while Xiu et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) provide a more detailed enumeration from which we retrieve the corresponding geographical coordinates of each guild origin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Empirical model\u003c/h2\u003e \u003cp\u003eWe estimate the following baseline regression model to test the effect of MGC on the information quality of MD\u0026amp;A disclosures:\u003c/p\u003e \u003cp\u003e \u003cem\u003eSimilarity\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eMGC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e +\u003cem\u003eCVs\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e +\u003cem\u003eFE\u003c/em\u003e\u0026thinsp;+\u0026thinsp;ε\u003c/p\u003e \u003cp\u003e(1)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ei\u003c/em\u003e indexes firms and \u003cem\u003et\u003c/em\u003e denotes years. \u003cem\u003eSimilarity\u003c/em\u003e measures the textual overlap between a firm\u0026rsquo;s MD\u0026amp;A in year \u003cem\u003et\u003c/em\u003e and that in year \u003cem\u003et-1\u003c/em\u003e. \u003cem\u003eMGC\u003c/em\u003e captures the degree of a firm\u0026rsquo;s exposure to MGC. \u003cem\u003eCVs\u003c/em\u003e denote firm-level control variables drawn from financial characteristics, governance attributes, and MD\u0026amp;A textual properties. \u003cem\u003eFE\u003c/em\u003e refers to industry and year fixed effects. A significantly negative (positive) coefficient on \u003cem\u003eMGC\u003c/em\u003e provides support for Hypothesis H1a (H1b).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Variable measurement and Descriptive statistics\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Merchant guild culture\u003c/h2\u003e \u003cp\u003eFollowing the literature on MGC (Du et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kanagaretnam et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, 2024; Zhang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we construct a geographic proximity-based measure to capture the extent to which a firm is influenced by MGC. Specifically, we first collect the geographical coordinates of the origins of the ten historical merchant guilds and the headquarters locations of listed firms. We then calculate the spherical distance between each firm and all merchant guild origins using the formula adopted by Du et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The minimum distance between a firm and the nearest merchant guild origin is selected to proxy for the firm\u0026rsquo;s exposure to MGC. A smaller distance indicates greater influence of MGC. For ease of interpretation, we multiply the distance by \u0026minus;\u0026thinsp;1 so that higher values consistently indicate stronger MGC influence.\u003c/p\u003e \u003cp\u003eIt is worth noting that there exists an alternative firm-level measure of MGC influence. Instead of the \u0026ldquo;nearest distance\u0026rdquo; approach, one may draw a circle with the firm\u0026rsquo;s headquarters as the center and count the number of merchant guild origins located within the circle. Regions with more origins are more likely to have cultivated and retained MGC (Kanagaretnam et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xiu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While this approach captures the potential overlapping effects of multiple guilds, the choice of radius remains ambiguous, and hence we only report it as a robustness test rather than as the main measure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 MD\u0026amp;A disclosure quality\u003c/h2\u003e \u003cp\u003eConsistent with prior research on MD\u0026amp;A disclosure quality (e.g., Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xie and Li, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we use the longitudinal similarity of MD\u0026amp;A texts (i.e., the cosine similarity between period \u003cem\u003et\u003c/em\u003e and period \u003cem\u003et\u0026ndash;1\u003c/em\u003e disclosures) as an inverse proxy for information content. A higher similarity score indicates greater overlap between the current and prior year\u0026rsquo;s MD\u0026amp;A reports, implying that the current MD\u0026amp;A provides fewer incremental disclosures. Economically, this corresponds to lower-quality disclosure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Control variables\u003c/h2\u003e \u003cp\u003eIn line with Du et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Meng et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Zhang et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we control for firm characteristics at three levels.\u003c/p\u003e \u003cp\u003e(1) Financial characteristics: firm size (\u003cem\u003eSizeR\u003c/em\u003e), financial leverage (\u003cem\u003eLev\u003c/em\u003e), profitability (\u003cem\u003eROA\u003c/em\u003e), and cash flow (\u003cem\u003eCashflow\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e(2) Corporate governance characteristics: ownership concentration (\u003cem\u003eTop\u003c/em\u003e), Independence of the Board (\u003cem\u003eID\u003c/em\u003e), CEO duality (\u003cem\u003eDual\u003c/em\u003e), and state ownership (\u003cem\u003eSOE\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e(3) MD\u0026amp;A textual characteristics: disclosure tone (\u003cem\u003ePosi\u003c/em\u003e) and readability (\u003cem\u003eWord\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eDetailed definitions of all variables are provided in Appendix 1. Appendix 2 reports the descriptive statistics of the variables defined above. The results are broadly consistent with those documented in prior studies.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Empirical result","content":"\u003ch2\u003e4.1 Univariate analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTo provide preliminary evidence on the relationship between the level of MGC influence and the information content of MD\u0026amp;A, we divide the sample into two groups based on the industry\u0026ndash;year median of MGC (\u003cem\u003eMGCdum\u003c/em\u003e) and examine the mean differences in other variables across groups. Table 1 reports that firms with greater MGC exposure (i.e., those located closer to historical merchant-guild origins) exhibit significantly higher longitudinal similarity in MD\u0026amp;A disclosures\u0026mdash;implying less incremental textual information\u0026mdash;lending support to Hypothesis H1b\u003c/p\u003e\n\u003cp\u003eIn addition, we observe that firms with stronger exposure to MGC tend to be smaller in size, less leveraged, more profitable, and better positioned in terms of cash flows. These firms are also more likely to combine the roles of CEO and chairperson and are more likely to be non-state-owned enterprises. Furthermore, they display more negative sentiment and disclose longer MD\u0026amp;A reports. By contrast, ownership concentration and the proportion of independent directors do not differ systematically with MGC exposure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e[Insert Table 1 here]\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e4.2 Baseline regressions\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTable 2 presents the hypothesis testing results based on the specification of Eq. (1). Column (1) reports the univariate regression controlling for fixed effects, whereas Columns (2) through (4) progressively add controls for financial attributes, corporate governance features, and MD\u0026amp;A disclosure characteristics. Column (4) thus corresponds to the full Eq.