Question 1

Randomization based on covariates is

Accepted Answer

A) not of practical importance since individuals are hardly ever assigned in this fashion. 
B) dependent on the covariances of the error term (serial correlation). 
C) a randomization in which the probability of assignment to the treatment group depends on one of more observable variables W. 
D) eliminates the omitted variable bias when using the difference estimator based on Yi = β0 + β1Xi + ui,where Y is the outcome variable and X is the treatment indicator. 
A) not of practical importance since individuals are hardly ever assigned in this fashion. 
B) dependent on the covariances of the error term (serial correlation). 
C) a randomization in which the probability of assignment to the treatment group depends on one of more observable variables W. 
D) eliminates the omitted variable bias when using the difference estimator based on Yi = β0 + β1Xi + ui,where Y is the outcome variable and X is the treatment indicator.

Question 2

Let the vertical axis of a figure indicate the average employment fast food restaurants.There are two time periods,t = 1 and t = 2,where time period is measured on the horizontal axis.The following table presents average employment levels per restaurant for New Jersey (the treatment group)and Eastern Pennsylvania (the control group).
   Enter the four points in the figure and label them   ,   ,   ,and   .Connect the points.Finally calculate and indicate the value for   .

Accepted Answer

@#IMG-DLM& = Δ @#IMG-DLM& - Δ @#IMG-DLM& = (21.03

Question 3

In the context of a controlled experiment,consider the simple linear regression formulation Yi = β0 + β1Xi + ui.Let the Yi be the outcome,Xi the treatment level,and ui contain all the additional determinants of the outcome.Then

Accepted Answer

A) the OLS estimator of the slope will be inconsistent in the case of a randomly assigned Xi since there are omitted variables present. 
B) Xi and ui will be independently distributed if the Xi be are randomly assigned. 
C) β0 represents the causal effect of X on Y when X is zero. 
D) E(Y   X = 0)is the expected value for the treatment group. 
A) the OLS estimator of the slope will be inconsistent in the case of a randomly assigned Xi since there are omitted variables present. 
B) Xi and ui will be independently distributed if the Xi be are randomly assigned. 
C) β0 represents the causal effect of X on Y when X is zero. 
D) E(Y   X = 0)is the expected value for the treatment group.

Question 4

The following is not a threat to external validity:

Accepted Answer

A) the experimental sample is not representative of the population of interest. 
B) the treatment being studied is not representative of the treatment that would be implemented more broadly. 
C) experimental participants are volunteers. 
D) partial compliance with the treatment protocol. 
A) the experimental sample is not representative of the population of interest. 
B) the treatment being studied is not representative of the treatment that would be implemented more broadly. 
C) experimental participants are volunteers. 
D) partial compliance with the treatment protocol.

Question 5

Heterogeneous population

Accepted Answer

A) implies that heteroskedasticity-robust standard errors must be used. 
B) suggest that multiple characteristics must be used to describe the population. 
C) effects can be captured through interaction terms. 
D) refers to circumstances in which there is unobserved variation in the causal effect with the population. 
A) implies that heteroskedasticity-robust standard errors must be used. 
B) suggest that multiple characteristics must be used to describe the population. 
C) effects can be captured through interaction terms. 
D) refers to circumstances in which there is unobserved variation in the causal effect with the population.

Question 6

Quasi-experiments

Accepted Answer

A) provide a bridge between the econometric analysis of observational data sets and the statistical ideal of a true randomized controlled experiment. 
B) are not the same as experiments,and lessons learned from the use of the latter can therefore not be applied to them. 
C) most often use difference-in-difference estimators,which are quite different from OLS and instrumental variables methods studied in earlier chapters of the book. 
D) use the same methods as studied in earlier chapters of the book,and hence the interpretation of these methods is the same. 
A) provide a bridge between the econometric analysis of observational data sets and the statistical ideal of a true randomized controlled experiment. 
B) are not the same as experiments,and lessons learned from the use of the latter can therefore not be applied to them. 
C) most often use difference-in-difference estimators,which are quite different from OLS and instrumental variables methods studied in earlier chapters of the book. 
D) use the same methods as studied in earlier chapters of the book,and hence the interpretation of these methods is the same.

Question 7

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.

Accepted Answer

Answer will differ by students.Students

Question 8

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.

Accepted Answer

Answers will vary by student.The omitted

Question 9

The following estimation methods should not be used to test for randomization when Xi,is binary:

Accepted Answer

A) linear probability model (OLS)with homoskedasticity-only standard errors. 
B) probit. 
C) logit. 
D) linear probability model (OLS)with heteroskedasticity-robust standard errors. 
A) linear probability model (OLS)with homoskedasticity-only standard errors. 
B) probit. 
C) logit. 
D) linear probability model (OLS)with heteroskedasticity-robust standard errors.

