printer friendly pageECON 177: Empirical Environmental Economics (SUSTAIN 130)
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?ve not used R before, that?s 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: Spr
| Units: 5
Instructors:
Allcott, H. (PI)
STATS 101: Data Science 101
This course will provide a hands-on introduction to statistics and data science. Students will engage with fundamental ideas in inferential and computational thinking. Each week consists of three lectures and two labs, in which students will manipulate real-world data and learn about statistical and computational tools. Topics covered include introductions to data visualization techniques, summary statistics, regression, prediction, sampling variability, statistical testing, inference, and replicability. The objectives of this course are to have students (1) be able to connect data to underlying phenomena and think critically about conclusions drawn from data analysis, and (2) be knowledgeable about how to carry out their own data analysis later. Some statistical background or programming experience is helpful, but not required. The class will start with a brief introduction to R but will move at a relatively fast pace. Freshmen and sophomores interested in data science, computing, and statistics are encouraged to attend. Also open to graduate students.
Terms: Spr, Sum
| Units: 5
| UG Reqs: GER: DB-NatSci, WAY-AQR
Instructors:
Gablenz, P. (PI)
;
Lu, S. (PI)
;
Ji, W. (TA)
;
Kanekar, R. (TA)
;
Liu, S. (TA)
;
Nair, Y. (TA)
;
Paul, D. (TA)
;
Shen, R. (TA)
;
Song, Z. (TA)
SUSTAIN 130: Empirical Environmental Economics (ECON 177)
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?ve not used R before, that?s 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: Spr
| Units: 5
Instructors:
Allcott, H. (PI)
;
Brown, M. (TA)
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