Using Big Data to Solve Economic and Social Problems

Raj Chetty and Gregory Bruich
Level: beginner
University: Harvard University
Platform: Opportunity Insights
Recurrence: flexible
Language: English
Discipline: Economics, Social Sciences
Attendance: free
Workload per week: 1.5 h

Source image: Opportunity Insights Youtube Channel

This course provides a simple introduction to problems that social scientists are working on (e.g. racial disparities, inequality and climate change) in a manner that does not require any prior background in Economics or Statistics.

Instead of starting out with abstract methods (such as supply and demand in canonical Economics courses), this course goes directly into the problems to analyse them. The course teaches methods such as experimental analysis using relevant example problems. These have become relevant and feasible due to the more recent availability of Big Data.

There is additional material available on the course website.

Lecture List:

  1. The Geography of Upward Mobility in America
  2. Causal Effects of Neighborhoods
  3. Moving to Opportunity vs. Place-Based Approaches
  4. The American Dream in Historical Perspective
  5. Upward Mobility, Innovation, and Economic Growth
  6. Higher Education and Upward Mobility
  7. The Causal Effect of Colleges
  8. Primary Education
  9. Teachers and Charter Schools
  10. Racial Disparities in Economic Opportunity
  11. Improving Health Outcomes
  12. The Economics of Health Care and Insurance
  13. Improving Judicial Decisions
  14. Effects of Air and Water Pollution
  15. Policies to Mitigate Climate Change
  16. Income Taxation
  17. Behavioural Public Economics
  18. Institutions and Economic Development

Take this course

This material has been suggested and edited by:

Donate

This project is brought to you by the Network for Pluralist Economics (Netzwerk Plurale Ökonomik e.V.).  It is committed to diversity and independence and is dependent on donations from people like you. Regular or one-off donations would be greatly appreciated.

 

Donate