(1). Across all specifications, the coefficient on MGC remains significantly positive at the 1% level (0.003 with t=4.303; 0.003 with t=4.012; 0.002 with t=2.779; 0.003 with t=4.366; and 0.002 with t=2.614).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEconomically, these results imply that stronger exposure to MGC is associated with greater vertical similarity in MD\u0026amp;A disclosures, thereby reducing their incremental information content and ultimately reflecting lower-quality disclosure. This evidence supports H1b, indicating that firms influenced more heavily by MGC tend to disclose less incremental information in MD\u0026amp;A, consistent with Zhang et al. (2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e[Insert Table 2 here]\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e4.3 Robustness checks\u003c/h2\u003e\n\u003cp\u003e4.3.1 Alternative Explanations\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA firm that operates in a steady manner without any new matters to report would naturally produce an MD\u0026amp;A that largely mirrors the previous year. Accordingly, the significant positive association between MGC and MD\u0026amp;A similarity might be interpreted differently: firms more deeply influenced by MGC may pursue inherently stable business models and strategies, thereby generating \u0026ldquo;no new information to disclose.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eTo test this possibility, we replace the dependent variable in Eq.(1) with the competitive strategy index of Hu et al. (2020) and the business strategy index of Bentley et al. (2013). Following Hu et al. (2020), we construct three measures: the proportion of differentiation-related words in MD\u0026amp;A reports (\u003cem\u003eDiff\u003c/em\u003e), the number of such words scaled by the number of cost-leadership (\u003cem\u003eDiff_Cost\u003c/em\u003e), and a binary variable indicating whether \u003cem\u003eDiff_Cost\u0026nbsp;\u003c/em\u003eexceeds the annual average (\u003cem\u003eStrategy_Diff\u003c/em\u003e). Higher values of these measures indicate a stronger preference for differentiation strategies. The business strategy index of Bentley et al. (2013) (\u003cem\u003eStrategy_Bent\u003c/em\u003e) captures strategic dynamics, with lower values reflecting greater stability. If the alternative explanation were valid, we would expect firms more influenced by MGC to avoid differentiation and to exhibit lower business strategy index values, consistent with a defensive posture.\u003c/p\u003e\n\u003cp\u003eThe results reported in Table 3. MGC loads significantly positively on the differentiation measures (0.014 with t=3.893; 0.032 with t=5.870; and 0.037 with t=4.109), while its coefficient on the business strategy index is insignificantly negative (-0.119 with t=-1.447). Taken together, these findings suggest that firms under stronger MGC influence are more inclined toward differentiation and not necessarily defensive strategies. This implies that such firms are likely to have new matters worth reporting. Hence, we find no evidence to support the alternative explanation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e[Insert Table 3 here]\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e4.3.2 Propensity Score Matching (PSM)\u003c/p\u003e\n\u003cp\u003eThe geographic distribution of listed firms is not random. Historical MGC origins, with their accumulated commercial experience and pro-business environment, may increase the likelihood of firms going public or motivate them to establish headquarters nearby. To mitigate endogeneity from such sample selection, we implement a PSM procedure. In particular, we use the \u003cem\u003eMGCdum\u003c/em\u003e from the univariate analysis as the treatment indicator, while including financial characteristics, corporate governance variables, and fixed effects from Eq.(1) as covariates. We then perform 1:1 nearest-neighbor matching to identify comparable control firms for those located closer to MGC origins. Column (1) of Table 4 presents the matched-sample results, which confirm that the significantly positive coefficient on MGC persists (0.003 with t = 2.381).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4.3.3 Omitted Variables\u003c/p\u003e\n\u003cp\u003eA potential concern for our baseline results is the possibility of omitted variables arising from auditor attributes and regional economic conditions. Prior research suggests that auditors, by virtue of their responsibility to verify firms\u0026rsquo; annual reports, may influence the information content of MD\u0026amp;A (Ge et al., 2020). To account for this, we incorporate three auditor-specific controls: BigN accounting firms (\u003cem\u003eBig4\u003c/em\u003e), changes in auditor or audit firm (\u003cem\u003eChange\u003c/em\u003e), and audit tenure \u003cem\u003e(Tenure\u003c/em\u003e). Column (2) of Table 4 shows that our main conclusion remains valid after including these controls (0.002 with t=2.205). Notably, and in contrast to Ge et al. (2020), we do not find significant evidence that auditor changes affect MD\u0026amp;A information content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further address potential bias from regional economic conditions, we introduce additional fixed effects, including provincial fixed effects (\u003cem\u003ePro\u003c/em\u003e) and joint industry\u0026ndash;year\u0026ndash;province fixed effects (\u003cem\u003eInd\u0026times;Year\u0026times;Pro\u003c/em\u003e). As reported in Columns (3) and (4) of Table 4, while statistical significance weakens somewhat, the coefficient on MGC remains significantly positive (0.005 with t=1.940; 0.007 with t=2.303).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e[Insert Table 4 here]\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4.3.4 Controlling for other regional /historical cultures\u003c/p\u003e\n\u003cp\u003eExisting research on the effects of informal institutions on accounting outcomes seldom controls for alternative informal institutions, raising the risk of omitted variable bias (Leventis et al., 2024). To mitigate this concern, we augment the baseline specification in Eq. (1) by incorporating \u003cem\u003eConfu\u003c/em\u003e and \u003cem\u003eClan\u003c/em\u003e, along with their interaction terms with \u003cem\u003eMGC\u003c/em\u003e. \u003cem\u003eConfu\u003c/em\u003e follows Du et al. (2015) and serves as a proxy for Confucian culture, while\u003cem\u003e\u0026nbsp;Clan\u003c/em\u003e is constructed at the city level, measured by the population share of the three largest surnames as an indicator of clan culture. Table 5 shows that the coefficients on Confucian and clan culture are insignificant; the interaction term \u003cem\u003eMGC\u0026times;Confu\u003c/em\u003e is likewise insignificant, whereas \u003cem\u003eMGC\u0026times;Clan\u003c/em\u003e is negative and significant at the 10% level.