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**Unformatted text preview: **Chapter 7: Using Indicator Variables Multiple Choice Review Questions 1. Which of the following terms is NOT commonly used to refer to an indicator variable? a.) dummy b.) binary c.) dichotomous d.) digital Ans; d Section: 7.1 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 Ans: b Section: 7.1 3. The following Mincer equation has been used to estimate wages: ln (Y) = ln (Yo) + β2EDU + β3 EXPER + β4 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 * EDU Ans: d Section: 7.1 4. The Chow test is a specific application of a(n) a.) z-‐test b.) χ2 test c.) F-‐test d.) t-‐test Ans: c Section 7.2 5. 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 Ans: b Section: 7.3 [highlighted term should have a “hat” over] 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. What hypothesis would you test to determine if the discrimination claim is valid? a.) H0:βMALE = 0 ; H1: βMALE ≥ 0 b.) H0:βMALE = βEDU = βEXPER = 0 ; H1: βMALE ≠ 0 and βEDU ≠ 0 and βEXPER ≠ 0 c.) H0:βMALE = βEDU = βEXPER = 0 ; H1: βMALE ≠ 0 or βEDU ≠ 0 or βEXPER ≠ 0 d.) H0:βMALE ≤ βEDU or βMALE ≤ βEXPER ; H1: βMALE > βEDU or βMALE > βEXPER Ans: a Section: 7.3 [highlighted term should have a “hat” over] 7. 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 Ans: c Section: 7.4 8. The following economic model predicts whether a voter will vote for an incumbent school board member INCUMBENT = β1 + β2 MALE + β3 PARTY + β4 MARRIED + β5 KIDS where INCUMBENT = 1 if the voter votes for them, 0 otherwise, MALE = 1 if the voter is a male, PARTY indicates the voter is registered with the same political party as the incumbent, MARRIED = 1 for married voters, 0 otherwise, and KIDS is the number of school age kids living with the voter. What is the probability that a married female without kids who is not registered with a political party will vote for the incumbent? a.) β1 + β4 b.) β1 c.) β1 + β2 + β3 + β5 d.) β2 + β3 + β5 Ans: a Section: 7.4 9. The following economic model predicts whether a voter will vote for an incumbent school board member INCUMBENT = β1 + β2 MALE + β3 PARTY + β4 MARRIED + β5 KIDS where INCUMBENT = 1 if the voter votes for them, 0 otherwise, MALE = 1 if the voter is a male, PARTY indicates the voter is registered with the same political party as the incumbent, MARRIED = 1 for married voters, 0 otherwise, and KIDS is the number of school age kids living in the voter’s house. How should we interpret β4? 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 Ans: d Section: 7.4 10. 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. Ans: a Section: 7.5 11. 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. Ans: d Section: 7.5 12. 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. Ans: a Section: 7.5 13. 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 Ans: b Section: 7.5 14. 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 Ans: c Section: 7.5 15. 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 Ans: c Section: 7.5 16. 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 Ans: a Section: 7.5 17. The following economic model predicts whether a voter will vote for an incumbent school board member INCUMBENT = β1 + β2 MALE + β3 PARTY + β4 MARRIED + β5 KIDS where INCUMBENT = 1 if the voter votes for them, 0 otherwise, MALE = 1 if the voter is a male, PARTY indicates the voter is registered with the same political party as the incumbent, MARRIED = 1 for married voters, 0 otherwise, and KIDS is the number of school age kids living in the voter’s house. 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 Ans: a Section: 7.2 18. The following economic model predicts whether a voter will vote for an incumbent school board member INCUMBENT = β1 + β2 MALE + β3 PARTY + β4 MARRIED + β5 KIDS where INCUMBENT = 1 if the voter votes for them, 0 otherwise. MALE = 1 if the voter is a male. PARTY indicates the voter is registered with the same political party as the incumbent. MARRIED = 1 for married voters, 0 otherwise. KIDS is the number of school age kids living in the voter’s house. 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 Ans: b, Section: 7.2 19. 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 Ans: b Section: 7.2 ...

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- Fall '13