Lehre – Aktuelle Kurse

Seminar

Prof. Dr. Markus Frölich

every term

Deutsch

Ziele und Inhalte des Moduls: Das Seminar umfasst aktuelle Themen bezogen auf Arbeits­märkte in Entwicklungs­länder­nmit einem empirischen mikroökonometrischen Fokus. Die Themen beinhalten unter anderem: Kinderarbeit,informelle Arbeits­märkte, Unternehmertum, die Schaffung von Firmen, Arbeits­markt­regulierungen, Mikrokredite, Mikro­versicherungen, etc. Die Seminartermine werden nach den Wünschen der Studierenden ausgewählt. Die­Studierenden sollen aktuelle Probleme von Entwicklungs­ländern erörtern und erkennen sowie empirische Studien zudiesen Fragen bewerten und diskutieren. In diesem Sinne ist es eine Mischung zwischen einem reinen Seminar zu­Entwicklungs­ländern und einem angewandten Ökonometrieseminar. Die Studierenden sollen also auch angewandteökonometrische Papiere verstehen, diskutieren und vorstellen, um die konkrete empirische Forschungs­weise zuerlernen. Das Seminar ist insbesondere auch als eine Vorbereitung auf eine mögliche Bachelor­arbeit im Bereich derangewandten empirischen Forschung gedacht, welche dann üblicherweise eine eigenständige ökonometrische Analysemit Sekundärdaten verlangt. Das Seminar stellt somit eine Brückenfunktion zwischen den Grundlagenvorlesungenzur Ökonometrie, welche eher das Methodenwissen vermitteln, und der eigenständigen empirischen Analyse in der­wissenschaft­lichen Forschung dar.

Erwartete Kompetenzen nach Abschluss des Moduls: Die Studierenden haben gelernt, einen Aufsatz zu einem Themaaus der Entwicklungs­ökonomie zu schreiben und zu präsentieren, wobei sie den Bezug zu mikroökonomischen­Modellen und insbesondere empirisch-ökonometrischer Analyse herausgearbeitet haben. Dies umfasst somit auch einekritische Analyse und Begutachtung von empirischen Studien und deren Methodik, insbesondere der Ökonometrie, der Datengrundlage und der Umsetzung der empirischen Herangehensweise.

Weitere Informationen: Bitte beachten Sie den gemeinsamen Anmeldezeitraum für Seminare des Bachelor­studien­gangs VWL.

Kontakt: Prof. Dr. Markus Frölich, Tel. 0621/181-1920 (Sekretariat: Anja Dostert), E-Mail: dostert(a)uni-mannheim.de, L7,3-5, Raum 1.21/1.22.

Dr. Arne Robert Weiß, Dr. Nick Barton

Each fall semester

English

Goals and contents of the module: The lecture will cover the “behavioral turn” in development economics, from “thinkingbig” to “thinking small”. We will cover the relevance of behavioral economics to development, structured along theoretical mechanisms and key concepts. It provides an overview on the central questions and topics in the field of behavioral development economics. The course takes the micro perspective to provide an understanding of the major determinantsand consequences of poverty and their impact on behavior. Furthermore, it will provide a summary of recent experimentalfindings and new developments in the fields of Behavioral Economics and Development Economics.

Expected competences acquired after completion of the module: Students will become familiar with key areas and concepts in behavioral development economics. This course exposes students to different methodological approachesin development policy design and provides students with the necessary tools to understand empirical and experimentalliterature in development economics and related fields that use similar methodological toolboxes. The overarching aim is totrain students to take part in the development discussion by discussing different approaches through the lens of behavioral economics.

Further information: Literature for exercise classes to be announced.

Contact Information: Dr. Arne Weiß (0621) 181-1805; email: arne.weiss@uni-mannheim-de,

Office: 1.01, Office hours: upon consultation.

Dr. Asmus Zoch-Gordon, Marc Gillaizeau

Each spring semester

English

Written exam (100 min, 50 %) and home assignments (50 %)

Goals and contents of the module: This course will focus on different micro-econometric models using actual empiricalstudies from the field of labour economics. Starting from the standard theory of competitive labour markets, we introducethe concept of human capital, to explain wage differences between individuals, and explore the role of education. Exploringthe Mincer earnings function, discrimination and unemployment, the students will learn how to analyse actual labour datasets using Stata. The first part of the course will deal with linear panel data models and instrumental regressions, thesecond part will focus on discrete choice models. This course will end with the introduction of non-parametric estimators.

Expected competences acquired after completion of the module: Ability to use Stata to conduct independent micro-econometric analysis and apply advanced micro-economic models.