\u003c/p\u003e\n\u003cp\u003eCrucially, even after controlling for these two informal institutions and their interaction effects, the coefficient on \u003cem\u003eMGC\u003c/em\u003e remains significantly positive. This evidence indicates that our main results are robust when alternative regional historical cultures are taken into account.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e[Insert Table 5 here]\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4.3.5 Alternative Measure of \u003cem\u003eMGC\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs outlined in our variable definitions, an alternative proxy for MGC is the count of historical birthplace sites located within a given distance (\u003cem\u003eR\u003c/em\u003e) from a firm\u0026rsquo;s headquarters. Table 6 presents regression results using cutoffs of 30, 50, 150, and 300 km. Across all specifications, the coefficients on \u003cem\u003eR\u003c/em\u003e are significantly positive (0.001 with t=2.384, 1.828, 4.585, and 5.515, respectively), thereby providing consistent support for H1b.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e[Insert Table 6 here]\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"5 Further tests","content":"\u003ch2\u003e5.1 Cross-sectional Tests: Business groups\u003c/h2\u003e\n\u003cp\u003eRelational transaction modes constitute a distinctive governance mechanism in China (Li, 2017). Within this framework, two micro-level firm attributes are particularly salient: business group affiliation and state ownership. In emerging markets, business groups often serve as substitutes for underdeveloped external institutions, such as weak capital markets or financing frictions (Poczter, 2018). During the mid-1980s, the Chinese government\u0026mdash;aiming, among other things, to enhance SOE competitiveness under economic liberalization\u0026mdash;adopted a series of policy initiatives to foster business group development (He et al., 2013). In subsequent decades, numerous private firms also evolved into business groups with multiple listed subsidiaries. By contrast, state ownership remains the most significant source of heterogeneity in Chinese firm studies, since managers of SOEs are typically appointed by the State-owned Assets Supervision and Administration Commission rather than recruited through market channels. Accordingly, SOE strategies, financial policies, and disclosure practices are less shaped by managerial discretion. In both contexts, managerial choices primarily accommodate the requirements of superior affiliates\u0026mdash;including governmental directives\u0026mdash;rather than being fully autonomous. As such, managerial orientations are less likely to be molded by broader social value norms.\u003c/p\u003e\n\u003cp\u003eBased on this reasoning, we expect MD\u0026amp;A disclosure strategies of business group affiliates and SOEs to be largely unaffected by social environments such as MGC. The cross-sectional tests reported in Table 7 confirm this view: for firms within business groups or SOEs, the coefficient on \u003cem\u003eMGC\u003c/em\u003e is statistically insignificant, while the main effect emerges more strongly and robustly among stand-alone firms and non-SOEs (0.004 with t=3.487; 0.003 with t=2.476). These findings align well with our expectations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e[Insert Table 7 here]\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e5.2 Moderating effects: Independent director centrality and marketization\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eIndependent directors carry supervisory responsibilities for disclosure, particularly when serving on audit committees. Prior research indicates that the network centrality of independent directors is associated with the information richness of managerial reporting: directors who occupy more central network positions\u0026mdash;signifying greater authority and influence\u0026mdash;tend to enhance MD\u0026amp;A informativeness (Lu et al., 2022). Building on Lu et al. (2022), we incorporate independent director centrality (\u003cem\u003eIDNet\u003c/em\u003e) into Eq.(1). Results reported in Column (1) of Table 8 show that the interaction term \u003cem\u003eMGC\u0026times;IDNet\u003c/em\u003e is insignificant and negative (-0.002 with t=-0.780). This suggests that managers are not intentionally lowering MD\u0026amp;A informativeness to conceal information; otherwise, authoritative independent directors would have been expected to mitigate the main effect. Furthermore, \u003cem\u003eIDNet\u003c/em\u003e itself is significantly positive (0.003 with t=5.000), implying that firms with more centrally positioned independent directors exhibit greater longitudinal similarity in MD\u0026amp;A. This finding diverges from the evidence reported in Lu et al. (2022).\u003c/p\u003e\n\u003cp\u003eThe interplay between formal and informal institutions represents a core theme in cultural research at the firm level (Williamson, 2000; Du et al., 2017). Du et al. (2017) find that higher regional marketization attenuates the constraining role of MGC on agency costs. Following their approach, we introduce a provincial marketization index (\u003cem\u003eMarket\u003c/em\u003e) developed by Fan et al. (2011) into Eq.(1) to investigate how marketization moderates the relationship between MGC and MD\u0026amp;A informativeness. Results in Column (2) of Table 8 indicate that the interaction term \u003cem\u003eMGC\u0026times;Market\u003c/em\u003e is significantly positive (0.001 with t=3.492). This implies that firms located in regions with higher levels of marketization, when influenced by MGC, are more inclined to reduce MD\u0026amp;A informativeness. Such evidence is consistent with our theoretical argument that firms adopt this disclosure strategy to hedge against competitive risks.\u003c/p\u003e\n\u003cp\u003eIn sum, the analyses in this section furnish additional support for our main conclusions. Were the high-level vertical similarity of MD\u0026amp;A under MGC merely a manifestation of managerial information hoarding, one would anticipate the effect to be mitigated by director network centrality and regional marketization\u0026mdash;through the internal monitoring role of influential independent directors and the external pressures of more developed markets. Yet, the evidence does not bear out this information-concealment hypothesis.\u003c/p\u003e\n\u003ch2\u003e5.3\u0026nbsp;Moderating effect: Micro-level organizational culture\u003c/h2\u003e\n\u003cp\u003eThe emergence of merchant guilds during the Ming and Qing dynasties was shaped by intensified market competition. To respond, ancient Chinese merchants increasingly relied on collective action, which fostered a cooperative ethos of mutual support and joint business ventures (Du et al., 2017; Wang et al., 2022; Zhang et al., 2025). Thus, the essence of MGC reflects a coexistence of cooperative and competitive values. These historically transmitted ideas have been internalized in modern firms as \u0026ldquo;micro-level cultures\u0026rdquo; ,namely organizational cultures. We posit that, because organizational culture is more closely tied to corporate behavior, it weakens the extent to which regional cultural contexts influence firms\u0026rsquo; disclosure practices.