ESG Divergence and the Inflow of High-Skilled Talent in Client Firms: Evidence from Chinese Listed Companies

ESG Divergence and the Inflow of High-Skilled Talent in Client Firms: Evidence from Chinese Listed Companies | 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 ESG Divergence and the Inflow of High-Skilled Talent in Client Firms: Evidence from Chinese Listed Companies Yunhui Zhao, Ziqi Liu, Xingxing Fu, Yang Liu, Xinyu Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8275340/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract High-skilled talent, as a core resource, is a key driving force for enterprises to achieve high-quality and sustainable development. Previous studies showed that ESG performance is a critical factor that impacts the inflow of high-skilled talents toward client firms. However, few studies have examined how inconsistency (divergence) in ESG performance between suppliers and client firms affects the inflow of high-skilled talent. This study uses data from Chinese listed companies and their suppliers from 2009 to 2022. It employs signaling theory and consistency theory. It explores the impact of ESG divergence between suppliers and client firms on the inflow of high-skilled talent. The perspective is dyadic supply chain relationships. The findings reveal that ESG divergence increases both reputational and operational risks for client firms, reduces operational efficiency, and thereby weakens their ability to attract high-skilled talent. However, when a client firm’s ESG performance aligns with the average ESG level of its industry or region, the negative effect of ESG divergence can be effectively mitigated. In addition, the negative effect of ESG divergence is asymmetric: when a client firm’s ESG performance is lower than that of its supplier, the detrimental impact on talent inflow is more pronounced. Furthermore, the negative influence of ESG divergence is amplified when client firms have a lower level of digital transformation, operate in highly competitive markets, or are connected to suppliers within more networked configurations. This study enriches the literature on supply chain ESG management and high-skilled talent inflow under the context of innovation-driven development. It underscores the importance of ESG signal consistency in attracting high-skilled talent. It provides practical implications for enterprises. They aim to optimize their talent structure and enhance innovation-driven growth. Business and commerce/Business and management Social science/Business and management Business and commerce/Economics Social science/Economics Business and commerce/Operational research High-skilled talent ESG divergence Supplier–client relationship Signal consistency Figures Figure 1 1 Introduction High-skilled talent is a key driver of corporate innovation and development. According to the China Industrial Digital Talent Development Report (2023), there is a significant structural imbalance in the digital talent pool, with a total shortage of 25–30 million people—a gap that continues to widen. This shortage undermines technological innovation, reduces economic competitiveness, and hinders sustainable development. Therefore, attracting high-skilled talent is essential for enterprises to achieve long-term sustainability. As climate change and environmental pollution intensify, many firms have integrated sustainability into their development strategies. By demonstrating a commitment to sustainable development, they enhance their organizational attractiveness (Umrani et al., 2022; Carballo-Penela et al., 2023) and draw in high-skilled professionals. Yet, despite various initiatives, the inflow of such talent remains limited due to the scarcity and uneven distribution of skilled labor. Thus, how firms can effectively attract high-skilled talent to support innovation-driven growth has become an urgent issue. High-skilled talent suited to innovation-driven development typically possess advanced knowledge, strong creativity, professional ethics, and a sense of social and environmental responsibility. When choosing employers, these individuals consider not only economic benefits and career opportunities but also the firm’s social responsibility and sustainability capabilities. In response, a growing number of firms are improving their performance across environmental, social, and governance (ESG) dimensions to strengthen their appeal to talent (Zhang & Gowan, 2012). While firms’ ESG engagement has been shown to expand employment (Edmans, 2011) and increase labor demand (Jiang et al., 2025), supply chain partners form a community of shared interests—prospering or declining together (Chiu et al., 2019). Therefore, a firm’s ability to attract high-skilled professionals depends not only on its own ESG performance but also on that of its partners. Focusing solely on internal ESG practices may fail to achieve the desired talent attraction effect, and inconsistency in ESG performance among partners may further weaken the inflow of high-skilled talent. Accordingly, it is necessary to examine, from a dyadic supply chain perspective, whether and how ESG divergence between suppliers and clients influences the inflow of high-skilled talent to client firms. Based on signaling theory and consistency theory, when ESG performance between suppliers and clients is inconsistent—referred to as ESG divergence—the client firm’s ESG commitments may not effectively attract talent. ESG divergence affects the inflow of high-skilled talent through three main mechanisms: First, ESG divergence generates reputational risk. Poor ESG performance by any supply chain member increases not only its own reputational risk but also that of its partners (Dai et al., 2021). Such divergence can trigger public skepticism and dissatisfaction toward client firms, damaging their reputation and undermining the appeal of their ESG commitments to skilled talent. For example, McDonald’s and KFC regularly disclose detailed corporate social responsibility (CSR) information in their annual reports. However, both companies faced widespread negative media coverage when upstream suppliers were reported to use expired raw materials (Hartmann & Moeller, 2014). Such negative publicity weakens the non-financial value signals associated with corporate sustainability and CSR commitments (Pedersen et al., 2021; Lewis & Carlos, 2023), thereby reducing the attractiveness to high-skilled talent. Second, ESG divergence increases business risk. It reflects incompatible ESG value orientations between suppliers and clients, and deviation from shared norms may induce ethical failures (Hill et al., 2009). For instance, when suppliers’ poor ESG performance leads stakeholders to question their legitimacy, they may face operational difficulties—such as disruptions in the supply of raw materials or components—thereby increasing operational uncertainty for client firms (Xiong et al., 2021). Higher business risk reduces firms’ willingness to invest in innovation, constrains R&D spending, suppresses demand for high-skilled labor, and ultimately reduces talent inflow. Third, ESG divergence reduces operational efficiency. When suppliers are penalized or socially boycotted due to poor ESG performance, it can lead to unstable product quality, delivery delays, or production interruptions—disrupting client firms’ production schedules. To maintain operational continuity, client firms may resort to emergency measures such as finding temporary alternative suppliers or adjusting production lines. These responses not only increase operational costs but also damage the structural and functional integrity of client operations, reducing efficiency (Liu et al., 2023). Declining operational efficiency leads firms to scale down production and innovation investment (Kale & Shahrur, 2005), diminishing demand for high-skilled workers and further restricting talent inflow. Compared with existing studies, the main contributions of this paper are as follows: First, against the backdrop of innovation-driven development, it explores the antecedent conditions of high-skilled talent inflow, constructs a theoretical model of the impact of ESG divergence on talent inflow, and provides new empirical evidence for the practice of innovation-driven development; Second, it incorporates upstream and downstream supply chain ESG consistency into the analytical framework, expanding research on ESG and labor from a dyadic supply chain perspective; Third, it integrates signaling theory and consistency theory, revealing the asymmetric negative impact of ESG divergence on client firms' ability to attract high-skilled talent, thereby deepening the theoretical understanding of signal consistency. 2 Theoretical Framework and Hypotheses To support innovation-driven growth, firms require more high-skilled talent aligned with strategic goals. Amid growing climate and environmental challenges, many enterprises have embedded ESG principles into their strategies to attract such talent. As extended producer responsibility systems improve, stakeholders now evaluate not only a firm’s own ESG performance but also that of its supply chain partners. This creates interdependence within the supply chain (Zheng Zhen et al., 2024), where suppliers' ESG performance influences client firms' ability to attract high-skilled labor. According to consistency theory, alignment among actors enhances organizational performance (Fry & Smith, 1987). In supply chains, shared values foster trust-based cooperation and organizational identification. For instance, consistency in social responsibility improves financial performance and market value (Liu et al., 2021). When ESG performance aligns across partners, it serves as a shared ethical standard and boosts supply chain outcomes. However, ESG divergence often arises in practice, creating imbalance in supplier–client relationships. Drawing on signaling theory, weak ESG performance by one firm sends negative signals about its sustainability commitment. Amplified by media and public attention, these signals harm perceptions of partners (Yu et al., 2023) and undermine the supply chain’s legitimacy (Aouadi & Marsat, 2018). For high-skilled professionals, employment decisions now consider not only the firm but also supply chain alignment. Inconsistent ESG signals between partners hinder client firms' talent attraction—poor supplier ESG cannot be fully offset by strong client performance, nor can a supplier’s high ESG fully compensate for a client’s weak performance. Based on this reasoning, the following hypothesis is proposed: H1: ESG divergence negatively affects the inflow of high-skilled talent into client firms. Theoretically, ESG divergence between suppliers and clients may increase client firms’ reputational and operational risks, reduce their operational efficiency, and thus weaken their inflow of high-skilled talent. This study elaborates on these effects through three mechanisms: the reputational risk mechanism, the operational risk mechanism, and the operational efficiency mechanism. 1. Reputational Risk Mechanism. The public often views supply chain networks as unified entities (Liu et al., 2021). When one member violates common norms, it impacts the reputation of all partners (Kumar et al., 2020). Based on consistency and signaling theories, ESG divergence between suppliers and clients weakens positive ESG signals from the stronger performer. Since the public often cannot accurately trace negative outcomes to their source, dissatisfaction tends to be directed at client firms—those closer to end consumers (Hartmann & Moeller, 2014; Liu et al., 2021). Thus, even strong ESG performance by a client may not fully offset reputational harm caused by a supplier's misconduct such as pollution or labor violations. Conversely, when a client's ESG performance is weak, public attention—typically focused on consumer-facing firms—makes it hard for a supplier's strong ESG reputation to improve perceptions. Stakeholders may even question why a responsible supplier would partner with an irresponsible client (Yu et al., 2023). Such negative scrutiny elevates the client’s reputational risk (Boone & Ivanov, 2012), creating doubts among high-skilled professionals about the firm’s commitment to sustainability and reducing its talent appeal.Hence, the following hypothesis is proposed: H2: ESG divergence weakens the inflow of high-skilled talent into client firms by increasing reputational risk. 2. Operational Risk Mechanism. Based on consistency theory, misaligned behavior among supply chain partners increases operational risk (Liu et al., 2021). ESG divergence reflects fundamentally inconsistent values and business philosophies between suppliers and clients. If suppliers face penalties for ESG violations, clients must grapple with practical challenges—such as finding replacements or ensuring business continuity (Gualandris et al., 2015)—introducing significant operational risk. Such violations also signal potential issues in product quality, labor rights, or environmental compliance, creating a chain of accountability that exposes clients to reputational harm and trust crises (XuJ et al., 2025), potentially undermining their resource access and operational stability (Zimmerman & Zeitz, 2002). When clients exhibit poor ESG performance, misalignment with suppliers may trigger conflicts over divergent values (Fry & Smith, 1987). Suppliers concerned about their own reputation may reduce or end cooperation (Liu et al., 2021), further raising clients’ operational risks. These risks divert management attention from long-term R&D and talent investment (Xu S et al., 2025), reducing demand for high-skilled labor. Moreover, ESG divergence can tighten financial constraints, limiting clients’ capacity to afford and manage high-quality professionals (Falato & Liang, 2016), thereby hindering the inflow of high-skilled talent.Accordingly, the following hypothesis is proposed: H3: ESG divergence increases client firms’ operational risk, thereby weakening their inflow of high-skilled talent. 3. Operational Efficiency Mechanism. ESG divergence undermines established behavioral norms and cooperative commitments in supply chains (Ashforth & Gibbs, 1990). When suppliers exhibit poor ESG performance, they signal higher operational and compliance risks (Liu Y Z et al., 2026), potentially leading to reputational damage or regulatory penalties (Feng H et al., 2025). This raises client concerns over reliability and product quality, possibly triggering procurement reductions or supplier replacements (Bai M & Astvansh V, 2025). Such adjustments disrupt normal operations and reduce efficiency. Similarly, if a client’s ESG performance lags behind its suppliers, the latter may question the client’s capabilities and demand stronger commitments or enhanced ESG disclosures (Liu et al., 2021). Though aimed at maintaining cooperation, these demands raise operating costs and impair efficiency. Inefficiencies—such as delayed decisions, resource waste, and poor after-sales service (Liu et al., 2021)—conflict with high-skilled professionals’ expectations for dynamic, innovative workplaces (Huang Qunhui & Sheng Fangfu, 2024), weakening external talent attraction. They may also lower satisfaction and retention among current employees, indirectly hindering future talent inflow. In summary, ESG divergence can affect client firms’ high-skilled talent inflow through reputational, operational, and efficiency-related mechanisms.Therefore, the following hypothesis is proposed: H4: ESG divergence weakens client firms’ inflow of high-skilled talent by reducing operational efficiency. 3 Research Design 3.1 Sample Selection and Data Sources Based on the China Stock Market & Accounting Research (CSMAR) database, this study constructs a matched dataset of Chinese listed firms and their suppliers. ESG data are obtained from the Huazheng ESG ratings in the Wind database, while firm-level information is derived from CSMAR. Since the Huazheng ESG rating system began in 2009, the sample period is set from 2009 to 2022. To ensure the reliability of the results, the following steps are taken:(1) samples with non-listed suppliers are excluded;(2) only observations where both suppliers and client firms have Huazheng ESG ratings are retained;(3) financial industry samples are excluded;(4) samples classified as ST or PT firms are removed; and(5) observations with missing key variables are dropped.After these steps, the final dataset includes 1,785 supplier–client–year observations, comprising 597 client firms and 724 suppliers. To mitigate the influence of extreme values, all continuous variables are winsorized at the top and bottom 1%. 