Module 5.7 Emperical Analysic Lecture

The Role of Economists

• Economists analyze data to answer various questions related to human behavior and societal issues.

• Applications range from understanding discrimination's effects on job prospects to examining factors influencing charitable giving and academic performance in college.

• Importance of statistics and econometrics: Essential for everyone, regardless of their field of study, to grasp basic statistical methods to discern valid arguments from misleading claims made by journalists and policymakers.

Causality vs. Correlation

Education and Income Relationship

• Observational data indicates a correlation: Individuals with higher education levels tend to earn more income.

• Causality Question: Economists assess whether education directly causes higher income.

• Human capital theory: Greater education may enhance productivity, leading to increased wages and supporting policies that encourage higher education.

Omitted and Confounding Variables

• Consider potential confounding variables that may influence both education and income.

• Example: Individuals with higher intrinsic abilities (like intelligence or work ethic) may achieve more education and subsequently earn higher incomes. This means high ability could be an omitted variable creating a false impression of causality between education and income.

• Economists emphasize the importance of evaluating "what would happen" under alternative scenarios, termed counterfactual analysis.

Counterfactual Analysis

• Central to understanding causality: Determining what influences income without a college degree or with alternative education levels.

• Importance in policy analysis: Evaluating the effectiveness of programs requires considering what would have happened in their absence.

• Example: If a literacy program claims a 5% improvement in test scores, one must ask what would have occurred without the program to assess genuine impact.

Income and Health Relationship

Observational Data

• Higher income is correlated with better health outcomes, but this does not imply causation.

• Potential Causation: More income leading to better access to healthcare and healthier living conditions.

• Reverse Causation: Healthier individuals might have higher income due to reduced absenteeism and lower healthcare costs.

Confounding Variables in Health Outcomes

• Omitted parental factors: Parents' resources could explain both income and health outcomes by enabling access to better education and healthcare.

• Importance of statistical evidence: Relying on anecdotal evidence (e.g., extreme cases) is inadequate for establishing causal relationships.

Importance of Data Size and Statistical Evidence

• Economists advocate for large observations to draw reliable conclusions, avoiding dependency on isolated cases that may misrepresent broader trends.

• Counterfactual scenarios remain key: Examining health outcomes relative to increased income helps clarify the true nature of the relationship.

Conclusion

• Economists play a crucial role in discerning the nuances of data-driven questions, focusing on causality rather than mere correlation.

• Understanding the importance of omitted variables, confounding variables, and counterfactuals allows for more robust economic analysis and policymaking.