PART A:
Consider the following model of wage determination:
wage = β0 + β1educ + β2exper + β3married + ε
where:
wage = hourly earnings in dollars
educ = years of education
exper = years of experience
married = dummy equal to 1 if married, 0 otherwise
Using data from the file ps2.dta, which contains wage data for a number of workers from across the United States, estimate the model shown above by OLS using the regress command in Stata. As always, be sure to include your Stata output (show the regression command used and the complete regression output).
Why are we unable to determine which of the included variables is the most important determinant of wages by simply looking at the size (and perhaps significance) of the estimated coefficients (even if we were confident that these estimates reflected unbiased causal impacts)?
PART B:
Estimate the model again in Stata, but now include the "beta" option and explain how the additional information provided helps to provide insight into this issue discussed in part (c). As part of your answer, provide a clear interpretation of the new Stata output corresponding to the educ variable.