3.2 Model Construction and Variable Measurement To examine the impact of ESG divergence on the inflow of high-skilled talent in client firms, the following baseline model is constructed: $$Labo{r_{i,t}}={\beta _0}+{\beta _1}ESG\_de{v_{i,t}}+\gamma {X^{\prime}_{i,t}}+Year+Industry+\varepsilon$$ 1 where i represents the client firm, t represents the year, X’ denotes control variables. Year and Industry represent year and industry fixed effects, respectively, while ε is the random disturbance term. Labor denotes the inflow of high-skilled talent in client firms. Following Edmans A (2011), employees with a postgraduate degree or above are defined as high-skilled talent, and talent inflow is measured as the net increase in such employees. Specifically, it is calculated as: ln(number of postgraduate or higher-degree employees + 1)i,t − ln(number of postgraduate or higher-degree employees + 1)i,t-1. ESG_dev represents ESG divergence between suppliers and client firms. Compared with ESG indices released by foreign rating agencies, the Huazheng ESG index better reflects the characteristics of the Chinese market and currently covers all A-share listed firms (Wang F et al., 2025). Therefore, using the Huazheng ESG index more effectively captures Chinese firms’ ESG performance. Based on the Huazheng ESG rating data, enterprise ESG ratings are assigned values from 9 (highest) to 1 (lowest) (Erik B et al., 2021). Referring to Kumar et al. (2020), ESG divergence is measured using the following formula: Weight* (C_ESG- S_ESG) 2 (2) where Weight represents the proportion of a supplier’s sales to the client firm’s total procurement. The weighted approach is adopted because suppliers with higher procurement shares may exert stronger influence on the results than those with lower shares. C_ESG and S_ESG denote the ESG ratings of client firms and suppliers, respectively. Following prior studies, this paper controls for several firm-level variables: client firm ownership type (SOE), firm size (Size), firm age (Age), leverage ratio (Lev), return on assets (ROA), employee wages (Wage), sales profit margin (Profit), asset turnover (ATO), Tobin’s Q (TobinQ), return on equity (ROE), current ratio (Liquid), financial leverage (FL), book-to-market ratio (BM), CEO duality (Dual), ownership concentration (Top3), client firm ESG rating (C_ESG), supplier ESG rating (S_ESG), regional per capita GDP (PGDP), and Herfindahl–Hirschman Index (HHI). All variables and their measurement methods are shown in Table 1 . Table 1 Variables and measurement methods. Variable Type Variable Name Symbol Measurement Method Dependent Variable High-skilled Talent Inflow Labor ln(Number of employees with postgraduate degree or above + 1)_i,t − ln(Number of employees with postgraduate degree or above + 1)_i,t − 1 Independent Variable Supplier–Client ESG Divergence ESG_dev Weight × (C_ESG − S_ESG)² Control Variables Ownership Type SOE 1 if state-owned enterprise; otherwise 0 Firm Size Size ln(Total Assets) Firm Age Age ln(Years since establishment + 1) Leverage Ratio Lev Total Liabilities / Total Assets Return on Assets ROA Return on Total Assets Employee Wage Wage ln(Employee Compensation Payable / Number of Employees) Sales Profit Margin Profit (Operating Revenue − Operating Cost) / Operating Revenue Asset Turnover Ratio ATO Operating Revenue / Average Total Assets Tobin’s Q TobinQ Tobin’s Q Value Return on Equity ROE Return on Equity Current Ratio Liquid Current Assets / Current Liabilities Financial Leverage FL (Net Profit + Income Tax + Financial Expenses) / (Net Profit + Income Tax) Book-to-Market Ratio BM Book Value / Market Value Dual Role Dual 1 if CEO also serves as Chairperson; otherwise 0 Ownership Concentration Top3 Shareholding Ratio of Top Three Shareholders Independent Directors Ratio Indep Number of Independent Directors / Total Board Members Client Firm ESG Rating C_ESG Huazheng ESG Rating Supplier ESG Rating S_ESG Huazheng ESG Rating Regional GDP per Capita PGDP ln(Provincial Per Capita GDP) Herfindahl–Hirschman Index HHI Herfindahl–Hirschman Index 4 Empirical Results and Analysis 4.1 Descriptive Statistics Table 2 presents the descriptive statistics for the main variables. The mean value of High-skilled Talent Inflow in client firms is 0.1944, with a standard deviation of 0.7209, a minimum of − 1.7918, and a maximum of 4.8363, indicating considerable variation in high-skilled talent inflow across firms. The mean value of ESG Divergence is 0.1283, with a standard deviation of 0.3747, a minimum of 0, and a maximum of 2.7662. Table 2 Summary statistics of the main variables. Variables Sample Size Mean Standard Deviation Min Max Labor 1785 0.1944 0.7209 -1.7918 4.8363 ESG_dev 1785 0.1283 0.3747 0.0000 2.7662 SOE 1785 0.5507 0.4976 0.0000 1.0000 Size 1785 23.4314 1.9262 20.2448 28.5052 Age 1785 2.9267 0.3127 1.9459 3.5553 Lev 1785 0.5148 0.1850 0.0790 0.8689 ROA 1785 0.0432 0.0440 -0.1125 0.1779 Wage 1785 9.6010 0.9904 6.0570 11.7911 Profit 1785 0.2340 0.1440 0.0133 0.7507 ATO 1785 0.8262 0.5783 0.0411 10.8130 TobinQ 1785 1.6179 0.9291 0.8130 6.2228 ROE 1785 0.0902 0.0978 -0.4794 0.3541 Liquid 1785 1.6439 1.4314 0.2535 10.8500 FL 1785 1.3667 2.5552 -60.0533 42.0505 BM 1785 0.7410 0.2518 0.1607 1.2300 Dual 1785 0.1871 0.3901 0.0000 1.0000 Top3 1785 52.5550 17.6583 16.7664 97.2327 Indep 1785 37.5092 5.4554 33.3300 57.1400 C_ESG 1785 4.4353 0.9985 2.0000 6.0000 S_ESG 1785 4.1501 1.1501 1.0000 6.0000 PGDP 1785 11.1903 0.4787 10.1396 12.1564 HHI 1785 0.0920 0.1022 0.0148 0.5454 4.2 Baseline Regression Table 3 reports the regression results on the impact of ESG divergence on high-skilled talent inflow in client firms. Columns (1) and (2) exclude control variables, while column (2) adds year and industry fixed effects. Column (3) introduces control variables based on column (1), and the coefficient of ESG_dev remains significantly negative. Column (4), which includes both control variables and fixed effects, shows an ESG_dev coefficient of − 0.0781, significant at the 5% level. These results indicate that ESG divergence negatively affects the inflow of high-skilled talent in client firms, supporting Hypothesis H1. Table 3 Baseline regression results of ESG divergence on high-skilled talent inflow in client firms. Variables (1) (2) (3) (4) Labor Labor Labor Labor ESG_dev -0.0705 *** (0.0270) -0.0632 * (0.0312) -0.0640 * (0.0345) -0.0781 ** (0.0398) SOE 0.0411 (0.0460) -0.0349 (0.0543) Size -0.0043 (0.0159) 0.0023 (0.0188) Age -0.2347 *** (0.0613) -0.0728 (0.0751) Lev -0.2257 (0.1880) -0.2500 (0.1967) ROA 0.0149 (0.9333) -0.6843 (1.0948) Wage 0.0303 (0.0238) 0.0615 ** (0.0263) Profit -0.1525 (0.1813) -0.0653 (0.2081) ATO 0.0849 (0.0853) 0.1524 (0.1095) TobinQ 0.0349 (0.0248) -0.0015 (0.0257) ROE 0.4789 (0.3318) 0.6748 (0.4192) Liquid -0.0237 * (0.0144) -0.0220 (0.0139) FL 0.0159 (0.0131) 0.0150 (0.0142) BM 0.2832 * (0.1454) 0.0036 (0.1477) Dual -0.0062 (0.0431) -0.0434 (0.0443) Top3 -0.0024 ** (0.0012) -0.0029 ** (0.0012) Indep -0.0014 (0.0029) 0.0002 (0.0029) C_ESG -0.0200 (0.0199) -0.0219 (0.0206) S_ESG 0.0103 (0.0150) 0.0173 (0.0142) PGDP -0.0532 * (0.0299) 0.0839 * (0.0459) HHI -0.0562 (0.2299) -0.7984 (0.5951) Constant 0.2009 *** (0.0169) 0.1999 *** (0.0158) 1.2940 *** (0.4530) -0.8958 (0.6904) Year and Industry Fixed Effects No Yes No Yes N 1938 1938 1785 1785 R2 0.0014 0.0942 0.0321 0.1253 Note: Values in parentheses represent robust standard errors clustered at the supplier–client level. ***, *, and * indicate significance at the 1%, 5%, and 10% levels, respectively. The same applies to all following tables. 4.3 Endogeneity Tests 4.3.1 Instrumental Variable Approach. To mitigate potential endogeneity between ESG divergence and high-skilled talent inflow, this study employs a two-stage least squares (2SLS) approach, using whether a supplier is audited by a Big Four firm as an instrumental variable (LiuC & XinZ, 2024). Big Four–audited suppliers tend to exhibit higher ESG performance due to stricter compliance standards, thereby influencing ESG divergence without directly affecting client firms' talent inflow. This approach helps address endogeneity concerns and enhances the robustness of the empirical findings. Table 4 , column (1), reports the first-stage results of the instrumental variable estimation. The coefficient of Instrument is 0.2590, significant at the 1% level, indicating that the instrument strongly affects the endogenous regressor. The Cragg–Donald Wald F-statistic equals 89.5980, far exceeding conventional critical thresholds, and the Kleibergen–Paap rk Wald F-statistic equals 15.7420, above the value of 10, effectively ruling out weak-instrument concerns. Column (2) presents the second-stage regression results, showing that the coefficient of ESG_dev is − 0.3485 and significant at the 5% level. This finding indicates that ESG divergence significantly reduces high-skilled talent inflow in client firms, further confirming the baseline regression results. 4.3.2 Heckman Two-Stage Model Regression. Although the China Securities Regulatory Commission encourages listed companies to disclose information on their top five suppliers and customers, firms ultimately retain discretion regarding whether to disclose such details (Branstetter G L et al., 2019). During the sample-matching process, this disclosure choice may introduce sample selection bias. To address this concern, the Heckman two-stage model is applied. In the first stage, a Probit model is estimated to examine the factors influencing a firm’s decision to disclose supplier information, incorporating control variables. From this, the inverse Mills ratio (IMR) is calculated. In the second stage, the IMR is added to the baseline regression to further test the research hypothesis. Column (3) of Table 4 reports the second-stage regression results: the coefficient of ESG_dev is − 0.0747, significant at the 10% level. This indicates that even after correcting for potential sample selection bias, ESG divergence continues to exert a significant negative impact on high-skilled talent inflow, confirming the robustness of the results. 4.3.3 Propensity Score Matching (PSM). To further test whether differences in control variables between high and low ESG-divergence samples drive potential endogeneity, the study employs propensity score matching (PSM). Specifically, firms are classified into high- and low-divergence groups based on the median ESG divergence within the same industry and year. Control variables from the baseline regression are used as covariates, and nearest-neighbor matching is applied. This method ensures comparability between the two groups on key characteristics, thereby reducing potential endogeneity. Figure 1 illustrates the kernel density distributions before and after matching. Before matching, the probability distributions differ substantially between the two groups; after matching, they become much more similar, suggesting an effective matching outcome. Column (4) of Table 4 presents the PSM-adjusted regression results, where the coefficient of ESG_dev is − 0.1651, significant at the 5% level. This again confirms the robustness of the findings. Table 4 Endogenity test results. Variables Instrumental Variable Method Heckman Two-Stage Model Propensity Score Matching (PSM) Method (1) (2) (3) (4) ESG_dev Labor Labor Labor ESG_dev -0.3486 ** (0.1781) -0.0747 * (0.0394) -0.1651 ** (0.0590) Instrument 0.2590 *** (0.0653) IMR 0.6899 * (0.3515) Controlled Variable Yes Yes Yes Yes Year and Industry Fixed Effects Yes Yes Yes Yes N 1785 1785 1775 885 R2 0.3375 0.1718 0.1297 0.1382 4.4 Robustness Tests 4.4.1 Alternative Measurement of the Dependent Variable. To further test robustness, this study adopts an alternative measure of high-skilled talent inflow—the difference in the proportion of employees with postgraduate degrees or higher between the current and previous year (Daron A & Pascual R, 2022). Column (1) of Table 5 reports a coefficient for ESG_dev of − 0.1936, significant at the 10% level, suggesting that ESG divergence significantly weakens high-skilled talent inflow in client firms. This result remains consistent with the baseline findings. 4.4.2 Alternative Measurement of the Independent Variable. In the baseline regression, ESG ratings are divided into nine discrete levels, which may introduce bias in assessing ESG divergence. To ensure robustness, the study uses the continuous Huazheng ESG score (ESG_a, 0–100) to construct a refined measure of ESG divergence (ESG_dev_a) (Kumar et al., 2020), as defined in Eq. ( 3 ). C_ESG_a i,t and S_ESG_a i,t denote the ESG scores of client firms and suppliers, respectively. Column (2) of Table 5 reports that the coefficient of ESG_dev_a is − 0.0880, significant at the 10% level, further validating the robustness of the results. $$ESG\_dev\_{a_{i,t}}=\frac{{Weight*{{(C\_ESG\_{a_{i,t}} - S\_ESG\_{a_{i,t}})}^2}}}{{(C\_ESG\_{a_{i,t}}+S\_ESG\_{a_{i,t}})/2}}$$ 3 4.4.3 Adding Fixed Effects. When examining the impact of ESG divergence on high-skilled talent inflow in client firms, geographical factors may have potential effects on firms’ attractiveness to talent. Differences among provinces in terms of economic development level, policy environment, and labor market conditions may influence firms’ ability to attract high-skilled workers. Therefore, to further control for the influence of such regional heterogeneity, this study adds provincial fixed effects to the baseline regression. Column (3) of Table 5 reports that the coefficient of ESG_dev is − 0.0952, significant at the 5% level, indicating that the findings remain robust. 4.4.4 Changing the Clustering Method. To more effectively control for potential autocorrelation and heterogeneity within industries, the study adjusts the clustering of standard errors to the industry level based on client firms. As shown in column (4) of Table 5 , the coefficient of ESG_dev is − 0.0781, significant at the 5% level, consistent with the baseline regression results. 4.4.5 Excluding the Impact of the COVID-19 Pandemic. The COVID-19 pandemic in 2020 had a significant impact on the global economy and labor markets (Albanesi & Kim, 2021), particularly affecting firms’ talent mobility and recruitment decisions. Therefore, to avoid abnormal fluctuations caused by the pandemic, this study excludes data for the year 2020 from the sample. Column (5) of Table 5 reports that the coefficient of ESG_dev is − 0.1005, significant at the 1% level, further confirming the robustness of the results. Table 5 Robustness test results. Variables Alternative Measurement of the Dependent Variable Alternative Measurement of the Independent Variable Adding Client Province Fixed Effects Industry-Clustered Standard Errors Excluding the Impact of the COVID-19 Pandemic (1) (2) (3) (4) (5) Labor_a Labor Labor Labor Labor ESG_dev -0.1936 * (0.1144) -0.0952 ** (0.0453) -0.0781 ** (0.0381) -0.1005 *** (0.0387) ESG_dev_a -0.0880 * (0.0533) Controlled Variable Yes Yes Yes Yes Yes Year and Industry Fixed Effects Yes Yes Yes Yes Yes Province Fixed Effects No No Yes No No N 1785 1785 1785 1785 1662 R2 0.0695 0.1248 0.1483 0.1253 0.1296 5 Mechanism and Heterogeneity Analysis 5.1 Mechanism Analysis 5.1.1 Reputation Risk Mechanism ESG divergence may deter high-skilled talent by amplifying a client firm’s reputation risk. When a supplier’s ESG performance surpasses the client’s, stakeholders may perceive the client as falling short of expected sustainability standards, casting doubt on its commitment to social responsibility (Yang & Jiang, 2023). Conversely, if the supplier underperforms in ESG, the client may face public criticism for associating with low-standard partners, signaling weak ESG risk control in the supply chain (Liu et al., 2024; Yang & Jiang, 2024). Since high-skilled professionals value corporate ethics and sustainability (Edmans, 2011), such reputational concerns may lead them to avoid the firm. Thus, ESG divergence undermines talent attraction by elevating reputation risk. Following Yu et al. (2023), this study measures firms’ ESG-related negative publicity ( News ) using the number of ESG-related negative news items collected from the DATAGO Financial Research Database. The causal inference approach proposed by J K P & F A H (2008) is adopted for mechanism testing. As shown in Table 6 , column (1), the coefficient of ESG_dev is 2.6747 and significantly positive at the 5% level. This result supports Hypothesis H2, indicating that ESG divergence among supply chain partners increases the client firm’s reputation risk, which subsequently reduces its inflow of high-skilled talent. 