\u003c/p\u003e\n\u003cp\u003eFollowing Pan et al. (2019), we use measures of cooperative culture (\u003cem\u003eCooperate\u003c/em\u003e) and competitive culture (\u003cem\u003eCompete\u003c/em\u003e) as moderating variables in Eq.(1). Results reported in Columns (3) and (4) of Table 8 show that the interaction terms \u003cem\u003eMGC\u0026times;Cooperate\u003c/em\u003e and \u003cem\u003eMGC\u0026times;Compete\u003c/em\u003e are significantly negative (-0.003 with t=-2.375; -0.005 with t=-2.973), while the main effect of MGC remains significantly positive (0.003 with t=3.540; 0.004 with t=3.861). These findings support our prediction: when firms exhibit clear value orientations and adhere to internally embedded organizational cultures, the influence of MGC on MD\u0026amp;A informativeness diminishes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e[Insert Table 8 here]\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e5.4 Economic Consequences: Market Reactions to Low-Quality Disclosure\u003c/h2\u003e\n\u003cp\u003eInvestors in capital markets consistently demand high-quality disclosure. Prior research shows that MD\u0026amp;A informativeness is negatively related to subsequent stock price crash risk (Meng et al., 2017). A central question, therefore, is whether market participants can discern managers\u0026rsquo; strategic withholding of information designed to safeguard proprietary knowledge. Their reactions provide a valuable perspective on how such disclosure strategies are evaluated.\u003c/p\u003e\n\u003cp\u003eBuilding on Di Giuli and Laux\u0026rsquo;s (2022) empirical framework, we develop following models to test market responses to MGC-driven low-informativeness disclosure:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;NCSKEW/ DUVOL/ SYN\u003csub\u003ei,t+1\u003c/sub\u003e=\u003c/em\u003e\u003cem\u003e\u0026beta;\u003csub\u003e0\u003c/sub\u003e+\u003c/em\u003e\u003cem\u003e\u0026beta;\u003csub\u003e1\u003c/sub\u003eSimilarity\u003csub\u003ei,t\u003c/sub\u003e+CVs\u003csub\u003ei,t\u003c/sub\u003e+FE+\u003c/em\u003e\u003cem\u003e\u0026epsilon;\u003csub\u003ei,t\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/sub\u003e\u003c/em\u003e(2)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;NCSKEW/ DUVOL/ SYN\u003csub\u003ei,t+1\u003c/sub\u003e=\u003c/em\u003e\u003cem\u003e\u0026beta;\u003csub\u003e0\u003c/sub\u003e+\u003c/em\u003e\u003cem\u003e\u0026beta;\u003csub\u003e1\u003c/sub\u003eSimilarity_hat\u003csub\u003ei,t\u003c/sub\u003e+CVs\u003csub\u003ei,t\u003c/sub\u003e+FE+\u003c/em\u003e\u003cem\u003e\u0026epsilon;\u003csub\u003ei,t\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/sub\u003e\u003c/em\u003e(3)\u003c/p\u003e\n\u003cp\u003eFirst, we estimate Eq.(2) to test the direct market responses triggered by low-informativeness MD\u0026amp;A disclosure. The dependent variables capture market outcomes, namely stock price crash risk (\u003cem\u003eNCSKEW\u003c/em\u003e and \u003cem\u003eDUVOL\u003c/em\u003e) and stock price synchronicity (\u003cem\u003eSYN\u003c/em\u003e), while the key explanatory variable mirrors the dependent variable of Model (1). Crash risk is computed following Kim et al. (2011), and synchronicity is measured following Durnev et al. (2003). Second, we estimate Eq.(3) to examine how the capital market interprets the low-informativeness MD\u0026amp;A disclosure of firms influenced by MGC. The difference between Eq.(3) and Eq.(2) lies in the explanatory variable. In Eq.(3), the explanatory variable (\u003cem\u003eSimilarity_hat\u003c/em\u003e) is the predicted value from Eq.(1), which isolates the portion of MD\u0026amp;A informativeness attributable to MGC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults reported in Columns (1), (3), and (5) of Table 9 indicate that\u003cem\u003e\u0026nbsp;Similarity\u003c/em\u003e is significantly positively associated with stock price crash risk (0.350 with t=2.609; 0.258 with t=2.873) and stock price synchronicity (0.079 with t=2.460). This suggests that boilerplate MD\u0026amp;A disclosures trigger adverse market responses\u0026mdash;greater crash risk and stronger synchronicity\u0026mdash;because investors cannot obtain decision-useful information from them. In contrast, Columns (2), (4), and (6) reveal that \u003cem\u003eSimilarity_hat\u003c/em\u003e no longer exhibits significant negative effects on future stock price crash risk (-10.962 with t=-1.394; 0.199 with t=0.038) and is in fact negatively related to synchronicity (-7.773 with t=-4.167). These results imply that investors are capable of recognizing managers\u0026rsquo; strategic non-disclosure of incremental information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e[Insert Table 9 here]\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"6 Conclusion and Discussion","content":"\u003cp\u003eThe MGC, with its dual emphasis on integrity and stakeholder protection, shapes corporate non-financial disclosure behavior in two opposing ways. On one hand, its value of integrity encourages managers to disclose information openly and honestly. On the other hand, in seeking to mitigate competitive risks and safeguard investor interests, responsible managers may be reluctant to provide full disclosure for fear of revealing trade secrets. Consequently, managers often adopt a cautious, even reticent, approach to disclosure. Drawing on annual data from Chinese non-financial listed firms from 2010 to 2023, we find consistent empirical evidence that stronger exposure to MGC leads managers to limit the information conveyed in their MD\u0026amp;A reports, resulting in more boilerplate disclosures. These results remain robust across a series of robustness checks.\u003c/p\u003e \u003cp\u003eWe then conducted several additional analyses. First, taking into account China\u0026rsquo;s distinctive relational transaction environment and firm-level heterogeneity, we find that the main effect is particularly pronounced for fully listed firms and non\u0026ndash;SOEs. Second, we examine the moderating roles of independent director centrality, regional marketization, and firm-level organizational culture. Our findings show that independent director centrality has little influence on the main relationship, whereas the effect intensifies in regions with higher levels of marketization but is attenuated in firms with strong organizational cultures. Finally, we explore the market consequences of low-information-content MD\u0026amp;A disclosures shaped by MGC. While templated disclosures typically provoke adverse outcomes\u0026mdash;such as heightened stock price crash risk and stronger price synchronicity\u0026mdash;we find that when such disclosures are made by firms more deeply embedded in MGC, they do not elicit negative reactions. In conclusion, our evidence indicates that templated disclosure should not be viewed as an unethical practice warranting strict regulatory intervention.