At Econometrica, we value intellectual curiosity and problem-solving skills. We collaborate on projects in a diverse set of markets across numerous subject matter areas. Staff in our Health, Data Analytics, and Housing & Finance groups, along with members of our various teams, provide innovative solutions and analytic excellence in a wide variety of services. No matter how different each project is, one thing remains constant—we dedicate ourselves to providing the highest quality service.
Are Social Networks Linked to Economic Mobility?
Client: The Brookings Institution
Key Work Areas: Focus Groups, Literature Review, Outreach to Program Participants, Policy Development, Program Evaluation, Survey Design and Data Collection, Data Scrubbing; Data Visualization, Programming Languages, Regulatory & Policy Analysis
This project focused on understanding how someone’s social network impacts his or her economic mobility. Social network analysis provides a lens through which the social capital available to individuals to achieve self-sufficiency and upward mobility may be examined. Econometrica’s analysis detailed how the size, formation, and function of someone’s social networks impacts their related to employment, stable housing, and educational opportunities. Econometrica used statistical testing to understand if factors such as race/ethnicity and income impact social networks. Our analysis also provided insight into how people used their social networks to cope with the COVID-19 public health emergency.
To execute the project, Econometrica recruited for, conducted, and analyzed a total of 262 virtual interviews across three locations: Washington, DC; San Francisco, CA; and Racine, WI. Our custom recruitment materials reflected the diverse nature of participants of all races and ethnicities that we sought to interview, including young African-American men, Spanish speakers, Asian individuals, single people, parents, high-income earners, and low-income earners. We found that opportunities were in fact impacted by social networks. In San Francisco, for example, Asian earning high incomes had the largest networks, while in Washington and Racine, White participants had larger networks than Black participants.