Question 10

With panel data,the causal effect

Accepted Answer

A) cannot be estimated since correlation does not imply causation. 
B) is typically estimated using the probit regression model. 
C) can be estimated using the "differences-in-differences" estimator. 
D) can be estimated by looking at the difference between the treatment and the control group after the treatment has taken place. 
A) cannot be estimated since correlation does not imply causation. 
B) is typically estimated using the probit regression model. 
C) can be estimated using the "differences-in-differences" estimator. 
D) can be estimated by looking at the difference between the treatment and the control group after the treatment has taken place.

Question 11

A potential outcome

Accepted Answer

A) is the outcome for an individual under a potential treatment. 
B) cannot be observed because most individuals do not achieve their potential. 
C) is the same as a causal effect. 
D) is none of the above. 
A) is the outcome for an individual under a potential treatment. 
B) cannot be observed because most individuals do not achieve their potential. 
C) is the same as a causal effect. 
D) is none of the above.

Question 12

Testing for the random receipt of treatment

Accepted Answer

A) is not possible,in general. 
B) entails testing the hypothesis that the coefficients on W1i,…,Wri are non-zero in a regression of Xi on W1i,…,Wr. 
C) is not meaningful since the LHS variable is binary. 
D) entails testing the hypothesis that the coefficients on W1i,…,Wri are zero in a regression of Xi on W1i,…,Wr. 
A) is not possible,in general. 
B) entails testing the hypothesis that the coefficients on W1i,…,Wri are non-zero in a regression of Xi on W1i,…,Wr. 
C) is not meaningful since the LHS variable is binary. 
D) entails testing the hypothesis that the coefficients on W1i,…,Wri are zero in a regression of Xi on W1i,…,Wr.

Question 13

A repeated cross-sectional data set is

Accepted Answer

A) a collection of cross-sectional data sets,where each cross-sectional data set corresponds to a different time period. 
B) the same as a balanced panel data set. 
C) what Card and Krueger used in their study of the effect of minimum wages on teenage employment. 
D) time series. 
A) a collection of cross-sectional data sets,where each cross-sectional data set corresponds to a different time period. 
B) the same as a balanced panel data set. 
C) what Card and Krueger used in their study of the effect of minimum wages on teenage employment. 
D) time series.

Question 14

In the context of a controlled experiment,consider the simple linear regression formulation Yi = β0 + β1Xi + ui.Let the Yi be the outcome,Xi the treatment level when the treatment is binary,and ui contain all the additional determinants of the outcome.Then calling   a differences estimator

Accepted Answer

A) makes sense since it is the difference between the sample average outcome of the treatment group and the sample average outcome of the control group. 
B) and   the level estimator is standard terminology in randomized controlled experiments. 
C) does not make sense,since neither Y nor X are in differences. 
D) is not quite accurate since it is actually the derivative of Y on X. 
A) makes sense since it is the difference between the sample average outcome of the treatment group and the sample average outcome of the control group. 
B) and   the level estimator is standard terminology in randomized controlled experiments. 
C) does not make sense,since neither Y nor X are in differences. 
D) is not quite accurate since it is actually the derivative of Y on X.

Question 15

The New Jersey-Pennsylvania study on the effect of minimum wages on employment mentioned in your textbook used a comparison in means "before" and "after" analysis.The difference-in-difference estimate turned out to be 2.76 with a standard error of 1.36.
The authors also used a difference-in-differences estimator with additional regressors of the type
ΔYi = β0 + β1Xi + β2W1,t + ...+ β1+ rWr,i + ui
where i = 1,…,410.X is a binary variable taking on the value one for the 331 observations in New Jersey.Since the authors looked at Burger King,KFC,Wendy's,and Roy Rogers fast food restaurants and the restaurant could be company owned,four W-variables were added.
(a)Given that there are four chains and the possibility of a company ownership,why did the authors not include five W-variables?
(b)OLS estimation resulted in   1 of 2.30 with a standard error of 1.20.Test for statistical significance and specify the alternative hypothesis.
(c)Why is this estimate different from the number calculated from Δ   - Δ   = 2.76? What is the advantage of employing this estimator of the simple difference-in-difference estimator?

Accepted Answer

(a)Including a fifth W-variable would ha

Question 16

If the causal effect is different for different people,then the population regression equation for a binary treatment variable Xi,can be written as

Accepted Answer

A) Yi = β0 + β1Xi + ui. 
B) Yi = β0 + β1iXi + ui. 
C) Yi = β0i β1iXi + ui. 
D) Yi = β0 + β1Gi + β2Dt + ui. 
A) Yi = β0 + β1Xi + ui. 
B) Yi = β0 + β1iXi + ui. 
C) Yi = β0i β1iXi + ui. 
D) Yi = β0 + β1Gi + β2Dt + ui.

Question 17

Describe the major differences between a randomized controlled experiment and a quasi-experiment.