Further information: Introductory literature:

• Wooldridge, Jeffrey M. (2002), Econometric Analysis of Cross Section and Panel Data, Cambridge, Mass.: MIT Press.Chapters 10-20.
• George J. Borjas, Labor Economics

Contact information: Name: Dr. Asmus Zoch-Gordon; Phone: (0621) 181-1842; Email: zoch uni-mannheim.de; Office: Room 123

Prof. Dr. Markus Frölich

Research seminar where Ph.D. students, who have completed their course work, present theirown research and receive feedback. (Topics in empirical development economics with microeconometric methods. Development economics can be subdivided into three branches: Macro, micro theory and empirical with micro data. Weonly cover the last area. Macro and micro theory have been the driving forces of development economics initially, but withthe increasing availability of microdata for Africa, Asia and Latin America in the last two decades, the foundation of the J-PAL network and the Nobel Prize in 2019, empirical development economics has been gaining attention.)

Competences acquired: Doctoral Students will know how to- identify a research question,- put a research question into context of the relevant literature,- present their current stage of research to their peers in a seminar environment.

Dr. Giulia Montresor, Dr. Katharina Richert

Every spring semester

English

80 % final exam (90 minutes), 20 % presentation (30 minutes including 5 minutes paper critique and 5 minutes group discussion)

Goals and contents of the module: The course is designed for introducing students to the main empirical strategies thatare typically used for impact evaluation: Randomized Control Trials, Identification on Observables, Instrumental Variables,Difference-in-Difference, Regression Discontinuity Design. Students will be both exposed to fundamental concepts behind the estimation of causal effects and related applied applications. Students will be asked to actively participate andprepare a presentation once during the tutorial session. The lecture and the tutorial will take place every week. Lecture contents will be practiced during Stata exercise sessions in the tutorial or deepened with discussions of the currentliterature presented by students. Every participating student will have to present one research article once. The 30-minutespresentations (+/-10%) will contain a 20 minute summary of the paper and a 5 minute discussion of positive and negativepaper aspects, potentially including secondary literature. Additionally, the presenting student will have to prepare 2-3questions suitable to motivate a 5 minute group discussion with all course participants. In order to participate in the group discussions, all students are required to read the suggested literature before the tutorial sessions.

Expected competences acquired after completion of the course:
• Understand what impact evaluation is and the different techniques used
• Understand the identifying assumptions underlying each impact evaluation technique
• Review the “parameters of interest”
• Make judgments about what specific impact evaluation technique is appropriate to use according to the context and type of intervention

Further information: Main reading: Frölich, M.& Sperlich, S. (2019): Impact Evaluation – Treatment effects and causalanalysis, Cambridge University Press.

Other useful material:
• Khandker S. et al. (2010): Handbook on Impact Evaluation: Quantitative Methods and Practices
• Angrist J. and Pischke, J. (2009): Mostly Harmless Econometrics
• Angrist J. and Pischke, J. (2015): Master­ing Metrics
• Caliendo M. and Kopeinig S. (2005): Some Practical Guidance for the Implementation of Propensity Score Matching
• Angrist, J., Imbens, G., and Rubin, D. (1996): Identification of causal effects using instrumental variables. Journal ofthe American Statistical Association, 91(434), 444-455.
• Lee, D., Lemieux, T., Regression discontinuity designs in economics (2010). Journal of economic literature, 48 (2),281-355.

Contact Information: Dr. Giulia Montresor; Phone: (0621) 181-1941; E-mail: montresor uni-mannheim.de;

Office: L7,3-5,room 131; Dr. Katharina Richert, E-mail: richert uni-mannheim.de

English

Goals and contents of the module: The course gives an introduction into the data management in Stata. That includes how to set up do-files, the preparation of data for analysis, the generation of variables, the use of macros in Stata, and themerging of data sets. Basic and advanced statistical procedures will be discussed in the course. For each model, therewill be an introduction to the statistical model and it will be shown how to analyze the corresponding data with Stata andhow to interpret the output of Stata. The models considered are some elementary statistical models, the linear regressionmodel with homoscedastic and heteroscedastic error terms, analysis of variance models, linear panel data models,nonlinear regression models and binary and multinomial models.

Expected competences acquired after completion of the module: The students know basic probabilistic and statisticalconcepts, e.g. the concept of a statistical test and how to compute and use p-values. The students can analyze data with Stata: The students are able to review a data set, generate summary statistics, and merge data sets. They know howto work with variables, matrices, and macros. They know how to perform elementary tests. The students can generateadvanced plots. They are able to set up a linear model with homoscedastic or heteroscedastic error terms and understandthe results provided by Stata. They can do an analysis of variance and test for heteroscedasticity in a linear regressionmodel. They understand the ideas of linear panel data regression and can analyze corresponding data. The students areable to estimate the parameters, perform tests for the parameters, and analyze the results in nonlinear regression modelsand binary choice models.

Further information: Literature: Cameron/Trivedi (2009). Microeconometrics using Stata. Stata Press.

Contact Information: Dr. Atika Pasha, E-mail: pasha uni-mannheim.de; Dr.Ingo Steinke; E-mail: isteinke rumms.uni-mannheim.de