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Completed PROJECT GRANT Swedish Research Council

Ownership and investment in Swedish unlisted companies

20.42M kr SEK

Funder Vinnova
Recipient Organization Institutet För Finansforskning
Country Sweden
Start Date Aug 01, 2022
End Date Jul 31, 2025
Duration 1,095 days
Number of Grantees 1
Roles Principal Investigator
Data Source Swedish Research Council
Grant ID 2022-01419_Vinnova
Grant Description

Purpose and goal:

How private firms are created, funded and governed is a first order issue for research and policy. This project aims build an ownership database of unlisted firms in Sweden and making it accessible to the academic community. The database will build on pdf records filed by firms to the Swedish Companies Registration Office.

The data give a comprehensive view of the firm´s ownership structure over time, the contractual terms of all firms´ financing, and a near-complete representation of Swedish firms´ investors. Expected results and effects:

The main tangible outcome of this project is an ownership database accessible to a large community of researchers, which will aid in improving the understanding of the financing landscape in the private sector. The database will be able to inform policy and provide a natural data source for researchers studying the entrepreneurial ecosystem. The granularity of the data makes it suitable to study, for example, the prevalence of and conditions that create gender imbalances in entrepreneurship and ownership.

Approach and implementation:

In collaboration with KPHW, we will construct a time series of the ownership of all privately held companies in Sweden from 2004 until today. The data will be processed and standardized to meet the academic community´s standards for functionality and quality and made available in two versions i) an aggregated database with no personal data and ii) a detailed dataset of individuals´ and firms´ investments and holdings in the private firms.

KPHW will provide the technology to read and extract the data at the required scale and develop algorithms to classify different owner types.

All Grantees

Institutet För Finansforskning

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