Welcome to the Chair of International Economic Policy!

The course will be taugh online (live sessions, Zoom)

Content

The course covers empirical labor economics and modern econometrics. Students should sign in at posit.cloud to get access to RStudio. I use Mentimeter as a Classroom Response System

Requirements

Students should install RStudio and Mentimeter (App) as a Classroom Response System before the first session.

If you want to participate, please register for the ILIAS course so that we can send you the Zoom link.

Time and Location

Examination Modalities

Literature

Main references:
Angrist, J.D., & Pischke, J. (2015): Mastering 'Metrics, The Path from Cause to Effect, Princeton University Press.

Boeri, T., & Van Ours, J. (2013). The economics of imperfect labor markets. 2nd edition. Princeton University Press.
Borjas, G.J. (2020): Labor Economics, 8^th ed., Mc GrawHill, New York.

Bueno de Mequita, E. & Fowler, A. (2021): Thinking Clearly with Data, Princeton University Press.

Green, Donald P. (2022): Social Science Experiments, A Hands-on Introduction, Cambridge University Press.

Heiss, F. (2016): Using R for Introductory Econometrics, Düsseldorf.

Horton, N.J. & Prium, R. & Kaplan, D.T. (2018): A Students’ Guide to R, Project Mosaic.

James, G. et al. (2017): An Introduction to Statistical Learning, Springer, New York.

Klinkhammer, D., & Spermann, A. (2020): Eine Einführung in die empirische Kausalanalyse und Machine Learning mit R, UTB-Lehrbuch, wbv, Gütersloh.

Taddy, M. (2019): Business Data Science, Mc GrawHill, New York.

Wooldridge, J. (2018): Introductory Econometrics, A Modern Approach, 7th edition, Cengage Learning.

Randomized Controlled Trials (RCTs)
Angrist, J., & Pischke, J.-S. (2017): Undergraduate Econometrics Instruction: Through Our Classes, Darkly, Journal of Economic Perspectives, 31, 125-144.
Athey, S., & Imbens, G.W. (2017): The State of Applied Econometrics: Causality and Policy Evalua-tion, Journal of Economic Perspectives, 31, 3-32.
Athey, S., & Luca, M. (2019): Economists (and Economics) in Tech Companies, Journal of Economic Perspectives, 33, 209-230.

Banerjee, A., & Duflo, E. et al. (2015): A multifaceted program causes lasting progress for the very poor: Evidence from six countries, Science, 348, Issue 6236.

Bertrand, M. & S. Mullainathan (2004): Are Emily and Greg More Employable Than Lakisha and Jamal? A Filed Experiment on Labor Market Discrimination, American Economic Review, 94(4), 991-1013.

Brodeur, A., & Cook, N., & Heyes, A. (2020): Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics, American Economic Review, 110(11), 3634-3660.
Fryer, R. (2014): Injecting Charter School Best Practices into Traditional Public Schools: Evidence from Field Experiments, Quarterly Journal of Economics, 1355-1407.

Heckman, J. & J.A. Smith (1995): Assessing the Case for Social Experiments, Journal of Economic Perspectives, 9 (2), 85-110.

Lübke, K., & Gehrke, M., & Horst, J. & Szepannek, G. (2020): Why We should teach Causal In-fererence: Examples in Linear Regression with Simulated Data, Journal of Statistics Education, 28:2, 133-139.
Imbens, G.W. (2020): Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics, Journal of Economic Literature, 58(4), 1129–1179.
Klinkhammer, D., & Spermann, A. (2020): Eine Einführung in die empirische Kausalanalyse und Machin Learning mit R, UTB-Lehrbuch, wbv, Gütersloh, R codes on Github.
Wooldridge, J. (2018): Introductory Econometrics, A Modern Approach, 7th edition, Cengage Learning, Chapters 2.7, 3.7, 4.7.

Regression Discontinuity Design (RDD)
Abdulkadiroglu, A., & Angrist, J.D., & Narita, Y., & Pathak, P.A., & Zarate, R.A. (2017): Regression Discontinuity in Serial Dictatorship: Achievement Effects at Chicago's Exam Schools, American Economic Review, P.P., 107/5), 240-245.
Angrist, J.D., & Lavy, V., & Leder-Luis, J. & Shany, A. (2019): Maimonides' Rule Redux, AER: Insights, 1(3), 309-324.
Klinkhammer, D., & Spermann, A. (2020): Eine Einführung in die empirische Kausalanalyse und Machine Learning mit R, UTB-Lehrbuch, wbv, Gütersloh, R codes on Github.
Roth, A. (2018): Marketplaces, Markets, and Market Design, American Economic Review, 108(7), 1609-1658.

