Applied Public Policy Evaluation

July 7-July 11, 2025 in Amsterdam, Zuidas 

Faculty 

Bas van der Klaauw is professor in policy evaluation at the Vrije Universiteit Amsterdam. Since October 2023, Bas van der Klaauw is the director of Tinbergen Institute. 

Pierre Koning is full professor “Economics of social insurance and the Labor Market” at the Vrije Universiteit of Amsterdam. Until December 2015, Pierre was employed as a Chief Science Officer at the Ministry of Social Affairs and Employment in the Netherlands

Meet the Lecturers

Course

Many policy related empirical questions in economics are answered using detailed individual data and require analyzing individual behavior. This often implies that a causal parameters should be estimated to assess how a public policy affects individual outcomes. In many settings regression models provide correlations which only in special cases have a causal interpretation. In this course we discuss methods dealing with these confounding factors, such as omitted variables, reverse causality, measurement errors and non-random sampling. In particular, we consider limited dependent variable models, instrumental variables estimation and panel data models. We introduce the potential outcomes model, which is the most general model for defining treatment effects such as average treatment effect, average treatment effect on the treated, quantile treatment effects and local average treatment effects. The emphasis of the course is on identification, estimation and interpretation rather than a thorough treatment of the asymptotic properties of the estimators.

During the course applications of the different methods are discussed in the fields of labor economics, public economics, health economics, and the economics of education. Students learn how to assess the intended and unintended welfare effects of public polies that can be inferred from empirical research. For this, they use the framework of “Marginal Value of Public Funds”(MVPF) applied to different policy contexts. This framework does not only provide guidance on the efficiency of policies, but also on equity effects targeted at vulnerable groups on the labor market. We then can think of policies that aim to augment the human capital of individuals or those that aim to provide income certainty.

Learning Objectives

The key objective of the course is to learn student how to empirically evaluate the effectiveness of public policies in the fields of health, labor and education. After the course students should be able to (i) decide about the appropriate econometric method, (ii) apply this method to data, (iii) properly interpret the estimation results within the institutional context of the public policy that is evaluated and (iv) obtain a broader picture on the current effectiveness and “bang for the buck” of existing public policies.

Topics

This course provides students with state of the art econometric methods for evaluation of public policy. As general framework the potential outcomes model is presented, which provides guidelines for the interpretation of empirical results. Methods for an empirical analysis that will be considered are randomized experiments, instrumental variables, difference-in-differences, non-parametric bounds, weighting, and principal stratification. As to the applications, we use a standard framework for welfare analysis (the MVPF) that is based on causal estimates of policy effects.

Literature 

During the course lecture slides will be distributed. The following list can be considered as background material:

• Cameron, A.C. and P.K. Trivedi (2004), Applied Microeconometrics, Cambridge University Press.
• Card, D., J. Kluve and A. Weber (2018), What works? A Meta Analysis of Recent Active Labor Market Program Evaluations, Journal of the European Economic Association, 16(3), 894-931.
• Chetty, R. and A. Finkelstein (2013), Social insurance: connecting theory to data, Section 3.1. Handbook of Public Economics, volume 5: 111-193.
• Cunningham, S. Causal Inference: The Mixtape: https://mixtape.scunning.com
• DiNardo, D. and J.S. Lee (2011), Program evaluation and research designs, in O. Ashenfelter and D. Card, Handbook of Labor Economics 4A, North-Holland, Amsterdam.
• Duflo, E., R. Glennerster and M. Kremer (2008), Using randomization in development economics research: a toolkit, Chapter 61 in Handbook of Development Economics, Volume 4, 3895{3962).
• Finkelstein, A. and N. Hendren (2020), Welfare Analysis Meets Causal Inference, Journal of Economic Perspectives, 34(4), 146-167.
• Heckman, J.J., R.J. LaLonde and J.A. Smith (2000), The economics and econometrics of active labor market programs, in O. Ashenfelter and D. Card (eds.), Handbook of Labor Economics, Volume 3, North-Holland, Amsterdam.
• Hendren N. and B. Sprung-Keyser (2020), A Unified Welfare Analysis of Government Policies, Quarterly Journal of Economics, 135(3), 1209–1318.
• Huntington-Klein, N. The Effect: An Introduction to Research Design and Causality:  https://theeffectbook.net
• Imbens, G. and T. Lemieux (2008), Regression discontinuity designs: a guide to practice, Journal of Econometrics 142, 615-635
• Imbens, G.W. and J.M. Wooldridge (2009), Recent developments in the econometrics of program evaluation, Journal of Economic Literature 47, 5-86.

Level

The Summer School welcomes master students, PhD students, post-docs as well as practitioners who are interested applied public policy evaluation.

Schedule 

The program spans a total of five days, commencing daily at 10:00 AM. 

• 10.00-12.30 Lecture
• 12.30-13.30 Lunch
• 13.30-16.00 Lecture

Friday afternoon is reserved for individual consultations and students have the opportunity to present their work

Admission requirements

Students are assumed to be familiar with the basic concepts in econometrics, such as linear regression, instrumental variables, panel data, logit/probit models and hypothesis testing. Preferably, students have followed an introductory course in econometrics at the graduate level, but students who completed a course in statistics or advanced research methods are also considered. Students should be familiar with a statistical package, preferably Stata. Alongside this, students have a background in standard microeconomics.