I was given this problem:

PART A:

Consider the following model of wagedetermination:

wage= 0+1educ+2exper+3married+?

where:    wage = hourly earnings indollars

    educ = years of education

    exper = years of experience

    married = dummy equal to 1 ifmarried, 0 otherwise

Using data from the file ps2.dta, which contains wagedata for a number of workers from across the United States,estimate the model shown above by OLS using the regress command inStata. As always, be sure to include your Stata output (show theregression command used and the complete regression output).

Why are we unable to determine which of the includedvariables is the most important determinant of wages by simplylooking at the size (and perhaps significance) of the estimatedcoefficients (even if we were confident that these estimatesreflected unbiased causal impacts)?

My answer to PART A:

. regress wage educ exper married

     Source |      SS df      MS Number of obs  =526

-------------+----------------------------------  F(3, 522) = 54.97

      Model | 1719.00074         3573.000246 Prob > F        =0.0000

   Residual |  5441.41355    522 10.4241639 R-squared      = 0.2401

-------------+----------------------------------  Adj R-squared = 0.2357

      Total | 7160.41429       525 13.6388844Root MSE        = 3.2286

------------------------------------------------------------------------------

       wage |   Coef. Std. Err.      tP>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

       educ |.6128507   .0542332 11.30 0.000    .5063084 .7193929

      exper |  .0568845 .0116387     4.89 0.000.0340201     .079749

    married |   .9894464.309198     3.20 0.001 .3820212   1.596872

      _cons | -3.372934   .7599027 -4.44   0.000-4.865777 -1.880091

We are unable to determine which of the independentvariables is the strongest predictor of wage because the predictorsuse different units of measurement.

Is this answer correct?

PART B:

Estimate the model again in Stata, but now include the“beta” option and explain how the additional information providedhelps to provide insight into this issue discussed in part (c). Aspart of your answer, provide a clear interpretation of the newStata output corresponding to the educ variable.  

My answer to PART B:

The “, beta” command, shows us the standardizedcoefficients and enables us to make a comparison of the independentvariables’ relationship to the dependent variable; the higher theabsolute value of the beta coefficient for each the independentvariable, the stronger predictor it is of the dependent variable.The beta coefficient shows how one unit change in the independentvariable’s standard deviation corresponds to a change in thestandard deviation of the dependent variable. From the STATAoutput, are able to see that educ has the highest beta coefficient,meaning that education is the strongest predictor of wage. Whetheror not someone is married is the weakest predictor ofwage.

regress wage educ exper married, beta

     Source |      SS df      MS Number of obs  =526

-------------+----------------------------------  F(3, 522) = 54.97

      Model | 1719.00074         3573.000246 Prob > F        =0.0000

   Residual |  5441.41355    522 10.4241639 R-squared      = 0.2401

-------------+----------------------------------  Adj R-squared = 0.2357

      Total | 7160.41429       525 13.6388844Root MSE        = 3.2286

------------------------------------------------------------------------------

       wage |   Coef. Std. Err.      tP>|t|        Beta

-------------+----------------------------------------------------------------

       educ |.6128507   .0542332 11.30 0.000                .4595065

      exper |  .0568845 .0116387     4.89 0.000    .2090517

    married |   .9894464.309198     3.20 0.001    .1308998

      _cons | -3.372934   .7599027 -4.44   0.000        .

Is my answer correct?