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 theworker'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.

Econometrics is used to explain economic phenomena using inferences from statistics, economic…

Consider the population regression of log earnings [Y₁, where Y,= In(Earnings;)] against two binary variables: whether a worker is married (D₁, where D₁ = 1 if the 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 + B₁ D₁1 + P₂D21+P3 (D₁1XD₂1) + Hi- The interaction term: OA. indicates the effect of being married on log earnings. B. does not make sense since it could be zero for married males. OC. allows the population effect on log earnings of being married to depend on gender. OD. cannot be estimated without the presence of a continuous variable.

Consider the population regression of log earnings [Y₁, where Y,= In(Earnings;)] against two binary variables: whether a worker is married (D₁, where D₁ = 1 if the 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 + B₁ D₁1 + P₂D21+P3 (D₁1XD₂1) + Hi- The interaction term: OA. indicates the effect of being married on log earnings. B. does not make sense since it could be zero for married males. OC. allows the population effect on log earnings of being married to depend on gender. OD. cannot be estimated without the presence of a continuous variable.

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

See similar textbooks

Similar questions

Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for non-married respondents who are not female?
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
Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for married respondents who are not female?
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
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?
E3
In the December, 1969, American Economic Review (pp. 886-896), Nathanial Leff reports thefollowing least squares regression results for a cross section study of the effect of age composition onsavings in 74 countries in 1964:log S/Y = 7.3439 + 0.1596 log Y/N + 0.0254 log G - 1.3520 log D1 - 0.3990 log D2 (R2= 0.57)log S/N = 8.7851 + 1.1486 log Y/N + 0.0265 log G - 1.3438 log D1 - 0.3966 log D2 (R2= 0.96)where S/Y = domestic savings ratio, S/N = per capita savings, Y/N = per capita income, D1 = percentage ofthe population under 15, D2 = percentage of the population over 64, and G = growth rate of per capitaincome. Are these results correct? Explain..
Suppose that a researcher, using wage per hour data on 250 randomly selected male workers and 280 female workers, estimates the following OLS regression wage - 12.68+2.79xMale (0.18) (0.84) R = 0.06, s-3.10 where Male is a dummy variable that takes the value 1 if the worker is male and 0 if female: s represents the standard error of the regression and in brackets homoskedastic std errors are reported. The researcher wants to find the gender pay gap as percentage bf the wagé per hour of women. According to this information the gender pay gap on average against women is approximately -22% -25% -38% O-16%
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.
We know that discrimination exists. It influences wages, but also many other dimensions over the life cycle which affect wages indirectly. I run OLS regression with variables wage, age, female and degree. The dependent variable is log(wage) and we replace the variables female and degree with the interaction term. However, discrimination is not included among the observed regressors. Given that omitting confounding variables from regression model can bias the coefficient estimates, omitting discrimination would lead to biased results. Could you please help me provide an example of how unobserved gender discrimination can affect my OLS estimates. [Hint: think about ways in which discrimination can invalidate OLS assumptions.].
Econometrics
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.