The sequence integrates research design issues with statistical methods to better motivate students to learn the “how” of using quantitative tools by emphasizing the “why” such tools are necessary. This first course introduces students to a typical programming language used by policy analysts, covering basics such as data input, output, and calculation of key summary statistics. The key concepts of correlation, along with how to measure it and the requirements for establishing causality, are a central theme to this course. This course concludes by covering regression analysis, the normal distribution, and the law of large numbers.

The sequence integrates research design issues with statistical methods to better motivate students to learn the “how” of using quantitative tools by emphasizing the “why” such tools are necessary. This second course introduces students to the concept of sampling uncertainty and why hypothesis testing is required to establish causal inference. The course then covers inclusion of control variables in a multiple regression framework and explores why data generated from natural experiments and randomized control trials are so valuable to policy analysts. The course concludes by covering modern regression techniques and the importance of proper measurement of variables of interest.

Being a water-scarce state, California exhibits special features that make it a microcosm for water policy challenges that confront other regions worldwide. Students will learn, evaluate, and discuss the main elements of the water economy in California, the problems it faces, and the approaches used to address them. By the end of this course, students should be able to: Describe contemporary water problems in California and in the other states/countries discussed; Describe reasons for the arising problems, and how different types of policy interventions may or may not be successful; Explain the major features of California water policies, and Discuss the efficiency and equity goals of and the challenges faced by water policy-makers.

The sequence integrates research design issues with statistical methods to better motivate students to learn the “how” of using quantitative tools by emphasizing the “why” such tools are necessary. This first course introduces students to how to use statistical principles and software to formulate, estimate, and interpret linear regression models commonly used in policy analysis. Impact of common violations of standard regression assumptions on estimated regression coefficient—including omitted variables, measurement error, and non-constant variance—as well as strategies used by policy analysts to overcome these violations will be covered. The purpose of the class is to learn how to use regression to establish causality.

Students will find it helpful if they have some familiarity with microeconomic theory, but the required basics will be covered in lectures and readings. We will examine the theory of externalities and other relevant market failures, evaluate different approaches to pollution control, explore the why and how of cost-benefit analysis, and become familiar with current major environmental policies impacting air pollution, water pollution, and climate change.