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 × SCHOOL + e;-(20.7) (1.35)Refer to Scenario 38-1. Suppose that the residual term is non zero and reflects all the forces that influence wages other than years of schooling. If ; is correlated with schooling, the estimate OLS makeson the effect of schooling on wages000is inaccurate.lies between $0.46 and $5.86.lies between $8 and $13.4.is accurate.

This conclusion is drawn because if the residual term 𝑒𝑖 is non-zero and correlates with the…

Answered: Scenario 38-1. Consider the following…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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23. An education economist estimated the wage equation to determine the rate of return to an additional year of education and obtained ln(WAGE) = -0.5220+0.1075×EDUC. it is anticipated that ability ( attributes, such as intelligence, creativity, perseverance and industriousness) is positively correlated with UDUC. If a measure of a bility is omitted from the model the estimated return to education A. Is biased downwards B. Is biased upwards C. Cannot be estimated D. All the above
Scenario 38-2. Consider the following OLS model applied to the data on workers salaries and years of job tenure: SALARY = -500.4+ 103.75 x YEARS, + - (80.3) (7.35) Refer to Scenario 38-2. Suppose that some workers are more capable than others, which is not easily measurable. If ability and years of tenure are not correlated, then 0000 researchers will need to use workers' parents education as a confounding variable. the estimates OLS makes will be unbiased. researchers will need to use workers' age as a confounding variable. the estimates OLS makes will be biased.
The following equation has been used to estimate wages: ln (Y) = ln (Yo) + b2EDU + b3 EXPER + b4 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? OPTIONS - a. include a binary variable for gender, MALE b. include an interaction term equal to MALE* EDUC 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 * EDUC
Compute the R² using the following information. What does this number mean? Wage = 4.43 +0.61Edu + e n = 1089, SST = 37211.6, SSE 37211.6, SSE 19848.2 R² = = = 0.4666. 46.66% of the variation in wage can be explained by education. R² = 0.5334. 53.34% of the variation in wage can be explained by education. R² = 0.61.61% of the variation in wage can be - explained by education. R² = 0.3043. 30.43% of the variation in wage can be explained by education.
Suppose a worker's skill is captured by his efficiency units of labor. In country A, the distribution of efficiency units in the population is such that worker 1 has one efficiency unit, worker 2 has two efficiency units, and so on. There are 100 workers in the population. In deciding whether to migrate to country B, these workers compare the present value of their future wage earnings at home (wa) with their potential wage earnings in country B (wB). The relationship between wages and skills in each of the two countries is given by WA = 100 + s and Wg = 110 + 0.75s where s is the number of efficiency units of the worker. (a) Assume there are no migration costs. What is the efficiency of the marginal worker in country A, i.e. the worker who is indifferent between staying in country A or moving to country B? (b) What is the average number of efficiency units among immigrants to country B from country A? Assume that the marginal worker stays. (c) Suppose the cost of migrating from…
Suppose wage (Y) is determined by years of schooling (yrsc) and sociability (soc) the following causal model: - Y = ßo + B₁yrsc + ß₂ soc+u, where u captures all other unobservable variables and is uncorrelated with both years of schooling and sociability. Assume B₁ > 0 for i 0, 1, 2. Furthermore, assume cov(yrsc, soc) > 0. What can you conclude about the short regression model: Y B + B₁yrsc+e where E(e) = 0 and cov(yrsc, e) = 0? = O B₁ =B₁ OB > B₁ There's not enough information. οβα < βι
To investigate the impact of wage on the number of workers employed two models of labour demand are estimated for British firms. Model 1 is a univariate model with labour employed (L) function of average yearly wage (W) with all variables measured in logs : L = 7.26 -0.51 × W Model 2 is a multivariate model with labour employed (L) function of output (Q) and average wage (W) with all variables measured in logs : L = -0.20 +0.85 × Q - 0.91 × W Select the terms from the drop down lists to complete the sentence below correctly: The coefficient of the wage variable in Model 2, -0.91, is the employed and it measures the relative change in labour dem overall biased partial growing impact of wage on labour relative increase in wage when
Suppose the researcher considers the following model : Wage = Bo+B,Female + u, and runs OLS, using wage data on 250 randomly selected male workers and 280 female workers. The researcher has obtained the estimated equation as Wage = 15 (1.00) 3 Female, R = 0.05. (0.5) In the equation, Wage is measured in dollars per hour, Female is a binary variable that is equal to 1 if the person is a female and O if the person is a male. The numbers in the parentheses are the standard errors of the coefficients. Which Statement is NOT correct? The coefficient of Female, -3.00, is statistically significant at 5%. O The p-value for the test that Ho: B = 0, H : B 0 is less than 0.05. This regression may suffer omitted variable bias. R Since insignificant. is too low, the wage difference between men and women is The researcher can increase R- by including more regressors in the model.
In this question we look at the relation between the logarithm of weakly earnings and years of education. Using data from the national longitudinal study of youth, we find the following results for a regression of log weekly earnings and years of education, experience, experience squared and an intercept: Log (earnings) = 4.016 + 0.092 . educi + 0.079 .expei + 0.002 . experi 2 ( 0.222) ( 0.008) ( 0.025) (0.001) c). Labour economist studying the relation between education and earnings are often concerned about what they call “ability bias”. Suppose that individuals differ in ability, and that the correct specification of the regression function is one that includes ability: log ( earnings )I = β1 + β 2 ⋅ educI + β 3 ⋅ experI − β 4 ⋅ experI2 + β5 ⋅ abilityI + εI . In this regression, what do you expect the sign of β5 (the coefficient on ability) to be?
Consider the following model of the factors that influence a person's annual wage: Y = β0 + β1X1 + β2X2 + β3X1X2 + β4X3 + u where Y = annual wage, in dollars X1= years of formal education X2 = age, in years X3 = variable equal to 1 if male, 0 otherwise U = error term Which of the following is the interpretation of β1 alone? The effect of X1 on Y when X2 = 0 The effect of X1 on Y when X2 is at its median value. The effect of X1 on Y, holding all else equal. The effect of X1 on Y when X2 is at its mean value.
a. b. Given the equation, L = 2r + 3k + 8(q-¹/2k¹/2) dL/dr = ? dL/dk = ?
Consider the population regression of log earnings [Y;, where Y,= In(Earnings,;)] against two binary variables: whether a worker is married (D₁, where D₁;= 1 if the th person is married) and the worker's gender (D2;, where D₂;= 1 if the th person is female), and the product of the two binary variables Y₁ = Po+B₁D₁+P₂D2i + P3 (D₁¡ × D₂i) + Hi- The interaction term: O A. indicates the effect of being married on log earnings. B. does not make sense since it could be zero for married males. C. allows the population effect on log earnings of being married to depend on gender. D. cannot be estimated without the presence of a continuous variable.

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