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

      name:  <unnamed>

       log:  C:\Documents and Settings\Michael Rosenfeld\My Documents\newer web pages\soc_

> meth_proj3\2010_logs\first_class.log

  log type:  text

 opened on:  26 Jan 2010, 14:23:02

 

. set mem 200m

 

* the first thing you are going to need to do is expand the memory.

 

 

Current memory allocation

 

                    current                                 memory usage

    settable          value     description                 (1M = 1024k)

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

    set maxvar         5000     max. variables allowed           1.909M

    set memory          200M    max. data space                200.000M

    set matsize         400     max. RHS vars in models          1.254M

                                                            -----------

                                                               203.163M

 

. use "C:\Documents and Settings\Michael Rosenfeld\Desktop\cps_mar_2000_new.dta", clear

 

. describe

 

Contains data from C:\Documents and Settings\Michael Rosenfeld\Desktop\cps_mar_2000_new.dt

> a

  obs:       133,710                         

 vars:            55                          1 Feb 2009 13:36

 size:    15,109,230 (92.8% of memory free)

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

              storage  display     value

variable name   type   format      label      variable label

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

year            int    %8.0g       yearlbl    Survey year

serial          long   %12.0g      seriallbl

                                              Household serial number

hhwt            float  %9.0g       hhwtlbl    Household weight

region          byte   %27.0g      regionlbl

                                              Region and division

statefip        byte   %57.0g      statefiplbl

                                              State (FIPS code)

metro           byte   %27.0g      metrolbl   Metropolitan central city status

metarea         int    %50.0g      metarealbl

                                              Metropolitan area

ownershp        byte   %21.0g      ownershplbl

                                              Ownership of dwelling

hhincome        long   %12.0g      hhincomelbl

                                              Total household income

pubhous         byte   %8.0g       pubhouslbl

                                              Living in public housing

foodstmp        byte   %8.0g       foodstmplbl

                                              Food stamp recipiency

pernum          byte   %8.0g       pernumlbl

                                              Person number in sample unit

perwt           float  %9.0g       perwtlbl   Person weight

momloc          byte   %8.0g       momloclbl

                                              Mother's location in the household

poploc          byte   %8.0g       poploclbl

                                              Father's location in the household

sploc           byte   %8.0g       sploclbl   Spouse's location in household

famsize         byte   %25.0g      famsizelbl

                                              Number of own family members in hh

nchild          byte   %18.0g      nchildlbl

                                              Number of own children in household

nchlt5          byte   %23.0g      nchlt5lbl

                                              Number of own children under age 5 in hh

nsibs           byte   %18.0g      nsibslbl   Number of own siblings in household

relate          int    %34.0g      relatelbl

                                              Relationship to household head

age             byte   %19.0g      agelbl     Age

sex             byte   %8.0g       sexlbl     Sex

race            int    %37.0g      racelbl    Race

marst           byte   %23.0g      marstlbl   Marital status

popstat         byte   %14.0g      popstatlbl

                                              Adult civilian, armed forces, or child

bpl             long   %27.0g      bpllbl     Birthplace

yrimmig         int    %11.0g      yrimmiglbl

                                              Year of immigration

citizen         byte   %31.0g      citizenlbl

                                              Citizenship status

mbpl            long   %27.0g      mbpllbl    Mother's birthplace

fbpl            long   %27.0g      fbpllbl    Father's birthplace

hispan          int    %29.0g      hispanlbl

                                              Hispanic origin

educ99          byte   %38.0g      educ99lbl

                                              Educational attainment, 1990

educrec         byte   %23.0g      educreclbl

                                              Educational attainment recode

schlcoll        byte   %45.0g      schlcolllbl

                                              School or college attendance

empstat         byte   %30.0g      empstatlbl

                                              Employment status

occ1990         int    %78.0g      occ1990lbl

                                              Occupation, 1990 basis

wkswork1        byte   %8.0g       wkswork1lbl

                                              Weeks worked last year

hrswork         byte   %8.0g       hrsworklbl

                                              Hours worked last week

uhrswork        byte   %13.0g      uhrsworklbl

                                              Usual hours worked per week (last yr)

