Search result: Catalogue data in Autumn Semester 2022

Doctorate Management, Technology, and Economics Information
More Information at: Link
Subject Specialisation
Management
NumberTitleTypeECTSHoursLecturers
364-1013-05LOrganizational Behavior Information Restricted registration - show details
Number of participants limited to 20.
W1 credit1SF. Magni
AbstractOrganizational behavior concerns the study of individual and group-level processes in organizations like creativity, motivation, and leadership. In this PhD course, an overview of major concepts and research insights in organizational behavior is provided. The participants are encouraged to discuss their own work situation as PhD students in relation to the OB insights covered in the course.
ObjectiveThe objectives of the course are:
• to provide an overview of OB research
• to discuss major research streams in OB
• to enable students to reflect their own work situation based on concepts used in OB.
364-1013-06LMarketing Theory Restricted registration - show details
Does not take place this semester.
Number of participants limited to 18.
W2 credits1GF. von Wangenheim
AbstractThe course is taught Florian Wangenheim (ETHZ)

It focuses on the theoretical foundations of marketing and marketing research.
ObjectiveThe purpose of the course is to confront students with current theoretical thinking in marketing, and currently used theories for understanding and explaining buyer and customer behavior in reponse to marketing action.
ContentIn the first class, current understanding of the marketing literature and marketing thought is discussed.
In the following classes, various theories are discussed, particularly in light of their importance for marketing. Economic, pschological and sociological theory will be related to current marketing thought.
364-1110-00LFoundations of Innovation Studies
Does not take place this semester.
W3 credits2GS. Brusoni
AbstractThis course will introduce some of the major theoretical threads and controversies in the broad field of innovation. During the first part of the course, the emphasis will be on the evolution of innovation studies. The final part of the course will focus on one of the directions in which those studies have evolved: the field of managerial cognition.
ObjectiveStudents will learn about various perspectives, examine different methodologies, explore some original empirical research, make connections between theory and empirical research, and practice reviewing and identifying insight in research.

1) Be able to display some knowledge on a few major theoretical streams in the area.
2) Be familiar with the methods, issues and current gaps in the area.
3) Have practiced skills in finding insight and reviewing the literature.
4) Have practiced skills in defining research problems and proposing empirical research in this area.
364-0553-00LInnovation in Digital Space Restricted registration - show details
Does not take place this semester.
W1 credit1GG. von Krogh, to be announced
AbstractThe purpose of this course is to review and discuss issues in current theory and research relevant to innovation in the digital space.
ObjectiveThrough in-depth analysis of published work, doctoral candidates will identify and appraise theoretical and empirical studies, formulate research questions, and improve the positioning of their own research within the academic debate.
ContentThe Internet has a twofold impact on the way individuals and firms innovate. First, firms increasingly draw on digital technology to access and capture innovation-relevant knowledge in their environment. Second, individuals, firms, and other organizations extensively utilize the Internet to create, diffuse, and commercialize new digital products and services. During the past decade, theory and research on innovation in the digital space has flourished and generated extensive insights of relevance to both academia and management practice. This has brought us better understanding of working models, and some fundamental reasons for innovation success or failure. A host of new models and research designs have been created to explore the innovation in the digital space, but these have also brought out many open research questions. We will review some of the existing streams of work, and in the process explore a new research agenda.

