You recall from your textbook that additional years of experience are supposed t

Question

You recall from your textbook that additional years of experience are supposed to result in higher earnings. You reason that this is because experience is related to "oil the job training." One frequently used measure for experience is "Age-Education-6." Explain the underlying rationale. Assuming, heroically, that education is constant across the 1, 744 individuals, you consider regressing earnings on age and a binary variable for gender. You estimate two specifications initially: Earn = 323.70 + 5.15 times Age - 169.78 times Female, R^2 = 0.13, SER = 274.75 (21.18) (0.55) (13.06) Ln(Earn) = 5.44 + 0.015 times Age - 0.421 times Female, R^2 = 0.17, SER = 0.75 (0.08) (0.002) (0.036) where Earn are weekly earnings in dollars. Age is measured in years, and Female is a binary variable, which takes on the value of one if the individual is a female and is zero otherwise. Interpret each regression carefully. For a given age. how much less do females earn on average? Should you choose the second specification on grounds of the higher regression R^2? (d) Your peer points out to you that age-earning profiles typically take on an inverted U-shape. To test this idea, you add the square of age to your log-linear regression. Ln(Earn) = 3.04 + 0.147 times Age - 0.421 times Female - 0.0016 Age^2, (0.18) (0.009) (0.033) (0.0001) R^2 = 0.28, SER = 0.68 Interpret the results again. Are there strong reasons to assume that this specification is superior to the previous one? Why is the increase of the Age coefficient so large relative to its value in (c)?

Explanation / Answer

c) Earn = 323.7 + 5.15*Age - 169.78*Female

           = 323.7 + 5.15Age - 169.78

          = 153.92 + 5.15Age for Females

          = 323.7 + 5.15Age (for Male set Female = 0)

    For a given age, Females earn $169.78 than their male counterparts.

   Using the log model, the equations are

   ln Earn = 5.44 + .015*Age for Males

   ln Earn = 5.019 + .015*Age for Females

   For a given age, Females earn 79.2*e.015*Age less which is strictly increasing function with age. Or, higher the

   age, more is the wage disparity.

.   NO. R2 comparions is valid onyly when the models in question have the same dependent variable.

d) For a given age, females earn (less) = .86*e.147*Age -.0016Age^2

    The exponential is a parabola or U shaped. It increases upto a certain point, then diminishes beyond threshold.

    This model is superior to log-linear in c as with more age(experience), the disparity should lessen between

    females and males.

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