printer friendly pageECON 118: Development Economics
The microeconomic problems and policy concerns of less developed countries. Topics include: health and education; risk and insurance; microfinance; agriculture; technology; governance. Emphasis is on economic models and empirical evidence. Prerequisites:
ECON 50,
ECON 102B.
Terms: Spr
| Units: 5
| UG Reqs: WAY-SI, GER:EC-GlobalCom, WAY-AQR
ECON 125: Economic Development, Microfinance, and Social Networks
An introduction to the study of the financial lives of households in less developed countries, focusing on savings, credit, informal insurance, the expansion of microfinance, social learning, public finance/redistribution, and social networks. Prerequisites-
Econ 51 or
Publpol 51 and
Econ 102B.
Terms: Spr
| Units: 5
| UG Reqs: WAY-SI, WAY-AQR, GER:EC-GlobalCom
Instructors:
Chandrasekhar, A. (PI)
ECON 134: Wealth of Nations (POLISCI 244C)
Why are there economic disparities across countries? Why did some countries grow steadily over the past 200 years while many others did not? What have been the consequences for the citizens of those countries? What has been the role of geography, culture, and institutions in the development process? What are the moral dilemmas behind this development process? These are some of the questions we will discuss in this course. Following a historical and cross-cultural perspective, we will study the origins of economic development and the path that led to the configuration of the modern global economy.
Terms: Spr
| Units: 5
| UG Reqs: WAY-SI
Instructors:
Mejia Cubillos, J. (PI)
ECON 136: Market Design
Use of economic theory and analysis to design allocation mechanisms and market institutions. Course focuses on three areas: the design of matching algorithms to solve assignment problems, with applications to school choice, entry-level labor markets, and kidney exchanges; the design of auctions to solve general resource allocation problems, with applications to the sale of natural resources, financial assets, radio spectrum, and advertising; and the design of platforms and exchanges, with applications to internet markets. Emphasis on connecting economic theory to practical applications. Students must write term paper.
Terms: Spr
| Units: 5
| UG Reqs: WAY-FR
ECON 137: Decision Modeling and Information
Effective decision models consider a decision maker's alternatives, information and preferences. The construction of such models in single-party situations with emphasis on the role of information. The course then evolves to two-party decision situations where one party has more information than the other. Models examined include: bidding exercises and the winner's curse, the Akerlof Model and adverse selection, the Principal-Agent model and risk sharing, moral hazard and contract design. Prerequisite:
ECON 102A or equivalent. Recommended:
Econ 50, Optimization and simulation in Excel.
Terms: Spr
| Units: 5
| UG Reqs: WAY-FR, WAY-AQR
Instructors:
McKeon, S. (PI)
ECON 139D: Directed Reading
May be repeated for credit.
Terms: Aut, Win, Spr
| Units: 1-10
| Repeatable
for credit
Instructors:
Abramitzky, R. (PI)
;
Allcott, H. (PI)
;
Amemiya, T. (PI)
...