5.1.2 Operational Risk Mechanism. High-skilled talent tends to seek stable work environments with manageable operational risks (Mann, 2018). Significant ESG divergence between suppliers and clients heightens such risks, undermining talent attraction. Specifically, ESG divergence can provoke negative responses from regulators, consumers, and investors, eroding market trust and investor confidence (Erik et al., 2021). Client firms may then divert resources from core operations to manage ESG-related supply chain risks, increasing operational uncertainty (Xu et al., 2024). As high-skilled professionals generally prefer lower-risk employers (Branstetter et al., 2019), ESG divergence reduces the appeal of client firms by amplifying operational risk. Following Haejun J et al.(2023), this study measures operational risk using firms’ downside risk, as specified in Eq. ( 4 ). ROA_ind i,t − 1 represents the average ROA of all firms in the same industry as firm i in year t − 1 . This indicator captures a firm’s vulnerability to financial distress and its potential loss risk when facing external shocks. As shown in Table 6 , column (2), the coefficient of ESG_dev is 3.1319 and significantly positive at the 5% level. This result supports Hypothesis H3, suggesting that ESG divergence significantly increases firms’ operational risk, thereby negatively affecting client firms’ high-skilled talent inflows. $$Ris{k_{i,t}}=\sqrt {\frac{1}{5}\sum\limits_{{t=1}}^{5} {(RO{A_{i,t - 1}} - ROAin{d_{i,t - 1}})} }$$ 4 5.1.3 Operational Efficiency Mechanism. ESG divergence can undermine client firms' operational efficiency, consequently reducing their appeal to high-skilled talent. Misalignment in ESG standards increases operational uncertainty and complicates supply chain coordination, forcing firms to expend extra resources on business integration. This often leads to supply chain disruptions, quality issues, or delivery delays (Wang et al., 2025), diverting resources from core activities to crisis management and resulting in efficiency losses and organizational inefficiency (Liang et al., 2023). Such operational inefficiencies gradually erode a firm's market competitiveness (Erik et al., 2021), making it less attractive to high-skilled professionals who value stable and efficient working environments. Following Ambulkar et al. (2023), operational efficiency is measured by industry-adjusted inventory turnover days, where a longer turnover duration indicates lower operational efficiency. As shown in Table 6 , column (3), the coefficient of ESG_dev is 1.3632 and significantly positive at the 1% level. This result supports Hypothesis H4, indicating that ESG divergence significantly undermines client firms’ operational efficiency, and the resulting decline in efficiency further negatively affects high-skilled talent inflow. Table 6 Mechanism analysis summary. Variables Reputation Risk Mechanism Operational Risk Mechanism Operational Efficiency Mechanism (1) (2) (3) News Orisk Efficiency ESG_dev 2.6747 ** (1.0757) 3.1319 ** (1.2922) 1.3632 *** (0.4083) Controlled Variable Yes Yes Yes Year and Industry Fixed Effects Yes Yes Yes N 1541 1389 1781 R2 0.4966 0.1456 0.1405 5.2 Heterogeneity Analysis 5.2.1 Heterogeneity of Digital Transformation. Based on the median level of digital transformation within the same industry and year, client firms are divided into high and low digital transformation subsamples. Following Erik B et al. (2021), the degree of digital transformation is measured by the proportion of digital transformation–related keywords in the annual report relative to total word count. Digital transformation enhances firms’ operational efficiency and information-processing capabilities (Wang F et al., 2025) and reduces coordination problems arising from ESG divergence. It also improves internal processes and transparency, mitigating public concerns over ESG mismanagement and reputation loss. Therefore, firms with high levels of digital transformation often possess more flexible response mechanisms, allowing them to rapidly adjust strategies to align with sustainability goals (Feng & Zhu, 2024), thereby weakening the adverse effect of ESG divergence on high-skilled talent inflow. Regression results in Table 7 , columns (1) and (2) indicate that higher levels of digital transformation effectively mitigate the negative impact of ESG divergence on client firms’ ability to attract high-skilled workers. 5.2.2 Heterogeneity of Market Competition. Using the median Herfindahl–Hirschman Index (HHI) by industry and year, the sample is split into high- and low-competition groups. In more competitive markets, client firms experience stronger external pressure and uncertainty. ESG misalignment with suppliers can more severely damage a client’s reputation and brand, signaling poor management of ESG risks in the supply chain. This, in turn, reduces the firm's appeal to high-skilled workers, who in competitive markets have more options and prefer employers with strong, consistent ESG practices. Thus, competition intensifies the negative effect of ESG divergence on attracting talent.Regression results in Table 7 , columns (3) and (4) confirm that client firms in highly competitive industries experience stronger negative effects of ESG divergence on high-skilled talent inflow. 5.2.3 Heterogeneity of Supplier Concentration or Network Structure. This study measures supplier concentration as the proportion of total purchases accounted for by the top five suppliers. The sample is split at the industry-year median into supplier-concentrated and supplier-networked groups. Under high supplier concentration, closer ties with key suppliers improve coordination and reduce ESG divergence, mitigating its negative impact. Such relationships also enable better monitoring of supplier ESG performance, partly offsetting the effect on talent attraction. In contrast, firms in supplier-networked settings rely on numerous suppliers, increasing complexity. Looser relationships and lower information efficiency make ESG divergence harder to address, reducing appeal to high-skilled professionals. Regression results in Table 7 , columns (5) and (6) demonstrate that under supplier-networked configurations, the negative impact of ESG divergence on client firms’ ability to attract high-skilled talent is significantly stronger. Table 7 Heterogeneity analysis results. Variables High Digital Transformation Low Digital Transformation High Market Competition Low Market Competition Supplier Concentration Supplier Digitalization (1) (2) (3) (4) (5) (6) Labor Labor Labor Labor Labor Labor ESG_dev -0.0269 -0.0907 ** -0.1318 *** -0.0364 -0.0217 -0.1697 ** (0.1076) (0.0454) (0.0488) (0.0734) (0.0522) (0.0725) Controlled Variables Yes Yes Yes Yes Yes Yes Year and Industry Fixed Effects Yes Yes Yes Yes Yes Yes N 633 1152 928 857 780 1005 R2 0.2420 0.1582 0.1737 0.1918 0.2384 0.1867 6 Further Analysis The preceding analysis has examined the impact of ESG divergence on the inflow of high-skilled talent in client firms. Naturally, several extended questions arise:(1) Given that ESG divergence between supply chain partners represents a dyadic relationship, there are two possible cases—client firms with higher ESG scores than their suppliers, and client firms with lower scores. Do these two types of ESG divergence have similar inhibitory effects on high-skilled talent inflow?(2) Are client firms with ESG performance aligned with their industry or region still negatively affected by ESG divergence when attracting high-skilled talent?(3) While the previous analysis focused on the scale of high-skilled labor inflows, it did not explore labor structure—does ESG divergence also affect the labor composition within client firms?(4) Finally, how do divergences across different ESG dimensions—environmental, social, and governance—affect high-skilled talent inflows? This section examines each of these four questions in turn. 6.1 Asymmetry of ESG Divergence The ESG performance of both suppliers and client firms is adjusted by industry, excluding cases where the two scores are identical. This classification produces two subgroups: client firms whose ESG performance exceeds that of their suppliers, and those whose ESG performance falls below their suppliers’. When the client firm’s ESG performance is lower than that of its supplier, the client firm faces greater legitimacy pressure because its ESG level fails to meet the higher supply chain standard and may be perceived as a “free rider.” This exposes the client firm to higher reputational risk and public scrutiny, undermining its ability to attract high-skilled professionals. Conversely, when the client firm’s ESG performance exceeds that of its supplier, although divergence still exists, the client’s superior ESG standing may partially mitigate the potential negative effects. Regression results in Table 8 , columns (1) and (2) demonstrate that client firms with ESG scores lower than their suppliers are more vulnerable to legitimacy and reputational pressures arising from divergence, thereby hindering their ability to attract high-skilled talent. These results provide evidence for the asymmetry of ESG divergence effects: client firms’ ESG underperformance relative to suppliers poses greater reputational and public opinion risks, leading to a decline in high-skilled talent inflows. By contrast, the adverse effects of suppliers’ ESG divergence may be offset by client firms’ strong ESG performance, thus showing a weaker or insignificant impact on high-skilled talent inflow. 6.2 Industry/Regional ESG Consistency Potential job seekers evaluate employers not only by internal conditions but also by their industry or regional standing. A firm’s alignment with industry or regional average ESG performance signals greater legitimacy. High-skilled professionals often assess sector-wide or regional ESG levels to gauge career prospects and workplace quality. Stronger ESG consistency with peers helps bolster a firm’s positive image, reduces reputation risk, and mitigates the adverse effect of ESG divergence on talent attraction. Based on legitimacy theory, the impact of ESG divergence on talent inflow may depend on a firm’s industry (ESG_ind) and regional (ESG_region) ESG consistency. Close alignment with industry or regional norms reflects stronger legitimacy and projects conformity with local expectations. Even when ESG divergence occurs, high consistency allows firms to demonstrate commitment to sustainability, reducing doubts about their legitimacy. Following Falcone & Ridge (2024), ESG industry and regional consistency are measured as follows: $$ESG\_in{d_{i,t}}= - \frac{{|ES{G_{i,t}} - \overline {{ESGin{d_{i,t}}}} |}}{{\sigma ESGin{d_{i,t}}}}$$ 5 $$ESG\_regio{n_{i,t}}= - \frac{{|ES{G_{i,t}} - \overline {{ESGre{g_{i,t}}}} |}}{{\sigma ESGre{g_{i,t}}}}$$ 6 Where \(\overline {{ESGin{d_{i,t}}}}\) represents the mean ESG score of the industry in which the client firm operates, \(\sigma ESGin{d_{i,t}}\) shows the corresponding standard deviation, \(\overline {{ESGre{g_{i,t}}}}\) is the mean ESG score of the province, and \(\sigma ESGre{g_{i,t}}\) is the provincial standard deviation. After taking the negative value, higher ESG_ind and ESG_region indicate closer proximity to the industry or regional ESG average. Based on the median ESG consistency within the same year and industry, the sample is divided into high and low ESG consistency groups. Regression results in Table 8 , columns (3)–(6) show that ESG_dev is insignificant when client firms exhibit high ESG industry or regional consistency, but significantly negative under low consistency. This indicates that stronger ESG industry or regional alignment mitigates the negative effect of ESG divergence on high-skilled talent inflow—that is, the closer a firm’s ESG performance is to its industry or regional average, the less it suffers from ESG divergence. These findings further underscore the importance of ESG signal consistency . When client firms’ ESG signals align with those of their industry or region, they not only reduce reputational risk and sustain a favorable image but also strengthen cooperative ties and operational stability. Higher industry or regional ESG consistency supports the establishment of adaptive mechanisms and steady operational processes within the same institutional context, reinforcing firms’ attractiveness to high-skilled professionals. Thus, alignment of client firms’ ESG performance with industry or regional norms can effectively buffer the adverse impact of ESG divergence on high-skilled talent inflow. Table 8 Asymmetry in ESG divergence and industry/regional ESE consistency analysis results. Variables Client ESG Higher than Supplier ESG Client ESG Lower than Supplier ESG High ESG Industry Consistency Low ESG Industry Consistency High ESG Regional Consistency Low ESG Regional Consistency (1) (2) (3) (4) (5) (6) Labor Labor Labor Labor Labor Labor ESG_dev -0.0449 -0.2268 ** 0.0297 -0.0990 ** 0.0443 -0.1230 ** (0.0876) (0.0960) (0.1102) (0.0483) (0.0754) (0.0553) Controlled Variable Yes Yes Yes Yes Yes Yes Year and Industry Fixed Effects Yes Yes Yes Yes Yes Yes N 553 543 405 1380 674 1111 R2 0.1771 0.3010 0.2413 0.1597 0.1783 0.1823 6.3 Impact of ESG Divergence on Labor Structure in Client Firms The empirical results show that ESG divergence influences the number of high-skilled employees in client firms. To further explore how it reshapes labor composition, this study examines employment structure by occupational category. High-skilled workers, equipped with strong technical and analytical capabilities, can adapt quickly to new technologies and drive innovation (Wei Wu et al., 2025). As innovation-driven development raises skill demands, such talent has become increasingly scarce. Many firms face persistent high-skilled labor shortages, while low-skilled workers struggle to meet new job requirements. Yet, few studies have explored whether and how ESG divergence affects labor structure. This paper thus investigates its role in shaping client firms’ workforce composition. In the baseline regression, employees were classified by education to identify high-skilled talent. For a more detailed analysis, this section distinguishes between occupational categories: production and clerical employees are defined as routine low-skilled labor (Routine), whereas technical, marketing and sales, and financial staff are defined as non-routine high-skilled labor (Non-Routine) (Acemoglu D & Restrepo P, 2018). Labor structure change is measured by the annual difference in the share of routine or non-routine employees. Results in Table 9 , column (1) show that the coefficient of ESG_dev is significantly negative at the 10% level, suggesting that ESG divergence significantly reduces the inflow of non-routine high-skilled labor. Column (2) shows that while the coefficient of ESG_dev for routine labor is negative, it is not statistically significant, indicating that ESG divergence does not significantly affect inflows of low-skilled workers. This implies that high-skilled professionals are more sensitive to ESG signals from firms and their supply chains. Consistency in ESG orientation among supply chain partners increases the number of high-skilled employees, improves labor resource allocation, and optimizes firms’ labor structure—helping to alleviate current imbalances in skilled labor markets and fostering innovation-driven growth. These findings also reaffirm that green development is foundational to high-quality growth: alignment of ESG orientation among supply chain partners underscores the complementarity between high-skilled labor and innovative development. ESG consistency within supply chains not only reshapes firms’ labor demand structures but also generates new occupations and positions, thereby increasing demand for high-skilled professionals. 