The Science of Economic Opportunity: New Insights from Big Data
Jul 13, 2024
The Science of Economic Opportunity: New Insights from Big Data
Introduction
Speaker: Professor Raj Chetty
Event: Charles and Martha Hitchcock Lecture Series
Co-Sponsors: Department of Economics, College of Data Science, Computing Data Science, Society, The Graduate Council, UC Berkeley
Professorship Origin: Established by Dr. Charles Hitchcock in 1885, expanded by his daughter Lily Hitchcock Coit; it supports bringing scholars to Berkeley.
About Professor Raj Chetty
Position: William A. Ackman Professor of Public Economics at Harvard University
Director: Opportunity Insights
Research Focus: Using big data to improve outcomes for disadvantaged children
Education: PhD from Harvard (2003)
Awards: MacArthur Fellowship, John Bates Clark Medal
Previous Roles: Faculty at UC Berkeley and Stanford
Key Themes of the Lecture
The American Dream
Central Statistic: Fraction of children earning more than their parents
Historical Context: In the 1940s, 92% of children earned more than their parents; by the 1980s, this had fallen to 50%
Importance: Reflects fundamental economic, social, and political changes
Big Data in Economic Research
Technological Breakthrough: Longitudinal administrative data such as tax records and social network data
Disaggregation: Allows for detailed analysis by race, geographic area, and income group
Research Methods: Quasi-experimental and experimental techniques
Geographic Variation in Upward Mobility
Data: Analyzed using data on 20 million children, divided into 740 areas
Findings: Significant geographic variation; rural Midwest has high upward mobility, while Southeast and industrial Midwest have low upward mobility
Implications: Indicates the importance of local contexts and environments
Job Growth and Upward Mobility
Analysis: No significant correlation between job growth rates and upward mobility in various cities
Conclusion: Simply increasing jobs is not a comprehensive solution for improving upward mobility
Race and Upward Mobility
Findings: Stark differences in upward mobility by race, particularly for black men vs. white men
Gender Differences: Black women have similar upward mobility outcomes as white women, unlike black men
Downward Mobility: Black men from high-income families are more likely to experience downward mobility
Implications: Addressing racial disparities requires a nuanced understanding of the intersection of race, gender, and environment
Importance of Neighborhoods
Fine-Grained Analysis: Examined data at the census tract level
Findings: Significant variation in outcomes within small geographic areas, indicating the importance of hyperlocal environments
Example: Housing developments in Oakland and Alameda showed different outcomes
Movers Analysis
Method: Studied families moving across neighborhoods at different children's ages
Findings: Younger children benefit more from moving to better neighborhoods; the effect decreases with age
Implications: Childhood environment is crucial; impact decreases with age, but investment beyond early childhood is still valuable
Determinants of Economic Mobility
Factors Identified: Various factors such as poverty rates, family structure, and social capital
Focus on Social Capital: Importance of networks and cross-class interactions
Social Capital and Economic Mobility
Data: Collaboration with Facebook to analyze social networks