We present a dataset of US CEO turnover types created with manual data collection through searches of news stories related to CEO turnover. The data identify the fiscal year of turnover and its type for top managers in publicly-traded US firms. We identify turnover cases based on changes in the CEO of the firm as recorded in secondary data sources of Compustat and Execucomp, and then we classify turnover types based on the examination of full-text news.

We start dataset construction with the ExecuComp executive-level data for the fiscal years 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. 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. 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. 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

There is a newer version of this dataset available online. Click the link for the hosting site.

There is a new version of this data available on Zenodo. Look in the version log on this site.

We have included a snapshot of the documentation file here to help with future use along with an Excel version of the file for non-STATA users. This document also includes information on submitting edits and corrections to the open source data, which we welcome and encourage. We will acknowledge the participation of editors in the versioning changes in the documentation file.

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

There is a newer version of this dataset available online. Click this link for the hosting site.

We have included a snapshot of the documentation file here to help with future use along with an Excel version of the file for non-STATA users. This document also includes information on submitting edits and corrections to the open source data, which we welcome and encourage. We will acknowledge the participation of editors in the versioning changes at the bottom of the documentation file.

This version updates the set to the current turnovers as of May 1, 2023 version of Execucomp database and adds/clarifies several variables. Please check the documentation for the change log. The file was shared and completed on November 9, 2023

If you would like to get an email notification when we update the database, sign-up here. We're happy to let you know when it is updated.

This data is from Gentry et al (2021) and can be accessed here: https://onlinelibrary.wiley.com/doi/10.1002/smj.3278?af=R

Please cite the data as:

Richard J. Gentry, Joseph Harrison, Timothy Quigley, & Steven Boivie. (2021). Open Sourced Database for CEO Dismissal 1992-2018 (Version 2021.02.03) [Data set]. Strategic Management Journal. Zenodo. http://doi.org/10.5281/zenodo.4543893

This is a database of qualitatively coded reasons for a CEO’s dismissal. Every attempt has been made to ensure accuracy and thoroughness but this is a living database that will be updated with new information and departures as well as improved over time. If you have observations about a particular turnover, contact this document’s owner to suggest improvements on the database. Revision suggestions will be cataloged and included in future revisions.

Collection Effort Overview

Data collection was done primarily by undergraduate students in a computer lab together. During the data collection effort, two doctoral students were present to answer questions and monitored students' work in real time using Google Docs. If a student was miscoding events or not coding with enough detail, the doctoral students coached the student how to improve. The data coders generally averaged 8 hours a week across two data coding sessions. The final 1,200 turnover events were outsourced to a data collection company and the final work product from that outsourcing effort was double checked by one of the undergraduate students. A final 180 observations that were coded as 8. Events prior to 2000 and some mergers post-2000 were also coded by an outsourcing firm. All items returned from the outsourcing firm were double checked for clarity and completeness.

If you would like to suggest a change/addition to one of the data items, submit those suggestions here

Use Guidelines

CEO turnover can be coded directly off execucomp by just seeing where there is a new ceo from one year to the next. That set can then be matched to this file to determine whether the turnover is a) actually a turnover rather than a coding error and b) the reason for that departure. Use of the dataset is free and open to use to the Open Data Commons Attribution License. We ask that authors who use the data a) cite the article that published the set and b) reference the specific version number that their paper employed. A log of revisions is below.

CEO Departure Reasons and Definitions

| Code | Title | Brief Description | | -- | -- | ----- | | 1 | Involuntary - CEO death | The CEO died while in office and did not have an opportunity to resign before health failed. | | 2 | Involuntary - CEO illness | Required announcement that the CEO was leaving for health concerns rather than removed during a health crisis. | | 3 | Involuntary – CEO dismissed for job performance | The CEO stepped down for reasons related to job performance. This included situations where the CEO was immediately terminated as well as when the CEO was given some transition period, but the media coverage was negative. Often the media cited financial performance or some other failing of CEO job performance (e.g., leadership deficiencies, innovation weaknesses, etc.). | | 4 | Involuntary - CEO dismissed for personal issues | The CEO was terminated for behavioral or policy-related problems. The CEO's departure was almost always immediate, and the announcement cited an instance where the CEO violated company HR policy, expense account cheating, etc. | | 5 | Voluntary - CEO retired | Voluntary retirement based on how the turnover was reported in the media. Here the departure did not sound forced, and the CEO often had a voice or comment in the succession announcement. Media coverage of voluntary turnover was more valedictory than critical. Firms use different mandatory retirement ages, so we could not use 65 or older and facing mandatory retirement as a cut off. We examined coverage around the event and subsequent coverage of the CEO’s career when it sounded unclear. | | 6 | Voluntary - new opportunity (new career driven succession) | Voluntary retirement based on how the turnover was reported in the media. Here the departure did not sound forced, and the CEO often had a voice or comment in the succession announcement. Media coverage of voluntary turnover was more valedictory than critical. Firms use different mandatory retirement ages, so we could not use 65 or older and facing mandatory retirement as a cut off. We examined coverage around the event and subsequent coverage of the CEO’s career when it sounded unclear. | | 7 | Other | xInterim CEOs, CEO departure following a merger or acquisition, company ceased to exist, company changed key identifiers so it is not an actual turnover, and CEO may or may not have taken over the new company. | | 8 | Missing | Despite attempts to collect information, the...