6.4 Impact of ESG Divergence across ESG Dimensions on High-Skilled Talent Inflow ESG encompasses three dimensions—environmental, social responsibility, and corporate governance—each exerting distinct influences on firm operations, reputation, and attractiveness to high-skilled professionals. Accordingly, this study conducts a dimension-specific analysis to capture how divergence between suppliers and client firms within each dimension affects talent inflow. Following the baseline measurement of overall ESG divergence, this study calculates dimension-specific indices based on the Huazheng ESG sub-ratings: E_dev (environmental divergence), S_dev (social responsibility divergence), and G_dev (governance divergence). Table 9 reports the results for dimension-specific ESG divergence. In column (4), the coefficient of S_dev is − 0.0484 and significant at the 10% level, indicating that divergence in the social responsibility dimension has the strongest negative effect on high-skilled talent inflow. This finding aligns with Edmans A (2011). The primary reason is that the social dimension encompasses labor rights, employee welfare, occupational health, and workplace safety—factors directly relevant to employees’ working conditions and career development prospects. Kim & Park (2011) also found that when business performance is challenged, corporate social responsibility can serve as an effective reputation management strategy to attract potential employees. Hence, it is unsurprising that the social responsibility dimension exerts the greatest influence on high-skilled talent inflow among all ESG dimensions. Table 9 Sub-dimensional Analysis of ESG Deviation and Client Firms’ Labor Structure Variables (1) (2) (3) (4) (5) Non_Routine Routine Labor Labor Labor ESG_dev -0.8903 * (0.5091) -0.0292 (0.7158) E_dev 0.0034 (0.0140) S_dev -0.0484 * (0.0252) G_dev -0.0326(0.0219) Controlled Variable Yes Yes Yes Yes Yes Year and Industry Fixed EFfects Yes Yes Yes Yes Yes N 1743 1743 1785 1785 1785 R 2 0.1226 0.2318 0.1242 0.1250 0.1246 7 Conclusion and Policy Implication Using matched data between Chinese listed firms and their suppliers from 2009 to 2022, this study examines how ESG divergence affects the inflow of high-skilled talent to client firms. The results show that ESG divergence significantly reduces high-skilled talent inflow, a finding that remains robust after a series of endogeneity and robustness tests. Mechanism analysis reveals that reputation risk, operational risk, and operational efficiency are the main channels through which ESG divergence influences talent attraction. Heterogeneity analysis indicates that the negative effect of ESG divergence is more pronounced when client firms have lower levels of digital transformation, operate in highly competitive markets, or maintain networked supplier structures. Further analysis reveals an asymmetric effect: the adverse impact is stronger when the client firm’s ESG performance is lower than its suppliers’, but is significantly mitigated when the client’s ESG performance is close to the industry or regional average. Among ESG dimensions, divergence in social responsibility has the strongest negative impact. This study highlights the asymmetric impact of ESG divergence on high-skilled talent inflow in client firms, enriching the literature on ESG management and labor mobility in supply chains, and underscoring the importance of alignment in ESG performance between firms and their supply chain partners in attracting talent. From a managerial perspective, in addition to strengthening their own ESG practices, firms should pay attention to ESG alignment with supply chain partners—particularly in social responsibility. Enhancing digital transformation and maintaining ESG performance consistent with industry or regional standards can help strengthen organizational legitimacy and talent attractiveness. From a policy perspective, it is advisable to promote ESG coordination across supply chain tiers by establishing industry or regional standards, promoting green supply chain management, and improving the transparency of ESG disclosures. These measures can encourage firms to integrate ESG into their core strategies, optimize talent inflow, and inject new momentum into high-quality economic development. This study has certain limitations. Due to data availability, the sample only includes listed firms and their suppliers. Future research could construct more comprehensive supply chain datasets and explore other potential mechanisms, such as corporate culture misalignment or information transparency. Declarations Ethical approval: This article does not contain any studies with human participants performed by any of the authors. Informed consent: This article does not contain any studies with human participants performed by any of the authors. Author Contribution Yunhui Zhao and Yang Liu completed the topic selection and research design of the paper. Yunhui Zhao and Ziqi Liu completed the writing of the whole paper. Xingxing Fu and Xinyu Yang completed the data collection and processing. All authors reviewed the paper. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. 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Xiong Yangchun,Lam Hugo K.S.,Hu Qiaoxuan,Yee Rachel W.Y. & Blome Constantin.(2021).The financial impacts of environmental violations on supply chains: Evidence from an emerging market.Transportation Research Part E,151,https://doi.org/10.1016/J.TRE.2021.102345. Yang Yang & Jiang Yan.(2023).Buyer-supplier CSR alignment and firm performance: A contingency theory perspective.Journal of Business Research,154,https://doi.org/10.1016/J.JBUSRES.2022.113340. Yang, Y., & Jiang, Y. (2024). The impact of suppliers' CSR controversies on buyers' market value: The moderating role of social capital. Journal of Purchasing and Supply Management, 30(1), 100904. Yu Haixu,Liang Chuanyu,Liu Zhaohua & Wang He.(2023).News-based ESG sentiment and stock price crash risk.International Review of Financial Analysis,88,https://doi.org/10.1016/J.IRFA.2023.102646. Lu Zhang & Mary A. Gowan.(2012).Corporate Social Responsibility, Applicants' Individual Traits, and Organizational Attraction: A Person–Organization Fit Perspective.Journal of Business and Psychology,27(3),345–362.https://doi.org/10.1007/s10869-011-9250-5. Monica A. Zimmerman & Gerald J. Zeitz.(2002).Beyond Survival: Achieving New Venture Growth by Building Legitimacy.The Academy of Management Review,27(3),414–431. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":156003,"visible":true,"origin":"","legend":"\u003cp\u003eKernel density distributions before (left) and after (right) matching.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8275340/v1/d26fb7876b112827397e3d21.jpeg"},{"id":100377534,"identity":"e328a6b8-67e5-4751-ac96-26382ab92206","added_by":"auto","created_at":"2026-01-16 08:47:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2203117,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8275340/v1/0ec3f87a-629a-4dab-b1da-c648fc56d6be.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ESG Divergence and the Inflow of High-Skilled Talent in Client Firms: Evidence from Chinese Listed Companies","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eHigh-skilled talent is a key driver of corporate innovation and development. According to the China Industrial Digital Talent Development Report (2023), there is a significant structural imbalance in the digital talent pool, with a total shortage of 25\u0026ndash;30\u0026nbsp;million people\u0026mdash;a gap that continues to widen. This shortage undermines technological innovation, reduces economic competitiveness, and hinders sustainable development. Therefore, attracting high-skilled talent is essential for enterprises to achieve long-term sustainability.\u003c/p\u003e \u003cp\u003eAs climate change and environmental pollution intensify, many firms have integrated sustainability into their development strategies. By demonstrating a commitment to sustainable development, they enhance their organizational attractiveness (Umrani et al., 2022; Carballo-Penela et al., 2023) and draw in high-skilled professionals. Yet, despite various initiatives, the inflow of such talent remains limited due to the scarcity and uneven distribution of skilled labor. Thus, how firms can effectively attract high-skilled talent to support innovation-driven growth has become an urgent issue.\u003c/p\u003e \u003cp\u003eHigh-skilled talent suited to innovation-driven development typically possess advanced knowledge, strong creativity, professional ethics, and a sense of social and environmental responsibility. When choosing employers, these individuals consider not only economic benefits and career opportunities but also the firm\u0026rsquo;s social responsibility and sustainability capabilities. In response, a growing number of firms are improving their performance across environmental, social, and governance (ESG) dimensions to strengthen their appeal to talent (Zhang \u0026amp; Gowan, 2012).\u003c/p\u003e \u003cp\u003eWhile firms\u0026rsquo; ESG engagement has been shown to expand employment (Edmans, 2011) and increase labor demand (Jiang et al., 2025), supply chain partners form a community of shared interests\u0026mdash;prospering or declining together (Chiu et al., 2019). Therefore, a firm\u0026rsquo;s ability to attract high-skilled professionals depends not only on its own ESG performance but also on that of its partners. Focusing solely on internal ESG practices may fail to achieve the desired talent attraction effect, and inconsistency in ESG performance among partners may further weaken the inflow of high-skilled talent. Accordingly, it is necessary to examine, from a dyadic supply chain perspective, whether and how ESG divergence between suppliers and clients influences the inflow of high-skilled talent to client firms.\u003c/p\u003e \u003cp\u003eBased on signaling theory and consistency theory, when ESG performance between suppliers and clients is inconsistent\u0026mdash;referred to as ESG divergence\u0026mdash;the client firm\u0026rsquo;s ESG commitments may not effectively attract talent. ESG divergence affects the inflow of high-skilled talent through three main mechanisms:\u003c/p\u003e \u003cp\u003eFirst, ESG divergence generates reputational risk. Poor ESG performance by any supply chain member increases not only its own reputational risk but also that of its partners (Dai et al., 2021). Such divergence can trigger public skepticism and dissatisfaction toward client firms, damaging their reputation and undermining the appeal of their ESG commitments to skilled talent. For example, McDonald\u0026rsquo;s and KFC regularly disclose detailed corporate social responsibility (CSR) information in their annual reports. However, both companies faced widespread negative media coverage when upstream suppliers were reported to use expired raw materials (Hartmann \u0026amp; Moeller, 2014). Such negative publicity weakens the non-financial value signals associated with corporate sustainability and CSR commitments (Pedersen et al., 2021; Lewis \u0026amp; Carlos, 2023), thereby reducing the attractiveness to high-skilled talent.\u003c/p\u003e \u003cp\u003eSecond, ESG divergence increases business risk. It reflects incompatible ESG value orientations between suppliers and clients, and deviation from shared norms may induce ethical failures (Hill et al., 2009). For instance, when suppliers\u0026rsquo; poor ESG performance leads stakeholders to question their legitimacy, they may face operational difficulties\u0026mdash;such as disruptions in the supply of raw materials or components\u0026mdash;thereby increasing operational uncertainty for client firms (Xiong et al., 2021). Higher business risk reduces firms\u0026rsquo; willingness to invest in innovation, constrains R\u0026amp;D spending, suppresses demand for high-skilled labor, and ultimately reduces talent inflow.\u003c/p\u003e \u003cp\u003eThird, ESG divergence reduces operational efficiency. When suppliers are penalized or socially boycotted due to poor ESG performance, it can lead to unstable product quality, delivery delays, or production interruptions\u0026mdash;disrupting client firms\u0026rsquo; production schedules. To maintain operational continuity, client firms may resort to emergency measures such as finding temporary alternative suppliers or adjusting production lines. These responses not only increase operational costs but also damage the structural and functional integrity of client operations, reducing efficiency (Liu et al., 2023). Declining operational efficiency leads firms to scale down production and innovation investment (Kale \u0026amp; Shahrur, 2005), diminishing demand for high-skilled workers and further restricting talent inflow.\u003c/p\u003e \u003cp\u003eCompared with existing studies, the main contributions of this paper are as follows: First, against the backdrop of innovation-driven development, it explores the antecedent conditions of high-skilled talent inflow, constructs a theoretical model of the impact of ESG divergence on talent inflow, and provides new empirical evidence for the practice of innovation-driven development; Second, it incorporates upstream and downstream supply chain ESG consistency into the analytical framework, expanding research on ESG and labor from a dyadic supply chain perspective; Third, it integrates signaling theory and consistency theory, revealing the asymmetric negative impact of ESG divergence on client firms' ability to attract high-skilled talent, thereby deepening the theoretical understanding of signal consistency.\u003c/p\u003e"},{"header":"2 Theoretical Framework and Hypotheses","content":"\u003cp\u003eTo support innovation-driven growth, firms require more high-skilled talent aligned with strategic goals. Amid growing climate and environmental challenges, many enterprises have embedded ESG principles into their strategies to attract such talent. As extended producer responsibility systems improve, stakeholders now evaluate not only a firm\u0026rsquo;s own ESG performance but also that of its supply chain partners. This creates interdependence within the supply chain (Zheng Zhen et al., 2024), where suppliers' ESG performance influences client firms' ability to attract high-skilled labor.\u003c/p\u003e \u003cp\u003eAccording to consistency theory, alignment among actors enhances organizational performance (Fry \u0026amp; Smith, 1987). In supply chains, shared values foster trust-based cooperation and organizational identification. For instance, consistency in social responsibility improves financial performance and market value (Liu et al., 2021). When ESG performance aligns across partners, it serves as a shared ethical standard and boosts supply chain outcomes. However, ESG divergence often arises in practice, creating imbalance in supplier\u0026ndash;client relationships.\u003c/p\u003e \u003cp\u003eDrawing on signaling theory, weak ESG performance by one firm sends negative signals about its sustainability commitment. Amplified by media and public attention, these signals harm perceptions of partners (Yu et al., 2023) and undermine the supply chain\u0026rsquo;s legitimacy (Aouadi \u0026amp; Marsat, 2018). For high-skilled professionals, employment decisions now consider not only the firm but also supply chain alignment. Inconsistent ESG signals between partners hinder client firms' talent attraction\u0026mdash;poor supplier ESG cannot be fully offset by strong client performance, nor can a supplier\u0026rsquo;s high ESG fully compensate for a client\u0026rsquo;s weak performance. Based on this reasoning, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1: ESG divergence negatively affects the inflow of high-skilled talent into client firms.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTheoretically, ESG divergence between suppliers and clients may increase client firms\u0026rsquo; reputational and operational risks, reduce their operational efficiency, and thus weaken their inflow of high-skilled talent. This study elaborates on these effects through three mechanisms: the reputational risk mechanism, the operational risk mechanism, and the operational efficiency mechanism.\u003c/p\u003e \u003cp\u003e1. Reputational Risk Mechanism. The public often views supply chain networks as unified entities (Liu et al., 2021). When one member violates common norms, it impacts the reputation of all partners (Kumar et al., 2020). Based on consistency and signaling theories, ESG divergence between suppliers and clients weakens positive ESG signals from the stronger performer. Since the public often cannot accurately trace negative outcomes to their source, dissatisfaction tends to be directed at client firms\u0026mdash;those closer to end consumers (Hartmann \u0026amp; Moeller, 2014; Liu et al., 2021).