Search result: Catalogue data in Autumn Semester 2023
Doctorate Management, Technology, and Economics  More Information at: https://www.ethz.ch/en/doctorate.html | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-1110-00L | Foundations of Innovation Studies | W | 3 credits | 2G | S. Brusoni, D. Laureiro Martinez | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-1140-00L | Hacking for Sciences - An Applied Guide to Programming with Data  Does not take place this semester. Basic experience with either R or Python, e.g., a stats course that was taught using R. | W | 3 credits | 2V | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Hacking 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 notes | A free and open online book (made with bookdown) is available from https://h4sci.github.io/h4sci-book/. 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: https://h4sci.github.io/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | A free and open online book (made with bookdown) is available from https://h4sci.github.io/h4sci-book/. 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: https://h4sci.github.io/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Basic experience with either R or Python, e.g., a stats course that was taught using R. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 364-0553-00L | Innovation in Digital Space Does not take place this semester. | W | 1 credit | 1G | G. von Krogh, to be announced | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The purpose of this course is to review and discuss issues in current theory and research relevant to innovation in the digital space. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Through 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Open 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 364-1013-06L | Marketing Theory Does not take place this semester. | W | 2 credits | 1G | F. von Wangenheim | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course is taught Florian Wangenheim (ETHZ) It focuses on the theoretical foundations of marketing and marketing research. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | In 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 364-1020-01L | Methods in Management Research: Methodological Fit in Management Research | W | 1 credit | 1S | F. Magni, K. N. Helmersen | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course covers available methodologies and research design in management research, measurement and validity issues, and a broad overview of the main quantitative and qualitative methods. Students will reflect on the fit between research question and research design in their own research field. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course aims to support students in: • knowing basic quantitative and qualitative research methods • understanding what data each method needs and what outcomes it can provide, as well as its advantages and disadvantages • understanding how to link a research question to an appropriate research design and method • acquiring a basic understanding of how each method works (e.g., which software to use) • having an idea of how to apply these methods to one's own research • having a group of peers to share ideas and feedback with | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course covers basic methodological topics relevant to research in the management field, including available methodologies (inductive, deductive) and research design (e.g., interviews, field survey, lab experiment, secondary data), the definition and measurement of constructs, validity, the choice of data collection and data analysis methods. A broad overview of the main quantitative (ANOVA, regression, path analysis, SEM, multilevel models, growth models) and qualitative methods (thematic analysis, grounded theory) currently used in management research will also be provided, together with a brief analysis of the advantages and disadvantages of each method. Topics related to research design, including pre-registration, power analysis, and data management, as well as level of analysis and temporal issues (in particular related to data collection) can also be discussed, depending on the interest of the class. Finally, the course will cover fit between research question, research design, and methods of data analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | (Refer to Syllabus and Moodle) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Students should: (1) Be able to read and understand academic papers, including both empirical papers and method papers, to facilitate and actively participate to the class discussions; (2) Download SPSS and R + R Studio before the course to be able to conduct hands-on exercises in class; (3) complete a short survey that the instructor will share before the course, with he goal of optimising course organisation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-1013-05L | Organizational Behavior | W | 1 credit | 1S | F. Magni | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Organizational 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-1025-00L | Advanced Microeconomics | E- | 3 credits | 2G | A. Bommier | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 364-1179-00L | Calibration in Macroeconomics | W | 3 credits | 2V | K. Walsh | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Macroeconomic models allow us to perform policy counterfactuals related to inequality, monetary policy, and trade. But to believe our predictions, the models’ parameters must be reasonable. Calibration is the process of choosing parameters (usually related to technology and preferences). This course explores common approaches with applications to inequality, trade, finance, and monetary policy. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Using examples from both classic and frontier papers in quantitative macroeconomics, this course teaches students popular approaches to calibrating models and evaluating model fit. While the emphasis is on calibration methodology, students will also learn about a variety of model solution algorithms, key datasets, standard parameter values, and the contributions/takeaways of the various papers. After taking this course, students will be able to: - understand and implement the main approaches to calibration - assess the plausibility and fit of calibrations in new papers or their own research - integrate standard functional forms and parameter values into their research - gather and analyse key datasets used in calibration - understand the basics of a variety of solution algorithms for equilibrium models Additionally, the course gives students a sense of the frontier of research in some of the fields covered. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course is designed for PhD students in economics, finance, and related fields, especially those who wish to use quantitative macroeconomic models for research or policy. Enrolling students should have experience with graduate-level economic theory and be able to code in one of the programming languages commonly used in macroeconomics (e.g., Matlab, Python, Julia, Fortran, etc.). The purpose of the course is to show students how calibration is and has been used in quantitative macroeconomics. The predictions and implications of macroeconomic models – the costs of trade barriers, the causes of changes in inequality, the effects of fiscal and monetary policy shocks, and the consequences of sovereign default for example – depend on the underlying parameters. Typical parameters include consumer risk aversion and patience, firm and consumer elasticities of substitution, the variance and persistence of shocks to firms and consumers, and credit constraints. To believe the welfare implications, counterfactuals, or forecasts of our models, the parameters must be set to “reasonable” values. Calibration is the process of choosing reasonable parameters using, for example, previous research, estimates from microeconomic data, or the comparison of model moments with empirical counterparts. Calibration is an essential tool in macroeconomics. It is employed in a large fraction of the academic literature as well as in many influential policy analyses. The course is directed towards researchers interested in the frontier of macroeconomic theory, but it is also relevant for anyone working on policy-related theoretical models in public finance, trade, and international finance. The instructor will prepare and present lecture slides, but class discussion is strongly encouraged. Students are expected to read the papers assigned for each week. Assessment is based on a final project: each student must replicate the main result of a paper from the class or another paper approved by the instructor. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The course is framed around papers that discuss or employ calibration, likely including: 1) Cooley (1997): “Calibrated Models,” Oxford Review of Economic Policy. 