This dataset covers all publically listed companies in the United States from 2000 to 2018, which are listed in the S&P index. The starting point of 2000 is due to the minimal data available in the BoardEX database before this time in relation to board directors' information. Compustat is the source of financial data. As previous research indicates, financial and utilities firms are excluded from the sample due to their distinct regulations, which expose their directors to liability risks that non-financial firms are not subject to (Adams and Mehran, 2012; Sila et al., 2016). The sample size of non-financial firms amounts to 17,220. Financial variable outliers are adjusted to the 98% level in accordance with Bharath and Shumway's (2008) study.

Entities who wish to influence City matters often hire lobbyists to represent them. A lobbying firm must register these entities as clients.

This is a replication package for "The Wealth Tax of 1942 and the Disappearance of Non-Muslim Enterprises in Turkey," published in the Journal of Economic History. Turkey imposed a controversial tax on wealth to finance the army in 1942. This tax was arbitrarily assessed and fell disproportionately on non-Muslim minorities. We study the heterogeneous impact of this tax on firms by assembling a new dataset of all enterprises in Istanbul between 1926 and 1950. We find that the tax led to the liquidation of non-Muslim-owned firms, which were older and more productive, reduced the formation of new businesses with non-Muslim owners, and replaced them with frailer Muslim-owned startups. The tax helped "nationalize" the Turkish economy but had negative implications for productivity and growth.

Summary statistics of business dynamism taken from the Longitudinal Business Database (LBD), UK.

The file contains the data used for the binary logistic regression.

Sudan SD: Firms Competing Against Unregistered Firms: % of Firms data was reported at 90.500 % in 2014. Sudan SD: Firms Competing Against Unregistered Firms: % of Firms data is updated yearly, averaging 90.500 % from Dec 2014 (Median) to 2014, with 1 observations. Sudan SD: Firms Competing Against Unregistered Firms: % of Firms data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sudan – Table SD.World Bank.WDI: Company Statistics. Firms competing against unregistered firms are the percentage of firms competing against unregistered or informal firms.; ; World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/).; Unweighted average;

This dataset provides information on 358 in Brazil as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Panama PA: Firms That Spend On R&D: % of Firms data was reported at 7.400 % in 2010. This records a decrease from the previous number of 26.400 % for 2006. Panama PA: Firms That Spend On R&D: % of Firms data is updated yearly, averaging 16.900 % from Dec 2006 (Median) to 2010, with 2 observations. The data reached an all-time high of 26.400 % in 2006 and a record low of 7.400 % in 2010. Panama PA: Firms That Spend On R&D: % of Firms data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Panama – Table PA.World Bank: Company Statistics. Percent of firms that spend on research and development.; ; World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/).; Unweighted average;

According to an analysis conducted in 2023 of over 200 companies targeting children and families in the United States, only 25 percent of the businesses had a privacy-protective mindset and did not sell data. Under the California Privacy Rights Act amendment, companies are supposed to disclose if they sell users' personal data. Around 13 percent of companies did not disclose whether they engaged in such practices.

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth?s surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.

The Flood Insurance Rate Map (FIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Datum of 1983.

This is the replication package for "A Model of Zombie Firms and the Perils of Negative Real Interest Rates," accepted in 2023 by the Journal of Political Economy.

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 survey conducted in April and May 2023 among the representatives of companies doing business in the United States found that less than half, only 45 percent, of the companies feel very prepared to comply with the state-level privacy laws in the U.S. A further 36 percent believed they were moderately prepared, while 13 percent said they were prepared slightly. The U.S. does not have comprehensive data privacy legislation. However, some states have already signed, and some are in the process of signing state-level laws for data privacy.

Graph and download economic data for Data Processing and Other Purchased Computer Services for Support Activities for Transportation, All Establishments, Employer Firms (DISCONTINUED) (EXPDPSEF488ALLEST) from 2012 to 2017 about support activities, computers, purchase, employer firms, processed, establishments, transportation, expenditures, services, and USA.

CL: Firms using Banks to Finance Investment: % of Firms data was reported at 44.800 % in 2010. This records an increase from the previous number of 29.100 % for 2006. CL: Firms using Banks to Finance Investment: % of Firms data is updated yearly, averaging 36.950 % from Dec 2006 (Median) to 2010, with 2 observations. The data reached an all-time high of 44.800 % in 2010 and a record low of 29.100 % in 2006. CL: Firms using Banks to Finance Investment: % of Firms data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chile – Table CL.World Bank.WDI: Company Statistics. Firms using banks to finance investment are the percentage of firms using banks to finance investments.;World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/).;Unweighted average;

According to a survey conducted at the EmTech Digital conference in March 2019, U.S. business leaders shared their opinions on trust issues with regard to AI data quality and privacy. Nearly half of respondents reported a lack of trust in the quality of AI data in their companies, showing that there is still a long way to go to get quality AI data.

Data were previously published in the Supplement to the Federal Reserve Bulletin, which ceased publication in December 2008. These tables will be discontinued with the final table released in April 2022. The source for these data is the Treasury International Capital System and future data publications can be found on Treasury’s website.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Benin in 2004, 2009 and 2016, as part of Africa Enterprise Surveys rollout, an initiative of the World Bank. The objective of the Enterprise Survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms.

Enterprise Surveys target a sample consisting of longitudinal (panel) observations and new cross-sectional data. Panel firms are prioritized in the sample selection, comprising up to 50% of the sample in the current wave. For all panel firms, regardless of the sample, current eligibility or operating status is determined and included in panel datasets.

Benin ES 2009 was conducted from May 18 to Sept. 30, 2009, Benin ES 2016 was carried out in July - October 2016. Stratified random sampling was used to select the surveyed businesses. Data was collected using face-to-face interviews.

Data from 497 establishments was analyzed: 128 businesses were from 2004 only, 53 - from 2009 only, 88 - from 2016 only, 70 - from 2004 and 2009 only, 56 - from 2009 and 2016 only and 102 firms were from 2004, 2009 and 2016.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively measure characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries- Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72).

For the Benin ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

In 2016 ES, regional stratification was done across five regions: Atlantique, Borgou, Mono, Ouémé and Littoral. In 2009 ES, Cotonou and Other were the two areas selected.

In 2016 ES, the sample frame consisted of listings of firms from three sources: for panel firms, the list of 150 firms from the Benin 2009 ES was used, and for fresh firms (i.e., firms not covered in 2009) lists obtained from National Statistical Institute and Tax Directorate (2013) and the Chamber of Commerce (2016) were used.

In 2009 ES, two sample frames were used. The first one included the official list "Repertoire of Companies in Benin" (2009) from the Chambre de Commerce et d' Industrie du Benin. The second frame (the panel sample) consisted of enterprises interviewed for the Enterprise Survey in 2004, which were to be re-interviewed where they were in the selected geographical regions and met eligibility criteria.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments were used for Benin ES 2009 and 2016: - Manufacturing Module Questionnaire - Services Module Questionnaire

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth. There is a skip pattern in the Service Module Questionnaire for questions that apply only to retail firms.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

This SAS script is written for the manuscript "Do Nonfinancial Firms Use Financial Assets to Take Risk" (Chen and Duchin, RCFS, 2023). It needs to use the sas data set,cashholding_list_maindata, in folder of data, and sas macros in the folder of macros