About This Course
This course offers a gentle introduction to empirical and experimental microeconomics, focusing on how data is used to inform public policy and social decisions. Participants familiarize with the key concepts of correlation and causality through examples from the scientific literature on economic policy, with particular focus on criminal and dishonest behaviors, such as tax evasion, corruption, and fraudulent conduct. Through these examples, the course shows what data is necessary to identify causal relationships, how to process and interpret this data, and how to use these insights to develop policy strategies.
The course also introduces key analytical tools for identifying causal relationships, including Machine Learning, Synthetic Control Methods, and the Difference-in-Differences approach. By the end of the course, participants will have developed a solid understanding of how empirical evidence can be used to shape effective policy decisions, with an emphasis on distinguishing between mere correlations and true causal effects.
The course is organized into four weeks, each exploring a different topic related to social behaviors and policy intervention:
- Week 1 - Why Do People Pay Taxes?
- Week 2 - Why Do People Engage in Corruption?
- Week 3 - Do Leaders Influence Ethical Behaviors?
- Week 4 - Synthetic Control Methods, Machine Learning, and DiD Methodology
Each week includes a variety of lessons featuring videos, textual materials, and assessment quizzes.
Target
This course is designed for anyone interested in data analysis for policy-making, as well as topics in microeconomics and experimental economics. It is open to anyone, whether they are students, professionals, or lifelong learners, with no prior knowledge or skills required.
Outcomes
By the end of this course, participants will have developed a strong understanding of how to use empirical evidence to inform and shape effective policy decisions. They will gain the skills to distinguish between simple correlations and robust causal relationships, applying these concepts to key issues such as tax compliance, corruption, and ethical leadership. Participants will also become familiar with key analytical methods used in social policy and intervention research, including experimental design and procedures, Machine Learning, Synthetic Control Groups, and Difference-in-Differences approaches.
Key Learning Outcomes:
- Understand the difference between correlation and causality.
- Explore various empirical and experimental methods used to estimate causal relationships.
- Analyze examples of policy research focused on criminal and dishonest behaviors.
- Develop skills in evaluating and interpreting data to inform policy-making.
- Understand strategies for preventing criminal behaviors based on empirical data.
Requirements
No prior knowledge of statistics, econometrics, or coding is required to attend and successfully follow the course. All empirical methods and case studies will be presented in an intuitive, non-technical way. A good level of English is highly recommended to read the material and delve into the case studies presented during the course.
At the end of the entire course, there will be an optional open-ended question designed to inspire and encourage participants to consider developing their own empirical or experimental project. This could serve as a starting point for personal research, or a thesis project within a study program, with the possibility of further guidance and supervision from the course instructor.
Participants must correctly pass at least three out of four quizzes at the end of each week.
Open Badge
Participants who complete the course will be awarded an Open Badge from BESTR. Participants who log in to the platform with University of Bologna, EDUGAIN, CIE or Spid authentication and answer correctly at least 60% of the questions in total, will receive an email with instructions to download their Open Badge from the BESTR website.
Subtitles
English subtitles available.
For better understanding, subtitles are available for each video and can be activated or not. If you want to revise some crucial passages you can move through the video content and click on the attached text.
ISCED-F
031 Social and behavioural sciences
Categories
- Transdisciplinarity
- Economics
- Political economics
- Cognitive science
- Gender studies
- Sociology
- Accounting and taxation
- Business, administration and law
- Statistics, applied
- Survey design
- Survey sampling
FAQ
For further information, see FAQ page.
Course Professor
Alice Guerra
Alice Guerra is an Associate Professor at the University of Bologna, and an Associate Editor at the International Review of Law and Economics. In 2015, she earned a European PhD in Law and Economics (Universities of Hamburg, Bologna, Rotterdam), and from 2016 to 2019, she was a Marie Curie Fellow and Assistant Professor at Copenhagen Business School. Her research spans several topics in the economic analysis of illegal behaviors and dishonesty. This includes research on tax evasion, corruption, financial fraud, addictive behaviors, sports tourism, civil and criminal liability. Webpage: https://sites.google.com/site/aliceguerrahome/.
