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 Empirical analysis
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. Conducting an Observational Study
L = α0 + α1wn + α2X1 + … + αnXn + ε
Dependent variable
Independent variables
Parameters
Stochastic error term
Regression analysis
Regression line
Standard error wn L
First callout identifies intercept term. Second callout identifies slope term.
Slope is α1
Intercept is α0 α0

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

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

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

14. Estimating Causation with Data We Actually Get: Observational Data
3 . 3
Estimating Causation with Data We Actually Get: Observational Data
Cross-Sectional Regression Analysis
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. Estimating Causation with Data We Actually Get: Observational Data
3 . 3
Estimating Causation with Data We Actually Get: Observational Data
Quasi-Experiments

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