Format:
The course is organized in one block of 2 days. The course is a combination of pre-readings, presentations by faculty and students, and discussions. The students prepare presentations of papers in order to facilitate analysis and discussion.
LiteratureOpen source (OS) as innovation model
1. Lerner, J., & Tirole, J. (2002). Some Simple Economics of Open Source. JIE
2. von Hippel, E., & Von Krogh, G. (2003). Open source software and the 'private-collective' innovation model: Issues for Organization Science. OrgSci
3. von Krogh, G., Spaeth, S., & Lakhani, K. R. (2003). Community, joining, and specialization in open source software innovation: A case study. RP
4. Lakhani, K., & Eric, A. (2000). von Hippel (2003),“How open source software works:" free" user-to-user assistance”. RP
5. Yoo, Y., Boland, R. J., Lyytinen, K., & Majchrzak, A. (2012). Organizing for Innovation in the Digitized World. OrgSci
Coordination in OS communities
6. Faraj, S., von Krogh, G., Monteiro, E., & Lakhani, K. (2016). Special Section Introduction - Online Community as Space for Knowledge Flows. ISR
7. Lindberg, A., Berente, N., Gaskin, J., & Lyytinen, K. (2016). Coordinating interdependencies in online communities: A study of an open source software project. ISR
8. Shaikh, M., & Vaast, E. (2016). Folding and unfolding: Balancing openness and transparency in open source communities. ISR
9. Ren, Y., Chen, J., & Riedl, J. (2016). The impact and evolution of group diversity in online open collaboration. ManSci
10. Jiang, Q., Tan, C. H., Sia, C. L., & Wei, K. K. (2019). Followership in an Open-Source Software Project and its Significance in Code Reuse. MISQ
11. Medappa, P. K., & Srivastava, S. C. (2019). Does Superposition Influence the Success of FLOSS Projects? An Examination of Open-Source Software Development by Organizations and Individuals. ISR
12. Howison, J., & Crowston, K. (2014). Collaboration through open superposition: A theory of the open source way. MISQ
Governance & Leadership
13. He. F., Puranam P., Shrestha Y. R., & von Krogh, G. (2020) Resolving governance disputes in communities: A study of software license decisions. SMJ
14. Gulati, R., Puranam, P., & Tushman, M. (2012). Meta-organization design: Rethinking design in interorganizational and community contexts. SMJ
15. Fjeldstad, Ø. D., Snow, C. C., Miles, R. E., & Lettl, C. (2012). The architecture of collaboration. SMJ
16. Klapper, H., & Reitzig, M. (2018). On the effects of authority on peer motivation: L earning from Wikipedia. SMJ
17. Johnson, S. L., Safadi, H., & Faraj, S. (2015). The emergence of online community leadership. ISR
18. Safadi, H., Johnson, S. L., & Faraj, S. (2020). Core-Periphery Tension in Online Innovation Communities. OrgSci
19. Germonprez, M., Kendall, J. E., Kendall, K. E., Mathiassen, L., Young, B., & Warner, B. (2017). A theory of responsive design: A field study of corporate engagement with open source communities. ISR
20. Greenstein, S., & Zhu, F. (2016). Open content, Linus’ law, and neutral point of view. ISR
21. Nagle, F. (2019) Open source software and firm productivity. ManSci
22. Fitzgerald, B. (2006). The transformation of open source software. MISQ
Motivation to collaborate
23. Spaeth, S., von Krogh, G., & He, F. (2015). Perceived Firm Attributes and Intrinsic Motivation in Sponsored Open Source Software Projects. ISR.
24. Shah, S. K. (2006). Motivation, governance, and the viability of hybrid forms in open source software development. ManSci
25. von Krogh, G., Haefliger, S., Spaeth, S., & Wallin, M. W. (2012). Carrots and rainbows: Motivation and social practice in open source software development. MISQ
26. Hwang, E. H., Singh, P. V., & Argote, L. (2015). Knowledge sharing in online communities: Learning to cross geographic and hierarchical boundaries. OrgSci
27. Bapna, S., Benner, M. J., & Qiu, L. (2019). Nurturing Online Communities: An Empirical Investigation. MISQ
28. Goes, P. B., Guo, C., & Lin, M. (2016). Do incentive hierarchies induce user effort? Evidence from an online knowledge exchange. ISR
364-1140-00LHacking for Sciences - An Applied Guide to Programming with Data Information Restricted registration - show details
Basic experience with either R or Python, e.g., a stats course that was taught using R.
W3 credits2VM. Bannert
AbstractThe vast majority of data has been created within the last decade. As a result, more and more fields of research start to consider and embrace programming to process and analyse data. This course teaches applied programming with data and aims to leverage the open source tech stack to deal with this new wealth and complexity of data.
ObjectiveThe idea behind Hacking for Social Sciences is build a solid understanding of core technologies and concepts to help researchers develop a data processing strategy and increase your possibilities when working with data. The course approach is to single out those concepts stemming from software development that are easy to adopt and useful to social scientists. The course has three major learning objectives:

- Understand the role of focal components in a data science tech toolbox.
Learn how technologies like R, Python, Git Version Control, docker or Cloud Computing could play together in your research project.
- Learn how to manage and version control source code.
Hacking for Social Sciences teaches how to use git version control to collaborate professionally, make your research reproducible and your code base persistent.
- Applied data sourcing and data transformation
Learn how to communicate with SQL databases. Learn how to consume data from different sources using machine to machine communication interfaces (APIs) such as the OpenStreetMap geocoding API / Routing Engine or the KOF data API for macroeconomic time series.