\u003c/p\u003e \u003cp\u003eThis study carries both theoretical and practical implications. On the theoretical front, it advances the literature on MGC and contemporary corporate behavior, extends the framework of cultural economics, and offers a fresh lens through which to understand how cultural forces shape firms\u0026rsquo; disclosure practices. Practically, our results inform investors\u0026rsquo; decision-making by shedding light on the logic behind ambiguous disclosure practices. When confronted with two equally conservative or opaque MD\u0026amp;A reports, investors should scrutinize the broader institutional setting and cultural context of the firm. In contrast to companies operating in highly marketized regions with transparent governance, firms more deeply embedded in MGC may disclose less due to the influence of informal governance mechanisms. Thus, investors are advised to incorporate cultural background, governance structure, and local marketization levels into their evaluations to make better-informed decisions and enhance market efficiency.\u003c/p\u003e \u003cp\u003eBeyond investors, our evidence also carries important implications for regulators. Firms deeply shaped by MGC often resort to ambiguous MD\u0026amp;A disclosure as a means of safeguarding investor interests. Yet such behavior does not provoke negative market reactions; rather, it may stimulate investment by aligning with culturally conditioned investor expectations. This finding resonates with the argument that mandating extensive nonfinancial disclosures does not necessarily serve investors well. Prior studies further suggest that compulsory disclosure standards may constrain managers\u0026rsquo; ability to convey valuable signals. Against this backdrop, regulators should take cultural contingencies into account, adopting differentiated supervisory approaches toward firms strongly influenced by MGC. Such policies should safeguard the quality of essential disclosures while allowing sufficient flexibility to avoid the pitfalls of one-size-fits-all regulation. Likewise, assessments of disclosure practices should resist oversimplification. Users of accounting information are encouraged to adopt a nuanced perspective: while excessive disclosure can undermine investor welfare, limited ambiguity may be reasonable in certain contexts. For firms whose disclosure is shaped by MGC, the goal should be to both regulate and respect cultural diversity, striking an optimal balance between tradition and modernity to improve market efficiency.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe author declares no competing interests.\u003c/p\u003e\n\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003ch2\u003eInformed consent\u003c/h2\u003e\n\u003cp\u003eThe manuscript is approved by all authors for publication.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eZ.Z. curated the data, validated the research findings, and created visualizations. Q.Z. provided necessary resources, developed software tools, and assisted with data collection. Z.X. supervised the project, critically reviewed and edited the manuscript, and managed project administration. H.Y. developed the study concept, designed the methodology, and wrote the original draft. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data generated during and/or analysed during the current study are available from the corresponding author on reasonable request. The raw data used in our study were obtained from the China Stock Market \u0026amp; Accounting Research Database (CSMAR, [https://data.csmar.com/](https:/data.csmar.com) ). The essential procedures for processing the raw data and relevant references have been documented in the manuscript. The dataset and do-file used for the empirical analysis tables in the manuscript are available at the following URL: [https://pan.baidu.com/s/1bpBFPLw04mSE-VdRNvRqhQ?pwd=t4kd]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmes, R. T., Hall, D. L. 2001. Focusing the familiar: A translation and philosophical interpretation of the Zhongyong. United States, Honolulu: University of Hawaii Press.\u003c/li\u003e\n\u003cli\u003eBentley, K. A., Omer, T. C., Sharp, N. Y. 2013. Business Strategy, Financial Reporting Irregularities, and Audit Effort. Contemporary Accounting Research, 30(2), 780-817. https://doi.org/10.1111/j.1911-3846.2012.01174.x\u003c/li\u003e\n\u003cli\u003eBryan, S. H. 1997. Incremental Information Content of Required Disclosures Contained in Management Discussion and Analysis. The Accounting Review, 72(2), 285\u0026ndash;301. http://www.jstor.org/stable/248557\u003c/li\u003e\n\u003cli\u003eCao, S. S., Ma, G., Tucker, J. W., Wan, C. 2018. 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Fund social network and MD\u0026amp;A disclosure quality. International Review of Financial Analysis, 102, 104047. https://doi.org/10.1016/j.irfa.2025.104047\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTABLE 1 Univariate analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cem\u003eMGCdum\u003c/em\u003e=1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cem\u003eMGCdum\u003c/em\u003e=0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003eT-test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eObs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eObs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eSimilarity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e3.951***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eSizeR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e21.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e21.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-6.620***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-8.838***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e6.756***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eCashflow\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e3.033***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eTop\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eID\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e8.543***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eSOE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-22.087***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003ePosi\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-5.121***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eWord\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e4.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e4.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e3.017***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes：\u003cem\u003eMGCdum\u003c/em\u003e is a binary variable defined by partitioning \u003cem\u003eMGC\u003c/em\u003e at the industry\u0026ndash;year median, taking the value of 1 when \u003cem\u003eMGC\u003c/em\u003e exceeds the median and 0 otherwise. Table 1 provides a preliminary overview of the correlations among key variables.。*, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2\u0026nbsp;\u003c/strong\u003eBaseline regressions.