# **2.2.1 Matching Lightcast job postings to Factset**

We identify 10 million unique Lightcast company names associated with at least one job posting spanning from 2012 to 2022 across 17 countries. The set of company names are extracted from the raw text of all job postings and are not cleaned or standardized, nor cross-referenced with other postings, allowing for several variations in the naming of a single company. To overcome this issue, we draw on the legal names of all entities in the FactSet Relationships dataset, and define a list of name variations to help us matching both Lightcast and FactSet datasets. We take these variations by converting company names to lowercase, removing punctuation and special characters, and eliminating company suffixes (e.g., Ltd., Inc., LLC, etc.). The specific list of variations is in Appendix A4.

We then use both exact and fuzzy matching approaches to match the Factset firm names to company names in Lightcast. As a first step, we narrow down both matching approaches to the country where each company’s headquarters are legally registered, according to FactSet. Using the different company name variations, we proceed to search for each FactSet company name within Lightcast, delimited by the headquarters’ country. This sequential exact matching allow us to reduce the list of Lightcast company names by eliminating those already exactly matched with FactSet before the fuzzy matching procedure. The additional fuzzy matching approach was considered necessary due to potential variations in spelling and punctuation in company names that could not be captured by the exact matching, leading to inaccurate matching rates. We utilize lowercase company names without punctuation marks to conduct the fuzzy matching approach, employing the term frequency-inverse document frequency (TF-IDF) method as the primary fuzzy matching technique, complemented by the Levenshtein distance method for additional verification of name matches. Merging the results from both exact and fuzzy matching, we then obtain an initial subset of 80,000 matched FactSet firms out of the total 169,420.

As a second step, we expand the FactSet Relationships dataset by integrating ownership and structural information, enabling us to search for affiliates of the 169,420 firms as well. For instance, if the FactSet firm were ‘Microsoft’, using the relationship structure data we can access the entire family of affiliates, including ‘GitHub’, ‘LinkedIn’ and ‘Skype Technologies’. This integration yielded a newly expanded dataset of nearly 2.5 million Factset entities, including parent companies from the original sample. Subsequently, we conduct a second exact and fuzzy matching exercise searching for this expanded list of company names in the Lightcast data without restricting the matching process to the headquarters country. This procedure allow us to identify 440,000 affiliated entities from FactSet in the Lightcast data, which are associated with 108,000 firms in the FactSet Relationships dataset. After consolidating the outcomes from both steps and removal of duplicate matches<sup>7</sup> , our final 7 The duplicate-elimination criteria is detailed in Appendix A4.

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