Non-Goals:
Hacking for Social Sciences is not a Statistics, Econometrics or Machine Learning course. Though experience in these fields will help inasmuch that students will have an easier time to motivate investing in programming and to come up with their own application examples, profound methodological knowledge is not a prerequisite.
ContentHacking for Social Scientists is a guide to programming with data. It is tailored to the needs of a field in which scholars’ typical curricula do not contain a strong programming component. Yet this course argues that what the open source community calls a ‘software carpentry’ level is totally within reach for a quantitative social scientist and well worth the investment: being able to code leverages field specific expertise and fosters interdisciplinary collaboration, as source code continues to become an important communication channel.

The course contains three blocks that are mostly based on the three learning objectives presented above. Hacking for Social Sciences explicitly plans to spread its three blocks over 1-2 months to give students the ability to work on applied examples in between sessions in order to get most out of the subsequent session.

The first block demonstrates the components of a modern data science tech stack, classifies technologies and gives a big picture overview: from languages such as R and Python to container technology such as docker. The second block focuses on git version control, the de facto industry standard to manage source code. Version control is not only crucial to knowledge management and reproducible research, but it is also the backbone of collaboration in distributed teams. The third and final block focuses on data themselves
and teaches how to obtain data through machine to machine communication. Furthermore, the third block discusses data management in a research project.
Lecture notesA free and open online book (made with bookdown) is available from Link. The book/script will be continuously updated during the course to account for questions and participants' questions.
All course materials including, slides, resources and source code will be made available through: Link
LiteratureA free and open online book (made with bookdown) is available from Link. The book/script will be continuously updated during the course to account for questions and participants' questions.
All course materials including, slides, resources and source code will be made available through: Link
Prerequisites / NoticeBasic experience with either R or Python, e.g., a stats course that was taught using R.
Economics
NumberTitleTypeECTSHoursLecturers
364-1090-00LResearch Seminar in Contract Theory, Banking and Money (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student.
UZH Module Code: 03SMDOEC0786

Mind the enrolment deadlines at UZH: Link
W3 credits2SH. Gersbach, University lecturers
AbstractRecent developments in the fields of contract theory, finance, banking, money and macroeconomics.
ObjectiveUnderstanding recent developments in the fields of contract theory, finance, banking and macroeconomics.
363-1036-00LEmpirical Innovation EconomicsW3 credits1GM. Wörter
AbstractThe course focuses on important factors that drive the innovation performance of firms, like innovation capabilities, the use of digital technologies, environmental and innovation policy and it shows how innovation activities relate to firm performance and to the technological dynamic of industries. We also discuss the implications of the findings for effective economic policy-making.
ObjectiveThe course provides students with the basic skills to understand and assess empirically the technological activities of firms and the technological dynamics of industries. In addition, the aim is to promote the understanding of the essential criteria for innovation policy-making.