hourwage        int    %8.0g       hourwagelbl

                                              Hourly wage

union           byte   %33.0g      unionlbl   Union membership

inctot          long   %12.0g                 Total personal income

incwage         long   %12.0g                 Wage and salary income

incss           long   %12.0g                 Social Security income

incwelfr        long   %12.0g                 Welfare (public assistance) income

vetstat         byte   %10.0g      vetstatlbl

                                              Veteran status

vetlast         byte   %26.0g      vetlastlbl

                                              Veteran's most recent period of service

disabwrk        byte   %34.0g      disabwrklbl

                                              Work disability

health          byte   %9.0g       healthlbl

                                              Health status

inclugh         byte   %8.0g       inclughlbl

                                              Included in employer group health plan last

                                                year

himcaid         byte   %8.0g       himcaidlbl

                                              Covered by Medicaid last year

ftotval         double %10.0g      ftotvallbl

                                              Total family income

perwt_rounded   float  %9.0g                  integer perwt, negative values recoded to 0

yrsed           float  %9.0g                  based on educrec

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

Sorted by:  race

 

. tabulate sex

 

        Sex |      Freq.     Percent        Cum.

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

       Male |     64,791       48.46       48.46

     Female |     68,919       51.54      100.00

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

      Total |    133,710      100.00

 

. tabulate race

 

                                 Race |      Freq.     Percent        Cum.

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

                                White |    113,475       84.87       84.87

                          Black/Negro |     13,626       10.19       95.06

         American Indian/Aleut/Eskimo |      1,894        1.42       96.47

            Asian or Pacific Islander |      4,715        3.53      100.00

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

                                Total |    133,710      100.00

 

. tabulate race [fweight= perwt_rounded]

 

                                 Race |      Freq.     Percent        Cum.

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

                                White |224,806,952       82.02       82.02

                          Black/Negro | 35,508,668       12.96       94.98

         American Indian/Aleut/Eskimo |  2,847,473        1.04       96.01

            Asian or Pacific Islander | 10,924,728        3.99      100.00

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

                                Total |274,087,821      100.00

 

. *There is a difference between the weighted and unweighted percentages. For instance, bl

> acks make up 10.19% of the unweighted but 12.96% of the weighted data.

 

.

. summarize  perwt_rounded

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

perwt_roun~d |    133710    2049.868    1083.244         93      14281

 

. *key to understand that tabulate is for categorical variables like race, and summarize is for continuous variables like weight and income, where every respondent might have a different value.

 

. *If you mix these up, you will get nonsensical results, for example

 

. tabulate  perwt_rounded

 

    integer |

     perwt, |

   negative |

     values |

 recoded to |

          0 |      Freq.     Percent        Cum.

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

         93 |          3        0.00        0.00

         96 |          1        0.00        0.00

         98 |          1        0.00        0.00

         99 |          3        0.00        0.01

        103 |          2        0.00        0.01

        104 |          1        0.00        0.01

        105 |          3        0.00        0.01

        109 |          1        0.00        0.01

        112 |          1        0.00        0.01

        115 |          1        0.00        0.01

        116 |          2        0.00        0.01

        117 |          2        0.00        0.02

        118 |          4        0.00        0.02

        120 |          3        0.00        0.02

        121 |          7        0.01        0.03

        122 |          1        0.00        0.03

        123 |          4        0.00        0.03

        124 |          1        0.00        0.03

        126 |          5        0.00        0.03

        128 |          4        0.00        0.04

        129 |          2        0.00        0.04

        131 |          3        0.00        0.04

        132 |          5        0.00        0.04

        133 |          2        0.00        0.05

        134 |          6        0.00        0.05

        135 |          1        0.00        0.05

        136 |          4        0.00        0.05

--Break--

r(1);

 

. *we didn't want that table anyway

 

. *another wrong thing to do is to summarize a categorical variable

 

. summarize  race

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

        race |    133710    132.4183    105.8387        100        650

 

. *This makes no sense (how can you take the average of race?), but it is possible to do so watch out

 

. tabulate race

 

                                 Race |      Freq.     Percent        Cum.

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

                                White |    113,475       84.87       84.87

                          Black/Negro |     13,626       10.19       95.06

         American Indian/Aleut/Eskimo |      1,894        1.42       96.47

            Asian or Pacific Islander |      4,715        3.53      100.00

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

                                Total |    133,710      100.00

 

. tabulate race, nolabel

 

       Race |      Freq.     Percent        Cum.

