F1ID 5030 - Evaluating Human Capital Policies: Experimental Methodology and Evidence

Despite a growing importance of human capital with the emergence of the knowledge economy, very large inequalities in human capital persist between and within countries, leading to poverty traps and socio-economic inequalities. In this context, human capital policies appear to be of primary importance, but their relative effectiveness needs to be rigorously assessed in order to allocate public resources in an optimal way. Randomized experiments are getting more and more popular to measure the impact of human capital policies and programs. The goal of this course is to provide a detailed understanding of this methodology and of its results. The first part of the course will address the methodology: Why and when is a rigorous impact evaluation needed? What are the common pitfalls of evaluations, and why does randomization help? What are the key components of a good randomized evaluation design? How do you determine the appropriate sample size, measure outcomes, and manage data? What kind of results do you get? The second part of the course will present examples of completed field experiments in the areas of education. The last part will discuss advantages and limitations of randomized evaluation and how they can be used to design better human capital policies. Throughout the sessions, students will be prepared to conceive a group presentation giving the main lines of a possible randomized evaluation. The course will be a balanced mix of practical and theoretical approaches.

LEARNING OUTCOMES

• Good knowledge of the core concepts of impact evaluation and Randomized experiments
• Experience on how to build a randomized experiment from A to Z
• Knowledge of what Randomized experiments can bring to human capital policies (and what they can’t)

PROFESSIONAL SKILLS

Team work, scientific literature review, oral skills, statistical skills, evaluation skills, reflexion skills

There is no specific prerequisite for this course.

This class intends to teach students some basics to advanced evaluation knowledge, especially related to Randomized Control Trials. There is minimal mathematics across the class (one class only is going deeper on statistics), but this course can perfectly fit for students that did not do any statistics in the past but who are still interested in evaluation and how to run rigorous evaluation in practice.

General feedback will be provided to students. Individual written feedback are also provided with the research paper summary.

Feedback on oral presentations are provided right after students’ presentations, during class time.

A short test (25% of final grade), a research paper summary (25%) and a group presentation (50%) towards the end of the semester.

Bonus for active participation during classes.

• Attendance: 4 hours a week / 24 hours a semester
• Online learning activities: 0 hours a week / 0 hours a semester
• Reading and Preparation for Class: 1 hours a week / 12 hours a semester
• Research and Preparation for Group Work: 1 hours a week / 12 hours a semester
• Research and Writing for Individual Assessments: 1 hours a week / 12 hours a semester