Module 7600
Applied Microeconometrics and Impact Analysis

Lecturers

Prof. Dr. Johannes Sauer, jo.sauer@tum.de
Dr. Fabian Frick, fabian.frick@tum.de
Dr. Maria Vrachioli

At the end of the course, students shall be:

familiar with the fundamental econometric techniques and able to apply them to real world problems,
able to reason on the appropriate application of different methods,
able to apply the basic experimental and quasi-experimental methods for impact evaluation,
able to work with R Studio to conduct econometric analysis using real data.

Skills: quantitative analysis, conceptual thinking, econometrics, impact analysis

Introduction
- Fundamentals: correlation, causality and randomization
- Selection bias and other sources of endogeneity
- The role of experimental and quasi-experimental research design
Cross-sectional, panel regression and theoretical assumptions
- Ordinary Least Squares (OLS)
- Fixed and Random Effects
- Gauss-Markov assumptions
- Identification strategies
Propensity Score Matching
- Logit/Probit regression
- General Procedure: Propensity Score and Matching Methods
- Exercise
Instrumental Variables
- Selection of Suitable Instruments
- Estimation and Interpretation
- Testing Instrument Strength
- Exercise
Difference-in-Difference Estimation
- General Setting and Assumptions
- Exercise
Regression Discontinuity Design
- Methodology and Assumptions
- Testing the Assumptions
- Exercise
Synthetic Control Groups
- General Setting and Assumptions
- Exercise

1 week block seminar with 40% lecture, 20% exercises, 20% seminar, 20% homework

Examination: Homework assignments based provided datasets

Grading: In-class and homework assignments, where a minimum score of 50% is required to pass the module

Textbooks

Angrist, J. & Pischke, J.-S. (2009) Mostly Harmless Econometrics, Princeton University Press
Greene, W. (2012): Econometric Analysis, 7th edition. Pearson
Khandker, S.R., Koolwal, G.B. & Samad, H.A. (2009), 1st edition, Handbook on Impact Evaluation: Quantitative Methods and Practices
Verbeek, M. (2012): A Guide to Modern Econometrics, 4th edition. John Wiley & Sons
Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Nelson Education.

Fundamentals

Heckman, J. J. (2008). ‘Econometric Causality,’ Cemmap Working Paper 1/08, IFS, London.
Heckman, J. J. and E. J. Vytlacil (2007). ‘Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation,’ Handbook of Econometrics, v. 6B, pp. 4779-4874.
Duflo, E., R. Glennerster, M. Kremer (2008). ‘Using Randomization in Development Economics Research: A Toolkit,’ Handbook of Development Economics, v. 4, forthcoming.

Instrumental Variables

Angrist, J. D. (1990). Lifetime earnings and the Vietnam era draft lottery: evidence from social security administrative records. The American Economic Review, 313-336.
Angrist, J. D., & Krueger, A. B. (2001). Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic perspectives, 15(4), 69-85.
Duflo, E., & Pande, R. (2007). Dams. The Quarterly Journal of Economics, 122(2), 601-646.

Difference-in-Difference Estimation

Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates?. The Quarterly journal of economics, 119(1), 249-275.
Card, D., & Krueger, A. B. (2000). Minimum wages and employment: a case study of the fast-food industry in New Jersey and Pennsylvania: reply. American Economic Review, 90(5), 1397-1420.
Ravallion, M., Galasso, E., Lazo, T., & Philipp, E. (2005). What can ex-participants reveal about a program’s impact?. Journal of Human Resources, 40(1), 208-230.

Propensity Score Matching

Becerril, J., & Abdulai, A. (2010). The impact of improved maize varieties on poverty in Mexico: a propensity score-matching approach. World development, 38(7), 1024-1035.
Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of economic surveys, 22(1), 31-72.
King, G., & Nielsen, R. (2016). Why propensity scores should not be used for matching.
Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical science: a review journal of the Institute of Mathematical Statistics, 25(1), 1.

Regression Discontinuity Design

Becker, S. O., Egger, P. H., & Von Ehrlich, M. (2010). Going NUTS: The effect of EU Structural Funds on regional performance. Journal of Public Economics, 94(9-10), 578-590.
Datar, G., & Del Carpio, X. V. (2009). Are irrigation rehabilitation projects good for poor farmers in Peru?. The World Bank
Hahn, Todd & Van der Klaauw (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Econometrica, 96(1), 201-209.
Imbens & Kalyanaraman (2012). Optimal bandwidth choice for the regression discontinuity estimator. The Review of Economic Studies, 79(3), 933-959.

Synthetic Control Groups

Abadie, A., Diamond, A., Hainmueller (2010). ‘Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program’, Journal of the American Statistical Association, Vol. 105, No. 490
McClelland, R., & Gault, S. (2017). The synthetic control method as a tool to understand state policy. Washington, DC: Urban-Brookings Tax Policy Center.

During the course, all exercises can be conducted in R (Studio) or Stata. Support will mainly be given in R and our handouts will be in R. A short introduction to R and R Studio will be provided, but we suggest you familiarize yourself with the software prior to the course.
http://www.r-tutor.com/r-introduction
https://cran.r-project.org/doc/contrib/Torfs+Brauer-Short-R-Intro.pdf