Verified Test Bank Chapter 7 Using Indicator Variables

File: Chapter 7 – Using Indicator Variables

Multiple Choice

1. Which of the following terms is NOT commonly used to refer to an indicator variable?

a. Dummy

b. Binary

c. Dichotomous

d. Digital

2. Which of the following wage premia is modeled with an indicator variable that shifts the intercept?

a. Height

b. Gender

c. Education

d. Weight

3. The following Mincer equation has been used to estimate wages: ln (Y) = ln (Y_o) + ₂EDU + ₃ EXPER + ₄ EXPER² + e, where Y is income, Y₀ is income of someone with no education or experience, EDU is years of education and EXPER is experience in the field. If you suspect males earn higher wages than females and that the wage difference increases with education how would you adjust the econometric model to estimate wages?

a. Include a binary variable for gender, MALE

b. Include an interaction term equal to MALE* EXPER

c. Include an indicator variable for MALE and one for FEMALE

d. Include a binary variable for MALE and an interaction term equal to MALE * EXPER

4. The Chow test is a specific application of a _____ test.

a. z

b. ^

c. F

d. t

5. If you perform a Chow test to compare two regressions and reject the null hypothesis, what should you conclude?

a. There is not sufficient evidence that the regressions are significantly different.

b. The regression equations are statistically different.

c. The regression equations are equivalent.

d. It depends on how you set up the null hypothesis.

6. A large company is accused of gender discrimination in wages. The following model has been estimated from the company’s human resource information ln(WAGE) = 1.439 + .0834 EDU + .0512 EXPER + .1932 MALE, where WAGE is hourly wage, EDU is years of education, EXPER is years of relevant experience, and MALE indicates the employee is male. How much more do men at the firm earn, on average?

a. $1.21 per hour more than females.

b. 19.32% more than females.

c. $19.32 per hour.

d. $19,320 more per year than females.

7. A large company is accused of gender discrimination in wages. The following model has been estimated from the company’s human resource information ln(WAGE) = 1.439 + .0834 EDU + .0512 EXPER + .1932 MALE, where WAGE is hourly wage, EDU is years of education, EXPER is years of relevant experience, and MALE indicates the employee is male. What hypothesis would you test to determine if the discrimination claim is valid?

a. H₀:_MALE = 0 ; H₁: _MALE ≥ 0

b. H₀:_MALE = _EDU = _EXPER = 0 ; H₁: _MALE ≠ 0 and _EDU ≠ 0 and _EXPER ≠ 0

c. H₀:_MALE = _EDU = _EXPER = 0 ; H₁: _MALE ≠ 0 or _EDU ≠ 0 or _EXPER ≠ 0

d. H₀:_MALE ≤ _EDU or _MALE ≤ _EXPER ; H₁: _MALE > _EDU or _MALE > _EXPER

8. When you have a multiple regression model with a binary dependent variable it is a _____.

a. dichotomous model

b. Bernoulli model

c. Linear Probability model

d. prediction model

9. (Use the above information.) What is the probability that a married female without kids who is not registered with a political party will vote for the incumbent?

a. ₁ + ₄

b. ₁

c. ₁ + ₂ + ₃ + ₅

d. ₂ + ₃ + ₅

10. (Use the above information.) How should we interpret ₄?

a. The likelihood the incumbent candidate is married

b. The percentage of married voters who vote for the incumbent

c. The probability a married person is registered to vote

d. The difference in probability a married voter will vote for the incumbent as opposed to an unmarried voter

11. (Use the above information.) If you hypothesize males and females might have a different willingness to vote for a candidate registered with a different political party, which variable should you add to the economic model to allow you to test the hypothesis?

a. MALE * PARTY

b. MALE * MARRIED

c. MARRIED * KIDS

d. MARRIED * PARTY

12. (Use the above information.) If you believe marriage affects male and female voters differently, which variable should you add to the economic model to allow you to test the hypothesis?

a. MALE * PARTY

b. MALE * MARRIED

c. MARRIED * KIDS

d. MARRIED * PARTY

13. Treatment effects are best estimated using data from _____.

a. randomized, controlled experiments

b. subjects that have already undergone the risky treatment

c. people most in need of the treatments

d. natural or quasi-experiments

14. Randomized, controlled experiments are needed to accurately measure treatment effects without _____.

a. the expense of having to treat everyone

b. the risk of discrimination bias

c. exposing everyone to untested treatments

d. selection bias

15. When certain characteristics cause a person to choose to be in a treatment group, selection bias can be overcome by using _____.

a. conditional randomization and fixed effects

b. difference-in-differences estimation

c. larger sample sizes

d. quasi-experiments

16. Treatment effects can be estimated from natural or quasi-experiments using which estimator?

a. Restricted least squares

b. Difference-in-differences estimator

c. Fixed effects

d. Quasi-Likelihood

17. Which of the following variables is not necessary in order to estimate treatment effects using difference-in-differences?

a.) A treatment/control indicator

b.) Pre-treatment / post-treatment indicator

c.) Treatment group * treatment time interaction term

d.) Post-treatment performance

18. Estimating treatment effects using difference-in-differences requires what kind of data?

a. Aggregate measures over time

b. Time-series data spanning the treatment length

c. Paired, panel data

d. Cross-section spanning the treated population

19. What benefit is gained by estimating treatment effects with fixed effects using panel data?

a. It controls for unobserved, individual characteristics.

b. It controls for changes in individuals over time.

c. It allows the treatment effect to vary with the length of treatment.

d. It “fixes” the treatment to the same time for each individual.

20. When a change in one variable is the direct consequence of a change in another variable, then there is a _____ between the variables.

a. correlation

b. causal relationship

c. treatment indicator

d. reference indicator

21. When individuals are not randomly assigned to treatment and control groups, then there is _____.

a. conditional mean independence

b. starting point bias

c. treatment bias

d. selection bias

22. _____ says that for each value of the control variable, it must be possible to see an individual in the treatment and control groups.

a. The overlap assumption

b. Conditional mean independence

c. The difference estimator

d. The casual effect