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This paper using panel data of 2008-2019 Shanghai and Shenzhen A-share listed companies as the research sample and employing the multiple regression method to tests the relationship between executive compensation incentives and R&D investment of listed companies in China, further investigates the path of the relationship between the two and the influence of government subsidy to the relationship. In this paper, the selected samples are excluded according to the following criteria: ①Companies with incomplete data on financial indicators and corporate governance indicators are excluded. ②Eliminate companies with negative asset-liability ratio or greater than 1. ③Exclude companies in the financial and insurance industry. ④Exclude listed companies less than 1 year. ⑤Exclude companies containing S, ST and *ST. ⑥Exclude the companies with extreme sample data. The risk-taking data involved in this paper came from the WIND database. Other data come from the CSMAR database.

Comprehensive dataset of 2,635 Executive search firms in United States as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Success.ai’s CEO Contact Data API empowers your organization to connect directly with top executives—CEOs, Founders, and Presidents—across industries and regions. With access to over 700 million verified global profiles, this API provides instant access to accurate, continuously updated contact details, ensuring every high-level outreach is both timely and effective.

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Executive and Senior Level Employee Pay tables (current and past years)

This is a database that comprises two datasets; one on the institutionalized authority of executive heads (EHs) in international organizations (IOs) and another on their personal traits. They cover 142 EHs from 18 IOs from 1980 to 2020. Please see the codebook for further details.

The initial sample of this study covers the A-share companies listed on the Shanghai and Shenzhen stock exchanges during the period 2008-2020. 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 1352 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.

Success.ai’s CEO Contact Data for C-level executives Worldwide enables businesses to connect directly with CEOs, CFOs, and other high-level decision-makers across industries. With access to over 170 million verified professional profiles, this dataset includes crucial contact details for top-tier executives, allowing you to streamline your outreach and engage with leaders who make critical decisions.

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5. Includes profiles of decision-makers such as CEOs, CFOs, COOs, and other executives in various industries.

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7. Continuously Updated Datasets

8. Real-time updates ensure that you have access to the most current and relevant information for your outreach efforts.

9. Ethical and Compliant

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Data Highlights:

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8. Tailor the dataset to your specific target industries or C-level roles for more effective and precise outreach.

APIs for Enhanced Functionality:

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Leverage Success.ai’s B2B Contact Data for C-Level Executives Worldwide to access verified work emails, phone numbers, and decision-maker profiles for top global executives. Our AI-validated data ensures accuracy and relevance, allowing you to optimize your outreach efforts and increase conversion rates.

Contact Success.ai today to discover how our B2B contact data for C-level executives can elevate your business strategy a...

We present a dataset created from merged secondary sources of ExecuComp and CompuStat and then augmented with manual data collection through searches of news stories related to CEO turnover.

We start dataset construction with the ExecuComp executive-level data for the period from 1992 through 2020. These data are merged with the CompuStat dataset of financial variables. As the dataset is intended for research on CEO turnover, we exclude observations in which the CEO at the start of the fiscal year is not well-defined; these are cases when there were co-CEOs and cases when the CEO was shared across different firms. The data set also excludes firm/year combinations that involve a restructuring of the firm – spinoff, buyout, merger, or bankruptcy.

We identify the CEO at the start of each year for each firm. This also helps identify the last year an individual served as CEO. In order to identify CEO turnover based on changes in the CEO from year to year, we require firm observations to extend over at least six contiguous years for the firm to remain in the sample. Cases involving the last year the firm is in the sample are excluded. We also exclude from the dataset cases when there was an interim CEO who stayed in the position for less than 2 years. This results in a sample of 3,100 firms reflecting 41,773 firm/year combinations.

For this sample, we examine news articles related to CEO turnover to confirm the reasons for each CEO departure case. We use the ProQuest full-text news database and search for the company name, the executive name, and the departure year. We identify news articles mentioning the turnover case and then classify the explanation of each CEO departure case into one of five categories of turnover. These categories represent CEOs who resigned, were fired, retired, left due to illness or death, and those who left the position but stayed with the firm in a change of duties, respectively.

The published data file does not include proprietary data from ExecuComp and CompuStat such as executive names and firm financial data. These data fields may be merged with the current data file using the provided ExecuComp and CompuStat identifiers.

