Syllabus Course title

Syllabus

Course title

Email WeberA@ceu.edu

Office hours by appointment

Credits 2 US credits (4 ECTS credits) Module (in which the course is offered)

Term Fall 2022/23

Course level PhD/Master’s Prerequisites Econometrics 2 Course drop

1. COURSE DESCRIPTION

Content. This course examines econometric identification issues in empirical microeconomics and public policy analysis. It builds on micro-econometric methods that are introduced in Econometrics 2 and provides a more in depth discussion of formal methodological aspects such as identification assumptions, implementation problems, and robustness and specification tests. In addition, we cover a range of relevant applications that highlight the strengths and weaknesses of each of the econometric methods. The applications will provide a guidance on how to implement the

methodology in a meaningful way, how to test for identification assumptions, and how to interpret estimation results. In homework assignments students practice with real world data and learn how to critically read academic articles applying micro-econometric methods.

Relevance. The course focuses on the sensible application of econometric methods to empirical problems. Thereby it provides a solid background on issues that arise when analyzing non-

experimental social science data and a guide for tools that are useful for applied research and policy analysis. The course also emphasizes how a basic understanding of economic theory and

institutions can help inform the analysis.

2. LEARNING OUTCOMES

Key outcomes. By the end of the course, students will be able to

• have a firm grasp of the types of research design that can lead to convincing analysis

• understand threats to uncovering causal effects from economic data

• be able to apply a range of micro-econometric tools and interpret results

• be comfortable working with large scale data sets

• be encouraged to develop independent research interests and applied research projects.

Other outcomes. The course will also help develop skills in the following areas.

Learning Area Learning Outcome

Critical Thinking Recognize convincing research designs to recover causal effects,

understand threats to identification,

learn how to critically read academic articles and write referee reports Quantitative

Reasoning

Develop ideas for own research projects and their implementation Technology Skills In depth knowledge of state of the art micro-econometric tools Interpersonal

Communication Skills

Practice in class presentations and discussions Management

Knowledge and Skills

none Cultural Sensitivity

and Diversity

none Ethics and Social

Responsibility

none

3. READING LIST

Textbooks:

• Josh Angrist and Jorn-Steffen Pischke, Mostly Harmless Econometrics, Princeton University Press

• Jeffrey Wooldridge, EconometricAnalysis of Cross Section and Panel Data (MIT Press).

Additional Readings: topic specific reading list with mandatory and optional papers (see below)

4. TEACHING METHOD AND LEARNING ACTIVITIES

The course will involve a mix of lectures, student presentations, and class discussions. Specifically, learning objectives will be achieved through

• Student presentations and class discussion of assigned reading material

• Each student will develop a research project, starting with an abstract outlining the research question in the beginning of the course, working on developing the identification and empirical strategy, and giving a final presentation in the last term

• Homework assignment of problem sets. Solutions are discussed in class.

5. ASSESSMENT

Grading will be based on the total score out of 100, in line with CEU’s standard grading guidelines 1 Project Abstract 15%

2 Homework assignments 15%

3 Class Participation, Discussion, and Class Presentations 20%

4 Final Presentation 50%

1 (15%). Students develop an empirical project idea, which they first introduce in a short abstract.

2 (15%).Homework assignments

4 (20%). Students are assigned applied research papers, which they will present in class. Each short presentation will lead to a class discussion.

3 (50%).Final Exam

6. TECHNICAL REQUIREMENTS

Stata or other software package to solve applied problem sets.