\u003c/p\u003e \u003cp\u003eThus, even strong ESG performance by a client may not fully offset reputational harm caused by a supplier's misconduct such as pollution or labor violations. Conversely, when a client's ESG performance is weak, public attention\u0026mdash;typically focused on consumer-facing firms\u0026mdash;makes it hard for a supplier's strong ESG reputation to improve perceptions. Stakeholders may even question why a responsible supplier would partner with an irresponsible client (Yu et al., 2023). Such negative scrutiny elevates the client\u0026rsquo;s reputational risk (Boone \u0026amp; Ivanov, 2012), creating doubts among high-skilled professionals about the firm\u0026rsquo;s commitment to sustainability and reducing its talent appeal.Hence, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2: ESG divergence weakens the inflow of high-skilled talent into client firms by increasing reputational risk.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e2. Operational Risk Mechanism. Based on consistency theory, misaligned behavior among supply chain partners increases operational risk (Liu et al., 2021). ESG divergence reflects fundamentally inconsistent values and business philosophies between suppliers and clients. If suppliers face penalties for ESG violations, clients must grapple with practical challenges\u0026mdash;such as finding replacements or ensuring business continuity (Gualandris et al., 2015)\u0026mdash;introducing significant operational risk. Such violations also signal potential issues in product quality, labor rights, or environmental compliance, creating a chain of accountability that exposes clients to reputational harm and trust crises (XuJ et al., 2025), potentially undermining their resource access and operational stability (Zimmerman \u0026amp; Zeitz, 2002).\u003c/p\u003e \u003cp\u003eWhen clients exhibit poor ESG performance, misalignment with suppliers may trigger conflicts over divergent values (Fry \u0026amp; Smith, 1987). Suppliers concerned about their own reputation may reduce or end cooperation (Liu et al., 2021), further raising clients\u0026rsquo; operational risks. These risks divert management attention from long-term R\u0026amp;D and talent investment (Xu S et al., 2025), reducing demand for high-skilled labor. Moreover, ESG divergence can tighten financial constraints, limiting clients\u0026rsquo; capacity to afford and manage high-quality professionals (Falato \u0026amp; Liang, 2016), thereby hindering the inflow of high-skilled talent.Accordingly, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3: ESG divergence increases client firms\u0026rsquo; operational risk, thereby weakening their inflow of high-skilled talent.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e3. Operational Efficiency Mechanism. ESG divergence undermines established behavioral norms and cooperative commitments in supply chains (Ashforth \u0026amp; Gibbs, 1990). When suppliers exhibit poor ESG performance, they signal higher operational and compliance risks (Liu Y Z et al., 2026), potentially leading to reputational damage or regulatory penalties (Feng H et al., 2025). This raises client concerns over reliability and product quality, possibly triggering procurement reductions or supplier replacements (Bai M \u0026amp; Astvansh V, 2025). Such adjustments disrupt normal operations and reduce efficiency.\u003c/p\u003e \u003cp\u003eSimilarly, if a client\u0026rsquo;s ESG performance lags behind its suppliers, the latter may question the client\u0026rsquo;s capabilities and demand stronger commitments or enhanced ESG disclosures (Liu et al., 2021). Though aimed at maintaining cooperation, these demands raise operating costs and impair efficiency. Inefficiencies\u0026mdash;such as delayed decisions, resource waste, and poor after-sales service (Liu et al., 2021)\u0026mdash;conflict with high-skilled professionals\u0026rsquo; expectations for dynamic, innovative workplaces (Huang Qunhui \u0026amp; Sheng Fangfu, 2024), weakening external talent attraction. They may also lower satisfaction and retention among current employees, indirectly hindering future talent inflow.\u003c/p\u003e \u003cp\u003eIn summary, ESG divergence can affect client firms\u0026rsquo; high-skilled talent inflow through reputational, operational, and efficiency-related mechanisms.Therefore, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4: ESG divergence weakens client firms\u0026rsquo; inflow of high-skilled talent by reducing operational efficiency.\u003c/b\u003e \u003c/p\u003e"},{"header":"3 Research Design","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample Selection and Data Sources\u003c/h2\u003e \u003cp\u003eBased on the China Stock Market \u0026amp; Accounting Research (CSMAR) database, this study constructs a matched dataset of Chinese listed firms and their suppliers. ESG data are obtained from the Huazheng ESG ratings in the Wind database, while firm-level information is derived from CSMAR. Since the Huazheng ESG rating system began in 2009, the sample period is set from 2009 to 2022.\u003c/p\u003e \u003cp\u003eTo ensure the reliability of the results, the following steps are taken:(1) samples with non-listed suppliers are excluded;(2) only observations where both suppliers and client firms have Huazheng ESG ratings are retained;(3) financial industry samples are excluded;(4) samples classified as ST or PT firms are removed; and(5) observations with missing key variables are dropped.After these steps, the final dataset includes 1,785 supplier\u0026ndash;client\u0026ndash;year observations, comprising 597 client firms and 724 suppliers. To mitigate the influence of extreme values, all continuous variables are winsorized at the top and bottom 1%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Model Construction and Variable Measurement\u003c/h2\u003e \u003cp\u003eTo examine the impact of ESG divergence on the inflow of high-skilled talent in client firms, the following baseline model is constructed:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Labo{r_{i,t}}={\\beta _0}+{\\beta _1}ESG\\_de{v_{i,t}}+\\gamma {X^{\\prime}_{i,t}}+Year+Industry+\\varepsilon$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere i represents the client firm, t represents the year, X\u0026rsquo; denotes control variables. Year and Industry represent year and industry fixed effects, respectively, while ε is the random disturbance term.\u003c/p\u003e \u003cp\u003eLabor denotes the inflow of high-skilled talent in client firms. Following Edmans A (2011), employees with a postgraduate degree or above are defined as high-skilled talent, and talent inflow is measured as the net increase in such employees. Specifically, it is calculated as: ln(number of postgraduate or higher-degree employees\u0026thinsp;+\u0026thinsp;1)i,t\u0026thinsp;\u0026minus;\u0026thinsp;ln(number of postgraduate or higher-degree employees\u0026thinsp;+\u0026thinsp;1)i,t-1.\u003c/p\u003e \u003cp\u003eESG_dev represents ESG divergence between suppliers and client firms. Compared with ESG indices released by foreign rating agencies, the Huazheng ESG index better reflects the characteristics of the Chinese market and currently covers all A-share listed firms (Wang F et al., 2025). Therefore, using the Huazheng ESG index more effectively captures Chinese firms\u0026rsquo; ESG performance. Based on the Huazheng ESG rating data, enterprise ESG ratings are assigned values from 9 (highest) to 1 (lowest) (Erik B et al., 2021). Referring to Kumar et al. (2020), ESG divergence is measured using the following formula:\u003c/p\u003e \u003cp\u003eWeight* (C_ESG- S_ESG)\u003csup\u003e2\u003c/sup\u003e (2)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eWeight\u003c/em\u003e represents the proportion of a supplier\u0026rsquo;s sales to the client firm\u0026rsquo;s total procurement. The weighted approach is adopted because suppliers with higher procurement shares may exert stronger influence on the results than those with lower shares. \u003cem\u003eC_ESG\u003c/em\u003e and \u003cem\u003eS_ESG\u003c/em\u003e denote the ESG ratings of client firms and suppliers, respectively.\u003c/p\u003e \u003cp\u003eFollowing prior studies, this paper controls for several firm-level variables: client firm ownership type (SOE), firm size (Size), firm age (Age), leverage ratio (Lev), return on assets (ROA), employee wages (Wage), sales profit margin (Profit), asset turnover (ATO), Tobin\u0026rsquo;s Q (TobinQ), return on equity (ROE), current ratio (Liquid), financial leverage (FL), book-to-market ratio (BM), CEO duality (Dual), ownership concentration (Top3), client firm ESG rating (C_ESG), supplier ESG rating (S_ESG), regional per capita GDP (PGDP), and Herfindahl\u0026ndash;Hirschman Index (HHI). All variables and their measurement methods are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables and measurement methods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasurement Method\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-skilled Talent Inflow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eln(Number of employees with postgraduate degree or above +\u0026thinsp;1)_i,t\u0026thinsp;\u0026minus;\u0026thinsp;ln(Number of employees with postgraduate degree or above +\u0026thinsp;1)_i,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupplier\u0026ndash;Client ESG Divergence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eESG_dev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeight \u0026times; (C_ESG\u0026thinsp;\u0026minus;\u0026thinsp;S_ESG)\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"19\" rowspan=\"20\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOwnership Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 if state-owned enterprise; otherwise 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirm Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eln(Total Assets)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirm Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eln(Years since establishment\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeverage Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Liabilities / Total Assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReturn on Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReturn on Total Assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployee Wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eln(Employee Compensation Payable / Number of Employees)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSales Profit Margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProfit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Operating Revenue\u0026thinsp;\u0026minus;\u0026thinsp;Operating Cost) / Operating Revenue\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsset Turnover Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOperating Revenue / Average Total Assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTobin\u0026rsquo;s Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTobinQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTobin\u0026rsquo;s Q Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReturn on Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReturn on Equity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiquid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCurrent Assets / Current Liabilities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinancial Leverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Net Profit\u0026thinsp;+\u0026thinsp;Income Tax\u0026thinsp;+\u0026thinsp;Financial Expenses) / (Net Profit\u0026thinsp;+\u0026thinsp;Income Tax)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBook-to-Market Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBook Value / Market Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDual Role\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 if CEO also serves as Chairperson; otherwise 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOwnership Concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTop3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShareholding Ratio of Top Three Shareholders\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndependent Directors Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of Independent Directors / Total Board Members\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClient Firm ESG Rating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC_ESG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuazheng ESG Rating\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupplier ESG Rating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS_ESG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuazheng ESG Rating\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegional GDP per Capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eln(Provincial Per Capita GDP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHerfindahl\u0026ndash;Hirschman Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHerfindahl\u0026ndash;Hirschman Index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Empirical Results and Analysis","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptive statistics for the main variables. The mean value of High-skilled Talent Inflow in client firms is 0.1944, with a standard deviation of 0.7209, a minimum of \u0026minus;\u0026thinsp;1.7918, and a maximum of 4.8363, indicating considerable variation in high-skilled talent inflow across firms. The mean value of ESG Divergence is 0.1283, with a standard deviation of 0.3747, a minimum of 0, and a maximum of 2.7662.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics of the main variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.7918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.8363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG_dev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.7662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.4314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.2448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.5052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.9459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.5553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.6010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.0570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.7911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.8130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobinQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.2228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.4794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiquid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.8500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-60.0533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.0505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.2300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.5550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.6583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.7664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.2327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.5092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.4554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.3300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.1400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC_ESG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.4353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS_ESG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.1501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.1903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.1396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.1564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Baseline Regression\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the regression results on the impact of ESG divergence on high-skilled talent inflow in client firms. Columns (1) and (2) exclude control variables, while column (2) adds year and industry fixed effects. Column (3) introduces control variables based on column (1), and the coefficient of ESG_dev remains significantly negative. Column (4), which includes both control variables and fixed effects, shows an ESG_dev coefficient of \u0026minus;\u0026thinsp;0.0781, significant at the 5% level. These results indicate that ESG divergence negatively affects the inflow of high-skilled talent in client firms, supporting Hypothesis H1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline regression results of ESG divergence on high-skilled talent inflow in client firms.