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

        100 |    113,475       84.87       84.87

        200 |     13,626       10.19       95.06

        300 |      1,894        1.42       96.47

        650 |      4,715        3.53      100.00

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

      Total |    133,710      100.00

 

* Without the labels, we can see that race is stored as a number. In fact all the variables are stored as numbers, whereas “White” and “Black/Negro” are just labels associated with the numbers

 

. sort sex

 

. by sex: summarize  incwelfr

 

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

-> sex = Male

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |     49353    11.35025    245.3368          0      13800

 

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

-> sex = Female

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |     53873    67.43862    618.6006          0      25000

 

 

. *What if we want only the welfare income for people who actually had welfare income

 

    incwelfr |      1101    3299.837    2839.866          1      25000

 

 

. by sex: summarize  incwelfr if  incwelfr>0, detail

 

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

-> sex = Male

 

             Welfare (public assistance) income

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

      Percentiles      Smallest

 1%            4              1

 5%          113              4

10%          240             12       Obs                 188

25%        829.5             12       Sum of Wgt.         188

 

50%         2481                      Mean           2979.622

                        Largest       Std. Dev.      2644.509

75%       4337.5           8892

90%         6600          11580       Variance        6993429

95%         8400          13200       Skewness       1.260811

99%        13200          13800       Kurtosis       4.869903

 

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

-> sex = Female

 

             Welfare (public assistance) income

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

      Percentiles      Smallest

 1%           48              1

 5%          280              1

10%          480              1       Obs                1101

25%         1074             12       Sum of Wgt.        1101

 

50%         2766                      Mean           3299.837

                        Largest       Std. Dev.      2839.866

75%         4692          15600

90%         7152          19999       Variance        8064841

95%         8400          23292       Skewness       1.863679

99%        12084          25000       Kurtosis       9.951343

 

 

. by sex: summarize  incwelfr if  incwelfr>0 [fweight= perwt_rounded]

 

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

-> sex = Male

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |    357702     2897.24    2577.316          1      13800

 

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

-> sex = Female

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |      1101    3100.608    2837.588          1      25000

 

 

. *There seems to be a bit of a bug in the number of weighted observations for women here (1101 is the unweighted N).

 

. tabulate sex [fweight= perwt_rounded] if  incwelfr>0

 

        Sex |      Freq.     Percent        Cum.

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

       Male | 31,176,879       49.59       49.59

     Female | 31,688,337       50.41      100.00

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

      Total | 62,865,216      100.00

 

. tabulate sex  if  incwelfr>0 [fweight= perwt_rounded]

 

        Sex |      Freq.     Percent        Cum.

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

       Male | 31,176,879       49.59       49.59

     Female | 31,688,337       50.41      100.00

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

      Total | 62,865,216      100.00

 

. *Not sure about that. Maybe a bug...

 

*after class I tried this, below, to get rid of the missing values (represented by the period)

 

. tabulate sex if incwelfr>0 & incwelfr!=. [fweight= perwt_rounded]

        Sex |      Freq.     Percent        Cum.
------------+-----------------------------------
       Male |    357,702       14.02       14.02
     Female |  2,193,544       85.98      100.00
------------+-----------------------------------
      Total |  2,551,246      100.00

 

. by sex: summarize  incwage

 

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

-> sex = Male

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

     incwage |     49353     25943.8    34862.55          0     364302

 

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

-> sex = Female

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

     incwage |     53873    13525.17    20172.65          0     333564

 

 

. *income is topcoded

 

. *another example of topcoding is age

 

. tabulate age

 

                Age |      Freq.     Percent        Cum.

