What you'll learn
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Learn the most important and popular tools for program evaluation: Randomization, Instrumental Variable, Difference-in-Difference, Regression Discontinuity Design, and Matching
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Conduct Data analysis with these tools using STATA and R
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Use real world data sets for practical application of all models, such as for the Health Insurance Subsidy Program
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Reproduce some well known work of the most established experts in the field, including of noble laureates to learn best practices
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Read, understand, and evaluate empirical studies on program evaluation
Course Description
As individuals in the field of social sciences or policy analysis, we work with organizations, firms, or government bodies to make decisions which affect people’s lives in many ways. As we design and deliver projects across fields such as education, health, poverty, and law, it is critical to then understand what impact our programs and policies have on the people we serve. This is what Program Impact Evaluation is about. Impact Evaluation helps us in determining the effectiveness of a program, identify best practices, improve accountability, help in decision making, and improve resource allocation towards creating the greatest impact.
The immense importance of such evaluations and increasing demand for professionals equipped with the necessary skills to conduct them properly has led us to design a well-rounded course especially for professionals working in social sciences and policy making. This course looks to cover the main econometric evaluation tools and techniques most in use today, without getting too technical. We will be covering randomised experiments, instrumental variables, regression discontinuity designs, and differences-in-differences methods, along with using real world data and practical application of all these tools.
This course is designed to enable participants to focus on research design, thinking clearly with data and learn program/impact evaluation tools with real life data applications through computer-based exercises and learn to replicate the results of published economics articles like Card and Krueger (1994). Philosophy of the course will be to engage participants in doing program evaluation through computer assisted tools. Main software for the course will be STATA and R with all programs/slides available for each topic after the training.
By the end of this course, participants will be familiar with tools to be applied for program evaluation and better understand research design.
Course Content
- Definition and key concepts
- Purpose and benefits of impact evaluation
- Different types of impact evaluation
- Challenges and Best practices
- Purpose and Use in causal inference
- Assumptions and Limitations
- Data Preparation, Identifying Treatment and Control Groups
- Regression Model and breaking down DiD and IV analysis
- Examples, Applications, Policy Implementations
Demo and assignments using Stata and R
- Purpose and Use in causal inference
- Assumptions and Limitations
- Data Preparation, Identifying Treatment and Control Groups
- Regression Models and breaking down RDD and Matching/Randomization analysis
- Examples, Applications, Policy Implementations
Demo and assignments using Stata and R
- Real-life examples of impact evaluation studies
- Applications of impact evaluation in different sectors: Health Insurance Subsidy Program and other data
- Practical exercises and group discussions
Week 1:
Program/Impact Evaluation Overview
- Definition and key concepts
- Purpose and benefits of impact evaluation
- Different types of impact evaluation
- Challenges and Best practices
Week 2:
Difference in Differences Estimators, Instrumental Variable
- Purpose and Use in causal inference
- Assumptions and Limitations
- Data Preparation, Identifying Treatment and Control Groups
- Regression Model and breaking down DiD and IV analysis
- Examples, Applications, Policy Implementations
Demo and assignments using Stata and R
Week 3:
Regression Discontinuity Design, Matching and Randomization
- Purpose and Use in causal inference
- Assumptions and Limitations
- Data Preparation, Identifying Treatment and Control Groups
- Regression Models and breaking down RDD and Matching/Randomization analysis
- Examples, Applications, Policy Implementations
Demo and assignments using Stata and R
Week 4:
Case Studies and Applications
- Real-life examples of impact evaluation studies
- Applications of impact evaluation in different sectors: Health Insurance Subsidy Program and other data
- Practical exercises and group discussions
Trainer
Dr Zahid Asghar
Dr. Zahid Asghar, Professor at Quaid-i-Azam University, is an applied econometrician and statistician with special interests in development economics and making sense of data through Data visualization. He has conducted a large number of trainings on data analytics using Excel, STATA, R and EVIEWS. Dr. Zahid has published on topics such as gender, food security and other socio-economic issues, both in national and international peer reviewed journals.