Cost-benefit analysis (CBA) involves the use of microeconomics to formally assess the costs and benefits of different projects or investments. CBA is required for major regulations in the United States and is frequently used as a key input into major policy decisions. Understanding its advantages and limitations, and being able to distinguish well-conducted from poor analyses, is an important skill for a policy analyst.
How to make decisions in light of pervasive uncertainties? How to think about incentive structures faced by decision-makers, and think through unintended consequences of one’s decisions?
Economics, for better or worse, is organized common sense. No more, also no less. This class makes use of the toolkit given to us by economics and applies them to real-world policy problems.
This course offers a hands-on opportunity for doctoral and advanced masters students to experience the practice of qualitative research. We will address the nature of qualitative research in the administrative and policy sciences, with ample opportunities to discuss the implications of the choices made in designing, implementing and reporting on the findings of a “mock” project which we will determine in class, with your input.
The goal of this course is to provide students with an introduction to advanced empirical methods. We begin by discussing a framework for causal inference and how randomized controlled trials provide a simple and powerful template for thinking about causal questions. We then develop a sequence of advanced empirical methods as alternatives to randomized trials, in settings where experiments are infeasible or not desirable. In particular we discuss regression discontinuity, matching methods, difference-in-differences and panel data, and instrumental variables.
Open only to students in the MSPP program. The goal of this course is to provide students with an introduction to key methods of quantitative policy analysis. We develop the statistical toolkit of regression analysis, reviewing the bivariate regression model and then continuing with multiple regression, and explore how these methods are applied to policy analysis in five benchmark techniques: randomized trials, direct regression analysis, instrumental variables, regression discontinuity, and difference in differences.
This course provides an introduction to the impact investment landscape, the evolution of impact investment as an asset class and the opportunities and challenges for investors seeking meaningful impact investment vehicles. The course will also teach the process by which an investor performs financial due diligence on a social enterprise to render a responsible investment decision.
In our increasingly data-reliant and data-saturated society, people who understand how to leverage data to generate insights have the power to change the world. Data visualization and storytelling is a crucial skill for policy and data analysts, communications and marketing professionals, and managers and decision-makers within nonprofits, social organizations and the government. With the advent of visualization tools that do not require coding, data storytelling in the digital age is also an attainable skill set for people with varying levels of technical ability.
This half-semester course will include a qualitative and a quantitative component. We will begin with a discussion of using focus groups as a tool for data collection. We will spend the rest of the course talking about survey designs (including probability and non-probability sampling) and questionnaire development, as well as implementation issues. Students will produce a short focus-group proposal or survey questionnaire.
The course video provides more information.
This is an advanced course for students who plan to become policy analysts. Students (a) extend their familiarity with methodologic issues, including research designs, measurement problems, and analytic approaches; (b) get hands-on experience with management, analysis, and presentation of data; and (c) develop skills in reading, critiquing, and reporting on policy-relevant research.
All public and nonprofit organizations must assemble and report information on their performance. The need for performance measures goes beyond legal and regulatory requirements. To provide services effectively and efficiently, managers need information to make decisions. This course focuses on what performance measures are needed, how they should be created and what forms of communication are most effective.
Effective development, planning, execution and communication of special projects are critical to all types of public service organizations. Service organization, health providers and other institutions constantly pursue new initiatives – offering new services, developing creative approaches to service delivery, beginning new program components, opening new facilities, introducing new technologies – to address the demands of their constantly changing environment. Nonprofit and government organizations face similar pressures are also rely on special projects to address them.
This course serves as an introduction to those evaluation tools most commonly used to assess the performance of publicly funded programs, in both the public and private sector. Topics include developing and assessing program theory, implementation and process assessment, methods of impact evaluation, and efficiency analysis (cost-benefit and cost-effectiveness analysis). The focus is on critical analysis and understanding of both the underlying programs and their evaluations.
Multiple regression is the core statistical technique used by policy and finance analysts in their work. In this course, you will learn how to use and interpret this critical statistical technique. Specifically you will learn how to evaluate whether regression coefficients are biased, whether standard errors (and thus t statistics) are valid, and whether regressions used in policy and finance studies support causal arguments.
This half-semester course will focus on the analysis of complex survey data. The focus will be on using secondary data of this type. Using Stata, we will discuss how regression analysis differs when using complex survey data as well as data-cleaning procedures, including trimming, variable transformations, and dealing with missing data. Students will produce a short memo, using real survey data, to analyze a simple research question.
The goal of this course is to train advanced students on the principles, practices, and technologies required for good database design, management, and security. An introduction to the concepts and issues relating to data warehousing, governance, administration, security, privacy and alternative database structures will be provided. The course concentrates on building a firm foundation in information organization, storage, management, and security.