Lecture Notes: Economics and Data Analysis

Introduction to Economics and Data

• Economists work with data to answer various questions.
• Economics is pervasive, covering diverse topics like:
  • Impact of discrimination on job prospects.
  • Influence of peer pressure on charitable giving.
  • Effect of alcohol consumption in college on grades.
• Importance of introductory courses in statistics or econometrics for understanding basic statistical methods.

Understanding Correlation vs. Causation

• Correlation: Two variables move together; more education is associated with higher income.
• Causation: One variable causes changes in another, which is a more complex relationship.

Education and Income

• On average, more education correlates with more income.
• Economists explore if education causes higher income:
  • Education may increase productivity (human capital), leading to higher wages.
  • Policies fostering education could have significant income effects.
• Omitted Variables: Other factors like ability could cause both higher education and income.
• Counterfactual Analysis: Consideration of alternative scenarios to determine causality.

Importance of Contextual Comparison

• Always ask: "Compared to what?"
• Evaluating educational and economic policies requires understanding the counterfactual:
  • Example: Effectiveness of literacy programs.
  • Consideration of opportunity cost and marginal analysis.

Income and Health

• Higher income is correlated with better health outcomes.
• Causality Questions:
  • Does higher income cause better health?
  • Could better health lead to higher income (reverse causality)?
  • Role of omitted variables like parental resources.

Pitfalls to Avoid in Data Analysis

• Avoid anecdotal evidence or conclusions from small samples.
• Importance of large datasets for meaningful conclusions.
• Recognize counterfactual scenarios to assess real impacts.

Conclusion

• Economists use data to answer complex questions, integrating economic principles.
• Key is distinguishing correlation from causation and considering counterfactuals.