The dataset consists of a single table containing the following fields: • gvkey – unique identifier for the firms retrieved from CompuStat database • firmid – unique firm identifier to distinguish distinct contiguous time periods created by breaks in a firm’s presence in the dataset • coname – company name as listed in the CompuStat database • execid – unique identifier for the executives retrieved from ExecuComp database • year – fiscal year • reason – reason for the eventual departure of the CEO executive from the firm, this field is blank for executives who did not leave the firm during the sample period • ceo_departure – dummy variable that equals 1 if the executive left the firm in the fiscal year, and 0 otherwise

Comprehensive dataset of 3 Executive search firms in Montana, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Our US Business Executives database delivers over 91M verified contacts with 95%+ accuracy. Supported by our in-house call centers, social media validation, and market research teams we ensure data is fresh, reliable, and optimized for B2B outreach, lead generation, and advanced market insights.

The authority granted to the County Executive by law to take a certain specific action or the means by which the County Executive exercises his general executive powers. An order generally directs a specific single action rather than establishing rules and standards. Examples of the use of Executive Orders granted by local law include the specific authority of the County Executive under Section 2A-17 of the County Code to issue traffic orders which direct the establishment of stop signs, fire lanes, no parking, etc., at particular designated locations. Examples of Executive Orders arising from his general authority under the Charter of Montgomery County include orders to acquire specific parcels of land for rights-of-way, to direct condemnation by the County Attorney, and to authorize the sale of surplus County property. Update Frequency : Monthly

1780 Global export shipment records of Executive Desk with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Comprehensive dataset of 39 Executive search firms in Washington, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The Veterans Health Administration (VHA) Leadership and Workforce Development System (VHALWD) has 36 separate databases that contain information on people, positions, and organizations, work groups, workforce, workforce and leadership classes, workforce development programs and participation, personal development plans, supervisory levels, mentor and coach attributes, High Performance Development Model (HPDM) core competency, intern data, Equal Employment Opportunity (EEO) reporting, succession planning, workforce planning, senior executive information, applicant tracking and recruitment, Executive Career Field (ECF) position and performance information, and education funding and programs. The VHA Executive Management Program consists of the functions that fall under the purview of the VHA Executive Resources Board (ERB) and the VHA Performance Review Board (PRB). Their functions include executive development, recruitment and placement, organizational analysis, succession planning, workforce planning, EEO and Alternative Dispute Resolution (ADR) assessment, workload tracking and reporting of human capital and HR, and individual and organizational performance assessment and recognition. The method used to collect this information is a proprietary system using relational database technology. Information from these databases are joined and expanded to inform programs and processes. This combination of information is used in the administration of talent management, VHA human capital objectives, and in the support of the ERB and PRB functions.

The State Executive Approval Dataset (SEAD) contains marginals from polls conducted on state-specific samples that measure the public's view of the governor. It contains data from Niemi et al's Job Approval Ratings (JAR) dataset and my own data collection efforts. It also contains a latent measure of governor approval generated using Stimson's dyad-ratio method, estimated quarterly and annually. Version 1, released here, contains data collected through the end of 2020.

The USA: Judicial constraints on executive: The latest value from 2023 is 0.934 index points, unchanged from 0.934 index points in 2022. In comparison, the world average is 0.571 index points, based on data from 171 countries. Historically, the average for the USA from 1960 to 2023 is 0.94 index points. The minimum value, 0.896 index points, was reached in 1960 while the maximum of 0.964 index points was recorded in 2016.

140 Global export shipment records of Executive with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

United States PPI: Employment Services: Executive Search data was reported at 93.300 Dec2008=100 in Jun 2018. This records an increase from the previous number of 92.100 Dec2008=100 for May 2018. United States PPI: Employment Services: Executive Search data is updated monthly, averaging 99.100 Dec2008=100 from Dec 2008 (Median) to Jun 2018, with 115 observations. The data reached an all-time high of 100.000 Dec2008=100 in Dec 2008 and a record low of 92.100 Dec2008=100 in May 2018. United States PPI: Employment Services: Executive Search data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I017: Producer Price Index: By Commodities.

New as of FY20, this is the current top-level dashboard for performance against OMB KPI targets, at Government-Wide, Category, and Department levels. (New version coming early FY22).