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Instructors:
Abramitzky, R. (PI)
;
Allcott, H. (PI)
;
Amemiya, T. (PI)
;
Athey, S. (PI)
;
Auclert, A. (PI)
;
Bagwell, K. (PI)
;
Baker, L. (PI)
;
Bernheim, B. (PI)
;
Bhattacharya, J. (PI)
;
Bloom, N. (PI)
;
Bocola, L. (PI)
;
Boskin, M. (PI)
;
Bresnahan, T. (PI)
;
Brynjolfsson, E. (PI)
;
Bulow, J. (PI)
;
Callander, S. (PI)
;
Chandrasekhar, A. (PI)
;
Clerici-Arias, M. (PI)
;
Cochrane, J. (PI)
;
Cuesta, J. (PI)
;
Diamond, R. (PI)
;
Duffie, D. (PI)
;
Duggan, M. (PI)
;
Dupas, P. (PI)
;
Einav, L. (PI)
;
Fafchamps, M. (PI)
;
Fearon, J. (PI)
;
Gentzkow, M. (PI)
;
Goldin, J. (PI)
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Goulder, L. (PI)
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Greif, A. (PI)
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Haber, S. (PI)
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Hall, R. (PI)
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Hammond, P. (PI)
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Harris, D. (PI)
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Harstad, B. (PI)
;
Hong, H. (PI)
;
Hoxby, C. (PI)
;
Imbens, G. (PI)
;
Jackson, M. (PI)
;
Jagadeesan, R. (PI)
;
Jha, S. (PI)
;
Kehoe, P. (PI)
;
Klenow, P. (PI)
;
Krueger, A. (PI)
;
Kurz, M. (PI)
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Lau, L. (PI)
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Levin, J. (PI)
;
Li, H. (PI)
;
MaCurdy, T. (PI)
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Mahoney, N. (PI)
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Makler, C. (PI)
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McKeon, S. (PI)
;
Milgrom, P. (PI)
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Miller, G. (PI)
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Morten, M. (PI)
;
Naylor, R. (PI)
;
Niederle, M. (PI)
;
Noll, R. (PI)
;
Ober, J. (PI)
;
Pencavel, J. (PI)
;
Persson, P. (PI)
;
Piazzesi, M. (PI)
;
Pistaferri, L. (PI)
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Reiss, P. (PI)
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Romano, J. (PI)
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Rossin-Slater, M. (PI)
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Rosston, G. (PI)
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Roth, A. (PI)
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Sargent, T. (PI)
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Schneider, M. (PI)
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Segal, I. (PI)
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Shoven, J. (PI)
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Singleton, K. (PI)
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Skrzypacz, A. (PI)
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Sorkin, I. (PI)
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Spiess, J. (PI)
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Starrett, D. (PI)
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Taylor, J. (PI)
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Tendall, M. (PI)
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Voena, A. (PI)
;
Wolak, F. (PI)
;
Wright, G. (PI)
ECON 154: Law and Economics (PUBLPOL 106, PUBLPOL 206)
In this course, we explore the role of law in promoting social well-being (happiness). Law, among its other benefits, can serve as a mechanism to harmonize private incentives with cooperative gains, to maintain an equitable division of those gains, and to deter social defection and dystopia. Law is thus an implementation of the social contract and essential to civilization. Economic analysis of law focuses on the welfare-enhancing incentive effects of law (and of law enforcement). More generally, we study the law's role in reducing the risks of cooperation, achieved by fixing expectations of what courts or the state will do in possible futures. Prerequisite:
ECON 50 or
PUBLPOL 50. Final paper instead of an exam. Instructor consent required for enrollment. Please email the instructor a short statement of interest (300 words max) explaining why you would like to enroll in the course.
Terms: Spr
| Units: 4-5
| UG Reqs: WAY-SI
Instructors:
Vasquez Duque, O. (PI)
ECON 163: Solving Social Problems with Data (COMM 140X, DATASCI 154, EARTHSYS 153, MS&E 134, POLISCI 154, PUBLPOL 155, SOC 127)
Introduces students to the interdisciplinary intersection of data science and the social sciences through an in-depth examination of contemporary social problems. Provides a foundational skill set for solving social problems with data including quantitative analysis, modeling approaches from the social sciences and engineering, and coding skills for working directly with big data. Students will also consider the ethical dimensions of working with data and learn strategies for translating quantitative results into actionable policies and recommendations. Lectures will introduce students to the methods of data science and social science and apply these frameworks to critical 21st century challenges, including education & inequality, political polarization, and health equity & algorithmic design in the fall quarter, and social media, climate change, and school choice & segregation in the spring quarter. In-class exercises and problem sets will provide students with the opportunity to use
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Introduces students to the interdisciplinary intersection of data science and the social sciences through an in-depth examination of contemporary social problems. Provides a foundational skill set for solving social problems with data including quantitative analysis, modeling approaches from the social sciences and engineering, and coding skills for working directly with big data. Students will also consider the ethical dimensions of working with data and learn strategies for translating quantitative results into actionable policies and recommendations. Lectures will introduce students to the methods of data science and social science and apply these frameworks to critical 21st century challenges, including education & inequality, political polarization, and health equity & algorithmic design in the fall quarter, and social media, climate change, and school choice & segregation in the spring quarter. In-class exercises and problem sets will provide students with the opportunity to use real-world datasets to discover meaningful insights for policymakers and communities. This course is the required gateway course for the new major in Data Science & Social Systems. Preference given to Data Science & Social Systems B.A. majors and prospective majors. Course material and presentation will be at an introductory level. Enrollment and participation in one discussion section is required. Sign up for the discussion section will occur on Canvas at the start of the quarter. Prerequisites:
CS106A (required),
DATASCI 112 (recommended as pre or corequisite). Limited enrollment. Please complete the interest form here:
https://forms.gle/8ui9RPgzxjGxJ9k29. A permission code will be given to admitted students to register for the class.
