Replication Data for: Linking Datasets on Organizations Using Half a Billion Open-Collaborated Records

Abstract: Scholars studying organizations often work with multiple datasets lacking shared unique identifiers or covariates. In such situations, researchers usually use approximate string (fuzzy) matching methods to combine datasets. String matching, although useful, faces fundamental challenges. Even when two strings appear similar to humans, fuzzy matching often struggles because it fails to adapt to the informativeness of the character combinations. In response, a number of machine-learning methods have been developed to refine string matching. Yet, the effectiveness of these methods is limited by the size and diversity of training data. This paper introduces data from a prominent employment networking site (LinkedIn) as a massive training corpus to address these limitations. We show how, by leveraging information from LinkedIn regarding organizational name-to-name links, we can improve upon existing matching benchmarks, incorporating the trillions of name pair examples from LinkedIn into various methods to improve performance by explicitly maximizing match probabilities inferred from the LinkedIn corpus. We also show how relationships between organization names can be modeled using a network representation of the LinkedIn data. In illustrative merging tasks involving lobbying firms, we document improvements when using the LinkedIn corpus in matching calibration and make all data and methods open source.