Syllabus

Title
1496 Microeconometrics
Instructors
Univ.Prof. Dr. Andrea Weber
Contact details
  • Type
    FS
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/14/20 to 09/18/20
Registration via LPIS
Notes to the course
Subject(s) Doctoral/PhD Programs
Dates
Day Date Time Room
Friday 10/23/20 08:00 AM - 11:00 AM TC.4.14
Friday 10/30/20 08:00 AM - 11:00 AM TC.4.14
Friday 11/13/20 08:00 AM - 11:00 AM TC.4.14
Friday 11/20/20 08:00 AM - 11:00 AM Online-Einheit
Friday 11/27/20 08:00 AM - 11:00 AM Online-Einheit
Friday 12/04/20 08:00 AM - 11:00 AM Online-Einheit
Friday 12/11/20 08:00 AM - 11:00 AM Online-Einheit
Friday 12/18/20 08:00 AM - 11:00 AM Online-Einheit

Procedure for the course when limited activity on campus

In case of limited activity on campus we will switch to a rotation mode.

A microsoft teams access will be created before the first class, so that students can also participate online. Microsoft Teams is used flexibly, either at the lecturer's desk in the lecture room (or - if it is not allowed to enter the lecture rooms - in the home office). The screen of the lecturer's desk is shared between the students in the lecture room and those at home. For the students in the lecture room, the lecturer's PC projects onto the whiteboard; students at home need their own laptop (tablet), with which MS-Teams can share the screen. The sound is transmitted from the lecture room via a microphone to the students in the home office. (It is not intended / necessary to film the lecturer.)

This mode is very flexible and the presence in the lecture room can vary between 0% - 100%.

Contents

This course examines econometric identification issues in empirical microeconomics and public policy analysis. It supplements topics covered in Econometrics with a focus on the sensible application of econometric methods to empirical problems. The course provides 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.

Learning outcomes

By the end of this course, students will:

  • 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 microeconometric tools and interpret results
  • be encouraged to develop independent research interests and applied research projects.

Attendance requirements

Attendance in class is compulsory. Students who miss a class must send an excuse by email.  

Teaching/learning method(s)

Readings for each week will be assigned one week in advance. Students are expected to read the material in advance and be prepared for class discussions.

 

Students give short presentations of papers assigned for reading

 

2-3 problem sets will be posted on the course website over the term.

Assessment

Final grades are based on 

  • Problem sets: in total 20% 
  • Participation in class discussions: 15%
  • Student presentations: 15%
  • Final exam: 50%

Readings

1 Author: Josh Angrist and Jorn-Steffen Pischke
Title:

Mostly Harmless Economietrics


Publisher: Princeton University Press
Content relevant for class examination: Yes
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book
2 Author: Jeffrey Wooldridge
Title:

EconometricAnalysis of Cross Section and Panel Data


Publisher: MIT Press
Content relevant for class examination: Yes
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book
3
Title:

Topic specific reading list with mandatory and optional papers


Recommendation: Essential reading for all students
Type: Journal

Prerequisites for participation and waiting lists

Prerequisites are MA level courses in Econometrics and Microeconomics

 

Recommended previous knowledge and skills

Recommended knowledge of basic data handling skills and Stata (or similar programming package)

Knowledge in one or more fields of applied microeconomics, such as labor economics, public finance, development economics, industrial organization etc.

Availability of lecturer(s)

For office hours please send an email and make an appointment.

Unit details

Unit Date Contents
1 10/23/20

Introduction

Decomposition Methods

Reading (still preliminary):

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” working paper.

Kline, Patrick, (2011) “Oaxaca-Blinder as a Reweighting Estimator”,  American Economic Review: Papers and Proceedings, 101, pp. 532-537

2 10/30/20

Linear Regression, Propensity Scores, Matching

Reading (still preliminary):

Mostly Harmless Econometrics, Chapter 3 


Alan B. Krueger (1993) "How Computers Have Changed the Wage Structure: Evidence from Microdata 1984 - 1989", The Quarterly Journal of Economics


John E. DiNardo and Jorn-Steffen Pischke (2004) "The Returns to Computer Use Revisited: Have Pencils Changed the Wage Structure Too?", The Quarterly Journal of Economics


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.


Ashenfelter, Orley (1987), "The Case for Evaluating Training Programs with Randomized Trials", Economics of Education Review, 6, 333-338.


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


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.


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.
 

3 11/13/20

Fixed Effects and Panel Data Methods, Differences-in-Differences, Event Study Designs

Reading (still preliminary):

Mostly Harmless Econometrics, Chapter 5


Card and Krueger (1994) “Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania“, American Economic Review, 84(4), 772 – 793.


Entorf, Horst, Michel Gollac and Francis Kramarz (1999), “New Technologies, Wages, and Worker Selection”, Journal of Labor Economics, 17, pp. 464-491.


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


Kline, Patrick. (2012). The Impact of Juvenile Curfew Laws on Arrests of Youth and Adults, American Law and Economics Review, 14(1): 44-67


Fadlon, Nielsen (2015) "Household Responses to Severe Health Shocks", NBER Working Paper 21352.
Halla, M., J. Schmieder, A. Weber (2017) "Job Displacement, Family Dynamics, and Spousal Labor Supply"
 

 

4 11/20/20

Synthetic Control Methods

Clustering Standard Errors

Reading (still preliminary):

Abadie, A., M. M. Chingos, and M. R. West “Endogenous stratification in randomized experiments”. Working Paper 2017.

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.
Edson Severnini, “The Power of Hydroelectric Dams: Agglomeration Spillovers” IZA Discussion Paper 8082, 2014.

Marianne Bertrand, Esther Duflo, and Sendhil Mullainathan. How much should we trust differences-in-differences estimates? The Quarterly Journal of Economics, 119(1):249–275, 2004.

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

5 11/27/20

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

Reading (still preliminary)


Mostly Harmless Econometrics, Chapter 4 


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.


Bhuller, Manudeep, Gordon Dahl, Katrine Loken, Magne Mogstad (2020) “Incarceration, Recidivism and Employment”, Journal of Political Economy, vol. 128, no. 4


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


Cornelissen, Thomas, Christian Dustmann, Anna Raute and Uta Schönberg (2016) “From LATE to MTE: Alternative Methods for the Evaluation of Policy Interventions”, Labour Economics ,41, 47–60.


Cornelissen, Thomas, Christian Dustmann, Anna Raute and Uta Schönberg (forthcoming) “Who benefits from universal childcare? Estimating marginal returns to early childcare attendance”, Journal of Political Economy.


P. Goldsmith-Pinkham, I. Sorkin, H. Swift “Bartik Instruments: What, When, Why, and How?”, 2018.
 

6 12/04/20

Regression Discontinuity Designs

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

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).

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.

 

Alex Solis (2017) “Credit Access and College Enrollment”, Journal of Political Economy 125, no. 2: 562-622.

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
 

7 12/11/20

Regression Kink Designs

Reading (still preliminary):


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. NBER Working Paper 20869 


Manoli Turner (2016) “Cash-on-Hand & College Enrollment: Evidence from Population Tax Data and Policy Nonliearities.” 
 

8 12/18/2020

Final Exam

Last edited: 2020-07-14



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