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$$\begin{array}{l}{\text { Use the data in WAGEI for this exercise. }} \\ {\text { (i) Use equation }(7.18) \text { to estimate the gender differential when educ }=12.5 .} \\ {\text { Compare this with the estimated differential when educ }=0 \text { . }}\end{array}$$$$\begin{array}{l}{\text { (ii) Run the regression used to obtain }(7.18), \text { but with female\cdot (educ }-12.5 ) \text { replacing female.educ. }} \\ {\text { How do you interpret the coefficient on female now? }} \\ {\text { (iii) Is the coefficient on female in part (ii) statistically significant? Compare this with }(7.18) \text { and }} \\ {\text { comment. }}\end{array}$$

This indicates that the gender-differential is 0.297 when educ$=12.5$ and 0.227 when $e d u c=0$

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Chapter 7

Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables

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thesis the regression result table for the wage equation. In this equation, which depends on gender education and some other explanatory variables, your task is to calculate the gender difference in which the difference in which between women and men is actually the combination of the beta had are female and by the head of the interaction term between female and education multiply with the value of education. Yeah, we will calculate the gender difference when education is 12.5 and when education is zero for the first case, the difference in which equal minus 0.227 that is bait ahead of female minus 0.6 That is the bait ahead of the interaction term times 12.5 and you would get minus 0.302 When education is zero. The difference in which is just the beta had a female, which is minus 0.227 so we can see that the differential of 12.5 years good of education is minus 0.227 minus minus point 302 equal 0.75 which is 7.5% points in this part, we replace the interaction. Term of female and education, with female and education minus 12.5. Doing so changes how we interpret the coefficient of females. The coefficient of female is the gender differential in which, but in the old model, the one in part one, it iss the gender differential when education is zero. In the new model, the model of this part it is the gender differential. When education is 12.5. You could also notice that the estimation of beta female is statistically significant in the new model. It was not significant in the old model. It is so because we are estimating the gender differential at a reasonable number of years of education 12.5, which is close to the average. There are no people of either gender with close to zero years of education, and so we don't want to estimate the gender differential at education equals zero

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Divide.$$\frac{7.22}{3.8}$$

Subtract.$$\begin{array}{r}805 \\-147 \\\hline\end{array…

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