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CHAPTER 2

Tools of Positive Analysis

Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin

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Tools for Positive Analysis

• Do tax rates have an effect on work effort.?

• Does environmental restrictions improve health outcomes?

• Do school vouchers improve students’ test scores?

• This chapter discusses the tools that economists use to estimate the impact of government programs on individual behavior.

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The Role of Theory

• Economic models– Virtue of simplicity(economic models are not

realistic) – Judging a model– Limitations of models

• Ex: Simple Labor Supply Model

• Empirical analysis is necessary to find out individual behavior.

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Causation vs. Correlation

• Statistical analysis

– Correlation( a measure of the extent to which two events move together)

– Control group(comparison group of individuals who are not subject to the intervention being)

– Treatment group(studied.the group of individuals who are subject to intervention being studied)

• 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

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Causation vs. Correlation

• It is found out that married people have higher wages. So there is a correlation between being married and having higher wages.

• Is it reasonable for government to institute financial incentives for people to marry?

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Experimental Studies

• Biased estimates(a different estimate than the true relationship)

• Counterfactual(the outcome for people in the treatment group had they not being treated.

• Experimental (or randomized) study (subjects are randomly assigned to be in the treatment or control group).

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Conducting an Experimental Study

• Random assignment to control and treatment groups

• Ex: UI and unemployment duration.

Random assignment of individuals to high UI or low UI.

As sample size increase all the characteristics of high UI and low UI groups will converge.

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Pitfalls of Experimental Studies

• Ethical issues

• Technical problems(people are not passive objects)

• Response bias(some people do not respond)

• Impact of limited duration of experiment

• Generalization of results to other populations, settings, and related treatments

• Black box aspect of experiments

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Observational Studies

• Observational study – empirical study relying on observed data not obtained from experimental study

• Sources of observational data– Surveys(household budget survey)

– Administrative records(hospital records on birth)

– Governmental data(tax revenue data)

• Econometrics– Regression analysis

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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

α0

Interceptis α0

Slopeis α1

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Types of Data

• Cross-sectional data

• Time-series data

• Panel data

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Pitfalls of Observational Studies

• Data collected in non-experimental settingIn wage and labor supply example, the reason why higher wage

earners work more can be explained by higher wage earner being more ambitious people. So, the difference in hours worked is not due to wage but it is due to ambition.

In regression analysis we try to add any variable that might effect hours worked as independent variable, such as ……?

Specification issues

type of relationship can be misspecified. (linear vs non-linear.

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Quasi-Experimental Studies

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

Uses observational data but there is random assignment.

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Conducting a Quasi-Experimental Study

• Difference-in-Difference quasi-experiments

• Example: tax on beer and reduction in teen traffic fatalities) some states increased tax from 89-92 and some states did not.

• Instrumental Variables quasi-experiments

• Regression-Discontinuity quasi-experiments

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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