7. TOPIC OUTLINE AND SCHEDULE

Session Topics Readings

Week 1 Regression, Decomposition methods

Week 2 Potential outcome model, Matching and Propensity Scores Week 3 Difference-in-difference, Event study Designs

Week 4 Synthetic control methods Week 5 Instrumental variables, LATE Week 6 Regression discontinuity designs

8. SHORT BIO OF THE INSTRUCTOR

Andrea Weber is professor of economics at the Central European University, where she has been teaching since 2017. She is a CEPR Research Fellow, a Fellow at IZA and the CESifo Research Networks. She is also a guest professor at the Vienna University of Economics and Business, a research consultant for the Austrian Institute of Economic Research in Vienna, and she serves as coeditor on the board of the Journal of Public Economics. Her work focuses on the role of institutions and labor market policies on individual labor supply decisions, the role of firms in the labor market, policies to reduce gender differences, and the effects of economic shocks such as job displacement on individual outcomes. At CEU Andrea Weber is teaching Labor Economics and Applied Micro-econometrics.

READING LIST (PRELIMINARY)

This is the combined list of references for Advanced Econometrics 2 and Advanced Microeconometrics:

Applications. At the beginning of the course a compulsory reading list will be announced.

1. Introduction: Research questions, the concept of causality

Abadie, A. and M. Cattaneo, “Econometric Methods for Program Evaluation”, Annual Review of Econmics 2018. 10:465–503.

S. Athey, G. Imbens “The State of Applied Econometrics: Causality and Policy Evaluation” Journal of Economic Perspectives. May 2017, Vol. 31, Issue 2, Pages 3-32.

2. Decomposition Methods

DiNardo, Fortin and Lemieux (1996) Labor Market Institutions and the Distribution of Wages, 1973- 1992: A Semiparametric Approach, Econometrica, Vol 64, 1001-1044.

Fortin, Nicole, Thomas Lemieux, and Sergio Firpo (2011) “Decomposition Methods in Economics”, Handbook of Labor Economics (Volume 4A)

Bell, Brian, Michael Böhm, Nicole Fortin (2017) “Top Earnings Inequality and the Gender Pay Gap:

Canada, Sweden, and the United Kingdom” Labour Economics, 47, 107-123.

Huber, M., Solovyeva, A. (2020) On the Sensitivity of Wage Gap Decompositions. Journal of Labor Research 41, 1–33.

4. Linear Regression, Propensity Scores, Matching

Imbens, Guido W. (2004) “Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review” Review of Economics and Statistics, 86, 4-29.

Rosenbaum, Paul R. and Donald B. Rubin (1984) “Reducing Bias in Observational Studies Using Subclassification on the Propensity Score” Journal of the American Statistical Association, 79, 516-524.

LaLonde, Robert J. (1986), "Evaluating the Econometric Evaluations of Training Programs with Experimental Data", American Economic Review, 76, 604-620.

Dehejia, Rajeev H. and Sadek Wahba (1999) "Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs" Journal of the American Statistical Association, 94, 1053-1062.

Guido Imbens (2015) “Matching Methods in Practice: Three Examples” J. Human Resources, vol. 50 no.

2 373-419

Leung, P. and Z. Pei (2020) “Further Education during Unemployment”, Princeton University Industrial Relations Section Working Paper #642

5. Fixed Effects and Panel Data Methods, Differences-in-Differences, Event Study Designs Davis, Lucas W. (2004) “The Effect of Health Risk on Housing Values: Evidence from a Cancer Cluster”, American Economic Review, 94(5), 1693 – 1704.

John Gruber (1994) ”The Incidence of Mandated Maternity Benefits”, American Economic Review, Vol 84, 622-641.

Jacobson, LaLonde, Sullivan (1993) "Earnings Losses of Displaced Workers", American Economic Review

Fadlon, Nielsen (2015) "Household Responses to Severe Health Shocks", NBER Working Paper 21352.

Halla, Schmieder, Weber (2017) "Job Displacement, Family Dynamics, and Spousal Labor Supply"

Abadie, A., M. M. Chingos, and M. R. West “Endogenous stratification in randomized experiments”.

Working Paper 2017.

Itay Saporta-Eksten, Ity Shurtz, Sarit Weisburd (2018) “Social Security, Labor Supply and Health of Older Workers: Quasi-Experimental Evidence from a Large Reform”, working paper

Bernardus Van Doornik, David Schoenherr, Janis Skrastins (2018) “Unemployment Insurance, Strategic Unemployment, and Firm-Worker Collusion”, working paper

M. Lachowska, A. Mas, S. Woodbury “Sources of Displaced Workers Long-term Earnings Losses”, NBER Working Paper 24217, Forthcoming, American Economic Review.

