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. 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

5. Experimental Studies Biased estimates Counterfactual Experimental (or randomized) study

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

7. 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

8. 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

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

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

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

12. Estimating Causation with Data We Actually Get: Observational Data 2002 CPS Data

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

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

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

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

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

18. 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

19. 19 of 23 Conclusion 3. 4 The central issue for any policy question is establishing a causal relationship between the policy in question and the outcome of interest. We discussed several approaches to distinguish causality from correlation. The gold standard for doing so is the randomized trial, which removes bias through randomly assigning treatment and control groups. Unfortunately, however, such trials are not available for every question we wish to address in empirical public finance. As a result, we turn to alternative methods such as time series analysis, cross-sectional regression analysis, and quasi-experimental analysis. Each of these alternatives has weaknesses, but careful consideration of the problem at hand can often lead to a sensible solution to the bias problem that plagues empirical analysis.