A researcher looking into the impact of gender, age, and the number of hours worked on...

 

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A researcher looking into the impact of gender, age, and the number of hours worked on the income of managers, created a model using the natural log of the individuals wage as the dependent variable. They used the number of years the individual was an adult (AGE - 18) as a proxy variable for experience. They also took the natural log of the number of hours worked in a typical week to see if the resulting value would be inelastic (|b| < 1), elastic(|b|> 1), or unit elastic (|b|= 1). Finally they included the Female variable to see the impact gender has on the wage of managers. They got the following results. Source Intercept AGE Ln(Hours) Male 7.501 0.005 0.888 0.202 Value Standard Error 0.263 0.002 0.068 0.039 28.528 3.349 13.064 5.231 t Pr>t <0.0001 0.001 <0.0001 <0.0001 Use these results to answer the following questions. a. How much would wage increase as age increases by 1 (use the quick estimate technique)? 1. The income would go up by 0.005% 2. The income would go up by 0.5 3. The income would go up by 0.5% 4. The income would go up by 0.005 b. When testing to see if the relationship between wage and hours worked is inelastic (|b| < 1), elastic(|b|> 1), or unit elastic (|b|= 1) they test the hypotheses Ho = 1 and Ha 21. What is the value of the t statistic they would use? (round to 3 decimal places) C. What is the accurate estimate for the impact of gender on wages? 1. The income of men is 22.4% higher than that of women 2. The income of men is 0.202 higher than that of women 3. The income of men is 20.2% higher than that of women 4. The income of men is 0.202% higher than that of women A researcher looking into the impact of gender, age, and the number of hours worked on the income of managers, created a model using the natural log of the individuals wage as the dependent variable. They used the number of years the individual was an adult (AGE - 18) as a proxy variable for experience. They also took the natural log of the number of hours worked in a typical week to see if the resulting value would be inelastic (|b| < 1), elastic(|b|> 1), or unit elastic (|b|= 1). Finally they included the Female variable to see the impact gender has on the wage of managers. They got the following results. Source Intercept AGE Ln(Hours) Male 7.501 0.005 0.888 0.202 Value Standard Error 0.263 0.002 0.068 0.039 28.528 3.349 13.064 5.231 t Pr>t <0.0001 0.001 <0.0001 <0.0001 Use these results to answer the following questions. a. How much would wage increase as age increases by 1 (use the quick estimate technique)? 1. The income would go up by 0.005% 2. The income would go up by 0.5 3. The income would go up by 0.5% 4. The income would go up by 0.005 b. When testing to see if the relationship between wage and hours worked is inelastic (|b| < 1), elastic(|b|> 1), or unit elastic (|b|= 1) they test the hypotheses Ho = 1 and Ha 21. What is the value of the t statistic they would use? (round to 3 decimal places) C. What is the accurate estimate for the impact of gender on wages? 1. The income of men is 22.4% higher than that of women 2. The income of men is 0.202 higher than that of women 3. The income of men is 20.2% higher than that of women 4. The income of men is 0.202% higher than that of women

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a The wage would increase by 05 as age increases by 1 use the quick ... View the full answer