Using Big Data to Solve Economic and Social Problems

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This course provides an introduction to modern applied economics in a manner that does not require any prior background in economics or statistics. It is intended to complement traditional Principles of Economics (Econ 101) courses. Topics include equality of opportunity, education, health, the environment, and criminal justice. In the context of these topics, the course provides an introduction to basic statistical methods and data analysis techniques, including regression analysis, causal inference, quasi-experimental methods, and machine learning.

Visit Opportunity Insights to view the lecture videos and find lecture materials.

Public Economics Lectures

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2nd Year Ph.D. Course

This Ph.D. course covers basic issues in the optimal design of tax and social insurance policies, with emphasis on combining theoretical models with empirical evidence. Topics include efficiency costs and incidence of taxation, income taxation, transfer and welfare programs, public goods and externalities, optimal social insurance (excluding social security), welfare analysis in behavioral models, corporate taxation, and education policy.

Lecture Recordings

Watch the complete 28-lecture course from Fall 2012 on YouTube.
Each lecture is 1.5 hours long.

Course Outline

The slides and reading list are divided into eight parts:

  • Introduction
  • Tax Incidence [3 lectures]
  • Efficiency Cost of Taxation [3 lectures]
  • Optimal Taxation [5 lectures]
  • Income Taxation and Labor Supply [4 lectures]
  • Social Insurance [5 lectures]
  • Public Goods and Externalities [4 lectures]
  • Corporate Taxation [4 lectures]
  • Education Policy [2 lectures; slides only, no videos]

Downloads

Lecture slides (joint with Gregory A. Bruich) in pdf format
Lecture slides, lite version  (faster downloading; lower image quality)
Reading list in pdf format
Source files in Scientific Workplace format

  • The corporate taxation and education policy source files are in LyX format.
  • The reading list is a .doc file.
  • The zip file contains .rap files which may be opened with the MacKichan Software. Download MacKichan Software.

Acknowledgements

We would like to thank Jon Gruber, Day Manoli, Emmanuel Saez, and many other colleagues whose comments and lecture notes contributed to the development of these slides.

Questions? Comments?

Please contact Gregory Bruich (gbruich@fas.harvard.edu) or Raj Chetty (chetty@fas.harvard.edu) if you have any questions or suggestions.

Lectures, videos, slides, and more