Personal and social skills are also addressed during the course. In particular, there is the possibility to improve communication and presentation skills, the ability to develop arguments for the positions of political representatives, policy-makers, pressure groups, or NGOs in connection with innovation policy-making.
ContentThe course consists of two parts. Part I provides an introduction into important topics in the field of the economics of innovation. Part II consists of empirical exercises based on various firm-level data sets, e.g., the KOF Innovation data, data about the digitization of firms, data about environmentally friendly innovations, or patent data. In part I, we will learn about ... a) market conditions that encourage firms to invest in R&D (Research and Development) and develop new products and processes. ... b) the role of competition and market structure for the R&D activities of companies. ...c) how digital and environmentally friendly technologies diffuse among firms. ...d) how the R&D activities of firms are affected by economic crises and how firms finance their R&D activities. ...e) how we can measure the returns to R&D activities. ...f) how environmental policies and innovation policies affect the technological activities of a firm. In part II we will use the KOF Innovation Survey data, patent data, data on digitization of firms, or other longitudinal data sources, to investigate empirically the technological activities of firms in relation to the topics introduced in part I.
Lecture notesWill be provided in the course and in the e-learning environment: Link
LiteratureLiterature will be presented in the course. For an introduction into the economics of innovation see G.M. Peter Swann, The Economics of Innovation - an Introduction, Edward Elgar, 2009.
For an overview of empirical innovation studies see W.M. Cohen (2010): Fifty Years of Empirical Studies of Innovation Activities and Performance, in: B.H Hall, N. Rosenberg (eds.), Handbook of Economics of Innovation, volume 1, Elsevier, pp. 129-213.
Prerequisites / NoticeCourse is directed to advanced Master-Students and PhD Students with an interest in empirical studies.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Media and Digital Technologiesassessed
Problem-solvingassessed
Project Managementfostered
Social CompetenciesCommunicationassessed
Cooperation and Teamworkfostered
Customer Orientationfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence fostered
Sensitivity to Diversityfostered
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingfostered
Critical Thinkingassessed
Integrity and Work Ethicsfostered
Self-awareness and Self-reflection fostered
Self-direction and Self-management assessed
364-0531-00LCER-ETH Research SeminarE-0 credits2SH. Gersbach, A. Bommier, L. Bretschger
AbstractResearch Seminar of Center of Economic Research CER-ETH
ObjectiveUnderstanding cutting-edge results of current research in the fields of the CER-ETH Professors.
ContentReferate zu aktuellen Forschungsergebnissen aus den Bereichen Ressourcen- und Umweltökonomie, theoretische und angewandte Wachstums- und Aussenwirtschaftstheorie sowie Energie- und Innovationsökonomie von in- und ausländischen Gastreferierenden sowie von ETH-internen Referierenden.
Prerequisites / NoticeBitte spezielle Ankündigungen beachten.

Studierende des GESS-Pflichtwahlfachs sollten sich vor Beginn mit der Seminarleitung in Verbindung setzen.
364-0556-00LDoctoral Workshop: Astute Modelling Restricted registration - show details
Prerequisite: Students are expected to attend the course 364-0559-00L "Dynamic Macroeconomics (Doctoral Course)", before registering for this workshop.
W3 credits1GH. Gersbach
AbstractIn this workshop, ongoing research is presented and the criteria and guidelines for astute modelling of economic, political, and social situations are discussed.
ObjectiveWe will learn how to craft models, how to present our own research and improve our analytical skills.
Prerequisites / NoticeStudents are expected to attend the doctoral course "Macroeconomic Dynamics" before registering for this workshop.
364-0585-01LPhD Course: Applied EconometricsW2 credits2VP. Egger
AbstractIn this course, we will address three blocs of selected problems: (i) estimation of fixed and random effects panel data models for single equations and systems of equations; (ii) estimation of models with endogenous treatment effects or sample selection; (iii) estimation of models with interdependent data (so-called spatial models).
ObjectiveThe main agenda of this course is to familiarize students with the estimation of econometric problems with three alternative types of problems: (i) estimation of fixed and random effects panel data models for single equations and systems of equations; (ii) estimation of models with endogenous treatment effects or sample selection; (iii) estimation of models with interdependent data (so-called spatial models). Students will be able to program estimation routines for such problems in STATA and apply them to data-sets. They will be given a data-set and will have to work out empirical problems in the context of a term paper.
Lecture notesFor panel data analysis, I will rely on the book:
Baltagi, Badi H. (2005), Econometric Analysis of Panel Data, Wiley: Chichester.

For sample selection and endogenous treatment effect analysis, I will rely on the book:
Wooldridge, Jeffrey M. (2002), Econometric Analysis of Cross Section and Panel Data, MIT Press: Cambridge, MA.

For spatial econometrics:
I will mostly use papers.

