Computational Social Science

Overview

<div>Social science research on many topics is often hampered by the limitations of survey data, including relatively small sample sizes, low response rates and high costs. However, the digital age has increased access to large, comprehensive data sources, such as public and private administrative databases, and new sources of information from online transactions, social-media interactions, and internet searches. New computational methods also allow for the extraction, coding, and analysis of large volumes of text. Advances in analytical methods for exploiting and analyzing data, including machine learning, have accompanied the rise of these data. The emergence of these new data and methods also raises questions about access, privacy and confidentiality.</div> <div></div> <div>The Russell Sage Foundation’s initiative on Computational Social Science (CSS) supports innovative social science research that brings new data and methods to bear on questions of interest in its core programs in Behavioral Economics, Future of Work, Race, Ethnicity and Immigration, and Social Inequality. Limited consideration will be given to research that focuses primarily on methodologies, such as causal inference and innovations in data collection.</div> <div></div> <div>Examples of research (some recently funded by RSF) that are of interest include, but are not restricted to, the following (only those relevant to MLFTC faculty listed below):</div> <div></div> <div><em><strong>Linked Administrative Data</strong></em><br>Linking public administrative records from different agencies or jurisdictions can help answer long-standing questions of interest. Chetty, Friedman and Rockoff (2014a; 2014b) linked school district administrative records with federal income tax data to identify which teachers, in the short term, have the largest impact on student achievement, and in the longer-term, to show the extent to which students assigned to teachers with higher value-added scores have higher college attendance and higher salaries as adults.</div> <div></div> <div><em><strong>Online Surveys and Experiments</strong></em><br>Survey response rates for in-person and telephone interviews have declined significantly and surveys are expensive to administer. Salganik and Levy (2015) highlight the advantage of Wiki surveys that have data collection instruments that can capture as much information as a respondent is willing to provide, collect information contributed by respondents that was unanticipated by the researcher, and modify the instrument as more information is obtained.</div> <div></div> <div>An extensive literature shows an association between race and economic outcomes, but it is difficult to determine the extent to which these associations are due to racial discrimination or characteristics correlated with race. Doleac and Stein (2013) use online classified advertisements to examine the effect of race on market outcomes by featuring a photograph of the item for sale, and experimentally manipulating the color of the seller’s hand (dark or light-skinned). They find that black sellers receive fewer and lower offers than white sellers, and that buyer communication with black sellers indicates lower levels of trust.</div>