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Lehre – Aktuelle Kurse

Herbst/-Wintersemester 2022

Teaching method (hours per week)	Lecture (2) + exercise (1)
Lecturer	Dr. Marc Gillaizeau, Dr. Viviana Urueña
Semester	fall term term 2022
Target Audience	Elective course for M. Sc. Economics
Start, End/ Time & Location	Please find the latest data under our course catalog
Course language	English
Prerequisites	E601- 603 (or equivalent). A background in development economics and Stata is helpful.
Examination	www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/
ECTS	7
Grading and ECTS Credits	Presentation (20 min during tutorial, 20%) + Stata Assignments (home assignment in week 3 and 7, 25% each) + final essay exam (45 min, 30%)
Course description	Goals and contents of the module: The course will introduce students to the main concepts of poverty and inequality measurements and the critical links between poverty and inequality and economic growth. Students will get an overview on theories of justice, methodological aspects of poverty & inequality measurement, gender inequalities, economic mobility, inequality and poverty in rich countries as well as development policy targeting poverty. The course will focus on low- and middle-income countries. It is structured as follows: 1. Introduction 2. Long Run Determinants of Growth 3. Concepts and Measurements of Poverty I 4. Concepts and Measurements of Poverty II 5. Poverty Alleviation I: (Micro-)finance 6. Poverty Alleviation II: Cash transfers 7. Concepts and Measurements of Inequality 8. Does Inequality Cause Growth? 9. Pro-Poor Growth 10. Inequality and Gender 11. Poverty and Inequality in High-Income Countries 12. Economic Mobility 13. The Behavioral Economics of Poverty 14. Recap
Expected competences acquired after completion of the module	The students become acquainted with the topics in poverty and inequality and learn to critically review and discuss empirical studies in the field.
Expected number of students in class	20
Contact information	Name: Dr. Marc Gillaizeau; Email: gillaizeau uni-mannheim.de; Office: L7, 3-5, room 1.19; Office hours: on request via email

Teaching method (hours per week)	Lecture (2) + Exercise (1)
Responsible teacher of the module	Prof. Dr. Markus Frölich
Cycle of offer	Each fall semester
ECTS Credits	7
Form and usability of the module	Elective course for M.Sc. in Economics
Start, End/ Time & Location	Please find the latest data under our course catalog
Course language	English
Prerequisites	E601 – 603 (or equivalent)
Examination	www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/
Grading	Final exam (120 min)
Course description	Topics will include counterfactual outcomes, heterogeneous treatment effects, (propensity) score matching, differences in differences, instrumental variables designs, randomized control trials, regression discontinuity design. The course content in 2022 will depend on the Covid situation. In the (possible) scenario that limitations to physical presence on campus are still in place in fall, there would be no computer exercise sessions. In such situation, only econometric theory will be considered and all practical aspects such as computer exercises and machine learning would be dropped and resumed again in fall 2023. Given unpredictability, the detailed course planning can only be done in September.
Expected competences acquired after completion of the module	The students become acquainted with modern methods in impact evaluation.
Further information	Impact Evaluation (Frölich, Sperlich, 2019, Cambridge University Press)
Expected number of students in class	20
Contact Information	Name: Anja Dostert; Email: dostert uni-mannheim.de; Office: L7, 3-5, room 1.21/1.22

Teaching method (hours per week)	Lecture (3) and exercise (1)
Lecturer	Dr. Ingo Steinke; Dr. Nicholas Barton, PhD
Semester	fall term 2022
Target Audience	Elective course for M. Sc. Economics
Start, End/ Time & Location	Please find the latest data under our course catalog
Course language	English
Prerequisites	E601 – 603 (or equivalent)
Examination	www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/
ECTS	9.5
Grading	Final exam (90 min)
Course description	Goals and contents of the module: Although Stata already offers a large number of econometric tools, novel approaches are often not available and have to be implemented by users. This course offers an introduction to advanced programming in Stata. Since comparatively few people know how to do so, Stata programming skills can be a competitive advantage. The lecture will start with an introduction to efficiently written do-files (including data processing). We will look at and discuss different data types. In hands-on sessions students will be taught how to prepare the data for analysis. Variables will be generated and their distributions explored; data will be merged; and regression results will be critically discussed. Moreover, in this course students will learn how to implement new commands for Stata and to conduct Monte Carlo simulations. These are important for verification of implementations and are used as a very important tool to analyse the small sample properties of estimators and to complement the theoretical properties of estimators making them an integral part of econometric analyses. We will also touch upon Stata's matrix programming language Mata, non-linear optimization, e.g. ML estimation and bootstrap methods.
Expected competences acquired after completion of the module	Students will be able to program quantitative methods using Stata independently. They are able to use Stata and Mata as programming languages and understand the standard syntax and the grammar of the languages. They will also be able to understand commands in Stata and edit these accordingly. Knowledge won from this module can be applied to various records. Students are capable of automatizing analysis and working efficiently. In addition to that, they will be able to conduct Monte Carlo simulations and interpret and use the results to estimate the quality of the estimation procedure. They can generate samples from a variety of distributions. Through Monte Carlo simulations, students will have a better comprehension of the uncertainty and quality of the estimation and test procedures.
Further information	Cameron/ Trivedi (2009). Microeconometrics using Stata. Stata Press
Expected number of students in class	21
Contact information	Name: Dr. Nicholas Barton; Email: nibarton mail.uni-mannheim.de Name: Dr. Ingo Steinke; Email: isteinke rumms.uni-mannheim.de