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG_dev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0705\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0632\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0312)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0640\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0345)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0781\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0398)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0411\u003c/p\u003e \u003cp\u003e(0.0460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0349\u003c/p\u003e \u003cp\u003e(0.0543)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0043\u003c/p\u003e \u003cp\u003e(0.0159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003cp\u003e(0.0188)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2347\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0728\u003c/p\u003e \u003cp\u003e(0.0751)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2257\u003c/p\u003e \u003cp\u003e(0.1880)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.2500\u003c/p\u003e \u003cp\u003e(0.1967)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0149\u003c/p\u003e \u003cp\u003e(0.9333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.6843\u003c/p\u003e \u003cp\u003e(1.0948)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0303\u003c/p\u003e \u003cp\u003e(0.0238)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0615\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0263)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1525\u003c/p\u003e \u003cp\u003e(0.1813)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0653\u003c/p\u003e \u003cp\u003e(0.2081)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0849\u003c/p\u003e \u003cp\u003e(0.0853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1524\u003c/p\u003e \u003cp\u003e(0.1095)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobinQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0349\u003c/p\u003e \u003cp\u003e(0.0248)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0015\u003c/p\u003e \u003cp\u003e(0.0257)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4789\u003c/p\u003e \u003cp\u003e(0.3318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6748\u003c/p\u003e \u003cp\u003e(0.4192)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiquid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0237\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0220\u003c/p\u003e \u003cp\u003e(0.0139)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0159\u003c/p\u003e \u003cp\u003e(0.0131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0150\u003c/p\u003e \u003cp\u003e(0.0142)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2832\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0036\u003c/p\u003e \u003cp\u003e(0.1477)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0062\u003c/p\u003e \u003cp\u003e(0.0431)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0434\u003c/p\u003e \u003cp\u003e(0.0443)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0024\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0029\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0014\u003c/p\u003e \u003cp\u003e(0.0029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003cp\u003e(0.0029)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC_ESG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0200\u003c/p\u003e \u003cp\u003e(0.0199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0219\u003c/p\u003e \u003cp\u003e(0.0206)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS_ESG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0103\u003c/p\u003e \u003cp\u003e(0.0150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0173\u003c/p\u003e \u003cp\u003e(0.0142)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0532\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0839\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0459)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0562\u003c/p\u003e \u003cp\u003e(0.2299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.7984\u003c/p\u003e \u003cp\u003e(0.5951)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2009\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1999\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2940\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.4530)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.8958\u003c/p\u003e \u003cp\u003e(0.6904)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear and Industry Fixed Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Values in parentheses represent robust standard errors clustered at the supplier\u0026ndash;client level. ***, *, and * indicate significance at the 1%, 5%, and 10% levels, respectively. The same applies to all following tables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Endogeneity Tests\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Instrumental Variable Approach.\u003c/h2\u003e \u003cp\u003eTo mitigate potential endogeneity between ESG divergence and high-skilled talent inflow, this study employs a two-stage least squares (2SLS) approach, using whether a supplier is audited by a Big Four firm as an instrumental variable (LiuC \u0026amp; XinZ, 2024). Big Four\u0026ndash;audited suppliers tend to exhibit higher ESG performance due to stricter compliance standards, thereby influencing ESG divergence without directly affecting client firms' talent inflow. This approach helps address endogeneity concerns and enhances the robustness of the empirical findings.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, column (1), reports the first-stage results of the instrumental variable estimation. The coefficient of Instrument is 0.2590, significant at the 1% level, indicating that the instrument strongly affects the endogenous regressor. The Cragg\u0026ndash;Donald Wald F-statistic equals 89.5980, far exceeding conventional critical thresholds, and the Kleibergen\u0026ndash;Paap rk Wald F-statistic equals 15.7420, above the value of 10, effectively ruling out weak-instrument concerns. Column (2) presents the second-stage regression results, showing that the coefficient of ESG_dev is \u0026minus;\u0026thinsp;0.3485 and significant at the 5% level. This finding indicates that ESG divergence significantly reduces high-skilled talent inflow in client firms, further confirming the baseline regression results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Heckman Two-Stage Model Regression.\u003c/h2\u003e \u003cp\u003eAlthough the China Securities Regulatory Commission encourages listed companies to disclose information on their top five suppliers and customers, firms ultimately retain discretion regarding whether to disclose such details (Branstetter G L et al., 2019). During the sample-matching process, this disclosure choice may introduce sample selection bias. To address this concern, the Heckman two-stage model is applied.\u003c/p\u003e \u003cp\u003eIn the first stage, a Probit model is estimated to examine the factors influencing a firm\u0026rsquo;s decision to disclose supplier information, incorporating control variables. From this, the inverse Mills ratio (IMR) is calculated. In the second stage, the IMR is added to the baseline regression to further test the research hypothesis. Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the second-stage regression results: the coefficient of \u003cem\u003eESG_dev\u003c/em\u003e is \u0026minus;\u0026thinsp;0.0747, significant at the 10% level. This indicates that even after correcting for potential sample selection bias, ESG divergence continues to exert a significant negative impact on high-skilled talent inflow, confirming the robustness of the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Propensity Score Matching (PSM).\u003c/h2\u003e \u003cp\u003eTo further test whether differences in control variables between high and low ESG-divergence samples drive potential endogeneity, the study employs propensity score matching (PSM). Specifically, firms are classified into high- and low-divergence groups based on the median ESG divergence within the same industry and year. Control variables from the baseline regression are used as covariates, and nearest-neighbor matching is applied. This method ensures comparability between the two groups on key characteristics, thereby reducing potential endogeneity. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the kernel density distributions before and after matching. Before matching, the probability distributions differ substantially between the two groups; after matching, they become much more similar, suggesting an effective matching outcome. Column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the PSM-adjusted regression results, where the coefficient of \u003cem\u003eESG_dev\u003c/em\u003e is \u0026minus;\u0026thinsp;0.1651, significant at the 5% level. This again confirms the robustness of the findings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEndogenity test results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eInstrumental Variable Method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeckman Two-Stage Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePropensity Score Matching (PSM) Method\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESG_dev\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG_dev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.3486\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1781)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0747\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0394)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1651\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0590)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstrument\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2590\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0653)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6899\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.3515)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControlled Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear and Industry Fixed Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Robustness Tests\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Alternative Measurement of the Dependent Variable.\u003c/h2\u003e \u003cp\u003eTo further test robustness, this study adopts an alternative measure of high-skilled talent inflow\u0026mdash;the difference in the proportion of employees with postgraduate degrees or higher between the current and previous year (Daron A \u0026amp; Pascual R, 2022). Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports a coefficient for ESG_dev of \u0026minus;\u0026thinsp;0.1936, significant at the 10% level, suggesting that ESG divergence significantly weakens high-skilled talent inflow in client firms. This result remains consistent with the baseline findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Alternative Measurement of the Independent Variable.\u003c/h2\u003e \u003cp\u003eIn the baseline regression, ESG ratings are divided into nine discrete levels, which may introduce bias in assessing ESG divergence. To ensure robustness, the study uses the continuous Huazheng ESG score (ESG_a, 0\u0026ndash;100) to construct a refined measure of ESG divergence (ESG_dev_a) (Kumar et al., 2020), as defined in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). C_ESG_a\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;i,t\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;and S_ESG_a\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;i,t\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;denote the ESG scores of client firms and suppliers, respectively. Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports that the coefficient of ESG_dev_a is \u0026minus;\u0026thinsp;0.0880, significant at the 10% level, further validating the robustness of the results.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$ESG\\_dev\\_{a_{i,t}}=\\frac{{Weight*{{(C\\_ESG\\_{a_{i,t}} - S\\_ESG\\_{a_{i,t}})}^2}}}{{(C\\_ESG\\_{a_{i,t}}+S\\_ESG\\_{a_{i,t}})/2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 Adding Fixed Effects.\u003c/h2\u003e \u003cp\u003eWhen examining the impact of ESG divergence on high-skilled talent inflow in client firms, geographical factors may have potential effects on firms\u0026rsquo; attractiveness to talent. Differences among provinces in terms of economic development level, policy environment, and labor market conditions may influence firms\u0026rsquo; ability to attract high-skilled workers. Therefore, to further control for the influence of such regional heterogeneity, this study adds provincial fixed effects to the baseline regression. Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports that the coefficient of \u003cem\u003eESG_dev\u003c/em\u003e is \u0026minus;\u0026thinsp;0.0952, significant at the 5% level, indicating that the findings remain robust.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.4.4 Changing the Clustering Method.\u003c/h2\u003e \u003cp\u003eTo more effectively control for potential autocorrelation and heterogeneity \u003cb\u003ewithin\u003c/b\u003e industries, the study adjusts the clustering of standard errors to the industry level based on client firms. As shown in column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the coefficient of \u003cem\u003eESG_dev\u003c/em\u003e is \u0026minus;\u0026thinsp;0.0781, significant at the 5% level, consistent with the baseline regression results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.4.5 Excluding the Impact of the COVID-19 Pandemic.\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic in 2020 had a significant impact on the global economy and labor markets (Albanesi \u0026amp; Kim, 2021), particularly affecting firms\u0026rsquo; talent mobility and recruitment decisions. Therefore, to avoid abnormal fluctuations caused by the pandemic, this study excludes data for the year 2020 from the sample. Column (5) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports that the coefficient of \u003cem\u003eESG_dev\u003c/em\u003e is \u0026minus;\u0026thinsp;0.1005, significant at the 1% level, further confirming the robustness of the results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness test results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlternative Measurement of the Dependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlternative Measurement of the Independent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdding Client Province Fixed Effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndustry-Clustered Standard Errors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExcluding the Impact of the COVID-19 Pandemic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabor_a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG_dev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1936\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0952\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0453)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0781\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1005\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0387)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG_dev_a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0880\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0533)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControlled Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear and Industry Fixed Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvince Fixed Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5 Mechanism and Heterogeneity Analysis","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Mechanism Analysis\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e5.1.1 Reputation Risk Mechanism\u003c/h2\u003e \u003cp\u003eESG divergence may deter high-skilled talent by amplifying a client firm\u0026rsquo;s reputation risk. When a supplier\u0026rsquo;s ESG performance surpasses the client\u0026rsquo;s, stakeholders may perceive the client as falling short of expected sustainability standards, casting doubt on its commitment to social responsibility (Yang \u0026amp; Jiang, 2023). Conversely, if the supplier underperforms in ESG, the client may face public criticism for associating with low-standard partners, signaling weak ESG risk control in the supply chain (Liu et al., 2024; Yang \u0026amp; Jiang, 2024). Since high-skilled professionals value corporate ethics and sustainability (Edmans, 2011), such reputational concerns may lead them to avoid the firm. Thus, ESG divergence undermines talent attraction by elevating reputation risk.