Teaching

The reading course focuses on the role of information and the media for the behavior of individuals in the marketplace, in the polity, and in bureaucratic hierarchies. Students are familiarized with the empirical approaches that analyze the relationship between the individual behavior and media presence or the availability of information.

In a tutorial, complementary material is presented and discussed.

Requirements

A good command of econometrics and solid knowledge of micro- and macroeconomics are required since most papers are empirical.

Time and Location

Examination Modalities

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 causes 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

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

Content

This updated course covers empirical labor economics, modern econometrics, and modern business analytics. The main textbooks are Taddy et al. (2023) and Huber (2023). R scripts are used for each session. Students should sign in at posit.cloud to get access to RStudio.

Each session will be recorded via Zoom.

Complementary YouTube-Videos are available on my channel (https://www.youtube.com/channel/UCi5oODsxMKzuMiI9PFp3CSw).

Lecture

!!! 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. (2020): Using R for Introductory Econometrics, 2nd edition, Düsseldorf.

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

Huber, M. (2023): Causal Analysis: Impact evaluation and Causal Machine Learning in R, Project Mosaic.

James, G. et al. (2021): An Introduction to Statistical Learning, 2nd edition, 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. & Hendrix, L. & Harding, M. (2023): Modern Business Analytics, 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. (2021): An Introduction to Statistical Learning, 2nd edition, 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. & Lechner, M & Moczall, A. & Wolff, J. (2020): Does the Estimation of the Propensity Score by Machine Learning Improve Matching Estimation? The Case of Germany's Programmes for Long Term Unemployed, Labour Economics, 65, 101855.
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. (2022): Econometrics, Princeton University Press, forthcoming.
Taddy, M. (2023): 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.