I will prepare a script (based on slides), covering all topics.
364-0581-00LMicroeconomics Seminar (ETH/UZH)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student.
UZH Module Code: 03SMDOEC6089

Mind the enrolment deadlines at UZH:
Link
E-0 credits2SH. Gersbach
AbstractResearch Seminar
research papers of leading researchers in Microeconomics are presented and discussed
ObjectiveResearch Seminar
research papers of leading researchers in Microeconomics are presented and discussed
ContentInvited Speakers present current research in Microeconomics
364-1025-00LAdvanced MicroeconomicsE-3 credits2GA. Bommier
AbstractThe objective of the course is to provide students with advanced knowledge in some areas of micro economic theory. The course will focus on 1) Individual behavior 2) Collective behavior 3) Choice under uncertainty 4) Intertemporal choice.
ObjectiveThe aim is to give to the students the opportunity to review the key results in rational individual behavior, collective models, choice under uncertainty, intertemporal choice, as well as to get some insights on more recent advances in those areas.
The course is therefore designed for students who have some interest for research in economics.
ContentThe following topics will be addressed;
1) Individual Behavior. Theory of the consumer (preferences, demand, duality, integrability). Theory of the firm.
2) Collective models. Cooperative and non cooperative models of household behavior.
2) Choice under uncertainty. The foundations of expected utility theory. Some insights on other approaches to choice under uncertainty.
3) Intertemporal choice. Dynamic model. Life cycle theory.
LiteratureThe course will be based on some chapters of the books "Advanced Microeconomic Theory" by Jehle and Reny (2011) and "Microeconomic Theory", by Mas-Colell, Whinston and Green (1995), as well as research articles for the most advanced parts.
364-1058-00LRisk Center Seminar SeriesZ0 credits2SH. Schernberg, D. Basin, A. Bommier, D. N. Bresch, S. Brusoni, L.‑E. Cederman, P. Cheridito, F. Corman, H. Gersbach, C. Hölscher, K. Paterson, G. Sansavini, B. Stojadinovic, B. Sudret, J. Teichmann, R. Wattenhofer, U. A. Weidmann, S. Wiemer, M. Zeilinger, R. Zenklusen
AbstractThis course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. Students and other guests are welcome.
ObjectiveParticipants should learn to get an overview of the state of the art in the field, to present it in a well understandable way to an interdisciplinary scientific audience, to develop novel mathematical models for open problems, to analyze them with computers, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to work scientifically on an internationally competitive level.
ContentThis course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. For details of the program see the webpage of the colloquium. Students and other guests are welcome.
Lecture notesThere is no script, but a short protocol of the sessions will be sent to all participants who have participated in a particular session. Transparencies of the presentations may be put on the course webpage.
LiteratureLiterature will be provided by the speakers in their respective presentations.
Prerequisites / NoticeParticipants should have relatively good mathematical skills and some experience of how scientific work is performed.
364-1015-00LKOF-ETH-UZH International Economic Policy Seminar (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student.
UZH Module Code: 03SMDOEC1028