\u003c/p\u003e \u003cp\u003eFollowing Yu et al. (2023), this study measures firms\u0026rsquo; ESG-related negative publicity (\u003cem\u003eNews\u003c/em\u003e) using the number of ESG-related negative news items collected from the DATAGO Financial Research Database. The causal inference approach proposed by J K P \u0026amp; F A H (2008) is adopted for mechanism testing. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, column (1), the coefficient of \u003cem\u003eESG_dev\u003c/em\u003e is 2.6747 and significantly positive at the 5% level. This result supports Hypothesis H2, indicating that ESG divergence among supply chain partners increases the client firm\u0026rsquo;s reputation risk, which subsequently reduces its inflow of high-skilled talent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e5.1.2 Operational Risk Mechanism.\u003c/h2\u003e \u003cp\u003eHigh-skilled talent tends to seek stable work environments with manageable operational risks (Mann, 2018). Significant ESG divergence between suppliers and clients heightens such risks, undermining talent attraction. Specifically, ESG divergence can provoke negative responses from regulators, consumers, and investors, eroding market trust and investor confidence (Erik et al., 2021). Client firms may then divert resources from core operations to manage ESG-related supply chain risks, increasing operational uncertainty (Xu et al., 2024). As high-skilled professionals generally prefer lower-risk employers (Branstetter et al., 2019), ESG divergence reduces the appeal of client firms by amplifying operational risk.\u003c/p\u003e \u003cp\u003eFollowing Haejun J et al.(2023), this study measures operational risk using firms\u0026rsquo; downside risk, as specified in Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eROA_ind\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;i,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026lt;/sub\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;represents the average ROA of all firms in the same industry as firm \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e. This indicator captures a firm\u0026rsquo;s vulnerability to financial distress and its potential loss risk when facing external shocks. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, column (2), the coefficient of \u003cem\u003eESG_dev\u003c/em\u003e is 3.1319 and significantly positive at the 5% level. This result supports Hypothesis H3, suggesting that ESG divergence significantly increases firms\u0026rsquo; operational risk, thereby negatively affecting client firms\u0026rsquo; high-skilled talent inflows.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$Ris{k_{i,t}}=\\sqrt {\\frac{1}{5}\\sum\\limits_{{t=1}}^{5} {(RO{A_{i,t - 1}} - ROAin{d_{i,t - 1}})} }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e5.1.3 Operational Efficiency Mechanism.\u003c/h2\u003e \u003cp\u003eESG divergence can undermine client firms' operational efficiency, consequently reducing their appeal to high-skilled talent. Misalignment in ESG standards increases operational uncertainty and complicates supply chain coordination, forcing firms to expend extra resources on business integration. This often leads to supply chain disruptions, quality issues, or delivery delays (Wang et al., 2025), diverting resources from core activities to crisis management and resulting in efficiency losses and organizational inefficiency (Liang et al., 2023). Such operational inefficiencies gradually erode a firm's market competitiveness (Erik et al., 2021), making it less attractive to high-skilled professionals who value stable and efficient working environments.\u003c/p\u003e \u003cp\u003eFollowing Ambulkar et al. (2023), operational efficiency is measured by industry-adjusted inventory turnover days, where a longer turnover duration indicates lower operational efficiency. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, column (3), the coefficient of \u003cem\u003eESG_dev\u003c/em\u003e is 1.3632 and significantly positive at the 1% level. This result supports Hypothesis H4, indicating that ESG divergence significantly undermines client firms\u0026rsquo; operational efficiency, and the resulting decline in efficiency further negatively affects high-skilled talent inflow.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism analysis summary.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReputation Risk Mechanism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOperational Risk Mechanism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOperational Efficiency Mechanism\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNews\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrisk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEfficiency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG_dev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6747\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.0757)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1319\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.2922)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3632\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.4083)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControlled Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear and Industry Fixed Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Heterogeneity Analysis\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1 Heterogeneity of Digital Transformation.\u003c/h2\u003e \u003cp\u003eBased on the median level of digital transformation within the same industry and year, client firms are divided into high and low digital transformation subsamples. Following Erik B et al. (2021), the degree of digital transformation is measured by the proportion of digital transformation\u0026ndash;related keywords in the annual report relative to total word count. Digital transformation enhances firms\u0026rsquo; operational efficiency and information-processing capabilities (Wang F et al., 2025) and reduces coordination problems arising from ESG divergence. It also improves internal processes and transparency, mitigating public concerns over ESG mismanagement and reputation loss. Therefore, firms with high levels of digital transformation often possess more flexible response mechanisms, allowing them to rapidly adjust strategies to align with sustainability goals (Feng \u0026amp; Zhu, 2024), thereby weakening the adverse effect of ESG divergence on high-skilled talent inflow. Regression results in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, columns (1) and (2) indicate that higher levels of digital transformation effectively mitigate the negative impact of ESG divergence on client firms\u0026rsquo; ability to attract high-skilled workers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2 Heterogeneity of Market Competition.\u003c/h2\u003e \u003cp\u003eUsing the median Herfindahl\u0026ndash;Hirschman Index (HHI) by industry and year, the sample is split into high- and low-competition groups. In more competitive markets, client firms experience stronger external pressure and uncertainty. ESG misalignment with suppliers can more severely damage a client\u0026rsquo;s reputation and brand, signaling poor management of ESG risks in the supply chain. This, in turn, reduces the firm's appeal to high-skilled workers, who in competitive markets have more options and prefer employers with strong, consistent ESG practices. Thus, competition intensifies the negative effect of ESG divergence on attracting talent.Regression results in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, columns (3) and (4) confirm that client firms in highly competitive industries experience stronger negative effects of ESG divergence on high-skilled talent inflow.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e5.2.3 Heterogeneity of Supplier Concentration or Network Structure.\u003c/h2\u003e \u003cp\u003eThis study measures supplier concentration as the proportion of total purchases accounted for by the top five suppliers. The sample is split at the industry-year median into supplier-concentrated and supplier-networked groups. Under high supplier concentration, closer ties with key suppliers improve coordination and reduce ESG divergence, mitigating its negative impact. Such relationships also enable better monitoring of supplier ESG performance, partly offsetting the effect on talent attraction. In contrast, firms in supplier-networked settings rely on numerous suppliers, increasing complexity. Looser relationships and lower information efficiency make ESG divergence harder to address, reducing appeal to high-skilled professionals. Regression results in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, columns (5) and (6) demonstrate that under supplier-networked configurations, the negative impact of ESG divergence on client firms\u0026rsquo; ability to attract high-skilled talent is significantly stronger.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity analysis results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh Digital Transformation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow Digital Transformation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Market Competition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow Market Competition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupplier Concentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupplier Digitalization\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eESG_dev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0907\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1318\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1697\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0734)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0522)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0725)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControlled Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear and Industry Fixed Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6 Further Analysis","content":"\u003cp\u003eThe preceding analysis has examined the impact of ESG divergence on the inflow of high-skilled talent in client firms. Naturally, several extended questions arise:(1) Given that ESG divergence between supply chain partners represents a dyadic relationship, there are two possible cases\u0026mdash;client firms with higher ESG scores than their suppliers, and client firms with lower scores. Do these two types of ESG divergence have similar inhibitory effects on high-skilled talent inflow?(2) Are client firms with ESG performance aligned with their industry or region still negatively affected by ESG divergence when attracting high-skilled talent?(3) While the previous analysis focused on the scale of high-skilled labor inflows, it did not explore labor structure\u0026mdash;does ESG divergence also affect the labor composition within client firms?(4) Finally, how do divergences across different ESG dimensions\u0026mdash;environmental, social, and governance\u0026mdash;affect high-skilled talent inflows?\u003c/p\u003e \u003cp\u003eThis section examines each of these four questions in turn.\u003c/p\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Asymmetry of ESG Divergence\u003c/h2\u003e \u003cp\u003eThe ESG performance of both suppliers and client firms is adjusted by industry, excluding cases where the two scores are identical. This classification produces two subgroups: client firms whose ESG performance exceeds that of their suppliers, and those whose ESG performance falls below their suppliers\u0026rsquo;.\u003c/p\u003e \u003cp\u003eWhen the client firm\u0026rsquo;s ESG performance is lower than that of its supplier, the client firm faces greater legitimacy pressure because its ESG level fails to meet the higher supply chain standard and may be perceived as a \u0026ldquo;free rider.\u0026rdquo; This exposes the client firm to higher reputational risk and public scrutiny, undermining its ability to attract high-skilled professionals. Conversely, when the client firm\u0026rsquo;s ESG performance exceeds that of its supplier, although divergence still exists, the client\u0026rsquo;s superior ESG standing may partially mitigate the potential negative effects.\u003c/p\u003e \u003cp\u003eRegression results in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, columns (1) and (2) demonstrate that client firms with ESG scores lower than their suppliers are more vulnerable to legitimacy and reputational pressures arising from divergence, thereby hindering their ability to attract high-skilled talent. These results provide evidence for the asymmetry of ESG divergence effects: client firms\u0026rsquo; ESG underperformance relative to suppliers poses greater reputational and public opinion risks, leading to a decline in high-skilled talent inflows. By contrast, the adverse effects of suppliers\u0026rsquo; ESG divergence may be offset by client firms\u0026rsquo; strong ESG performance, thus showing a weaker or insignificant impact on high-skilled talent inflow.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Industry/Regional ESG Consistency\u003c/h2\u003e \u003cp\u003ePotential job seekers evaluate employers not only by internal conditions but also by their industry or regional standing. A firm\u0026rsquo;s alignment with industry or regional average ESG performance signals greater legitimacy. High-skilled professionals often assess sector-wide or regional ESG levels to gauge career prospects and workplace quality. Stronger ESG consistency with peers helps bolster a firm\u0026rsquo;s positive image, reduces reputation risk, and mitigates the adverse effect of ESG divergence on talent attraction.\u003c/p\u003e \u003cp\u003eBased on legitimacy theory, the impact of ESG divergence on talent inflow may depend on a firm\u0026rsquo;s industry (ESG_ind) and regional (ESG_region) ESG consistency. Close alignment with industry or regional norms reflects stronger legitimacy and projects conformity with local expectations. Even when ESG divergence occurs, high consistency allows firms to demonstrate commitment to sustainability, reducing doubts about their legitimacy.\u003c/p\u003e \u003cp\u003eFollowing Falcone \u0026amp; Ridge (2024), ESG industry and regional consistency are measured as follows:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$ESG\\_in{d_{i,t}}= - \\frac{{|ES{G_{i,t}} - \\overline {{ESGin{d_{i,t}}}} |}}{{\\sigma ESGin{d_{i,t}}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$ESG\\_regio{n_{i,t}}= - \\frac{{|ES{G_{i,t}} - \\overline {{ESGre{g_{i,t}}}} |}}{{\\sigma ESGre{g_{i,t}}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\overline {{ESGin{d_{i,t}}}}\\)\u003c/span\u003e\u003c/span\u003e represents the mean ESG score of the industry in which the client firm operates, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sigma ESGin{d_{i,t}}\\)\u003c/span\u003e\u003c/span\u003e shows the corresponding standard deviation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\overline {{ESGre{g_{i,t}}}}\\)\u003c/span\u003e\u003c/span\u003e is the mean ESG score of the province, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sigma ESGre{g_{i,t}}\\)\u003c/span\u003e\u003c/span\u003e is the provincial standard deviation. After taking the negative value, higher ESG_ind and ESG_region indicate closer proximity to the industry or regional ESG average. Based on the median ESG consistency within the same year and industry, the sample is divided into high and low ESG consistency groups. Regression results in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, columns (3)\u0026ndash;(6) show that \u003cem\u003eESG_dev\u003c/em\u003e is insignificant when client firms exhibit high ESG industry or regional consistency, but significantly negative under low consistency. This indicates that stronger ESG industry or regional alignment mitigates the negative effect of ESG divergence on high-skilled talent inflow\u0026mdash;that is, the closer a firm\u0026rsquo;s ESG performance is to its industry or regional average, the less it suffers from ESG divergence.