Exam 13: Experiments and Quasi-Experiments

The following are reasons for studying randomized controlled experiment in an econometrics course, with the exception of

For quasi-experiments,

In the case of heterogeneous causal effects, the following is not true: a. in the circumstances in which OLS would normally be consistent (when $\left. E \left( u _ { i } \mid X _ { i } \right) = 0 \right)$ , the OLS estimator continues to be consistent. b. OLS estimation using heteroskedasticity-robust standard errors is identical to TSLS. c. the OLS estimator is properly interpreted as a consistent estimator of the average causal effect in the population being studied. d. the TSLS estimator in general is not a consistent estimator of the average causal effect if an individual's decision to receive treatment depends on the effectiveness of the treatment for that individual.

The following is not a threat to external validity:

Your textbooks gives several examples of quasi experiments that were conducted.The following is not an example of a quasi experiment:

Present alternative estimators for causal effects using experimental data when data is available for a single period or for two periods.Discuss their advantages and disadvantages.

The Hawthorne effect refers to

Program evaluation

Define the $\beta _ { 1 } ^ { d i f f - i n - d i f f s }$ in terms of observable differences in the treatment and control group, before and after the treatment. Explain why this presentation is the equivalent of calculating the coefficient in a regression framework.

If the causal effect is different for different people, then the population regression equation for a binary treatment variable Xi, can be written as a. $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { i } + u _ { i }$ . b. $Y _ { i } = \beta _ { 0 } + \beta _ { 1 i } X _ { i } + u _ { i }$ . c. $Y _ { i } = \beta _ { 0 i } + \beta _ { 1 i } X _ { i } + u _ { i }$ . d. $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } G _ { i } + \beta _ { 2 } D _ { t } + u _ { i }$ .

Canada and the United States had approximately the same aggregate unemployment rates from the 1920s to 1981.In 1982, a two percentage point gap appears, which has roughly persisted until today, with the Canadian unemployment rate in the third quarter of 2002 being 7.6 percent while the American rate stood at 5.9 percent in the same period. Several authors have investigated this phenomenon.One study, published in 1990, contained the following statement: "It is a clichė that, as compared to analysis in the physical sciences, economic analysis is hampered by the lack of controlled experiments. In this regard, study of the Canadian economy can be much facilitated by comparison with the behaviour of the US …" Discuss what the authors may have had in mind.List some potential threats to internal and external validity when comparing aggregate unemployment rate behavior between countries.

To test for randomization when $X _ { \mathrm { i } }$ is binary,

In the context of a controlled experiment, consider the simple linear regression formulation $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { i } + u _ { i }$ Let the $Y _ { \mathrm { i } }$ be the outcome, $X _ { \mathrm { i } }$ the treatment level when the treatment is binary, and u_i contain all the additional determinants of the outcome. Then calling $\widehat { \beta } _ { 1 }$ a differences estimator

You want to study whether or not the use of computers in the classroom for elementary students has an effect on performance.Explain in some detail how you would ideally set up such an experiment and what threats to internal and external validity there might be.

Specify the multiple regression model that contains the difference-in-difference estimator (with additional regressors).Explain the circumstances under which this model is preferable to the simple difference-in-difference estimator.Explain how the W's can be used to test for randomization.How does the interpretation of the W variables change compared to the differences estimator with additional regressors?

Earnings functions provide a measure, among other things, of the returns to education.It has been argued these regressions contain a serious omitted variable bias due to differences in abilities.Furthermore, ability is hard to measure and bound to be highly correlated with years of schooling.Hence the standard estimate of about a 10 percent return to every year of schooling is upward biased.Suggest some ways to address this problem.One famous study looked at earnings of identical twins.Explain how this can be viewed as a quasi-experiment, and mention some of the threats to internal and external validity that such a study might encounter.

The major distinction between the experiments and quasi-experiments chapter and earlier chapters is the

The following does not represent a threat to internal validity of randomized controlled experiments:

One of the major lessons learned in the chapter on experiments and quasi-experiments

Consider the simple population regression model where the treatment is the same for the members of the treatment group, and hence X is a binary variable.Explain why the coefficient on X represents the difference between two means.How is the test for the statistical significance of the coefficient on X related to the test for differences in means between two populations, when their variances are different? Write down the null and alternative hypothesis in each case.