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Syllabus

Introduction

This course combines economic theory, econometric literature and institutional literature to examine current issues in U.S. education. Topics include (but will not be limited to):

  • the basic theory of investments in education (human capital theory)
  • the empirical problem of disentangling the return to education from the return to innate ability
  • the role of education in national economic growth
  • the association between education and individual earnings and reasons why that relationship has changed over time
  • the main approaches to K-12 school reform (money, choice, and educational standards)
  • the problem of increasing access to higher education.

Along the way, we will discuss computers both in their effect on educational requirements and their potential role in teaching skills. We will also examine the role of education in individual mobility and in national growth.

Many issues we discuss will involve significant controversy - e.g. the effect of increased spending or parental school choice on student achievement. For this reason, we will rely heavily on discussion of the readings. The reading load can be heavy and I would like you to form reading groups to share the load. A three-people-per-group rule of thumb seems about right, and we will talk in class about how these groups should function.

Grading

Grading will be based on problem sets (20 percent), a mid-term (30 percent), a final exam (40 percent) and class participation (10 percent). There will be some opportunities for a reading group to earn extra credit by presenting a particular debate in the literature. The grading table is as follows:


ACTIVITIES PERCENTAGES
Problem Sets 20%
Midterm Exam 30%
Final Exam 40%
Class Participation 10%

Prerequisite

14.01 or its equivalent is required and 14.30 or its equivalent is recommended. Many of the articles and some exercises will require understanding basic regression analysis. The teaching assistant will undertake a review of regressions at the beginning of the course, but life will be much easier if you have already had some exposure to regressions.