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

       Under 1 year |      1,713        1.28        1.28

                  1 |      1,932        1.44        2.73

                  2 |      1,950        1.46        4.18

                  3 |      1,939        1.45        5.63

                  4 |      1,965        1.47        7.10

                  5 |      1,998        1.49        8.60

                  6 |      2,059        1.54       10.14

                  7 |      2,176        1.63       11.77

                  8 |      2,163        1.62       13.38

                  9 |      2,243        1.68       15.06

                 10 |      2,202        1.65       16.71

                 11 |      2,083        1.56       18.27

                 12 |      2,035        1.52       19.79

                 13 |      2,047        1.53       21.32

                 14 |      1,979        1.48       22.80

                 15 |      2,046        1.53       24.33

                 16 |      1,965        1.47       25.80

                 17 |      1,998        1.49       27.29

                 18 |      1,847        1.38       28.67

                 19 |      1,826        1.37       30.04

                 20 |      1,722        1.29       31.33

                 21 |      1,687        1.26       32.59

                 22 |      1,638        1.23       33.81

                 23 |      1,622        1.21       35.03

                 24 |      1,662        1.24       36.27

                 25 |      1,666        1.25       37.52

                 26 |      1,640        1.23       38.74

                 27 |      1,726        1.29       40.03

                 28 |      1,801        1.35       41.38

                 29 |      1,995        1.49       42.87

                 30 |      1,907        1.43       44.30

                 31 |      1,991        1.49       45.79

                 32 |      1,890        1.41       47.20

                 33 |      1,898        1.42       48.62

                 34 |      2,024        1.51       50.13

                 35 |      2,134        1.60       51.73

                 36 |      2,123        1.59       53.32

                 37 |      2,099        1.57       54.89

                 38 |      2,064        1.54       56.43

                 39 |      2,228        1.67       58.10

                 40 |      2,190        1.64       59.74

                 41 |      2,115        1.58       61.32

                 42 |      2,137        1.60       62.92

                 43 |      2,091        1.56       64.48

                 44 |      2,114        1.58       66.06

                 45 |      2,118        1.58       67.64

                 46 |      1,939        1.45       69.10

                 47 |      1,957        1.46       70.56

                 48 |      1,827        1.37       71.93

                 49 |      1,767        1.32       73.25

                 50 |      1,865        1.39       74.64

                 51 |      1,802        1.35       75.99

                 52 |      1,825        1.36       77.35

                 53 |      1,695        1.27       78.62

                 54 |      1,301        0.97       79.59

                 55 |      1,323        0.99       80.58

                 56 |      1,324        0.99       81.57

                 57 |      1,304        0.98       82.55

                 58 |      1,128        0.84       83.39

                 59 |      1,129        0.84       84.24

                 60 |      1,154        0.86       85.10

                 61 |      1,051        0.79       85.89

                 62 |      1,073        0.80       86.69

                 63 |        938        0.70       87.39

                 64 |        952        0.71       88.10

                 65 |      1,014        0.76       88.86

                 66 |        869        0.65       89.51

                 67 |        926        0.69       90.20

                 68 |        908        0.68       90.88

                 69 |        904        0.68       91.56

                 70 |        913        0.68       92.24

                 71 |        885        0.66       92.90

                 72 |        770        0.58       93.48

                 73 |        797        0.60       94.08

                 74 |        814        0.61       94.68

                 75 |        796        0.60       95.28

                 76 |        704        0.53       95.81

                 77 |        646        0.48       96.29

                 78 |        687        0.51       96.80

                 79 |        602        0.45       97.25

                 80 |        514        0.38       97.64

                 81 |        476        0.36       97.99

                 82 |        425        0.32       98.31

                 83 |        427        0.32       98.63

                 84 |        325        0.24       98.87

                 85 |        306        0.23       99.10

                 86 |        248        0.19       99.29

                 87 |        209        0.16       99.44

                 88 |        172        0.13       99.57

                 89 |        155        0.12       99.69

90 (90+, 1988-2002) |        416        0.31      100.00

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

              Total |    133,710      100.00

 

. *topcoding (here age topcoded to 90) is for purposes of confidentiality

 

. by sex: summarize  incwage if incwage>0

 

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

-> sex = Male

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

     incwage |     34897    36690.96     36394.4          1     364302

 

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

-> sex = Female

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

     incwage |     32504    22416.97    21797.73          1     333564

 

 

. *stata has a built-in calculator

 

. display 36690.96-22426.97

14263.99

 

 

. by sex: summarize  incwage if incwage>0 & age>19 & age<40

 

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

-> sex = Male

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

     incwage |     16234    31833.01    29178.08          5     362302

 

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

-> sex = Female

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

     incwage |     14777     21108.5    19573.02          1     333564

 

 

 

. display 31833.01-21108.5

10724.51

 

. ttest incwage if incwage>0 & age>19 & age<40

by() option required

r(100);

 

. ttest incwage if incwage>0 & age>19 & age<40, by(sex)

 

Two-sample t test with equal variances

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

   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]

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

    Male |   16234    31833.01    229.0045    29178.08    31384.13    32281.88

  Female |   14777     21108.5    161.0144    19573.02    20792.89     21424.1

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

combined |   31011    26722.69    145.5435    25630.13    26437.42    27007.96

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

    diff |            10724.51    284.9786                10165.94    11283.08

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

    diff = mean(Male) - mean(Female)                              t =  37.6327

Ho: diff = 0                                     degrees of freedom =    31009

 

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0

 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

 

. *This is one way of answering the question of whether the difference between men and women in earnings is real, or could be explained by random chance. The T-test says it is a big difference, very statistically significant.