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable=\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 437px;\"\u003e\n \u003cp\u003e\u003cem\u003eSimilarity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMGC\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(4.303)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(4.012)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2.779)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(4.366)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2.614)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003eSizeR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(-3.917)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(-1.614)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.010***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.011***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(-5.390)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(-6.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(0.437)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(-1.510)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003eCashflow\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(-0.799)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(-0.902)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003eTop\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(-0.705)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(-1.218)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003eID\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(1.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(1.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.002***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.002***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(3.806)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(3.429)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003eSOE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.005***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.005***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(-8.980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(-8.448)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003ePosi\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.013***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.015***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(5.214)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(5.845)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003eWord\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.009***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.012***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(-6.162)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(-8.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.943***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.966***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.943***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.980***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1.007***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(3,166.712)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(219.283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(535.957)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(143.887)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e(118.399)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eADJ-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e18.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e26.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e29.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e30.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e29.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: Table 2 reports the stepwise regression results of Eq. (1). Across all specifications, the coefficient on MGC remains significantly positive, consistent with the prediction of H1b. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3 Alternative Explanations\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable=\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cem\u003eDiff\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cem\u003eDiff_Cost\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cem\u003eStrategy_Diff\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cem\u003eStrategy_Bent\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eMGC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.014***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.032***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.037***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(3.893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(5.870)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(4.109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-1.447)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eCVs \u0026amp; FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e22,453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e22,446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e22,490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e20,980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003er2_a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e352.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e275.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e299.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e360.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: Table 3 presents the robustness tests described in Section 4.3.1, which exclude a competing explanation for the main effect\u0026mdash;namely, that firms simply had no new information to disclose. In these models, the dependent variable in Eq. (1) is replaced by strategy-related proxies. Specifically, \u003cem\u003eDiff\u003c/em\u003e, \u003cem\u003eDiff_Cost\u003c/em\u003e, and \u003cem\u003eStrategy_Diff\u0026nbsp;\u003c/em\u003eare constructed following Hu et al. (2020)\u0026rsquo;s strategic competition index, while\u003cem\u003e\u0026nbsp;Strategy_Bent\u003c/em\u003e is based on Bentley et al. (2013)\u0026rsquo;s business strategy index. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4 PSM and Omitted Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable=\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 414px;\"\u003e\n \u003cp\u003e\u003cem\u003eSimilarity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eMGC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(2.381)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(2.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(1.940)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(2.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eBig4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.004***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(3.646)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eChange\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(-0.978)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eTenure\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.002***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(4.461)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eCVs \u0026amp; FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003ePro\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eInd\u003c/em\u003e\u003cem\u003e\u0026times;\u003c/em\u003e\u003cem\u003eYear\u003c/em\u003e\u003cem\u003e\u0026times;\u003c/em\u003e\u003cem\u003ePro\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e12,002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e22,493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e21,063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003er2_a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e12.