Accepted Answer

Answers will vary by student.Some of the

Question 18

The Hawthorne effect refers to

Accepted Answer

A) subjects dropping out of the study after being randomly assigned to the treatment or control group. 
B) the failure of individuals to follow completely the randomized treatment protocol. 
C) the phenomenon that subjects in an experiment can change their behavior merely by being included in the experiment. 
D) assigning individuals,in part,as a result of their characteristics or preferences. 
A) subjects dropping out of the study after being randomly assigned to the treatment or control group. 
B) the failure of individuals to follow completely the randomized treatment protocol. 
C) the phenomenon that subjects in an experiment can change their behavior merely by being included in the experiment. 
D) assigning individuals,in part,as a result of their characteristics or preferences.

Question 19

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 behavior 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.

Accepted Answer

It should be clear that the authors were

Question 20

Failure to follow the treatment protocol means that

Accepted Answer

A) the OLS estimator cannot be computed. 
B) instrumental variables estimation of the treatment effect should be used where the initial random assignment is the instrument for the treatment actually received. 
C) you should use the TSLS estimator and regress the outcome variable Y on the initial random assignment in the first stage to get predicted values of the outcome variable. 
D) the Hawthorne effect plays a crucial role. 
A) the OLS estimator cannot be computed. 
B) instrumental variables estimation of the treatment effect should be used where the initial random assignment is the instrument for the treatment actually received. 
C) you should use the TSLS estimator and regress the outcome variable Y on the initial random assignment in the first stage to get predicted values of the outcome variable. 
D) the Hawthorne effect plays a crucial role.

Randomization based on covariates is

In the context of a controlled experiment,consider the simple linear regression formulation Yi = β0 + β1Xi + ui.Let the Yi be the outcome,Xi the treatment level,and ui contain all the additional determinants of the outcome.Then

The following is not a threat to external validity:

Heterogeneous population

Quasi-experiments

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.

The following estimation methods should not be used to test for randomization when Xi,is binary:

With panel data,the causal effect

A potential outcome

Testing for the random receipt of treatment

A repeated cross-sectional data set is

In the context of a controlled experiment,consider the simple linear regression formulation Yi = β0 + β1Xi + ui.Let the Yi be the outcome,Xi the treatment level when the treatment is binary,and ui contain all the additional determinants of the outcome.Then calling a differences estimator

If the causal effect is different for different people,then the population regression equation for a binary treatment variable Xi,can be written as

Describe the major differences between a randomized controlled experiment and a quasi-experiment.

The Hawthorne effect refers to

Failure to follow the treatment protocol means that

Economic Questions and Data

Review of Probability

Review of Statistics

Linear Regression With One Regressor

Regression With a Single Regressor: Hypothesis Tests and Confidence Intervals

Linear Regression With Multiple Regressors

Hypothesis Tests and Confidence Intervals in Multiple Regression

Nonlinear Regression Functions

Assessing Studies Based on Multiple Regression

Regression With Panel Data

Regression With a Binary Dependent Variable

Instrumental Variables Regression

Introduction to Time Series Regression and Forecasting

Estimation of Dynamic Causal Effects

Additional Topics in Time Series Regression

The Theory of Linear Regression With One Regressor

The Theory of Multiple Regression

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Exam 13: Experiments and Quasi-Experiments

Randomization based on covariates is

In the context of a controlled experiment,consider the simple linear regression formulation Yi = β0 + β1Xi + ui.Let the Yi be the outcome,Xi the treatment level,and ui contain all the additional determinants of the outcome.Then

The following is not a threat to external validity:

Heterogeneous population

Quasi-experiments

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.

The following estimation methods should not be used to test for randomization when Xi,is binary:

With panel data,the causal effect

A potential outcome

Testing for the random receipt of treatment

A repeated cross-sectional data set is

In the context of a controlled experiment,consider the simple linear regression formulation Yi = β0 + β1Xi + ui.Let the Yi be the outcome,Xi the treatment level when the treatment is binary,and ui contain all the additional determinants of the outcome.Then calling a differences estimator

If the causal effect is different for different people,then the population regression equation for a binary treatment variable Xi,can be written as

Describe the major differences between a randomized controlled experiment and a quasi-experiment.

The Hawthorne effect refers to

Failure to follow the treatment protocol means that

Economic Questions and Data

Review of Probability

Review of Statistics

Linear Regression With One Regressor

Regression With a Single Regressor: Hypothesis Tests and Confidence Intervals

Linear Regression With Multiple Regressors

Hypothesis Tests and Confidence Intervals in Multiple Regression

Nonlinear Regression Functions

Assessing Studies Based on Multiple Regression

Regression With Panel Data

Regression With a Binary Dependent Variable

Instrumental Variables Regression

Introduction to Time Series Regression and Forecasting

Estimation of Dynamic Causal Effects

Additional Topics in Time Series Regression

The Theory of Linear Regression With One Regressor

The Theory of Multiple Regression

Filters