DE / EN

Lehre – Aktuelle Kurse

Herbst/-Wintersemester 2022

  • E5024 Poverty and Inequality

    Teaching method (hours per week)

    Lecture (2) + exercise (1)

    Lecturer

    Dr. Marc Gillaizeau, Dr. Viviana Urueña

    Semester

    fall term term 2022

    Target AudienceElective 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 CreditsPresentation (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 moduleThe 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 class20
    Contact informationName: Dr. Marc Gillaizeau; Email: gillaizeau uni-mannheim.de; Office: L7, 3-5, room 1.19; Office
    hours: on request via email
  • E5040 Impact Evaluation and Causal Inference

    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 Credits7
    Form and usability of the moduleElective 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 moduleThe students become acquainted with modern methods in impact evaluation.
    Further informationImpact Evaluation (Frölich, Sperlich, 2019, Cambridge University Press)
    Expected number of students in class20
    Contact InformationName: Anja Dostert; Email: dostert uni-mannheim.de; Office: L7, 3-5, room 1.21/1.22
  • E5026 Programming in Stata

    Teaching method (hours per week)Lecture (3) and exercise (1)

    Lecturer

    Dr. Ingo Steinke; Dr. Nicholas Barton, PhD

    Semester

    fall term 2022

    Target AudienceElective 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

    GradingFinal 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 moduleStudents 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 informationCameron/ Trivedi (2009). Microeconometrics using Stata. Stata Press
    Expected number of students in class21
    Contact informationName: Dr. Nicholas Barton; Email: nibarton mail.uni-mannheim.de
    Name: Dr. Ingo Steinke; Email: isteinke rumms.uni-mannheim.de
  • E603 Advanced Econometrics

    Teaching method (hours per week)Lecture (4) + exercise (2)

    Lecturer

    Prof. Dr. Markus Frölich

    Semester

    Each fall semester
    Form and usability of the moduleCore 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 moduleOn 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 class65
    Contact informationName: Prof. Dr. Markus Frölich; Email: froelich uni-mannheim.de; Office: L7, 3-5, room 114;
    Office hours: upon appointment
  • E8040 Research Seminar – Topics in Experimental Econometrics and Causal Inference

    Course Type

    Research Seminar (3 and 4th year)

    Lecturer

    Prof. Dr. Markus Frölich

    Semester

    fall term 2022

    PrerequisitesSuccessful 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.

  • Entwicklungsökonomie

    Lehrmethode

    Blockseminar

    Art und Verwendbarkeit des ModulsWahlveranstaltung 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-Punkte6
    Lehrmethode (Umfang)Blockseminar (2 SWS)
    ArbeitsaufwandPräsenzzeit Seminar: 21 Stunden; Zeit für die Anfertigung der Seminararbeit, für die Vorbereitung der Referate sowie für das Selbststudium 147 Stunden.
    Unterrichtssprache

    Deutsch

    TeilnahmevoraussetzungenGrundlagen der Ökonometrie

    Examination

    TBA
    Benotungschriftliche Seminararbeit (50%), Vortrag (25%), Koreferat (25%)
    Erwartete Zahl der Teilnehmer/innenmax. 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 ModulsDie 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 InformationenBitte beachten Sie den gemeinsamen Anmeldezeitraum für Seminare des Bachelorstudiengangs VWL.
    KontaktProf. 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

  • Statistics and Stata

    Form and usability of the moduleelective course for B. Sc. Economics
    Responsible teachers of the moduleDr. Atika Pasha, Dr. Ingo Steinke
    Cycle of offerevery spring term
    ECTS credits7
    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 classdepends 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

  • Impact Evaluation

    Form and usability of the moduleElective course for B. Sc. Economics

    Responsible teachers of the module

    Dr. Katharina Richert, Dr. Benjamin Chibuye

    Cycle of offer

    Every spring semester

    Duration1 semester
    ECTS Credits7
    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 moduleThe 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 informationMain 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 class41

     

    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

  • E5031 Applied Labour Economics

    Form and usability of the moduleElective course for M. Sc. Economics

    Responsible teacher of the module

    Dr. Asmus Zoch-Gordon, Marc Gillaizeau

    Cycle of offer

    Each spring semester

    ECTS Credits9
    Teaching method (hours per week)Lecture (2) + exercises (2)
    Start, End/
    Time & Location
    Please find the latest data under our course catalog
    Workload270 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 class25

    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 informationFurther 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 informationDr. Asmus Zoch-Gordon; Phone: (0621) 181-1842; Email: zoch uni-mannheim.de; Office: Room 123