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 x 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

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 x 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

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

See similar textbooks

Similar questions

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.
Suppose we are interested in evaluating the impact on wages of being a college graduate and of residing in California. To this end we define the following dummy variables if college graduate if not college graduate coll = ncoll = if not college graduate if college graduate 1 cali = if residing in California S 1 if not residing in California ncali = if not residing in California if residing in California and for wages denoting yearly wages, we estimate the following model of returns to education wages = B1 coll+ B2ncoll + B3 cali + B4(coll x cali) + e where E[e|coll, cali] = 0. Which parameter or combination of parameters measures the increase in expected wages from obtaining a college degree (comparing to not having a college degree) when residing California? O a. None of the other options is correct O b. Bi – B2 O c. B1 – B2 + ß4 O d. B4 O e. Bi + B3 – ßi – B4 O f. Bi + B4 – ß2 – B4 Clear my choice
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.
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…
The Weibracht Corporation designs and manufactures custom beer steins for some of the numerous brew pubs in western North Carolina. Each stein costs Weibracht $8 to produce. The brew pubs purchase the steins from Weibracht for resale to their customers and require a 51% margin. Weibracht’s marketing director has conducted a regression analysis using historical data, resulting in the following regression line: q = -12 * p + 326, where q = slope * retail price + MWB What is the profit maximizing price that Weibracht should charge the brew pubs for these custom steins?
We have a pooled cross section of data with data from 2009 and 1999 on women’s wages and their years of education. We consider the model: wage = β0 + β1Year2009 + β2educ + β3Year2009 × educ + u where wage is the hourly wage and educ is the years of education. Year2009 is a dummy variable equal to 1 in the year 2009 and 0 otherwise. Which of the following measures the change in the effect of education on the wage between 1999 and 2009? a) β0 b) β1 + β2 + β3 c) none of these answers d) β1 + β3 e) β3
A professor is interested in understanding the factors on which the grades a student obtains in her final exam depends. She chooses the student's intelligence quotient (X,), the grades obtained in the mid-term exam (X2), the number of days the student was present in the class in the whole semester (X3) as regressors. She randomly selects 180 students and estimates the following regression function: Ý = 49 + 1.85X, + 0.98X2 + 0.98X3, + u,. where Y,, X, X2j, and X3, denote the predicted value of grades obtained by the " student, her intellegence quotient, the grades obtained by her in the mid-term exam, and the number of days she was present in the class in the whole semester, respectively. She calculates the value of the regression R to be 0.45. The value of the adjusted R (R) will be (Round your answer to two docimal places). Which of the following statements is not true about the R? (Chock all that apply.) DA The value of R is always greater than the R given that the number of…