printer friendly pageECON 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 165: International Finance
This is a first course in open economy macroeconomics. The course's objective is to build the analytical foundation for understanding key macro issues in the world economy such as global capital flows, the behavior of exchange rates, currency and sovereign debt crises. While a significant portion of the course will be theoretical, there will be several occasions for linking the theory to real-world events. Prerequisite:
ECON 52. Summer session students wishing to enroll who feel they have the appropriate prerequisites from another institution may submit that information, transcript or syllabus that is similar to
Econ 52, to econ-undergrad@stanford.edu.
Terms: Sum
| Units: 5
| UG Reqs: WAY-SI
ECON 166: International Trade
Explaining patterns of trade among nations; characterizing the sources of comparative advantage in production and the prospect of gains from economies of scale. Enumerating and accounting for the net aggregate gains from trade, and identifying winners and losers from globalization. Analyzing the effects of international labor migration, foreign direct investment, outsourcing, and multinational companies. Strategic trade policy; international trade agreements; labor and environmental implications. We will review relevant theoretical frameworks, examine empirical evidence, and discuss historical and contemporary policy debates as covered in the popular press; active class participation is an important part of the course. Prerequisite:
ECON 51 (Public Policy majors may take
PUBLPOL 51 as a substitute for
ECON 51).
Terms: Aut
| Units: 5
| UG Reqs: WAY-SI
Instructors:
Tendall, M. (PI)
;
Laski, R. (TA)
ECON 167G: Game Theory and Social Behavior
Game theory is the formal toolkit for analyzing situations in which payoffs depend not only on your actions (say, which TV series you watch), but also others' (whether your friends are watching the same show). You've probably already heard of some famous games, like the prisoners' dilemma and the costly signaling game. We'll teach you to solve games like these, and more, using tools like Nash equilibrium, subgame perfection, Bayesian Nash equilibrium, and the one-off deviation principle. Game theory has traditionally been applied to understand the behavior of highly deliberate agents, like heads of state, firms in an oligopoly, or participants in an auction. However, we'll apply game theory to social behavior typically considered the realm of psychologists and philosophers, such as why we speak indirectly, in what sense beauty is socially constructed, and where our moral intuitions come from. Nearly each week, students are expected to complete a problem set, to read 2-3 academic papers
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Game theory is the formal toolkit for analyzing situations in which payoffs depend not only on your actions (say, which TV series you watch), but also others' (whether your friends are watching the same show). You've probably already heard of some famous games, like the prisoners' dilemma and the costly signaling game. We'll teach you to solve games like these, and more, using tools like Nash equilibrium, subgame perfection, Bayesian Nash equilibrium, and the one-off deviation principle. Game theory has traditionally been applied to understand the behavior of highly deliberate agents, like heads of state, firms in an oligopoly, or participants in an auction. However, we'll apply game theory to social behavior typically considered the realm of psychologists and philosophers, such as why we speak indirectly, in what sense beauty is socially constructed, and where our moral intuitions come from. Nearly each week, students are expected to complete a problem set, to read 2-3 academic papers, and to complete a 1-2 page response to short essay questions (`prompts') on these readings. All assignments can be completed in groups of two. There will also be a final exam. Prerequisites: Although there are no formal prerequisites for this course, we will make frequent use of probability theory (Bayes¿ Rule; conditional probabilities), set theory notation, and proofs. Students without a background in these tools have historically found some of the later problem sets to be challenging. TA sessions are not required, but are recommended for students without the necessary math background. Not sure if this class is for you? Take our self assessment, then see how your answers compare with ours. (Assessments and solutions can be found here:
https://economics.stanford.edu/undergraduate/forms)
Terms: Aut
| Units: 5
| UG Reqs: WAY-SI
Instructors:
Yoeli, E. (PI)
;
Saitto, R. (TA)
ECON 177: Empirical Environmental Economics (SUSTAIN 130, SUSTAIN 230)
Are you interested in environmental and energy policy? Do you want to improve your data science skills? If so, Empirical Environmental Economics is for you. In the first few weeks of class, you'll use data and microeconomic modeling to quantify the harms from pollution, including estimating the social cost of carbon emissions. For the rest of the quarter, you'll use more data and microeconomic modeling to evaluate major environmental policies such as pollution taxes, cap-and-trade programs, and subsidies for clean technologies. You will consider overall benefits and costs as well as the distributional equity, which can inform discussions of environmental justice. You will learn and practice useful data science skills, including applied econometrics/causal inference methods (e.g., difference-in-differences, instrumental variables, and regression discontinuity) and equilibrium modeling. The class has weekly problem sets involving data analysis in R, plus a final paper. Class sessions fea
