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

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter7: Production Economics

Section: Chapter Questions

Problem 1.3CE

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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 log-wage regression results for women (W) and men (M) where wages are predicted by schooling (S) and age (A). wW = 2.23 + 0.077Sw + 0.017Aw and wM = 2.33 + 0.0745SM + 0.026AM. Sample means for the variables by gender are: women average a logged wage of 3.90, 12.7 years of schooling, and 40.8 years-old; men average a logged wage of 4.53, 14.2 years of schooling, and 43.9 years-old. Decompose the raw difference in average logged wages using the Oaxaca-Blinder decomposition. Specifically, decompose the raw difference into the portion due to differences in schooling, differences in age, and the portion left unexplained, possibly due to gender discrimination.
5 1 point Scenario 38-4. Consider the following OLS model estimating the effect of job tenure and IQ on worker salary: SALARY = =-49.11.86 × YEARS +0.57 × IQi. (29.1) (1.41) (0.35) Refer to Scenario 38-4. This model is an example of a natural experiment. a singular regression. a control experiment. a multiple regression.
Oil Recovery A U.S. state's Bureau of Economic Geology published a study on the economic impact of using carbon dioxide enhanced oil recovery (EOR) technology to extract additional oil from fields that have reached the end of their conventional economic life. The following table gives the approximate number of jobs for the citizens that would be created at various levels of recovery. Percent Recovery (%) 20 40 80 100 Jobs Created (Millions) 6 6 12 18 Find the regression line. j(r) = Use the regression line to estimate the number of jobs (in millions) that would be created at a recovery level of 50%. million jobs
13. Collinearity in a multiple regression analysis Suppose you want to examine the effects of a training program on future earnings using the following model: earn98= 4.64 +2.376train +0.371earn96 +0.366educ- 1.86 age +2.534 married (1.14) (0.43) (0.016) (0.062) (0.013) (0.4) where earn 98- 1998 earnings, in thousands of dollars train -1 if the individual participated in the training program, and =0 otherwise earn 96- 1996 earnings, in thousands of dollars educ years of education age = age, in years married-1 if the individual is married, and -0 otherwise Suppose that there is a high degree of correlation (but not perfect) between earnings in 1996, education, age, and marital status. True or False: We should be concerned about this high degree of correlation because it affects our ability to reliably estimate the impact of the training program on 1998 earnings, T. True False
Consider the following regression model: wage-Bi+Bamalerpumalexedu Buedutu, where wage is the hourly wage measured in dollars: male is a dummy variable for males edu is the years of education: maleedu is the interaction of male and edu variables. The parameter estimates for B parameters are P-1.27: B1.29: Br-0,16: Be-0.82. What is the predicted marginal effect of years of education for males?
The measure of standard error can also be applied to the parameter estimates resulting from linear regressions. For example, consider the following linear regression equation that describes the relationship between education and wage: WAGE: = Bo + B₁ EDUC; + &i where WAGE; is the hourly wage of person i (i.e., any specific person) and EDUC; is the number of years of education for that same person. The residual ₂ encompasses other factors that influence wage, and is assumed to be uncorrelated with education and have a mean of zero. Suppose that after collecting a cross-sectional data set, you run an OLS regression to obtain the following parameter estimates: WAGE;= -10.7+ 3.1 EDUC; If the standard error of the estimate of B₁ is 1.04, then the true value of B₁ lies between grows, you would expect this range to in size. and . As the number of observations in a data set
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.
The measure of standard error can also be applied to the parameter estimates resulting from linear regressions. For example, consider the following linear regression equation that describes the relationship between education and wage: WAGEi=β0+β1EDUCi+εi where WAGEi is the hourly wage of person i (i.e., any specific person) and EDUCiEDUCi is the number of years of education for that same person. The residual εiεi encompasses other factors that influence wage, and is assumed to be uncorrelated with education and have a mean of zero. Suppose that after collecting a cross-sectional data set, you run an OLS regression to obtain the following parameter estimates: WAGEi=−12.3+4.4 EDUCi If the standard error of the estimate of β1 is 1.29, then the true value of β1 lies between (2.465, 3.11, 3.755, 1.82) and (5.69, 6.98, 5.045) . As the number of observations in a data set grows, you would expect this range to (INCREASE OR DECREASE) in size.
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?
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
Consider the following regression estimates (ID6) Source Model Residual Total wage educ exper exper2 _cons inverse u-shaped SS C. linear d. exponential 1927.87673 5232.53756 7160.41429 df -.0046123 -3.96489 642.625576 3 522 10.0240183 525 Coef. Std. Err. .5953429 .0530251 .268287 .0368969 .000822 .7521526 MS 13.6388844 Number of obs F(3, 522) Prob > F R-squared Adj R-squared Root MSE t P>|t| 0.000 11.23 7.27 0.000 -5.61 0.000 -5.27 0.000 = .4911741 .1958023 -.006227 -5.442508 = = = 526 64.11 0.0000 0.2692 0.2650 3.1661 [95% Conf. Interval] .6995118 .3407717 where wage is hourly wage in US$, educ is years of education, exper is years of work experience and exper2 is experience squared (exper* exper). According to these estimates, what is the functional form of the relationship between experience and predicted wage when we hold education constant? a. u-shaped b.i -.0029975 -2.487272