Teaching method (hours per week)	Lecture (4) + exercise (2)
Lecturer	Prof. Dr. Markus Frölich
Semester	Each fall semester
Form and usability of the module	Core course for M.Sc. Economics
Start, End/ Time & Location	Please find the latest data under our course catalog
Course language	English
Prerequisites	Undergraduate level of econometrics
Examination	www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/
ECTS	10
Grading	Final exam (120 min, 100%)
Course description	Goals and contents of the module: The goal of the module is to offer advanced treatment of econometric theory and to serve as the gate way to further advanced theoretical and applied econometric modules offered in the economics graduate program at the Department of Economics in Mannheim. The module offers a revision of undergraduate level econometrics before moving on to extensive coverage of large-sample theory and some organizing estimation principles such as GMM estimators. Asymptotic properties of these estimators are also the focus of the module as well as non-linear models and the treatment of serial correlation.
Expected competences acquired after completion of the module	On successful completion of the module, students are expected to attain the following competences: • Attain advanced theoretical knowledge in econometrics in the specific topics the module covers at a high technical and mathematical level. • Be familiar with current theories and recent developments in the specific topics of focus for the module. • Attain a higher/advanced level of analytical capability. • Be in a position to take on follow-up advanced theoretical and applied econometrics modules. • Attain the level of competence that permits independent undertakings in search of new knowledge in the specialist areas the module covers. • Attain the level of competence required to carry out (theoretical) research-oriented projects independently. • To be in a position to exchange information, ideas, and solutions with experts of the field on a scientific level as well as with laymen. • To be able to communicate and to work effectively and efficiently with people and in groups. • Graduates are able to communicate precisely in the English specialist language.
Further information	Recommended textbooks: •Econometrics; Bruce E. Hansen; University of Wisconsin; www.ssc.wisc.edu/~bhansen/econometrics/ • Wooldridge (2010): Econometric Analysis of Cross Section and Panel Data. MIT Press
Expected number of students in class	65
Contact information	Name: Prof. Dr. Markus Frölich; Email: froelich uni-mannheim.de; Office: L7, 3-5, room 114; Office hours: upon appointment

Course Type	Research Seminar (3 and 4th year)
Lecturer	Prof. Dr. Markus Frölich
Semester	fall term 2022
Prerequisites	Successful completion of first two years of PhD programme
Start, End/ Time & Location	Please find the latest data under our course catalog
Course language	English
Requirements for the assignment of ECTS Credits and Grades:	A written seminar paper on a topic of own choice and a presentation in class.
Examination	www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/
ECTS	5
Course description	Course content: Research seminar where Ph.D. students, who have completed their course work, present their own research and receive feedback. This seminar is intended to discuss topics around theoretical as well as applied research in the area of causal inference as well as randomized experiments and experimental design. Students are encouraged to review literature on a topic within this field, and explore if such research field may reflect or support their development of their own PhD project. Seminar topics normally refer to either Econometric Theory, i.e. identification or design development as well as estimators and their properties, or the applicability of methods that are linked to causal identification. 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.

Lehrmethode	Blockseminar
Art und Verwendbarkeit des Moduls	Wahlveranstaltung im Bachelorstudiengang Volkswirtschaftslehre
Modulverantwortliche/r	Prof. Dr. Markus Frölich
Semester	every term
Start, End/ Time & Location	Please find the latest data under our course catalog
ECTS-Punkte	6
Lehrmethode (Umfang)	Blockseminar (2 SWS)
Arbeitsaufwand	Präsenzzeit Seminar: 21 Stunden; Zeit für die Anfertigung der Seminararbeit, für die Vorbereitung der Referate sowie für das Selbststudium 147 Stunden.
Unterrichtssprache	Deutsch
Teilnahmevoraussetzungen	Grundlagen der Ökonometrie
Examination	TBA
Benotung	schriftliche Seminararbeit (50%), Vortrag (25%), Koreferat (25%)
Erwartete Zahl der Teilnehmer/innen	max. 13
Course description	Ziele und Inhalte des Moduls: Das Seminar umfasst aktuelle Themen bezogen auf Arbeitsmärkte in Entwicklungsländern mit 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. Die Studierenden sollen aktuelle Probleme von Entwicklungsländern erörtern und erkennen sowie empirische Studien zu diesen Fragen bewerten und diskutieren. In diesem Sinne ist es eine Mischung zwischen einem reinen Seminar zu Entwicklungsländern und einem angewandten Ökonometrieseminar. Die Studierenden sollen also auch angewandte ökonometrische Papiere verstehen, diskutieren und vorstellen, um die konkrete empirische Forschungsweise zu erlernen. Das Seminar ist insbesondere auch als eine Vorbereitung auf eine mögliche Bachelorarbeit im Bereich der angewandten empirischen Forschung gedacht, welche dann üblicherweise eine eigenständige ökonometrische Analyse mit Sekundärdaten verlangt. Das Seminar stellt somit eine Brückenfunktion zwischen den Grundlagenvorlesungen zur Ökonometrie, welche eher das Methodenwissen vermitteln, und der eigenständigen empirischen Analyse in der wissenschaftlichen Forschung, dar.
Erwartete Kompetenzen nach Abschluss des Moduls	Die Studierenden haben gelernt, einen Aufsatz zu einem Thema aus der Entwicklungsökonomie zu schreiben und zu präsentieren, wobei sie den Bezug zu mikroökonomischen Modellen und insbesondere empirisch-ökonometrischer Analyse herausgearbeitet haben. Dies umfasst somit auch eine kritische 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 uni-mannheim.de, L7, 3 – 5, Raum 1.21/1.22

