Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/282868 
Authors: 
Year of Publication: 
2023
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2023-055/VIII
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
When merging firms across large databases in the absence of common identifiers, text algorithms can help. I propose a high-performance fuzzy firm name matching algorithm that uses existing computational methods and works even under hardware restrictions. The algorithm consists of four steps, namely (1) cleaning, (2) similarity scoring, (3) a decision rule based on supervised machine learning, and (4) group identification using community detection. The algorithm is applied to merging firms in the Amadeus Financials and Subsidiaries databases, containing firm-level business and ownership information, to applicants in PATSTAT, a worldwide patent database. For the application the algorithm vastly outperforms an exact string match by increasing the number of matched firms in the Amadeus Financials (Subsidiaries) database with 116% (160%). 53% (74%) of this improvement is due to cleaning, and another 41% (50%) improvement is due to similarity matching. 18.1% of all patent applications since 1950 are matched to firms in the Amadeus databases, compared to 2.6% for an exact name match.
Subjects: 
Fuzzy name matching
supervised machine learning
name disambiguation
patents
JEL: 
C81
C88
O34
Document Type: 
Working Paper

Files in This Item:
File
Size
732.84 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.