Luca Maria Pesando is an Associate Professor of Social Research and Public Policy at New York University - Abu Dhabi, as well as Associate Program Director and Director of the Inequality research cluster. Previously, he was an Assistant Professor of Sociology and Demography and William Dawson Scholar at the Department of Sociology and Centre on Population Dynamics, McGill University. His research lies in the areas of social, economic, and digital demography, with a strong focus on public policy. He is interested in issues of family poverty, inequality, gender, stratification, intra- and inter-generational processes, technology adoption, and interactions between life-cycle events and human capital accumulation, primarily in sub-Saharan Africa, Latin America, and South-East Asia. He has also conducted research using social media data from Google and Facebook. Having worked with JPAL, the OECD, UNICEF, and other NGOs, Luca has extensive experience in the policy world. His work has appeared in different outlets such as Demography, Population and Development Review, Journal of Marriage and Family, Proceedings of the National Academy of Sciences, etc. He holds a Ph.D. in Demography and Sociology (2018) and an MA in Demography (2016) from the University of Pennsylvania, and an MSc (2012) and a BA (2010) in Economics and Social Sciences from Bocconi University.
Talk: What’s a Parent to Do? Measuring Cultural Logics of Parenting with Text Analysis   
Abstract: Leading theories on parenting in the United States suggest that parenting styles vary widely by socioeconomic status, with middle-class parents practicing “concerted cultivation”—marked by parents’ intensive efforts to foster their children’s development— and working-class parents engaging in the “accomplishment of natural growth”—with children given more freedom to manage their own time. While frequently inferred that these parenting practices reflect different cultural logics of parenting, such logics are inherently hard to measure. Our paper proposes a new way to study parenting logics using computational text analysis applied to a nationally representative survey where respondents provided parenting advice across three hypothetical parenting situations. Analyzing this advice using Biterm Topic Modeling we find that nearly all parenting logics reflect some form of intensive parenting, but within that are multiple nuanced versions varying across two dimensions: (1) assertive vs negotiated parenting, and (2) pedagogic vs pragmatic parenting. Using fractional multinomial logistic regression, we find little difference in how parenting logics vary by race/ethnicity, education, and income, suggesting more similarity across groups and more variability within groups than commonly understood. These findings also demonstrate how computational techniques may provide complementary tools to enrich the study of long-standing questions in social science research.