Next Generation Data Sets for Measuring Child Development

November 2-3, 2018, New York City, New York

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

Program

November 2nd

2:00–3:30PM

Session 1: Education and child development
Janet Currie, Princeton University
Cate Hartley, New York University
Discussant: Steven Durlauf, The University of Chicago

3:30–4:00PM

Coffee Break

4:00–5:30PM

Session 2: Biological measurement and child development
Daniel Notterman, Princeton University
David Cesarini, New York University
Discussant: Amy Schwartz, Syracuse University

November 3rd

8:30–9:00AM

Breakfast and Coffee

9:00–10:30AM

Session 3: Social Networks and child development
Yan Chen, University of Michigan
Rachel Kranton, Duke University
Discussant: Andrew Caplin, New York University

10:30–11:00AM

Coffee Break

11:00–12:30PM

Session 4: Building holistic data architectures for next generation studies of child development
Andrew Caplin, New York University
Hannah Bayer, Data Cubed
Discussant: Ed Vytlacil, Yale University

12:30–1:30PM

Lunch Break