 

. tabulate sex

 

        Sex |      Freq.     Percent        Cum.

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

       Male |     64,791       48.46       48.46

     Female |     68,919       51.54      100.00

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

      Total |    133,710      100.00

 

. tabulate sex, nolabel

 

        Sex |      Freq.     Percent        Cum.

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

          1 |     64,791       48.46       48.46

          2 |     68,919       51.54      100.00

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

      Total |    133,710      100.00

 

. gen male=0

 

*gen is short for generate, which creates a new variable

 

. replace male=1 if sex==1

(64791 real changes made)

 

. tabulate sex male

 

           |         male

       Sex |         0          1 |     Total

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

      Male |         0     64,791 |    64,791

    Female |    68,919          0 |    68,919

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

     Total |    68,919     64,791 |   133,710

 

 

. label define male_label 0 "female" 1 "male"

 

. label values male male_label

 

. tabulate sex male

 

           |         male

       Sex |    female       male |     Total

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

      Male |         0     64,791 |    64,791

    Female |    68,919          0 |    68,919

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

     Total |    68,919     64,791 |   133,710

 

 

. regress incwage male if incwage>0 & age>19 & age<40

 

      Source |       SS       df       MS              Number of obs =   31011

-------------+------------------------------           F(  1, 31009) = 1416.22

       Model |  8.8972e+11     1  8.8972e+11           Prob > F      =  0.0000

    Residual |  1.9481e+13 31009   628232693           R-squared     =  0.0437

-------------+------------------------------           Adj R-squared =  0.0436

       Total |  2.0371e+13 31010   656903664           Root MSE      =   25065

 

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

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

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

        male |   10724.51   284.9786    37.63   0.000     10165.94    11283.08

       _cons |    21108.5   206.1898   102.37   0.000     20704.36    21512.64

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

 

. *simple linear regression is just another way of asking the question about whether the 10K difference income between men and women is greater than we could expect by chance. The answer is yes.

 

. regress incwage male if incwage>0 & age>19 & age<40 [iweight= perwt_rounded]

 

      Source |       SS       df       MS              Number of obs =65026619

-------------+------------------------------           F(  1,65026617) =       .

       Model |  1.9498e+15     1  1.9498e+15           Prob > F      =  0.0000

    Residual |  4.2925e+1665026617   660120490           R-squared     =  0.0434

-------------+------------------------------           Adj R-squared =  0.0434

       Total |  4.4875e+1665026618   690105438           Root MSE      =   25693

 

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

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

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

        male |   10966.31   6.380798  1718.64   0.000     10953.81    10978.82

       _cons |   21614.93   4.626849  4671.63   0.000     21605.86       21624

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

 

. regress incwage male if incwage>0 & age>19 & age<40 [aweight= perwt_rounded]

(sum of wgt is   6.5027e+07)

 

      Source |       SS       df       MS              Number of obs =   31011

-------------+------------------------------           F(  1, 31009) = 1408.54

       Model |  9.2986e+11     1  9.2986e+11           Prob > F      =  0.0000

    Residual |  2.0471e+13 31009   660163046           R-squared     =  0.0434

-------------+------------------------------           Adj R-squared =  0.0434

       Total |  2.1401e+13 31010   690127682           Root MSE      =   25694

 

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

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

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

        male |   10966.31   292.1976    37.53   0.000     10393.59    11539.03

       _cons |   21614.93   211.8786   102.02   0.000     21199.64    22030.22

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

 

. *Note: The number of observations in the dataset that have positive incwage and meet the age criteria (age 20-39) is 31,101. The unweighted regression at top gives us a difference in income between men and women of 10,724, which we have seen before, and a T-statistic of 37.63. If we use the weights as iweights or fweights, as we do in the second regression, the number of observations is 2000X greater, the difference is income between men and women is only slightly changed, but the T statistic is much bigger (1718) because we told Stata that there are really 65 million instead of 31 thousand people in this little experiment. The third and bottom panel uses aweights, which rescales the weights to average 1, but still uses the weights. The result is that the coefficient reflects the weights, but the number of observations is still 31 thousand, which is the right number when doing statistical tests.

 

. save "C:\Documents and Settings\Michael Rosenfeld\Desktop\cps_mar_2000_new.dta", replace

file C:\Documents and Settings\Michael Rosenfeld\Desktop\cps_mar_2000_new.dta saved

 

* I saved because I created a new variable, the male variable. Like use and log, save is best done through the menus.

 

. exit, clear

 

* exit is another menu function