Next Generation Data Sets for Measuring Child Development
Traditional survey methods and administrative data have revealed the importance of a broad range of factors in successful child development outcomes. These factors include socioeconomic status, maternal health, early childhood experiences and educational opportunities. However, revealing the mechanisms by which these factors influence developmental outcomes requires a granularity of measurement that has not previously been achievable. Advances in mobile technology, digitized administrative data, genetic sequencing and advanced analytic capabilities have now made it possible to gather to the data necessary to understand the complex interplay of forces that shape how children develop. In this meeting we will discuss how the latest advances in data collection can be informed by theory to jumpstart future insights into the factors that influence child development outcomes.
Organizers
- Andrew Caplin, NYU
- Hannah Bayer, Data Cubed
Videos & Program
Janet Currie, Princeton University
Cate Hartley, New York University
Discussant: Steven Durlauf, The University of Chicago
Daniel Notterman, Princeton University
David Cesarini, New York University
Discussant: Amy Schwartz, Syracuse University
Yan Chen, University of Michigan
Rachel Kranton, Duke University
Discussant: Andrew Caplin, New York University
Andrew Caplin, New York University
Hannah Bayer, Data Cubed
Discussant: Ed Vytlacil, Yale University