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e26.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e25.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e22.353\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: Table 4 reports the analyses in Sections 4.3.2 and 4.3.3, which address potential endogeneity stemming from sample selection bias and omitted variable concerns. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 5 Controlling for other regional /historical cultures\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable=\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e\u003cem\u003eSimilarity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cem\u003eMGC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.004***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(2.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(2.797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.928)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(2.167)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cem\u003eConfu\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(-1.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(-0.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cem\u003eMGC\u003c/em\u003e\u003cem\u003e\u0026times;\u003c/em\u003e\u003cem\u003eConfu\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.551)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cem\u003eClan\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cem\u003eClan\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(-0.774)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cem\u003eMGC\u003c/em\u003e\u003cem\u003e\u0026times;\u003c/em\u003e\u003cem\u003eClan\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(-1.911)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.011***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.007***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.014***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.013***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(106.466)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(118.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(113.208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(113.774)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eCVs \u0026amp; FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e20,230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e20,230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003er2_a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e27.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e25.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e26.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e24.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: Table 5 presents the test corresponding to Section 4.3.4. Following Leventis et al. (2024), who highlight the importance of controlling for other informal institutions beyond the variable of interest in studies of informal institutions and accounting outcomes, we additionally include the interaction terms between MGC and other regional cultures. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 6 Alternative Measure of \u003cem\u003eMGC\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e=30km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e=50km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e=150km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e=300km\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable=\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 414px;\"\u003e\n \u003cp\u003e\u003cem\u003eSimilarity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(2.384)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(1.828)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(4.585)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e(5.515)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eCVs \u0026amp; FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003er2_a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e29.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e29.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e30.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e31.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: Table 6 reports the tests in Section 4.3.5, where alternative measures of MGC are employed, and the conclusions remain robust. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 7 Cross-sectional Tests: Business groups\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eGroup\u003c/em\u003e=1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eGroup\u003c/em\u003e=0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eSOE\u003c/em\u003e=1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eSOE\u003c/em\u003e=0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable=\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003e\u003cem\u003eSimilarity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eMGC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.004***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e(-0.776)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e(3.487)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e(0.695)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e(2.476)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eCVs \u0026amp; FE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7,731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e14,503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e9,614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e12,890\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e5.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e21.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e17.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: Table 7 presents the cross-sectional tests in Section 5.1 regarding business groups.\u003cem\u003e\u0026nbsp;Group\u003c/em\u003e indicates whether a sample firm belongs to a group with multiple listed entities, while \u003cem\u003eSOE\u003c/em\u003e denotes whether controlling ownership lies with state-owned capital. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 8 Moderating effects: Independent director centrality, marketization and Micro-level organizational culture\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable=\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 355px;\"\u003e\n \u003cp\u003e\u003cem\u003eSimilarity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003eMGC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.006***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.003***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.004***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(2.