Basics in Quantitative Methods for Public Policy Analysis
Bruno Cautrès, Sciences Po
Whether they should or they shouldn’t, numbers, data and quantitative methods matter in policy and in policy analysis. Policy and political analysts use numbers treated with quantitative methods in evidence-based research about whether policy interventions are successful. Policymakers use numbers to support (sometimes normative) arguments about whether government should (or should not) provide particular services or engage in policy change and reforms. This course is the first course in a twocourse (Module 2 and Module 3) sequence designed to teach you the quantitative methods that you need for a career in public policy and also to be able to read publications using these methods.
This course runs January 14-18, 2019.
TEACHING FELLOW: Kelly Senters, University of Illinois at Urbana-Champaign
This first module introduces participants to major basic concepts and tools for quantitative evaluation of public policies. Among the many concepts used by specialists of causal reasoning and policy evaluation, the following will be clarified : counterfactual, potential outcomes, quasi-experiment, treatment effect, before-after effect. The course will then show how statistical methods can be useful to face these problems and to create a formal framework for quasi-experimental reasoning. The basics of regression models will be presented in relationship to testing the effect of « treatment » (like a policy change or like to be exposed to political reforms). On this aspect, the course will cover regression group comparisons (treated versus control groups) through different techniques (dummies, interactions, Chow test). At the end of the course, participants will have a clear view on the relationship between comparing treated versus control groups and the regression methods framework.
Basic knowledge about descriptive statistics and introduction to statistical inference tests. As the course will use Stata as the software, a background in using this software is helpful, but not required. Lab sessions will include replication of some published papers that will permit participant to acquire practical skills for working with empirical data.