Liyang Sun, Sarah Abraham “Estimating dynamic treatment effects in event studies with heterogeneous treatment effects”, Journal of Econometrics, 2021.

Borusyak, K., X. Jaravel, and J. Spiess (2021) “Revisiting event study designs: Robust and efficient estimation”, Working paper.

Barbara Biasi and Heather Sarsons (2021) “Flexible Wages, Bargaining, and the Gender Gap” QJE Appendix

Andrew Goodman-Bacon (2021) “Difference-in-differences with variation in treatment timing” Journal of Econometrics

Brantly Callaway, Pedro Sant’ Anna (2021) “Difference-in-Differences with Multiple Time Periods”

Journal of Econometrics

6. Synthetic Control Methods

A. Abadie, A. Diamond, J. Hainmueller “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, June 2010.

A. Abadie, A. Diamond, J. Hainmueller “Comparative Politics and the Synthetic Control Method”, American Journal of Political Science, Vol. 59, No. 2, April 2015, Pp. 495–510.

G. Peri, V. Yasenov, “The Labor Market Effects of a Refugee Wave: Synthetic Control Method Meets the Mariel Boatlift”, IZA Discussion Paper 10605, 2017.

Damon Jones, Ioana Marinescu (2018) The Labor Market Impacts of Universal and Permanent Cash Transfers: Evidence from the Alaska Permanent Fund, NBER Working Paper 24312.

7. Clustering Standard Errors

Bertrand, Marianne, Esther Duflo, Sendhil Mullainathan, 2004. " How Much Should We Trust Differences-in-Differences Estimates?," The Quarterly Journal of Economics,

Cameron, C. and D.L. Miller “A Practitioner’s Guide to Cluster-Robust Inference” Journal of Human Resources, 50(2):317–372, 2015.

Abadie, Athey, Imbens, Wooldridge “When Should We Adjust Standard Errors for Clustering?”, Working paper, 2017.

8. Instrumental Variables Estimation, Control Functions, Local Average Treatment Effects, Marginal Treatment Effects

Bound, Jaeger, and Baker (1995) Weak Instruments

Angrist, Joshua D. and Alan B. Krueger (1991) “Does Compulsory School Attendance Affect Schooling and Earnings?” The Quarterly Journal of Economics, 106, 979-1014.

Angrist, Joshua D., Guido W. Imbens and Donald B. Rubin (1996) “Identification of Causal Effects Using Instrumental Variables”, Journal of The American Statistical Association, 91, 444-455.

Garen, John (1984) “The Returns to Schooling: A Selectivity Bias Approach with a Continuous Choice Variable”, Econometrica, 52, 1199-1218.

Card, David and Laura Giuliano (2014) “Do Gifted Education Programs Work? For Which Students?,”

NBER Working Paper No. w20453.

Card, David and Laura Giuliano (2016) Universal Screening Increases the Representation of Low Income and Minority Students in Gifted Education” Proceedings of the National Academy of Sciences, 113(48):

13678-13683, with David Card.

Bhuller, Manudeep, Gordon Dahl, Katrine Loken, Magne Mogstad (2016) Incarceration, Recidivism and Employment, working paper, University of Bergen

Dobbie, Grönqvist, Niknami, Palme, Priks, 2018. “Intergenerational Effects of Parental Incarcaration”, NBER Working Paper 24186.

Autor, Kostol, Mogstad, Setzler, (2017) “Disability Benefits, Consumption Insurance, and Household Labor Supply”, NBER Working paper 23466

Autor, David H. and Mark G. Duggan. 2003. “The Rise in the Disability Rolls and the Decline in Unemployment.” Quarterly Journal of Economics 118 (1):157–205.

