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

      name:  <unnamed>

       log:  C:\Users\Michael\Documents\newer web pages\soc_meth_proj3\soc_180B_win2013\class1.log

  log type:  text

 opened on:  10 Jan 2013, 14:20:48

 

* The first thing you should always do when starting a Stata session is open and save a log, in .log (simple text) format. The log will continually save your typed commands and Stata output, so once you have opene the log you don’t need to worry about saving it. The dataset is a different file that would need to be saved if you make changes, such as adding variables.

 

* describe tells you what is in your dataset.

 

. describe

 

Contains data from C:\Users\Michael\Desktop\cps_mar_2000_new_unchanged.dta

  obs:       133,710                         

 vars:            55                          1 Feb 2009 13:36

 size:    15,109,230 (71.2% 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:  sex

 

 

 

. 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 [fweight= perwt_rounded]

 

        Sex |      Freq.     Percent        Cum.

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

       Male |133,932,994       48.86       48.86

     Female |140,154,827       51.14      100.00

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

      Total |274,087,821      100.00

 

* One key to keep in mind is that there are 133,710 subjects in our CPS dataset, but if we apply the weights we can see that the sample frame (the noninstitutional population of the US) had 274 million people.

 

. summarize  perwt_rounded

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

perwt_roun~d |    133710    2049.868    1083.244         93      14281

 

* The average weight in the dataset is 2050, meaning each person had about a 1-in-2050 chance of being sampled in the CPS.

 

. 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

 

* Also note that there are no missing values for race. We know this because there are exactly 133,710 subjects in the dataset, and every one of them has a race. How can this be? The answer is that the Census Bureau imputes values for some variables when respondents leave those variables blank. It is possible to figure out whose values have been imputed by looking at the data allocation flags, but we won’t be worrying about those in this class.

 

. tabulate race [aweight= perwt_rounded]

 

                                 Race |      Freq.     Percent        Cum.

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

                                White |    109,669       82.02       82.02

                          Black/Negro | 17,322.419       12.96       94.98

         American Indian/Aleut/Eskimo | 1,389.1008        1.04       96.01

            Asian or Pacific Islander | 5,329.4793        3.99      100.00

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

                                Total |    133,710      100.00

 

* In the weighted data, the black percentage of the population is higher, because blacks are more urban and have lower response rates to the CPS, so blacks have higher average weight. There were 35.5 million blacks in the (noninstitutional) US in March, 2000.

 

. 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

 

. 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

 

* Another thing to keep in mind is that all the variables, including categorical variables like race, are stored as numbers. The “white” and “Black/Negro” labels are just added on to the dataset after the fact. The fact that race is stored as a number means you COULD summarize race but you SHOULD NOT because the numbers don’t mean anything. Please be careful to distinguish between variables that have units which you can take the average of (like years of education, or dollars of income) versus variables where the numbers are just placeholders for categories.

 

* Summarize is for continuous variables like income, not categorical variables like race. So please do NOT do this:

 

. summarize race

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

        race |    133710    132.4183    105.8387        100        650

 

 

* For income, however, summarize makes sense.

 

. summarize incwelfr

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |    103226    40.62242    478.8231          0      25000

 

* Here we are averaging the 1999 welfare income over all persons in the dataset, and we get an answer of $40.62. Why is it so low?

 

* Well, if we look at the detail, we see that more than 95% percent of all people report zero welfare income for 1999.

 

. summarize incwelfr, detail

 

             Welfare (public assistance) income

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

      Percentiles      Smallest

 1%            0              0

 5%            0              0

10%            0              0       Obs              103226

25%            0              0       Sum of Wgt.      103226

 

50%            0                      Mean           40.62242

                        Largest       Std. Dev.      478.8231

75%            0          15600

90%            0          19999       Variance       229271.5

95%            0          23292       Skewness       16.98146

99%          804          25000       Kurtosis       403.6187

 

* The average welfare income for people who received welfare was $3253, which sounds more reasonable.

 

 

. summarize incwelfr if incwelfr>0

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |      1289    3253.134    2813.505          1      25000

 

 

 

. tabulate age, nolab

 

        Age |      Freq.     Percent        Cum.

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

          0 |      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 |        416        0.31      100.00

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

      Total |    133,710      100.00

 

* When we tabulate age, we find that the highest value is 90. Where did the really old people go? The answer, if you look at the ipums documentation for variable age, is that 90 is the top code. Everyone who was older than 90 got recoded to 90, to help preserve respondent confidentiality.

 

. 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

 

* Just as summarize is for continuous variables (variables it makes sense to take the average of), tabulate is for categorical variables, that is variables with just a few categories. If you tabulate a continuous variable, you will get one row for every value, and that is almost certainly not what you want, so hit the little red stop sign at the top of your Stata window, and interrupt the process.

 

 

. tabulate incwelfr

 

    Welfare |

    (public |

assistance) |

     income |      Freq.     Percent        Cum.

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

          0 |    101,937       98.75       98.75

          1 |          4        0.00       98.76

          4 |          1        0.00       98.76

         12 |          5        0.00       98.76

         26 |          5        0.00       98.77

         30 |          1        0.00       98.77

         36 |          1        0.00       98.77

         40 |          1        0.00       98.77

         45 |          1        0.00       98.77

         48 |          1        0.00       98.77

         52 |          1        0.00       98.77

         53 |          1        0.00       98.77

         71 |          2        0.00       98.77

         75 |          2        0.00       98.78

         88 |          1        0.00       98.78

         98 |          1        0.00       98.78

        100 |          7        0.01       98.79

        105 |          1        0.00       98.79

        106 |          1        0.00       98.79

        113 |          1        0.00       98.79

        117 |          1        0.00       98.79

        120 |          3        0.00       98.79

        129 |          1        0.00       98.79

--Break--

r(1);

 

* How does welfare look by race? First sort race, and then summarize by race.

 

. sort race

 

. by race: summarize incwelfr

 

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

-> race = White

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |     88334    31.34195    425.1302          0      25000

 

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

-> race = Black/Negro

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |      9916    108.1088    754.8848          0      23292

 

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

-> race = American Indian/Aleut/Eskimo

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |      1320    120.6235    748.9787          0      12816

 

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

-> race = Asian or Pacific Islander

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |      3656     52.9267    584.7742          0      13200

 

* The above shows welfare income by race, including the zeros. Below excludes the zeros.

 

 

. by race: summarize incwelfr if incwelfr>0

 

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

-> race = White

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |       839    3299.833    2872.782          1      25000

 

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

-> race = Black/Negro

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |       348     3080.48    2664.567         26      23292

 

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

-> race = American Indian/Aleut/Eskimo

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |        57    2793.386    2369.272          1      12816

 

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

-> race = Asian or Pacific Islander

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |        45        4300    3119.071          1      13200

 

 

 

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

 

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

-> race = White

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |   1488839    3110.566    2899.053          1      25000

 

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

-> race = Black/Negro

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |    901251    2896.298    2592.647         26      23292

 

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

-> race = American Indian/Aleut/Eskimo

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |     74860    3202.088    3061.035          1      12816

 

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

-> race = Asian or Pacific Islander

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |     86296     4131.57    2745.261          1      13200

 

* What the above, using the fweights, tells us is that there were 1.49 million white people and 901 thousand black people with welfare income in the US in 1999.

 

 

. log close

      name:  <unnamed>

       log:  C:\Users\Michael\Documents\newer web pages\soc_meth_proj3\soc_180B

> _win2013\class1.log

  log type:  text

 closed on:  10 Jan 2013, 15:32:49

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