Spring 2005 Course Description
Political Economy
Description: This course offers an introduction to the practical application of microeconomic principles and cutting-edge statistical techniques to the evaluation of social programs. The substantive focus will be on programs in health, education, welfare, and workforce training. Students will be invited to work through a series of concrete program evaluations conducted for a number of international and national organizations ranging from the World Bank and the Mexican and Bolivian governments, to the Appalachian Commission and the Austin-San Antonio Corridor Council. While the course does touch on cost-benefit analysis (prospective evaluation before a program is in place), the primary focus is on the design and execution of program evaluations (the assessment of on-going programs or of programs after the fact). This will call for a solid treatment of:
- The formal logic of experimental and quasi-experimental research design
- Multivariate statistical techniques (including factor analysis, cluster analysis, Q-methodology)
- The management of large databases
- The use of statistical analysis packages (primarily Stata and SAS)
- Statistical techniques for dealing with selection bias and other commonly encountered threats to validity
- And much more?
Readings draw from the literature on program evaluation, quasi-experimental design, and econometrics. The econometrics of evaluation has become a particularly exciting area of research in recent years. While oftentimes challenging, it has important implications on public policy analysis. Students will write occasional commentaries on real-world studies, analyze large real-world datasets, and be responsible for an empirically based term paper using the methods and techniques developed in the course.
Prerequisites: Open to qualified PhD and MPAff students. Requires basic microeconmic theory at the level of PE I. More important, it is essential that students have a reasonable command of intermediate graduate-level econometrics (up through maximum likelihood estimation and logit/probit analysis at a minimum) and be comfortable working with summation and matrix notation. AQA II may not be sufficient to meet this requirement.
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