Specifically, the objective is to develop company name disambiguation and industry mapping solutions using a large resume dataset covering the universe of firms in the U.S. and a wide range of firms abroad. The employment dataset comes from a collaboration with a private partner.  The dataset spans approximately 1 billion individuals globally and is maintained in accordance with major data protection policies. These data offer a comprehensive view of each firm’s company profile, departments, and employees. The result of the project will consist of a corpus of company name variants, mapped to unique company identifiers and corresponding industry classifications including private firms and international firms. This corpus will support a wide variety of business research questions, including entity matching in unstructured data (e.g., news, patents, job postings, and information scraped from online sources such as Glassdoor).

As firms seek better ways to measure and manage cultural change, researchers and practitioners alike are keen to understand the mechanisms by which such change occurs. While most cultural change efforts focus on internal policies, practices, and procedures, firms also recognize that the infusion of senior leaders from other organizations can often serve as a catalyst for change. Indeed, executives are often brought in from the outside in the hopes that they will serve as cultural carriers who can transmit desirable cultural features from one organization to another. Yet it remains unclear what kinds of leaders—for example, based on career stage or the sequence of prior career moves—have the capacity to be cultural carriers and the contexts—for example, the degree of similarity between the prior and new organization or financial health of the new organization—in which they prove to be most effective.

In this project, we partner with an executive search firm that has compiled a number of datasets on executive profiles, career histories, and organizational culture profiles. Applying methods in computational social science, we integrate data on the career mobility of senior leaders, internal cultural assessments of individuals and firms, external measures of organizational culture based on natural language processing of Glassdoor employee reviews, and Compustat data on firm characteristics and performance. The resulting data set will provide a rich portrait of how individual-level career mobility shapes organizational culture change and, ultimately, firm performance.

By merging this with a panel phone survey that collects information about migration, social networks and employment, we employ machine learning methods to predict these outcomes for the whole sample of phone users using only the signatures in the cell data. This allows us to create new insights and analytics into the process of refugee migration, assimilation and job finding patterns in urban environments, typically out of view of traditional administrative datasets.