Difference-in-Differences (DiD)
Abadie, A. (2021): Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects, Journal of Economic Literature, Journal of Economic Literature 2021, 59(2), 391–425.
Angrist, J.D., & Pischke, J.-S. (2009): Mostly harmless econometrics, Princeton University Press.
Klinkhammer, D., & Spermann, A. (2020): Eine Einführung in die empirische Kausalanalyse und Machine Learning mit R, UTB-Lehrbuch, wbv, Gütersloh, R codes on Github.
Wooldridge, J. (2018): Introductory Econometrics, A Modern Approach, 7th edition, Cengage Learning, Chapters 13.2.

IV and LATE
Angrist, J.D., & Pischke, J.-S. (2009): Mostly harmless econometrics, Princeton University Press.
Angrist, J.D., & Pischke, J. (2015): Mastering 'Metrics, The Path from Cause to Effect, Princeton University Press.
Deaton, A. (2010): Instruments, Randomization, and Learning about Development, Journal of Economic Literature, 48, 424-455.
Imbens, G.W. (2020): Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics, Journal of Economic Literature, 58(4), 1129–1179.
Klinkhammer, D., & Spermann, A. (2020): Eine Einführung in die empirische Kausalanalyse und Machine Learning mit R, UTB-Lehrbuch, wbv, Gütersloh, R codes on Github.

Propensity Score Matching (PSM) and Introduction to Machine Learning
Chatri, A., & Hadef, K.,& Samoudi, N. (2021): Micro‑econometric evaluation of subsidized employ-ment in morocco: the case of the "Idmaj" program, Journal for Labor Market Research, 55:17
https://doi.org/10.1186/s12651-021-00300-5.
Imbens, G. (2005): Matching Methods in Practice, Journal of Human Resources, 50, 373-420.
James, G. et al. (2017): An Introduction to Statistical Learning, Springer, New York, Chapter 5 & 6.
Jerger, J., & Pohnke, C., & Spermann, A. (2001): Gut betreut in den Arbeitsmarkt? Eine mikroöko-nometrische Analyse der Mannheimer Arbeitsvermittlungsagentur, Mitteilungen aus der Arbeits-markt- und Berufsforschung, 34, 567-576.
Goller, D. et al. (2019): Does the Estimation of the Propensity Score by Machine Learning Improve Matching Estimation? The Case of Germany's Programmes for Long Term Unemployed, IZA Discus-sion Papers, No. 12526.
Klinkhammer, D., & Spermann, A. (2020): Eine Einführung in die empirische Kausalanalyse und Machine Learning mit R, UTB-Lehrbuch, wbv, Gütersloh, R codes on Github.

Simulation Based Inference (Shuffling and Resampling/Bootstrapping)
Hansen, B. (2021): Econometrics, Princeton University Press, forthcoming.
Taddy, M. (2019): Business Data Science, Mc GrawHill, New York.

Causal Machine Learning
Athey, S., & Imbens, G.W. (2019): Machine Learning Methods Economists should know about, Annual Review of Economics, 11, 685-725.

Cocks, B. & Lechner, M. & Bollens, J. (2023): Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium, Labour Economics, 80, 102306.

Hansen, B. (2021): Econometrics, Princeton University Press, forthcoming.
Huber, M. (2021): Causal Analysis, University of Fribourg, unpublished.
Taddy, M. (2019): Business Data Science, Mc GrawHill, New York

At the core of almost every dysfunctional economy lies a primordial disease – corruption. Yet, the discipline of economics has only recently turned its attention to study this dire phenomenon. This reading course invites you to join the most recent developments in empirical research on the caus-es and consequences of corruption around the world by surveying cutting edge scientific articles.

Our primary focus will be on the econometric design and the empirical methods behind the main findings of the literature. By the end of the course, the participants are expected to excel at reading, understanding, and interpreting the economic papers. These skills are essential for any economist and apply beyond the topic of corruption.

In a tutorial, additional complementary material is presented by the students and discussed in the group.

Requirements

Knowledge of econometrics at an intermediate level is strongly recommended. The course requires intensive reading prior to the reading sessions and active participation in the discussions.

Time and Location

Examination Modalities

Please note: this is not a language or grammar course!! The aim of this workshop is to prepare for writing papers in economics. It will involve various exercises and group work.

Topics covered are:

• (How to find an interesting/relevant topic?)
• How to find/organize relevant literature?
• How to read scientific papers?
• How to develop a concept?
• How to structure your own paper?
• Some formal rules on writing

Requirements

To apply for a slot, please send us an email including information on:

• Your program of studies
• In which semester you are studying
• If you have already written seminar papers (if so, how many and with whom
• The title of the topic you want to work on during the workshop and in which context you are writing it (seminar paper or diploma/master thesis at which chair)

For our information, please also indicate if you have attended any courses at our chair (which ones?). By applying, you formally acknowledge that you are willing and able to attend ALL meetings (of one of the workshops)!

Time and Location

Workshop 1 (Gan Jin)

Examination Modalities