Autor, David H., David Dorn, and Gordon H. Hanson. 2013. “The China Syndrome: Local Labor Market Effects of Import Competition in the United States.” American Economic Review 103 (6):2121–2168.

9. Regression Discontinuity Designs

Thistlethwaite and Campbell (1960) “Regression-Discontinuity Analysis: An Alternative to the Ex-Post Facto Experiment”

Van der Klaaw (2002) “Estimating the Effect of Financial Aid Offers on College Enrollment: A Regression-Discontinuity Approach” International Economic Review,Vol 43(4).

Imbens, Guido W. and Thomas Lemieux (2008) "Regression Discontinuity Designs: A Guide to Practice"

Journal of Econometrics, 142, 615-635.

David S. Lee and Thomas Lemieux (2010) "Regression Discontinuity Designs in Economics" Journal of Economic Literature, 48, 281-355.

David Card, Raj Chetty, Andrea Weber, (2007), "Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market", Quarterly Journal of Economics, 122(4), 1511-1560 Angrist, Joshua D. and Victor Lavy (1999) “Using Maimonides’ Rule to Estimate the Effect of Class Size on Scholastic Achievement” The Quarterly Journal of Economics, 114, 533-575.

Miguel Urquiola and Eric Verhoogen (2009), Class-Size Caps, Sorting, and the Regression-Discontinuity Design, American Economic Review, 99:1, 179–215.

McCrary, Justin (2008) “Manipulation of the running variable in the regression discontinuity design: A density test”, Journal of Econometrics, 142, 698–714.

Card, David and David S. Lee (2005) “Regression Discontinuity Inference with Specification Error”, Journal of Econometrics, 142(2) 655-674.

Calonico, S., Cattaneo, M. D., and Titiunik, R. (2014), “Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs”, Econometrica, 82, 2295-2326.

Calonico, S., Cattaneo, M. D., and Titiunik, R. (2014), “Robust Data-Driven Inference in the Regression- Discontinuity Design?” Stata Journal, 14, 909 - 946.

Calonico, S., Cattaneo, M. D., and Titiunik, R. (2015), “Optimal Data-Driven Regression Discontinuity Plots”, JASA, 110, 1753 – 1769

Zhuan Pei, Christina Korting, Carl Lieberman, Jordan Matsudaira, and Yi Shen (2019) Visual Inference and Graphical Representation in Regression Discontinuity Designs, Princeton University Industrial Relations Section Working Paper #638.

10. Regression Kink Designs

Card, David, David Lee, Zhuan Pei and Andrea Weber (2015) “Inference on Causal Effects in a Generalized Regression Kink Design”, Econometrica, 83(6), 2453–2483.

Card, David, David Lee, Zhuan Pei and Andrea Weber (2017) “Regression Kink Design: Theory and Practice”, Advances in Econometrics, volume 38, 341 – 382.

Card, David, Andrew Johnston, Pauline Leung, Alexandre Mas, and Zhuan Pei, (2015) “The Effect of Unemployment Benefits on the Duration of Unemployment Insurance Receipt: New Evidence from a Regression Kink Design in Missouri, 2003-2013,” American Economic Review: Papers and Proceedings, 105 (5), 126–130.

Card, David, Andrew Johnston, Pauline Leung, Alexandre Mas, and Zhuan Pei, (2015) “The Effect of Unemployment Benefits on the Duration of Unemployment Insurance Receipt: New Evidence from a Regression Kink Design in Missouri, 2003-2013,” NBER Working Paper 20869

11. Meta Analyis

Card, David, Jochen Kluve and Andrea Weber “What Works? A Meta Analysis of Recent Active Labor Market Program Evaluations”. Journal of the European Economic Association, 16(3), 894 – 931, 2018.

Card, David, Jochen Kluve and Andrea Weber “Active Labor Market Policy Evaluations: A Meta- analysis”, The Economic Journal, 120, F452-F477, 2010

Havranek,Tomaš (2015) Measuring Intertemporal Substitution: The Importance of Method Choices and Selective Reporting, Journal of the European Economic Association, 13(6).