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(4.357)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(3.540)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(3.861)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003eIDNet\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.003***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(5.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003eMGC\u003c/em\u003e\u003cem\u003e\u0026times;\u003c/em\u003e\u003cem\u003eIDNet\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(-0.780)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003eMarket\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(-1.226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003eMGC\u003c/em\u003e\u003cem\u003e\u0026times;\u003c/em\u003e\u003cem\u003eMarket\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(3.492)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003eCooperate\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.003***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(6.616)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003eMGC\u003c/em\u003e\u003cem\u003e\u0026times;\u003c/em\u003e\u003cem\u003eCooperate\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(-2.375)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003eCompete\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.007***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(10.932)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003eMGC\u003c/em\u003e\u003cem\u003e\u0026times;\u003c/em\u003e\u003cem\u003eCompete\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.005***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(-2.973)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003eCVs \u0026amp; FE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e21,957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e22,058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e22,504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003er2_a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e26.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e25.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e29.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e34.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: Table 8 reports the moderation tests in Sections 5.2 and 5.3. We find that director network centrality does not alter the main effect, regional marketization strengthens it, and micro-level organizational culture weakens it. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 9 Economic Consequences: Market Reactions to Low-Quality Disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cem\u003eNCSKEW\u003csub\u003et+1\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cem\u003eDUVOL\u003csub\u003e\u0026nbsp;t+1\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cem\u003eSYN\u003csub\u003e\u0026nbsp;t+1\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cem\u003eSimilarity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.350***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.258***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.079**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e(2.609)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e(2.873)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e(2.460)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cem\u003eSimilarity_hat\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-10.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-7.773***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e(-1.394)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e(0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e(-4.167)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cem\u003eCVs \u0026amp; FE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e19,771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e19,771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e19,771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e19,771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e19,771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e19,771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003er2_a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e10.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e10.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e9.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e9.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e189.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e190.736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: Table 9 presents the tests in Section 5.4, which investigate how investors perceive seemingly low-quality MD\u0026amp;A disclosures. The results indicate that boilerplate MD\u0026amp;A narratives generally provoke adverse market responses, including elevated crash risk and greater synchronicity. However, when such disclosures originate from firms more strongly shaped by MGC, they do not generate such reactions. The robust T-values are reported in parentheses. *, **, and *** denote significance at the 10 %, 5 %, and 1 % levels, respectively.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"MD\u0026A disclosure, Merchant guild culture, Informal institutions, Social norms","lastPublishedDoi":"10.21203/rs.3.rs-8821313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8821313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research investigate how merchant guild culture (MGC) rooted in imperial China continues to shape the disclosure practices in contemporary firms. Drawing on a set of competing theoretical hypotheses, we find that firms more deeply embedded in MGC tend to disclose MD\u0026amp;A reports of lower informational quality, as evidenced by higher year-to-year (vertical) textual similarity. We further explore cross-sectional tests, moderating effects, and the capital market\u0026rsquo;s response to such disclosure practices. Our evidence suggests that boilerplate MD\u0026amp;A disclosure represents a deliberate, strategic response by managers to mitigate competitive risks and safeguard stakeholder interests, rather than a manifestation of opportunistic concealment.\u003c/p\u003e","manuscriptTitle":"Merchant Guild Culture and MD\u0026amp;A Disclosure Quality: Transparency versus Reticence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 17:13:28","doi":"10.21203/rs.3.rs-8821313/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b00ce76e-6a64-4f9b-943e-05181be389fe","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65836034,"name":"Business and commerce/Business and management"},{"id":65836035,"name":"Social science/Business and management"},{"id":65836036,"name":"Business and commerce/Finance"},{"id":65836037,"name":"Social science/Finance"}],"tags":[],"updatedAt":"2026-04-14T15:41:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 17:13:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8821313","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8821313","identity":"rs-8821313","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}