Question 1

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

Accepted Answer

A) frequent use of binary variables. 
B) type of data analyzed and the special opportunities and challenges posed when analyzing experiments and quasi-experiments. 
C) superiority of TSLS over OLS. 
D) use of heteroskedasticity-robust standard errors. 
A) frequent use of binary variables. 
B) type of data analyzed and the special opportunities and challenges posed when analyzing experiments and quasi-experiments. 
C) superiority of TSLS over OLS. 
D) use of heteroskedasticity-robust standard errors.

Question 2

Define the   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.

Accepted Answer

@#IMG-DLM& = ( @#IMG-DLM& - @#IMG-DLM& )- ( @#IMG-DLM& - @#IMG-DLM& )
= Δ @#IMG-DLM& - Δ @#IMG-DLM& .Consider

Question 3

A repeated cross-sectional data set

Accepted Answer

A) is also referred to as panel data. 
B) is a collection of cross-sectional data sets,where each cross-sectional data set corresponds to a different time period. 
C) samples identical entities at least twice. 
D) is typically used for estimating the following regression model Yit = β0 + β1Xit + β2W1,it + ...+ β1+ rWr,it + uit 
A) is also referred to as panel data. 
B) is a collection of cross-sectional data sets,where each cross-sectional data set corresponds to a different time period. 
C) samples identical entities at least twice. 
D) is typically used for estimating the following regression model Yit = β0 + β1Xit + β2W1,it + ...+ β1+ rWr,it + uit

Question 4

To analyze the effect of a minimum wage increase,a famous study used a quasi-experiment for two adjacent states: New Jersey and (Eastern)Pennsylvania.A   was calculated by comparing average employment changes per restaurant between to treatment group (New Jersey)and the control group (Pennsylvania).In addition,the authors provide data on the employment changes between "low wage" restaurants and "high wage" restaurants in New Jersey only.A restaurant was classified as "low wage," if the starting wage in the first wave of surveys was at the then prevailing minimum wage of $4.25.A "high wage" restaurant was a place with a starting wage close to or above the $5.25 minimum wage after the increase.
(a)Explain why employment changes of the "high wage" and "low wage" restaurants might constitute a quasi-experiment.Which is the treatment group and which the control group?
(b)The following information is provided
   Where FTE is "full time equivalent" and the numbers are average employment per restaurant.
Calculate the change in the treatment group,the change in the control group,and finally   .Since minimum wages represent a price floor,did you expect   to be positive or negative?
(c)The standard error for   is 1.48.Test whether or not this is statistically significant,given that there are 174 observations.

Accepted Answer

(a)In the above example,the increase in

Question 5

Small sample sizes in an experiment

Accepted Answer

A) biases the estimators of the causal effect. 
B) may pose a problem because the assumption that errors are normally distributed is dubious for experimental data. 
C) do not raise threats to the validity of confidence intervals as long as heteroskedasticity-robust standard errors are used. 
D) may affect confidence intervals but not hypothesis tests. 
A) biases the estimators of the causal effect. 
B) may pose a problem because the assumption that errors are normally distributed is dubious for experimental data. 
C) do not raise threats to the validity of confidence intervals as long as heteroskedasticity-robust standard errors are used. 
D) may affect confidence intervals but not hypothesis tests.

Question 6

Experimental data are often

Accepted Answer

A) observational data. 
B) binary data,in that the subject either does or does not respond to the treatment. 
C) panel data. 
D) time series data. 
A) observational data. 
B) binary data,in that the subject either does or does not respond to the treatment. 
C) panel data. 
D) time series data.

Question 7

A causal effect for a single individual

Accepted Answer

A) can be deduced from the average treatment effect. 
B) cannot be measured. 
C) depends on observable variables only. 
D) is observable since it is used as part of calculating the mean of individual causal effects. 
A) can be deduced from the average treatment effect. 
B) cannot be measured. 
C) depends on observable variables only. 
D) is observable since it is used as part of calculating the mean of individual causal effects.

Question 8

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

Accepted Answer

A) labor market effects of immigration. 
B) effects on civilian earnings of military service. 
C) the effect of cardiac catheterization. 
D) the effect of unemployment on the inflation rate. 
A) labor market effects of immigration. 
B) effects on civilian earnings of military service. 
C) the effect of cardiac catheterization. 
D) the effect of unemployment on the inflation rate.

Question 9

To test for randomization when Xi is binary,

Accepted Answer

A) you regress Xi,on all W's and compute the F-statistic for testing that all the coefficients on the W's are zero.(The W's measure characteristics of individuals,and these are not affected by the treatment. ) 
B) is not possible,since binary variables can only be regressors. 
C) requires reordering the observations randomly and re-estimating the model.If the coefficients remain the same,then this is evidence of randomization. 
D) requires seeking external validity for your study. 
A) you regress Xi,on all W's and compute the F-statistic for testing that all the coefficients on the W's are zero.(The W's measure characteristics of individuals,and these are not affected by the treatment. ) 
B) is not possible,since binary variables can only be regressors. 
C) requires reordering the observations randomly and re-estimating the model.If the coefficients remain the same,then this is evidence of randomization. 
D) requires seeking external validity for your study.

Question 10

Your textbook mentions use of a quasi-experiment to study the effects of minimum wages on employment using data from fast food restaurants.In 1992,there was an increase in the (state)minimum wage in one U.S.state (New Jersey)but not in neighboring location (Eastern Pennsylvania).To calculate the   you need the change in the treatment group and the change in the control group.To do this,the study provides you with the following information
   Where FTE is "full time equivalent" and the numbers are average employment per restaurant.
(a)Calculate the change in the treatment group,the change in the control group,and finally   .Since minimum wages represent a price floor,did you expect   to be positive or negative?
(b)If you look at   ,is this number primarily due to a change in the treatment group or the control group? Is this what you expected?
(c)The standard error for   is 1.36.Test whether or not the coefficient is statistically significant,given that there are 410 observations.If you believed that the benefit from small minimum wage increases outweighed the cost in terms of employment loss,would finding that this coefficient was not statistically significant discourage you?

Accepted Answer

(a)change in treatment group: + 0.59,cha

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

Define the 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.

A repeated cross-sectional data set

Small sample sizes in an experiment

Experimental data are often

A causal effect for a single individual

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

To test for randomization when Xi is binary,

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

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

Define the 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.

A repeated cross-sectional data set

Small sample sizes in an experiment

Experimental data are often

A causal effect for a single individual

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

To test for randomization when Xi is binary,

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