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Question: Use gss2006_ chapter8.dta. Imagine that you heard somebody at the bar say that there was no reason to provide more educational opportunities for women because so many of them just stay at home anyway. Smack him. Then run some tests to see if there is any merit to this claim. Describe the variables educ and hrs 1. Do a correlation and regression of hour worked in the last week on years of education. Next, to separate analyses for men and women. Interpret the correlation and the slope for the overall sample, and then for women and men separately. Substantively, what does this say about women and education? Does this mean that, as that jerk in the bar said, we shouldn’t provide educational opportunities for women?

Solution: The following descriptive statistics are obtained:

Then, the following scatterplot is constructed:

The graph above suggests that there is not a significant association between EDUC and HRS. The following regression results are obtained:

The regression model is significant, F(1, 6385) = 44.86, p < .001. But the amount of explained variation is 0.7%, which is extremely small. This indicates that even though the association is significant, it is not of practical significance. The model is

HRS1 = 36.41293 + 0.4120023*EDUC

Now we do it by gender

Both of the linear regression models is significant, for males and females, but still, the amount of explained variation in HRS1 is extremely low in both cases (0.46% and 1.49% respectively).

The correlation for males is r = 0.0681, whereas the correlation for females is r = 0.1220. For males, an extra year of education brings extra 0.3189 hours of work, on average, whereas, for females, an extra year of education brings extra 0.6076 hours of work, on average.