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name:  <unnamed>

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

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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

362         7  Financial managers

125         8  Human resources and labor

relations managers

450        13  Managers and specialists in

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

. gen byte lawyers=0

. replace lawyers=1 if occ1990==178

. 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

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

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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

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*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

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

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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

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* 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)

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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