Data on various ESG ratings of sample companies and selected personal data of company executives

The initial sample of this study covers the A-share companies listed on the Shanghai and Shenzhen stock exchanges during the period 2008-2021. We then screened and processed the initial sample data, including (a) Screening for companies with both RepRisk's ESG rating and Bloomberg's ESG rating. Specifically, the selection is based on samples with the same ISIN code and companies' English names in the Bloomberg and RepRisk lndex (RRI) databases. The ISIN code is a securities coding standard developed by the International Organization for Standardization (ISO) and is a unique code used to identify securities in each country or region around the world. We exclude samples that do not provide ISIN codes or have inconsistent English names. (b) We exclude observations with missing values for the main variables. (c) We exclude the ST, *ST and PT trading status samples during the observation period. Our final sample contains 1456 firm-year observations.The ESG disclosure score data and ESG performance score data required for the ESG-washing construction are respectively obtained from the Bloomberg database and the RepRisk Index (RRI) database of the Wharton Research Centre for Data Studies (WRDS). Positive media coverage data is sourced from the China Research Data Services Platform (CNRDS), while the instrumental variable (IV_population) is obtained from the EPS database and Juhe Data (https://www.gotohui.com/). Unless otherwise stated, all other data in this study are from the China Stock Market and Accounting Research (CSMAR) database.Data on executive company changes were collected manually by the authors back-to-back and independently. Then we compared and reconciled the data collected by each, and where there were discrepancies, we again collected and calibrated the data to maximize their reliability. We first obtained executive biographies from the CSMAR database, and the missing values were retrieved from Sina Finance ( https://finance.sina.com.cn/). Due to the unstructured nature of the resume data, we manually processed more than 30,000 resumes of executives to get the data of executives' company changes, based on which we calculated the per capita number of job hops of all executives in each company. The number of part-time jobs held by executives also reflects their pursuit of career changes and development, so in the robustness test the per capita mean of the number of part-time jobs held by executives is used as a proxy variable for careerist orientation. These data can be obtained directly from the CSMAR database.

本研究的初始样本覆盖2008年至2021年期间在上交所与深交所上市的A股公司。随后我们对初始样本数据开展筛选与处理工作，具体如下：(a) 筛选同时拥有RepRisk ESG评级与彭博(Bloomberg) ESG评级的公司。具体而言，选取彭博与RepRisk指数(RRI)数据库中具有相同国际证券识别码(ISIN)及公司英文名称的样本。国际证券识别码(ISIN)是由国际标准化组织(ISO)制定的证券编码标准，为全球各国及地区识别证券的唯一代码。我们剔除未提供ISIN码或英文名称不一致的样本。(b) 剔除主要变量存在缺失值的观测样本。(c) 剔除观测期内处于ST、*ST及PT交易状态的样本。最终本研究的有效样本包含1456个公司-年度观测值。
用于ESG漂绿(ESG-washing)构建的ESG披露得分数据与ESG表现得分数据，分别取自沃顿研究数据服务中心(WRDS)旗下的彭博数据库与RepRisk指数(RRI)数据库。正面媒体报道数据源自中国研究数据服务平台(CNRDS)，工具变量(IV_population)则取自EPS数据库与聚合数据(Juhe Data, https://www.gotohui.com/)。除非另有说明，本研究其余所有数据均取自中国股票市场与会计研究(CSMAR)数据库。
高管团队公司变动数据由作者二人背对背独立手动收集。随后我们对各自收集的数据开展比对与协调工作，若存在分歧，则再次进行数据收集与校准，以最大化数据可靠性。我们首先从CSMAR数据库获取高管简历，缺失部分则从新浪财经(https://finance.sina.com.cn/)补充获取。由于简历数据属于非结构化数据，我们手动处理了逾3万份高管简历，以此得到高管团队的公司变动数据，并据此计算每家公司所有高管的人均跳槽次数。高管兼任的兼职职位数量同样反映了其职业变动与发展诉求，因此在稳健性检验中，我们以高管兼职数量的人均均值作为职业主义导向的代理变量。此类数据可直接从CSMAR数据库获取。