2) Aiyagari (1994): “Uninsured Idiosyncratic Risk and Aggregate Saving,” Quarterly Journal of Economics. 3) Hubmer, Krusell, and Smith (2020): “Sources of U.S. Wealth Inequality: Past, Present, and Future,” Macroeconomics Annual. 4) Toda and Walsh (2020): “The Equity Premium and the One Percent,” Review of Financial Studies. 5) Chatterjee and Eyigungor (2012): “Default Risk and Income Fluctuations in Emerging Markets,” American Economic Review. 6) Miranda-Pinto, Murphy, Walsh, Young (2022): “A Model of Expenditure Shocks,’’ Working Paper. 7) Caliendo and Parro (2015): “Estimates of the Trade and Welfare Effects of NAFTA,” Review of Economic Studies. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Knowledge of graduate-level economic theory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-0531-00L | CER-ETH Research Seminar | E- | 0 credits | 2S | H. Gersbach, A. Bommier, L. Bretschger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Research Seminar of Center of Economic Research CER-ETH | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Understanding cutting-edge results of current research in the fields of the CER-ETH Professors. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Referate 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 / Notice | Bitte spezielle Ankündigungen beachten. Studierende des GESS-Pflichtwahlfachs sollten sich vor Beginn mit der Seminarleitung in Verbindung setzen. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-0556-00L | Doctoral Workshop: Astute Modelling Prerequisite: Students are expected to attend the course 364-0559-00L "Dynamic Macroeconomics (Doctoral Course)", before registering for this workshop. | W | 3 credits | 1G | H. Gersbach | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this workshop, ongoing research is presented and the criteria and guidelines for astute modelling of economic, political, and social situations are discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | We will learn how to craft models, how to present our own research and improve our analytical skills. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Students are expected to attend the doctoral course "Macroeconomic Dynamics" before registering for this workshop. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 363-1136-00L | Dynamic 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. | W | 3 credits | 2V | S. Zelzner, H. Gersbach | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introducing 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After the course, students will be familiar with dynamic general equilibrium theory and the basic workhorse models in macroeconomics. Participants will be able to 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 will get an idea how digitization and artificial intelligence might drive economic growth. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | 1. Introduction 2. The Solow Model 3. The Neoclassical Growth 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | 1. 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 / Notice | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-1168-00L | Economics of Inequality Does not take place this semester. | W | 3 credits | 2V | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | We 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). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The 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, 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 write a referee report or a research proposal, starting from a paper we discussed in the course. 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 and active participation is expected. At the end of the semester, they will write a referee report with possible suggestions for future research or develop a research proposal. The written assignment is due by January 24, 2024. Topics (suggestive) 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 363-1036-00L | Empirical Innovation Economics Does not take place this semester. | W | 3 credits | 1G | M. Wörter | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The 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 notes | Will be provided in the course and in the e-learning environment: https://moodle-app2.let.ethz.ch/course/view.php?id=15120 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Literature 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 / Notice | Course is directed to advanced Master-Students and PhD Students with an interest in empirical studies. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-1015-00L | KOF-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: 03SMDOEC1051 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html | W | 2 credits | 2S | P. Egger, J.‑E. Sturm, University lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | On 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 364-0581-00L | Microeconomics 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: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html | E- | 0 credits | 2S | H. Gersbach | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Research Seminar research papers of leading researchers in Microeconomics are presented and discussed | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Research Seminar research papers of leading researchers in Microeconomics are presented and discussed | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Invited Speakers present current research in Microeconomics | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-0585-01L | PhD Course: Applied Econometrics | W | 2 credits | 2V | P. Egger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In 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). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The 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 notes | For 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 364-1090-00L | Research 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: 03SMDOEC1096 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html | W | 3 credits | 2S | H. Gersbach, University lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Recent developments in the fields of contract theory, finance, banking, money and macroeconomics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Understanding recent developments in the fields of contract theory, finance, banking and macroeconomics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-1058-00L | Risk Center Seminar Series | Z | 0 credits | 2S | H. 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, S. Wiemer, R. Zenklusen | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Participants 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This 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 notes | There 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Literature will be provided by the speakers in their respective presentations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Participants should have relatively good mathematical skills and some experience of how scientific work is performed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-1064-00L | Doctoral Retreat - Inaugural Workshop and Seminar on Ethics and Scientific Integrity Pre-registration upon invitation required. Once your pre-registration has been confirmed, a registration in myStudies is possible. Information on the online Ethic Moodle course will be passed on to registered doctoral candidates in due time. | W | 1 credit | 1S | U. Renold, A. Bommier | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The 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 - sensitise doctoral candidates to ethical issues that may occur during their doctorate. - familiarise doctoral candidates with resources that can assist them with ethical decision-making | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course is geared towards first and second-year doctoral candidates of MTEC. It is held as in a workshop style. Doctoral candidates attending this seminar will benefit from interdisciplinary discussions and insights into current and future work in business and economics research. The Doctoral Retreat is new connected with a course on “Ethics and Scientific Integrity”. The first part is an online self-paced e-learning Moodle course which consists of 5 modules and should be completed before the retreat starts. The second, face-to-face part of the Ethic course focuses on discipline-specific aspects and takes place on the 2nd day of the retreat. It provides an interactive learning environment. Doctoral candidates get to apply their knowledge, and they are encouraged to reflect on ethical problems and to critically discuss them with fellow doctoral colleagues. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| » Educational Science for Teaching Diploma and TC | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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