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Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

This project contains the data of a study on CEO life events, marriage and parenthood in particular, and its effects on firm growth. Dataset contains data on S&P100 firms and their CEOs (2003-2013). Sources include common databases such as Compustat, Execucomp, MSCI ESG, and Boardex. Data on the private life events was collected using news articles, company websites, and biographical websites such as; Referenceforbusiness, Britannica, Notablebiographies, Marquis Who’s Who in Finance and Industry, and the Notable Names Database.

We have included a snapshot of the documentation file here to help with future use along with an Excel version of the file for non-STATA users. This document also includes information on submitting edits and corrections to the open source data, which we welcome and encourage. We will acknowledge the participation of editors in the versioning changes at the bottom of the documentation file.

This version updates the set to the current turnovers as of May 30, 2022 version of Execucomp database and adds/clarifies several variables. Please check the documentation for the change log.

for updates check: https://zenodo.org/records/7591606

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According to the Rock Health Funding Database, digital health companies in the U.S. Midwest are least likely to have a female CEO when compared with the rest of the country. This data shows the percentage of U.S. digital health companies with female CEOs in 2018, by geographic region.

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.

United States CEO Economic Outlook Index data was reported at 109.300 % in Sep 2018. This records a decrease from the previous number of 111.100 % for Jun 2018. United States CEO Economic Outlook Index data is updated quarterly, averaging 84.700 % from Dec 2002 (Median) to Sep 2018, with 64 observations. The data reached an all-time high of 118.600 % in Mar 2018 and a record low of -5.000 % in Mar 2009. United States CEO Economic Outlook Index data remains active status in CEIC and is reported by Business Roundtable. The data is categorized under Global Database’s United States – Table US.S018: CEO Economic Outlook Survey.

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Context

getlatka.com is a SaaS Database, which contains data from over 1,000 manual interviews with CEO's of SAAS (Software as a service) tech companies.

At the time we harvested the data there was a statement on the website that said "You are only seeing a very small percentage of data. Click here to unlock it all and export." It was also stated that there are 1,082 companies in the database. The dataset we have contains 606 rows. So we have 56% of the available data.

The dataset is rare in that the information is all manually generated and contains metrics on private companies typically not publicly available. Having listened to a few of the podcasts it's surprising that Nathan Latka is able to get this information out of these CEO's. Some metrics include Number of Customers, Revenue, Churn Rates, Customer LTV and CEO Age and much more.

If you wish to purchase the data you should. As of March 2019 you are only seeing approx. 50% of it.

Acknowledgements

This data has been provided courtesy of elementive.io

The original blog post - Characteristics of Successful Entrepreneurs (SAAS)

.CEO Whois Database, discover comprehensive ownership details, registration dates, and more for .CEO TLD with Whois Data Center.

Dataset

This dataset was created by matúš Chlepko

Released under Data files © Original Authors

Contents

Files include: (1) an open sourced database of CEO duality and board chair orientations developed by scaling human coded data using supervised machine learning techniques (in both .dta and .csv formats), as well as (2) the accompanying training and scoring scripts to scale human coded data.

Users may apply the scoring script to score the same variables from company proxy statements, or may adapt the training/scoring scripts and retrain models to scale human coded data of other constructs or measures.

We note that early steps in the process to develop our database and script required web-scraping of company filings from SEC Edgar and text extraction from collected filings. We relied on other publicly available scripts to develop our own fetcher and extraction scripts. Users seeking to duplicate those parts of the process may benefit from the following resources from Kai Chen and pipy.org:

For resources from Kai Chen: see https://www.kaichen.work/?p=681 and https://www.kaichen.work/?p=946

For resources from pipy.org, see sec-edgar-downloader and sec-api

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Schema Preview

Business_Name, First_Name, Last_Name, Address, City, State, ZIP, Email_Address, Phone_Number, Executive_Level, Age, Gender, Income, Marital_Status, DNC_Flag

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