2. The following model allows the return to education to depend upon the total amount of both parents' education, called pareduc: log(wage) = Bo + Bieduc+ Beduc * pareduc + Bzerper+ Batenure + u (a) What sign do you expect for B2? Why? (b) Using the data in WAGE2.RAW, the estimated equation is log(wage) = 5.65+0.047educ + 0.00078educ * pareduc + 0.019erper + 0.010tenure, (0.13) (0.010) (0.00021) (0.004) (0.003)

2. The following model allows the return to education to depend upon the total amount of both parents' education, called pareduc: log(wage) = Bo + Bieduc+ Beduc * pareduc + Bzerper+ Batenure + u (a) What sign do you expect for B2? Why? (b) Using the data in WAGE2.RAW, the estimated equation is log(wage) = 5.65+0.047educ + 0.00078educ * pareduc + 0.019erper + 0.010tenure, (0.13) (0.010) (0.00021) (0.004) (0.003)

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
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
With respect to a given product, describe the connection that exists between equalibrium/disequalibrium and prediction. cite an unique example
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.
Q.7 Given following separate equations. Variable educ measures years of schooling, while age indicates age of the individual. Wage measures monthly wage (in hundred Kina) of individual. log(wage) = -0.9048 + ₁.educ (1) log(wage) = -0.6742 + ₂.log (age) (2) wage = wage -0.8709+ B3. educ (3) -0.7635+ B4. log (educ) (4) = What is the interpretation of B₁, B₂, B3, B4?
MP believe that there is a causal relationship between earnings and years of schooling. They show you an estimate of the impact of years of schooling on annual earnings: There are two groups of individuals – group 1 and group 2. Group 1 on average earns £60,000 per year with the average level of post-compulsory education of 6 years, while group 2 earns on average £40,000 with the average post-compulsory education of 4 years. The MPs conclude that the annual return to an additional year of education is 25% using the following equation [(£60,000-£40,000)/40,000]/(6-4)=25%. Ignoring time preferences and discounting, and if both groups face the same cost of schooling, you need to explain to the MPs why this is not a good estimate of the annual return to an additional year of education. Propose methods to the MPs to deal with the problem
Himig has two part-time jobs, as an assistant worker in a fast food chain (Job 1) and and as a receptionist at a small hotel (Job 2). Due to her heavy load in her studies, Himig can only render 12 hours a week in her jobs. She has determined that for every hour she works at Job 1, she needs 2 hours of preparation time, and for every hour she works at Job II, she needs one hour of preparation time, and she cannot spend more than 16 hours for preparation. If Himig makes Php 40 an hour at Job 1, and Php30 an hour at Job 2, how many hours should she work per week at each job to maximize her income? (Let x = Job 1; y = Job 2).
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.
Consider the following regression estimates (FN2) Linear regression belavg abvavg female married _cons b. 1 wage O C. 5 O d. 4 Robust Coef. Std. Err. 3047845 3150202 2820787 -1.063254 .0693348 -2.751963 .9686236 .2612646 6.699098 .2889831 Number of obs F(4, 1255) Prob > F R-squared Root MSE t P>|t| -3.49 0.001 0.22 0.826 -9.76 0.000 3.71 0.000 23.18 0.000 |||||||||| = = .45606 6.132155 = -1.661197 -.5486894 -3.305361 = = [95% Conf. Interval] 1,260 56.35 0.0000 0.1121 4.3987 assume that MLR 1-6 hold. In the regression above, how many coefficients (including the constant) are statistically significant at the 1% level? a. 3 -.4653108 .687359 -2.198565 1.481187 7.266042
A commonly used functional form for wage equations is InW = a + bR + cR2, where W is the annual wage, R is the fatal accident rate, and the other job and employee characteristics are omitted for simplicity. 20 20 1. Derive an expression for the value of a statistical life. 2. Why is it important to include R2 in the wage equation?
The following model allows the return to education to depend upon the total amount of both parents' education, called pareduc: log(wage) = Bo + Bjeduc + Bzeduc-pareduc + Bzexper + Butenure + u. (i) Show that, in decimal form, the return to another year of education in this model is Alog(wage)/Aeduc = ßi + Brpareduc. What sign do you expect for B,? Why? (ii) Using the data in WAGE2, the estimated equation is log(wage) = 5.65 + .047 educ + .00078 educ-pareduc + (.13) (.010) (.00021) .019 exper + .010 tenure (.004) (.003) n = 722, R = .169. (Only 722 observations contain full information on parents' education.) Interpret the coefficient on the interaction term. It might help to choose two specific values for pareduc-for example, pareduc = 32 if both parents have a college education, or pareduc = 24 if both parents have a high school education-and to compare the estimated return to educ. (iii) When pareduc is added as a separate variable to the equation, we get: log(wage) = 4.94 + .097…
7 Suppose the following model describes the relationship between annual salary (salary) and the number of previous years of labor market experience (exper): log(salary) = 10.6 + .027 exper. (i) What is salary when exper = 0? When exper = 5? (Hint: You will need to exponentiate.) (ii) Use equation (A.28) to approximate the percentage increase in salary when exper increases by five years. (iii) Use the results of part (i) to compute the exact percentage difference in salary when exper = 5 and еxper = 0. Comment on how this compares with the approximation in part (ii).