Mind the enrolment deadlines at UZH:
Link
W2 credits2SP. Egger, J.‑E. Sturm, University lecturers
AbstractIn this seminar series, which is held jointly with Prof. Dr. Woitek and Prof. Dr. Hoffman from the University of Zurich, distinguished international researchers present their current research related to international economic policy. The participating doctoral students are expected to attend the presentations (bi-weekly). Moreover, a critical review has to be prepared for 1 of the papers presented
ObjectiveOn the one hand, participating students are exposed to research at the frontier of international economic policy research. On the other hand, skills such as critical thinking and preparing reviews are learned.
364-0513-00LEmpirical Methods in Energy and Environmental Economics
Does not take place this semester.
W3 credits2VM. Filippini, to be announced
AbstractThis course is designed for PhD & advanced Masters students who are interested in energy and environmental economics. The focus of the lectures/seminars is on methods of applied econometrics in these fields. The course is composed of lectures on specific topics and a seminar. In the seminar, students will have an opportunity to present own papers or to present and discuss empirical studies.
ObjectiveThe objectives of this course are twofold: first, students will learn about the application of econometric techniques in the fields of energy and environmental economics. Second, through the presentation of their papers or the presentation and discussion of the existing literature, students will also get a sense of how critical thinking can be used to assess empirical research in energy and environmental economics.
ContentDay 1: Thursday, January 9
09:00 – 10:30 Session 1: Multinomial choice, heterogeneity (instructor: Greene)
11:00 – 12:30 Session 2: Multinomial choice, heterogeneity (instructor: Greene)
13:30 – 15:00 Session 3: Latent class and Mixed logit (instructor: Greene)
15:30 – 16:30 Session 3: Latent class and Mixed logit (instructor: Greene)
Day 2: Friday, January 10
08:30 – 10:00 Session 1: Measurement of the energy efficiency (instructor: Filippini)
10:30 – 12:00 Session 2: Structural models (instructor: Houde)
13:00 – 14:30 Session 3: Student Presentations
15:00 – 16:30 Session 3: Student Presentations
Day 3: Saturday, January 11
08:30 – 09:30 Session 1: Seminar by Prof. Kenneth Gillingham (Yale University)
09:30 – 10:30 Session 1: Seminar by Prof. Beat Hintermann (Basel University)
10:30 – 11:30 Session 1: Seminar by Prof. Matt Kotchen (Yale University)
10:30 – 12:30 Session 2: Student Presentations
13:30 – 15:30 Session 3: Student Presentations
Lecture notesLecture notes will be made available to the students.
Prerequisites / NoticeStudents are expected to have attended courses in advanced microeconomics and in econometrics.
363-1136-00LDynamic Macroeconomics, Innovation and Growth
Students who have successfully completed the course "Dynamic Macroeconomics" (364-0559-00L) or
"Economics of Innovation and Growth" (363-0562-01L) can not register for this course.
W3 credits2VS. Zelzner
AbstractIntroducing dynamic models and workhorses in macroeconomics, understanding the role of innovation and institutions for economic development and discussing policies to foster innovation and economic growth, with a perspective on how digitization and artificial intelligence will affect our economies.
ObjectiveAfter the course, students will be familiar with dynamic general equilibrium theory and the basic workhorses in macroeconomics. Participants will be able to speak the Arrow-Debreu and recursive language and apply the frameworks to interesting issues, such as innovation and growth. Moreover, students will understand how the world has developed over the last centuries and the proximate and fundamental causes of innovation and economic growth. Students will understand and apply the basic models of economic growth and will be able to identify policies to foster innovation and growth and to reduce the large wealth differences in the world. Finally, they understand how digitization and artificial intelligence will drive the economies.
Content1. Introduction

2. The Arrow-Debreu Approach and Sequential Markets

3. The Neoclassical Growth Model and the Representative Agent Model (with Mathematical Background)

4. Technological Progress and how the World has developed

5. Innovations and Growth (New Growth Theory)

6. Growth Policies and Fundamental Causes for Growth

7. Digitization and Artificial Intelligence
Literature1. Acemoglu, D. (2009): Introduction to Modern Economic Growth. Princeton University Press, Cambridge MA.

2. Stokey, N. and Lucas, R. (1989): Recursive Methods in Economic Dynamics. Harvard University Press, Cambridge, Massachusetts, United States and London, England.

3. Ljungqvist, L. and Sargent, T. (2004): Recursive Macroeconomic Theory, MIT Press, Cambridge, Massachusetts, United States and London, England.