Frühjahrs-/Sommersemester 2022

Form and usability of the module	elective course for B. Sc. Economics
Responsible teachers of the module	Dr. Atika Pasha, Dr. Ingo Steinke
Cycle of offer	every spring term
ECTS credits	7
Teaching method (hours per week)	lecture (2) + exercise (2)
Start, End/ Time & Location	Please find the latest data under our course catalog
Workload: time in class:	lecture 21 hours and exercise 21 hours; independent study time and preparation for the exam 154 hours.
Course language	English
Prerequisites	Statistik I + II, Grundlagen der Ökonometrie
Examination	www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/
Grading	programming exam (90 min.)
Expected number of students in class	depends on students´choice (max. 41)
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 the merging of data sets. Basic and advanced statistical procedures will be discussed in the course. For each model, there will be an introduction to the statistical model and it will be shown how to analyze the corresponding data with Stata and how to interpret the output of Stata. The models considered are some elementary statistical models, the linear regression model 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 statistical concepts, 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 how to work with variables, matrices, and macros. They know how to perform elementary tests. The students can generate advanced plots. They are able to set up a linear model with homoscedastic or heteroscedastic error terms and understand the results provided by Stata. They can do an analysis of variance and test for heteroscedasticity in a linear regression model. They understand the ideas of linear panel data regression and can analyze corresponding data. The students are able to estimate the parameters, perform tests for the parameters, and analyze the results in nonlinear regression models and binary choice models.
Further information:	Literature: Cameron/Trivedi (2009). Microeconometrics using Stata. Stata Press.
Contact Information:	Dr. Ingo Steinke; Phone: (0621) 181 1940; Email: isteinke rumms.uni-mannheim.de Dr. Atika Pasha; Email: pasha uni-mannheim.de

Form and usability of the module	Elective course for B. Sc. Economics
Responsible teachers of the module	Dr. Katharina Richert, Dr. Benjamin Chibuye
Cycle of offer	Every spring semester
Duration	1 semester
ECTS Credits	7
Start, End/ Time & Location	Please find the latest data under our course catalog
Teaching method (hours per week)	Lecture (2) + exercise (2)
Workload/ time in class	lecture 21 hours and exercise 21 hours, independent study time and preparation for the exam 154 hours
Course language	English
Prerequisites	Statistik I + II, Grundlage der Ökonometrie
Examination	www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/
Grading	exam (90 minutes) and presentation, 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 that are 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 and prepare 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 current literature presented by students. Every participating student will have to present one research article once. The 30-minutes presentations (+/-10%) will contain a 20-minute summary of the paper and a 5-minute discussion of positive and negative paper aspects, potentially including secondary literature. Additionally, the presenting student will have to prepare 2–3 questions 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 judgements 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 causal analysis, 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 of the 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.
Maximum number of students in class	41
	Contact Information: Office: L7,3-5, room 131; Dr. Katharina Richert, E-mail: richert uni-mannheim.de Office L7,3-5, room 102; Dr. Benjamin Chibuye, E-mail: bchibuye mail.uni-mannheim.de

Form and usability of the module	Elective course for M. Sc. Economics
Responsible teacher of the module	Dr. Asmus Zoch-Gordon, Marc Gillaizeau
Cycle of offer	Each spring semester
ECTS Credits	9
Teaching method (hours per week)	Lecture (2) + exercises (2)
Start, End/ Time & Location	Please find the latest data under our course catalog
Workload	270 hours in total, containing 33 hours class time and 237 hours for independent studies, project and exam preparation
Course language	English
Prerequisites	E601 – E603 (or equvalent)
Examination	www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/
Grading	Written exam (100 min, 50%), take-home assignments (50%)
Expected number of students in class	25
Goals and contents of the module	This course will focus on different micro-econometric models using actual empirical studies from the field of labour economics. Starting from the standard theory of competitive labour markets, we introduce the concept of human capital, to explain wage differences between individuals, and explore the role of education. Exploring the Mincer earnings function, discrimination and unemployment, the students will learn how to analyse actual labour data sets using Stata. The first part of the course will deal with linear panel data models and instrumental regressions, the second 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	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	Dr. Asmus Zoch-Gordon; Phone: (0621) 181-1842; Email: zoch uni-mannheim.de; Office: Room 123