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Analyzing Societal Effects: Tools and Pitfalls

In Chapter 2, delve into the tools of positive analysis including economic models and empirical analysis. Learn about causation vs. correlation, experimental and observational studies, pitfalls to avoid, and the conditions required for government action to cause societal effects. Understand statistical and regression analysis, limitations of models, and ethical considerations in research. Explore how random assignment is key in conducting experimental and quasi-experimental studies. Gain insights into different data types and sources, and the challenges of generalizing results in various settings.

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Analyzing Societal Effects: Tools and Pitfalls

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  1. CHAPTER 2 Tools of Positive Analysis

  2. The Role of Theory • Economic models • virtue of simplicity • judging a model • limitations of models • Empirical analysis

  3. Causation vs. Correlation • Statistical analysis • Correlation • Control group • Treatment group • Conditions required for government action X to cause societal effect Y • X must precede Y • X and Y must be correlated • Other explanations for any observed correlation must be eliminated

  4. Experimental Studies • Biased estimates • Counterfactual • Experimental (or randomized) study

  5. Conducting an Experimental Study • Random assignment to control and treatment groups

  6. Pitfalls of Experimental Studies • Ethical issues • Response bias • Impact of limited duration of experiment • Generalization of results to other populations, settings, and related treatments • Black box aspect of experiments

  7. Observational Studies • Observational study – empirical study relying on observed data not obtained from experimental study • Sources of observational data • Surveys • Administrative records • Governmental data • Econometrics • Regression analysis

  8. L = α0 + α1wn + α2X1 + … + αnXn + ε Dependent variable Independent variables Parameters Stochastic error term Regression analysis Regression line Standard error L wn Conducting an Observational Study Slopeis α1 Interceptis α0 α0

  9. Types of Data • Cross-sectional data • Time-series data • Panel data

  10. Pitfalls of Observational Studies • Data collected in non-experimental setting • Specification issues

  11. Quasi-Experimental Studies • Quasi-experimental study (= natural experiment) – observational study relying on circumstances outside researcher’s control to mimic random assignment

  12. Pitfalls of Quasi-Experimental Studies • Assignment to control and treatment groups may not be random • Not applicable to all research questions • Generalization of results to other settings and treatments

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