Terms: Aut, Spr
| Units: 5
| UG Reqs: WAY-SI, WAY-AQR
Instructors:
Nobles, M. (PI)
;
Weinstein, J. (PI)
;
Castro Pena, D. (TA)
...
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Instructors:
Nobles, M. (PI)
;
Weinstein, J. (PI)
;
Castro Pena, D. (TA)
;
Charmot, S. (TA)
;
Dann, C. (TA)
;
Ma, S. (TA)
;
Ross, D. (TA)
;
Upadhyay, R. (TA)
;
Vasilenok, N. (TA)
ECON 185: Data Science for Environmental Business (PUBLPOL 185, SUSTAIN 135, SUSTAIN 235)
Are you interested in clean tech and sustainability? Do you like working with data or plan to manage data scientists? Do you want to find a socially impactful job? If so, Data Science for Environmental Business is for you. Each week, we'll have a guest speaker from a utility, venture capital firm, clean tech startup, renewable energy developer, or some other sustainability-related business. We'll do a quantitative case study of one of the speaker's business problems, such as carbon footprint measurement, supply chain decarbonization, techno-economic analysis, where to site renewable energy facilities, how to value electricity storage, or predicting demand for electric vehicles. Then in the next class, we'll discuss the analytical decisions you made on the case study and the business implications of your results. We aim to draw a mix of students from the GSB, engineering, sustainability, data science, computer science, economics, math, and other fields. Students registering through the
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Are you interested in clean tech and sustainability? Do you like working with data or plan to manage data scientists? Do you want to find a socially impactful job? If so, Data Science for Environmental Business is for you. Each week, we'll have a guest speaker from a utility, venture capital firm, clean tech startup, renewable energy developer, or some other sustainability-related business. We'll do a quantitative case study of one of the speaker's business problems, such as carbon footprint measurement, supply chain decarbonization, techno-economic analysis, where to site renewable energy facilities, how to value electricity storage, or predicting demand for electric vehicles. Then in the next class, we'll discuss the analytical decisions you made on the case study and the business implications of your results. We aim to draw a mix of students from the GSB, engineering, sustainability, data science, computer science, economics, math, and other fields. Students registering through the GSB should expect a roughly standard MBA class workload. Students registering through non-GSB course numbers should expect a serious data science course where you'll learn and apply new methods. We hope to develop a pipeline of students working for the guest speakers and similar firms. Prerequisites: You must know basic statistics and regression analysis (e.g.,
ECON 102 or 108,
CS 129,
EARTHSYS 140,
HUMBIO 88,
POLISCI 150C, or
STATS 60 or 101). You should also have at least some experience with data analysis in R, python, Stata, MATLAB, or something similar. If you plan to take microeconomics (e.g.,
ECON 1, 50, or 51) or empirical environmental economics (
ECON 177), we recommend you take those either beforehand or concurrently.
Terms: Spr
| Units: 5
Instructors:
Imbens, G. (PI)
;
Allcott, H. (SI)
ECON 198: Junior Honors Seminar (PUBLPOL 197)
For juniors (advanced sophomores will be considered) who expect to write an honors thesis in Economics or Public Policy. Weekly sessions go through the process of selecting a research question, finding relevant bibliography, writing a literature review, introduction, and study design, culminating in the write-up of an honors thesis proposal (prospectus) and the oral presentation of each student's research project. Students also interact with potential advisors, and outline a program of study for their senior year. To apply, complete the application at
https://economics.stanford.edu/undergraduate/forms.
Terms: Spr
| Units: 5
Instructors:
Clerici-Arias, M. (PI)
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