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      name:  <unnamed>

       log:  C:\Users\Michael\Documents\newer web pages\soc_meth_proj3\fall_2016_logs\class6.log

  log type:  text

 opened on:  12 Oct 2016, 10:12:46

 

. use "C:\Users\Michael\Desktop\cps_mar_2000_new_unchanged.dta", clear

 

 

 

 

. codebook occ1990, tab(1000)

 

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occ1990                                                                                 Occupation, 1990 basis

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

 

                  type:  numeric (int)

                 label:  occ1990lbl

 

                 range:  [4,999]                      units:  1

         unique values:  379                      missing .:  0/133710

 

            tabulation:  Freq.   Numeric  Label

                            14         4  Chief executives and public

                                          administrators

                           362         7  Financial managers

                           125         8  Human resources and labor

                                          relations managers

                           450        13  Managers and specialists in

                                          marketing, advertising, and

                                          public relations

                           443        14  Managers in education and

                                          related fields

                           374        15  Managers of medicine and health

                                          occupations

                           747        17  Managers of food-serving and

                                          lodging establishments

                           279        18  Managers of properties and real

                                          estate

--Break--

r(1);

 

*Because there are so many occupations, it is easier in HW2 to generate the dummy variables by hand as follows. For a comparison between two occupations, you need one dummy variable. For a comparison among 3 occupations, you need two dummy variables. With k categories you will always have k-1 dummy variables, because the constant term absorbs the comparison, or first category.

 

 

. gen byte nurses=0

 

. replace nurses=1 if occ1990==95

(966 real changes made)

 

. gen byte lawyers=0

 

. replace lawyers=1 if occ1990==178

(441 real changes made)

 

. regress inctot nurses lawyers

 

      Source |       SS       df       MS              Number of obs =  103226

-------------+------------------------------           F(  2,103223) = 1294.98

       Model |  2.5972e+12     2  1.2986e+12           Prob > F      =  0.0000

    Residual |  1.0351e+14103223  1.0028e+09           R-squared     =  0.0245

-------------+------------------------------           Adj R-squared =  0.0245

       Total |  1.0611e+14103225  1.0279e+09           Root MSE      =   31667

 

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

      inctot |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

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

      nurses |   15233.13    1023.69    14.88   0.000     13226.71    17239.55

     lawyers |   73688.55   1511.213    48.76   0.000     70726.59    76650.51

       _cons |   25554.04   99.24125   257.49   0.000     25359.52    25748.55

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

 

*If we don’t limit ourselves to only nurses, lawyers, and sociologists, the n is much larger, and the constant in the above model is the actual average inctot for everyone except the nurses and lawyers.

 

. regress inctot nurses lawyers if occ1990==178| occ1990==95| occ1990==125

 

      Source |       SS       df       MS              Number of obs =    1413

-------------+------------------------------           F(  2,  1410) =  262.68

       Model |  1.0359e+12     2  5.1795e+11           Prob > F      =  0.0000

    Residual |  2.7802e+12  1410  1.9718e+09           R-squared     =  0.2715

-------------+------------------------------           Adj R-squared =  0.2704

       Total |  3.8161e+12  1412  2.7026e+09           Root MSE      =   44405

 

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

      inctot |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

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

      nurses |  -3576.166   18184.46    -0.20   0.844    -39247.68    32095.35

     lawyers |   54879.25   18251.16     3.01   0.003     19076.91    90681.59

       _cons |   44363.33   18128.25     2.45   0.015     8802.086    79924.58

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

 

* If we limit ourselves to the 3 occupations above, notice that the constant is the actual income of sociologists, and the nurses and lawyers are compared to sociologists.

 

. table occ1990 if occ1990==178| occ1990==95| occ1990==125, contents(freq mean inctot)

 

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

Occupation, 1990      |

basis                 |        Freq.  mean(inctot)

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

    Registered nurses |          966    40787.1677

Sociology instructors |            6   44363.33333

              Lawyers |          441   99242.58277

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

 

. display 40787-44363

-3576

 

 

 

. exit, clear