Ch. 2 Tools of Positive Economics

1. Ch. 2 Tools of Positive Economics

2. Theoretical Tools of Public Finance • theoretical tools The set of • tools designed to understand • the mechanics behind economic decision making. • empirical tools The set of tools designed to analyze data and answer questions raised by theoretical analysis.

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

4. There are many examples where causation and correlation can get confused. • In statistics, this is called the identification problem: given that two series are correlated, how do you identify whether one series is causing another?

5. 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

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

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

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

9. 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 (American Wind Energy Association, 2007)

10. 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

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

12. 3 . 3 • Time Series Analysis • Estimating Causation with Data We Actually Get: Observational Data

13. 3 . 3 • Time Series Analysis • Estimating Causation with Data We Actually Get: Observational Data • When Is Time Series Analysis Useful?

14. 3 . 3 • Cross-Sectional Regression Analysis • Estimating Causation with Data We Actually Get: Observational Data • Example with Real-World Data

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

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

17. 3 . 3 • Quasi-Experiments • Estimating Causation with Data We Actually Get: Observational Data

18. Conducting a Quasi-Experimental Study • Difference-in-difference quasi-experiments • Instrumental Variables quasi-experiments • Regression-Discontinuity Quasi-Experiments

19. 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