Part I: Wage, Gender, Education, and Experience: Consider the following model: wage Bo+B₁female + B₂ (educ≥6) + 83 experience + u. (1) where wage is the hourly wage in U.S. dollars. (educ≥6] is a dummy variable that equals 1 if an individual has 6 or more years of education and equals zero otherwise. For example, [educ≥6]=1 for an individual with 7 years of education, and [educ≥6]=0 for an individual with 3 years of education. experience is the number of years of experience an individual has. female is a dummy variable that equals 1 for female and equals 0 for male. An OLS regression for the above model gives us: wage = 9.50 0.11 female + 0.08 * [educ> 6] +0.03 * experience. (2.75) (0.05) (0.03) (0.01) where the numbers in parentheses (below the coefficients) are the standard errors. D Question 1 Obtain the predicted wage for a female with 7 years of education and 7 years of experience (round to 2 decimal places). Question 2 What is the estimated wage difference between: • a female with 4 years of education and 9 years of experience, and • a male with 8 years of education and 10 years of experience? (round to 2 decimal places).

Consider the following panel model to examine the effect of retirement on consumption expenditure, consit, of individual i over years t=1,…,3: (B1) log⁡(consit) = β0 + β1retiredit + β2ageit + β3marriedit + β4healthit + δ1Yr2t + δ2Yr3t + ai + uit Where: retiredit is a dummy variable equal to 1 if individual i is retired on year t and 0 otherwise ageit is the individual's age in years marriedit is an indicator variable for whether the individual is married (1) or not (0) in year t healthit is an indicator variable equal to 1 if the individual is in 'good health' and 0 otherwise Yr2 is a dummy variable equal to 1 in year t=2 and 0 otherwise Yr3 is a dummy variable equal to 1 in year t=3 and 0 otherwise We obtain the following results when we estimate Model (B1) using RE and FE methods: Variable Random Effects Fixed Effects retired -0.072*** (0.024) -0.054** (0.025) age -0.008*** (0.003) - married 0.031*** (0.010) 0.028 (0.022) health 0.050*** (0.021) 0.052***…

You are given a sample of 600 working individuals, with data on their location and earnings. You construct the following model: wage=Bo+Binorth+u, where the dependent variable is the annual earnings (wage) in $1,000. The explanatory variable is north (which equals one if a worker lives in the north, and equals zero otherwise). An OLS regression yields B 24.8. B₁ = 2.1. = As we have learned in class, education matters a lot in explaining wages-more years of education lead to higher wages. In the above econometric model, education belongs to the error term, u. We also know that in our population of interest, workers in the north have lower drop-out rates than workers in the south. These facts imply that B₁ is biased and inconsistent because the estimation is subject to omitted variable bias. The correct model should be: wage Bo+Binorth + B₂educ + e, where educ denotes the number of years of education. The OLS estimate of B1 from our incorrect model presented at the beginning of this…

1. Suppose output (Q) is related to labor (L) and capital (K) in the following nonlinear way: Q = albKc When taking log to this equation, it is transformed into a linear LnQ = Ina + b In(L) + c Ln (K) One hundred twenty-three observations are used to obtain the following regression results: Dependant Variable: Observations: Variable Intercept L K Q 123 5.5215 Parameter Standard Estimate error 0.650 R-square 0.350 0.7547 0.9750 0.2950 0.1450 F-ratio 184.56 t-ratio 5.66 2.20 2.41 p-value on F 0.00001 p-value 0.0001 0.0295 0.0173 a. Write the regression equation based on the output either in the transformed linear form or the original non-linear form.

Assume that the coefficient estimated in the second regression is correct. Forget about the effect of the Return variable, whose effect seems small and statistically insignificant. Calculate the correlation between Female and In(MarketValue) using the omitted variable bias equation. Let X Female, u=MarketValue, and =0.51. The correlation between Female and In(MarketValue), Px is -0.333. (Round your response to three decimal places.) Are large firms more likely to have female top executives than small firms? A. Yes. B. There is no relationship between the genders. C. No.

When labor usage is at 12units, the output is 36 units. From this, we may infer that. A) the marginal product of labor is 3 b) the total product of labor is 1/3 c)the average product of labor is 3 d) none of the above

Scenario 38-1. Consider the following OLS model applied to the data for workers wages and years of schooling, where e; stands for the residual: WAGE = -10.7+3.16 x SCHOOL + e. (20.7) (1.35) Refer to Scenario 38-1. Suppose that the residual term has a zero mean and is uncorrelated with the independent variable. According to the estimated model, each year of schooling decreases a worker's wage by $3.16. 000 decreases a worker's wage by $10.7. increases a worker's wage by $3.16. increases a worker's wage by $10.7.