\u003c/p\u003e \u003cp\u003eThese findings further underscore the importance of ESG \u003cem\u003esignal consistency\u003c/em\u003e. When client firms\u0026rsquo; ESG signals align with those of their industry or region, they not only reduce reputational risk and sustain a favorable image but also strengthen cooperative ties and operational stability. Higher industry or regional ESG consistency supports the establishment of adaptive mechanisms and steady operational processes within the same institutional context, reinforcing firms\u0026rsquo; attractiveness to high-skilled professionals. Thus, alignment of client firms\u0026rsquo; ESG performance with industry or regional norms can effectively buffer the adverse impact of ESG divergence on high-skilled talent inflow.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAsymmetry in ESG divergence and industry/regional ESE consistency analysis results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClient ESG Higher than Supplier ESG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClient ESG Lower than Supplier ESG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh ESG Industry Consistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow ESG Industry Consistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh ESG Regional Consistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow ESG Regional Consistency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eESG_dev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2268\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0990\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1230\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0960)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0754)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0553)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControlled Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear and Industry Fixed Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Impact of ESG Divergence on Labor Structure in Client Firms\u003c/h2\u003e \u003cp\u003eThe empirical results show that ESG divergence influences the number of high-skilled employees in client firms. To further explore how it reshapes labor composition, this study examines employment structure by occupational category. High-skilled workers, equipped with strong technical and analytical capabilities, can adapt quickly to new technologies and drive innovation (Wei Wu et al., 2025). As innovation-driven development raises skill demands, such talent has become increasingly scarce. Many firms face persistent high-skilled labor shortages, while low-skilled workers struggle to meet new job requirements. Yet, few studies have explored whether and how ESG divergence affects labor structure. This paper thus investigates its role in shaping client firms\u0026rsquo; workforce composition.\u003c/p\u003e \u003cp\u003eIn the baseline regression, employees were classified by education to identify high-skilled talent. For a more detailed analysis, this section distinguishes between occupational categories: production and clerical employees are defined as routine low-skilled labor (Routine), whereas technical, marketing and sales, and financial staff are defined as non-routine high-skilled labor (Non-Routine) (Acemoglu D \u0026amp; Restrepo P, 2018). Labor structure change is measured by the annual difference in the share of routine or non-routine employees. Results in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, column (1) show that the coefficient of ESG_dev is significantly negative at the 10% level, suggesting that ESG divergence significantly reduces the inflow of non-routine high-skilled labor. Column (2) shows that while the coefficient of ESG_dev for routine labor is negative, it is not statistically significant, indicating that ESG divergence does not significantly affect inflows of low-skilled workers. This implies that high-skilled professionals are more sensitive to ESG signals from firms and their supply chains. Consistency in ESG orientation among supply chain partners increases the number of high-skilled employees, improves labor resource allocation, and optimizes firms\u0026rsquo; labor structure\u0026mdash;helping to alleviate current imbalances in skilled labor markets and fostering innovation-driven growth. These findings also reaffirm that green development is foundational to high-quality growth: alignment of ESG orientation among supply chain partners underscores the complementarity between high-skilled labor and innovative development. ESG consistency within supply chains not only reshapes firms\u0026rsquo; labor demand structures but also generates new occupations and positions, thereby increasing demand for high-skilled professionals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e6.4 Impact of ESG Divergence across ESG Dimensions on High-Skilled Talent Inflow\u003c/h2\u003e \u003cp\u003eESG encompasses three dimensions\u0026mdash;environmental, social responsibility, and corporate governance\u0026mdash;each exerting distinct influences on firm operations, reputation, and attractiveness to high-skilled professionals. Accordingly, this study conducts a dimension-specific analysis to capture how divergence between suppliers and client firms within each dimension affects talent inflow.\u003c/p\u003e \u003cp\u003eFollowing the baseline measurement of overall ESG divergence, this study calculates dimension-specific indices based on the Huazheng ESG sub-ratings: E_dev (environmental divergence), S_dev (social responsibility divergence), and G_dev (governance divergence). Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e reports the results for dimension-specific ESG divergence. In column (4), the coefficient of S_dev is \u0026minus;\u0026thinsp;0.0484 and significant at the 10% level, indicating that divergence in the social responsibility dimension has the strongest negative effect on high-skilled talent inflow. This finding aligns with Edmans A (2011). The primary reason is that the social dimension encompasses labor rights, employee welfare, occupational health, and workplace safety\u0026mdash;factors directly relevant to employees\u0026rsquo; working conditions and career development prospects. Kim \u0026amp; Park (2011) also found that when business performance is challenged, corporate social responsibility can serve as an effective reputation management strategy to attract potential employees. Hence, it is unsurprising that the social responsibility dimension exerts the greatest influence on high-skilled talent inflow among all ESG dimensions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSub-dimensional Analysis of ESG Deviation and Client Firms\u0026rsquo; Labor Structure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon_Routine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoutine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLabor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG_dev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.8903\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.5091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0292\u003c/p\u003e \u003cp\u003e(0.7158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE_dev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003cp\u003e(0.0140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS_dev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0484\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG_dev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0326(0.0219)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControlled Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear and Industry Fixed EFfects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"7 Conclusion and Policy Implication","content":"\u003cp\u003eUsing matched data between Chinese listed firms and their suppliers from 2009 to 2022, this study examines how ESG divergence affects the inflow of high-skilled talent to client firms. The results show that ESG divergence significantly reduces high-skilled talent inflow, a finding that remains robust after a series of endogeneity and robustness tests. Mechanism analysis reveals that reputation risk, operational risk, and operational efficiency are the main channels through which ESG divergence influences talent attraction.\u003c/p\u003e \u003cp\u003eHeterogeneity analysis indicates that the negative effect of ESG divergence is more pronounced when client firms have lower levels of digital transformation, operate in highly competitive markets, or maintain networked supplier structures. Further analysis reveals an asymmetric effect: the adverse impact is stronger when the client firm\u0026rsquo;s ESG performance is lower than its suppliers\u0026rsquo;, but is significantly mitigated when the client\u0026rsquo;s ESG performance is close to the industry or regional average. Among ESG dimensions, divergence in social responsibility has the strongest negative impact.\u003c/p\u003e \u003cp\u003eThis study highlights the asymmetric impact of ESG divergence on high-skilled talent inflow in client firms, enriching the literature on ESG management and labor mobility in supply chains, and underscoring the importance of alignment in ESG performance between firms and their supply chain partners in attracting talent.\u003c/p\u003e \u003cp\u003eFrom a managerial perspective, in addition to strengthening their own ESG practices, firms should pay attention to ESG alignment with supply chain partners\u0026mdash;particularly in social responsibility. Enhancing digital transformation and maintaining ESG performance consistent with industry or regional standards can help strengthen organizational legitimacy and talent attractiveness.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, it is advisable to promote ESG coordination across supply chain tiers by establishing industry or regional standards, promoting green supply chain management, and improving the transparency of ESG disclosures. These measures can encourage firms to integrate ESG into their core strategies, optimize talent inflow, and inject new momentum into high-quality economic development.\u003c/p\u003e \u003cp\u003eThis study has certain limitations. Due to data availability, the sample only includes listed firms and their suppliers. Future research could construct more comprehensive supply chain datasets and explore other potential mechanisms, such as corporate culture misalignment or information transparency.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval:\u003c/h2\u003e \u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent:\u003c/strong\u003e \u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYunhui Zhao and Yang Liu completed the topic selection and research design of the paper. Yunhui Zhao and Ziqi Liu completed the writing of the whole paper. Xingxing Fu and Xinyu Yang completed the data collection and processing. All authors reviewed the paper.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcemoglu Daron \u0026amp; Restrepo Pascual.(2022).Tasks, Automation, and the Rise in U.S. Wage Inequality.Econometrica,90(5),1973\u0026ndash;2016.https://doi.org/10.3982/ECTA19815.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichael G. Hertzel,Zhi Li,Micah S. Officer \u0026amp; Kimberly J. 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(2024). The impact of suppliers' CSR controversies on buyers' market value: The moderating role of social capital. Journal of Purchasing and Supply Management, 30(1), 100904.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Haixu,Liang Chuanyu,Liu Zhaohua \u0026amp; Wang He.(2023).News-based ESG sentiment and stock price crash risk.International Review of Financial Analysis,88,https://doi.org/10.1016/J.IRFA.2023.102646.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Zhang \u0026amp; Mary A. Gowan.(2012).Corporate Social Responsibility, Applicants' Individual Traits, and Organizational Attraction: A Person\u0026ndash;Organization Fit Perspective.Journal of Business and Psychology,27(3),345\u0026ndash;362.https://doi.org/10.1007/s10869-011-9250-5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonica A. Zimmerman \u0026amp; Gerald J. Zeitz.(2002).Beyond Survival: Achieving New Venture Growth by Building Legitimacy.The Academy of Management Review,27(3),414\u0026ndash;431.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"High-skilled talent, ESG divergence, Supplier–client relationship, Signal consistency","lastPublishedDoi":"10.21203/rs.3.rs-8275340/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8275340/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-skilled talent, as a core resource, is a key driving force for enterprises to achieve high-quality and sustainable development. Previous studies showed that ESG performance is a critical factor that impacts the inflow of high-skilled talents toward client firms. However, few studies have examined how inconsistency (divergence) in ESG performance between suppliers and client firms affects the inflow of high-skilled talent. This study uses data from Chinese listed companies and their suppliers from 2009 to 2022. It employs signaling theory and consistency theory. It explores the impact of ESG divergence between suppliers and client firms on the inflow of high-skilled talent. The perspective is dyadic supply chain relationships.\u003c/p\u003e \u003cp\u003eThe findings reveal that ESG divergence increases both reputational and operational risks for client firms, reduces operational efficiency, and thereby weakens their ability to attract high-skilled talent. However, when a client firm\u0026rsquo;s ESG performance aligns with the average ESG level of its industry or region, the negative effect of ESG divergence can be effectively mitigated. In addition, the negative effect of ESG divergence is asymmetric: when a client firm\u0026rsquo;s ESG performance is lower than that of its supplier, the detrimental impact on talent inflow is more pronounced. Furthermore, the negative influence of ESG divergence is amplified when client firms have a lower level of digital transformation, operate in highly competitive markets, or are connected to suppliers within more networked configurations.\u003c/p\u003e \u003cp\u003eThis study enriches the literature on supply chain ESG management and high-skilled talent inflow under the context of innovation-driven development. It underscores the importance of ESG signal consistency in attracting high-skilled talent. It provides practical implications for enterprises. They aim to optimize their talent structure and enhance innovation-driven growth.\u003c/p\u003e","manuscriptTitle":"ESG Divergence and the Inflow of High-Skilled Talent in Client Firms: Evidence from Chinese Listed Companies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 21:03:02","doi":"10.21203/rs.3.rs-8275340/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-17T04:08:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T08:15:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135882064952667279374441482844587314236","date":"2026-03-25T12:13:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T03:24:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149448588208118058891482363780269591455","date":"2026-01-10T05:45:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T09:19:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-22T07:28:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-18T07:28:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-17T08:22:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-12-17T08:15:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4a75ce8a-e4ec-453f-bc69-938cd0f778f7","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":60729397,"name":"Business and commerce/Business and management"},{"id":60729398,"name":"Social science/Business and management"},{"id":60729399,"name":"Business and commerce/Economics"},{"id":60729400,"name":"Social science/Economics"},{"id":60729401,"name":"Business and commerce/Operational research"}],"tags":[],"updatedAt":"2026-04-17T04:25:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 21:03:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8275340","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8275340","identity":"rs-8275340","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}