4. Barro, R.J. and X. Sala-i-Martin (2004): Economic Growth. MIT Press.

5. Aghion P. and P. Howitt (1998): Endogenous Growth Theory. MIT Press.

6. Aghion P. and S. Durlauf (eds. 2005): Handbook of Economic Growth. Elsevier, chapter 6.

7. Romer, D. (2001): Advanced Macroeconomics. McGraw-Hill.

8. Bretschger, L. (1999): Growth Theory and Sustainable Development. Edward Elgar.

9. Romer, P. (1990): Endogenous Technological Change, Journal of Political Economy, Vol. 98(5).

10. Aghion, P. and P. Howitt (1992):A Model of Endogenous Growth through Creative Destruction. Econometrica, Vol. 60(2).

11. Lucas, R. (1988): On the Mechanics of Economic Development, Journal of Monetary Economics, Vol. 22.

12. Rebelo, S. (1991): Long-Run Policy Analysis and Long-Run Growth. Journal of Political Economy, Vol. 99(3).

13. Piketty, T. (2014): Capital in the Tewnty-First Century. Harvard University Press, Cambridge, MA.

14. Current Literature on Digitization and Artificial Intelligence
Prerequisites / NoticeStudents who have successfully completed the course "Dynamic Macroeconomics" (364-0559-00L) or "Economics of Innovation and Growth" (363-0562-01L) can not register for this course.
364-1168-00LEconomics of InequalityW3 credits2VI. Martinez
AbstractWe discuss research on inequality in different areas of economics. Possible topics include distributional national accounts, heterogeneous returns, inheritances, intergenerational mobility, gender inequality in the labor market (topics will also be decided upon depending on the students' interests). Students will present a paper and critically comment on it (as if they would referee the paper).
ObjectiveAfter the course, participants will have a solid understanding of the current state of research on inequality in different fields in economics and, starting from there, will be able to develop their own research ideas. They will further learn how to critically assess and referee a paper, as it is common practice during the referee process, and they will practice their presentation skills and give feedback to each other. The students will therefore also acquire competences for conferences and participation in the scientific discourse.
ContentThe target group of this course are PhD students who are interested in writing a paper related to economic inequality. Advanced Master students who are interested in taking the course, especially those who plan to pursue a PhD in Economics afterwards, are welcome, too. The topic is intentionally kept broad to leave room for individual research interests and cover different areas. This will allow students to get to know the current state of research in different, but related areas and help them develop their own research question.

By critically examining the literature, students will also learn what makes a well-written paper. By presenting papers, students will further train their presentation skills and we will take time to give feedback in class on the presentations, too. Oral and written presentation of research are both integral parts of a successful academic career. In the written assignment, finally, students will learn how to write a referee report.

The course will start with an introduction into the topic and an overview of inequality research in economics. Inequality has become a buzz-word in many paper titles and abstracts, but different areas of economics have sometimes very different approaches to this popular topic. The main part of the course will consist of reading and presenting papers that belong to different areas of economics, including Macroeconomics, Public Economics, and Microeconomics / Labour Economics.

Below you find the suggestive syllabus for this course. I will provide a list of papers in each of the six blocks at the beginning of the semester, and students will choose a paper to present during the semester (suggestions to present a paper that is not on the list are welcome). Students are required to read all papers discussed in the course. At the end of the semester, they will write a referee report with possible suggestions for future research. The written assignment is due in early January.


Syllabus
Aggregate trends in income and wealth inequality
- Top income and wealth shares
- Distributional national accounts DINA
- Wealth income ratios

Measurement of top wealth and its difficulties
- Capitalization and heterogeneous returns
- Tax data and tax evasion
- Alternative data and its limitations

Inheritances
- Their role for wealth inequality
- Optimal taxation of inheritances

Intergenerational mobility
- Measurement
- Exogenous variation and causal identification

Gender Inequality in the labour market
- Gender wage gap
- Child penalties

Pandemics and their effects on inequalities
- Covid-19
- 1918 Influenza Pandemic (“Spanish Flu”)
- The plague
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Social CompetenciesCommunicationassessed
Personal CompetenciesCritical Thinkingassessed
Additional Courses
NumberTitleTypeECTSHoursLecturers
364-1064-00LInaugural Seminar - Doctoral Retreat Restricted registration - show details
Pre-registration upon invitation required.
Once your pre-registration has been confirmed, a registration in myStudies is possible.
W1 credit1SU. Renold, A. Bommier, P. Egger, R. Finger, G. Grote
AbstractThis course is geared towards first and second-year doctoral candidates of MTEC. It is held as in a workshop style. Students attending this seminar will benefit from interdisciplinary discussions and insights into current and future work in business and economics research.
ObjectiveThe purpose of this course is to
- introduce doctoral candidates to the world of economics, management and systems research at MTEC
- make doctoral candidates aware of silo-thinking in the specific sub-disciplines and encourage them to go beyond those silos
- discuss current issues with regard to substantive, methodological and theoretical domains of research in the respective fields
Transferable Skills
NumberTitleTypeECTSHoursLecturers
900-0100-DRLTransferable Skills Course I (1-3 days) Restricted registration - show details
Only for doctoral students.

Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate.
W1 credit2SLecturers
AbstractAcquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days.
ObjectiveAcquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days.
900-0101-DRLTransferable Skills Course II (1-3 days) Restricted registration - show details
Only for doctoral students.

Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate.
W1 credit2SLecturers
AbstractAcquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days.
ObjectiveAcquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days.
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