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Are you interested in environmental and energy policy? Do you want to improve your data science skills? If so, Empirical Environmental Economics is for you. In the first few weeks of class, you'll use data and microeconomic modeling to quantify the harms from pollution, including estimating the social cost of carbon emissions. For the rest of the quarter, you'll use more data and microeconomic modeling to evaluate major environmental policies such as pollution taxes, cap-and-trade programs, and subsidies for clean technologies. You will consider overall benefits and costs as well as the distributional equity, which can inform discussions of environmental justice. You will learn and practice useful data science skills, including applied econometrics/causal inference methods (e.g., difference-in-differences, instrumental variables, and regression discontinuity) and equilibrium modeling. The class has weekly problem sets involving data analysis in R, plus a final paper. Class sessions feature active learning, discussions, and small-group case studies. You should only enroll if you expect to attend regularly and complete the problem sets on time. Prerequisites: You must have experience with regression analysis (e.g.,
ECON 102 or 108,
CS 129,
EARTHSYS 140,
HUMBIO 88,
POLISCI 150C, or
STATS 60 or 101). If you plan to take microeconomics (e.g.,
ECON 1, 50, or 51), we recommend you take those either beforehand or concurrently. If you have no economics background, you may still be comfortable in class if you are strong in math, statistics, and/or computer science. If you have not used R before, that is OK: we will guide you from the beginning. If you have used R before, you can still learn a lot in this class through the applications.
Terms: Aut
| Units: 4-5
ECON 178: Behavioral Economics
The field of behavioral economics draws on insights from other disciplines, especially psychology, to enrich our understanding of economic behavior. In this course, we will discuss how psychological considerations can create behavioral patterns that diverge from the predictions of standard economic models, the implications of those behavioral patterns for market outcomes and public policies, and the ways in which economists incorporate those considerations into their theories. We will also examine how social motives (such as altruism or concerns about fairness, equity, status, or image) impact economic behavior. We will learn about classical findings and leading theories in behavioral economics. The treatment of psychological phenomena in this course involves tools similar to those employed in other economics courses. Prerequisites:
ECON 50 and
ECON 102A.
Econ 51 and 102B are recommended.
Terms: Win
| Units: 5
| UG Reqs: WAY-SI
ECON 179: Experimental Economics
Methods and major subject areas that have been addressed by laboratory experiments. Focus is on a series of experiments that build on one another. Topics include decision making, two player games, auctions, and market institutions. How experiments are used to learn about preferences and behavior, trust, fairness, and learning. Final presentation of group projects. Prerequisites:
ECON 51 (Public Policy majors may take
PUBLPOL 51 as a substitute for
ECON 51),
ECON 102A.
Last offered: Autumn 2019
| UG Reqs: WAY-AQR, WAY-SI
ECON 180: Honors Game Theory
Rigorous introduction to game theory and applications. Topics include solution concepts for static and dynamic games of complete and incomplete information, signaling games, repeated games, bargaining, and elements of cooperative game theory. Applications mainly from economics, but also political science, biology, and computer science. Prerequisites: Experience with abstract mathematics and willingness to work hard. No background in economics required.
Last offered: Autumn 2019
| UG Reqs: GER:DB-SocSci, WAY-FR, WAY-SI
ECON 184: Institutional Investment Management: Theory and Practice
This course provides an introduction to the theory and practice of institutional investment management, including asset allocation and manager selection across public and private equity, absolute return, real assets, and fixed income. The course is taught by the CIO of Stanford's endowment, along with other members of the investment team, and takes the perspective of an institution with a long-term investment horizon. We introduce and apply a framework for assessing investment strategies and investment firms. Students put theory into practice with guest speakers from leading investment firms, including partners at venture capital firms, real estate partnerships, and hedge funds. Enrollment is capped at 20. All majors are welcome. To apply please send a one to two paragraph statement of interest and an unofficial transcript to
econ184@
smc.stanford.edu by December 3, 2023.
Econ 1 and
Econ 102A,
Stats 60, or equivalent courses recommended and may be taken concurrently. Lunch will be provided for each Monday lecture.
Terms: Win
| Units: 4
Instructors:
Wallace, R. (PI)
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)
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