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

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

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 Google Doc.

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.

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

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 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.

The Corporate Financial Fraud project is a study of company and top-executive characteristics of firms that ultimately violated Securities and Exchange Commission (SEC) financial accounting and securities fraud provisions compared to a sample of public companies that did not. The fraud firm sample was identified through systematic review of SEC accounting enforcement releases from 2005-2010, which included administrative and civil actions, and referrals for criminal prosecution that were identified through mentions in enforcement release, indictments, and news searches. The non-fraud firms were randomly selected from among nearly 10,000 US public companies censused and active during at least one year between 2005-2010 in Standard and Poor's Compustat data. The Company and Top-Executive (CEO) databases combine information from numerous publicly available sources, many in raw form that were hand-coded (e.g., for fraud firms: Accounting and Auditing Enforcement Releases (AAER) enforcement releases, investigation summaries, SEC-filed complaints, litigation proceedings and case outcomes). Financial and structural information on companies for the year leading up to the financial fraud (or around year 2000 for non-fraud firms) was collected from Compustat financial statement data on Form 10-Ks, and supplemented by hand-collected data from original company 10-Ks, proxy statements, or other financial reports accessed via Electronic Data Gathering, Analysis, and Retrieval (EDGAR), SEC's data-gathering search tool. For CEOs, data on personal background characteristics were collected from Execucomp and BoardEx databases, supplemented by hand-collection from proxy-statement biographies.

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...