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

      name:  <unnamed>

       log:  C:\Users\Michael\Documents\newer web pages\soc_meth_proj3\fall_2015_381_logs\class1.log

  log type:  text

 opened on:  21 Sep 2015, 10:08:54

 

. use "C:\Users\Michael\Desktop\cps_mar_2000.dta", clear

 

* Start every Stata session with a log! Then open your dataset

 

. *class starts here

 

. 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:    14,574,390                          

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

              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

     Note:  dataset has changed since last saved

 

. 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, missing

 

                                 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

 

*Note that there are no missing values for race. Why? Because missing values get imputed by the census bureau.

 

. 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

 

* The universe of the CPS (people living in non-institutional settings in the US) had 274 million people in March 2000. Also note that percentage of people who are black in the US is a little higher than the percentage of people who are black in the CPS. Why? Because the response rate for black respondents was a little lower than average, so they were assigned higher weights.

 

. summarize perwt_rounded

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

perwt_roun~d |    133710    2049.868    1083.244         93      14281

 

* If you take 274 million and divide it by 133 thousand you get about 2 thousand. The average weight is about 2 thousand, because the CPS is a 1-in-2000 representative survey of US households.

 

. 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, nolab

 

       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

 

. codebook race

 

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

race                                                                        Race

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

 

                  type:  numeric (int)

                 label:  racelbl

 

                 range:  [100,650]                    units:  10

         unique values:  4                        missing .:  0/133710

 

            tabulation:  Freq.   Numeric  Label

                        1.1e+05      100  White

                         13626       200  Black/Negro

                          1894       300  American Indian/Aleut/Eskimo

                          4715       650  Asian or Pacific Islander

 

* Race, like the other nominal categorical variables, is coded numerically, and the text value labels are added to the numbers later.

 

. summarize age

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

         age |    133710    35.17964    22.21722          0         90

 

* What happened to the people older than 90? 90 turns out to be the topcode for age. Topcodes are used to reduce the possibility of identification of individuals. Crucially, you need to look at the ipums documentation for variables to know what the topcodes are, what the missing value codes are, what the question wording is, and so on.

 

. summarize yrsed

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

       yrsed |    103226    12.77328    3.156011          0         17

 

* If you run a tabulation, and you get fewer than 133,710 cases, you need to ask yourself where the other cases went. It turns out that children are not asked about educational attainment.

 

. tabulate age if yrsed==.

 

                Age |      Freq.     Percent        Cum.

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

       Under 1 year |      1,713        5.62        5.62

                  1 |      1,932        6.34       11.96

                  2 |      1,950        6.40       18.35

                  3 |      1,939        6.36       24.71

                  4 |      1,965        6.45       31.16

                  5 |      1,998        6.55       37.71

                  6 |      2,059        6.75       44.47

                  7 |      2,176        7.14       51.61

                  8 |      2,163        7.10       58.70

                  9 |      2,243        7.36       66.06

                 10 |      2,202        7.22       73.28

                 11 |      2,083        6.83       80.12

                 12 |      2,035        6.68       86.79

                 13 |      2,047        6.71       93.51

                 14 |      1,979        6.49      100.00

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

              Total |     30,484      100.00

 

. sort sex

 

. by sex: summarize yrsed if age>=25 & age<=34

 

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

-> sex = Male

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

       yrsed |      9027    13.31212    2.967666          0         17

 

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

-> sex = Female

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

       yrsed |      9511    13.55657    2.854472          0         17

 

* Do young women have more educational attainment than young men? In the CPS, the answer is obviously yes: 13.55>13.31. But what about in the US? Is this difference consistent with a null hypothesis that young women and young men in the US have the same educational levels? Another way of asking this question is: if young men and young women in the US had the same educational levels, what would be the probability of finding a difference as large as 0.24 years in a representative sample of 18K young people? That is the key statistical question we are going to be focusing on.

 

 

. ttest yrsed if age>=25 & age<=34, by(sex)

 

Two-sample t test with equal variances

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

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

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

    Male |    9027    13.31212    .0312351    2.967666    13.25089    13.37335

  Female |    9511    13.55657    .0292693    2.854472    13.49919    13.61394

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

combined |   18538    13.43753    .0213921    2.912627     13.3956    13.47946

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

    diff |           -.2444469    .0427623               -.3282649   -.1606289

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

    diff = mean(Male) - mean(Female)                              t =  -5.7164

Ho: diff = 0                                     degrees of freedom =    18536

 

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

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

 

* So we run the t-test, and Stata reports a T-statistic of -5.7164. Is that large or small?

 

. display 1- ttail(18536, -5.7164)

5.524e-09

 

* It turns out that the t-statistic of -5.7, is very far from zero, much further than we would expect to get by chance. The probability of getting such a large difference by chance is about 5 chances in a billion. If we double the P value (because we might ask how large was the probability of getting a result this large in either direction, that is with the men or the women in the sample having 0.24 or more years of educational advantage, the answer is 1 in 100 million. In other words, very unlikely. If our data is totally inconsistent with the null hypothesis, that generally means that we reject the null hypothesis. So the we are reasonably sure that young women in the US are more educated than young men.

 

. summarize incwelfr

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |    103226    40.62242    478.8231          0      25000

 

. summarize incwelfr if age>=15 & incwelfr>0 & incwelfr~=.

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |      1289    3253.134    2813.505          1      25000

 

. summarize incwelfr if age>=15 & incwelfr>0 & incwelfr~=. [fweight= perwt_rounded]

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

    incwelfr |   2551246    3072.095    2803.442          1      25000

 

* Here I generate a new variable, then replace the values that need to be replaced, then I add labels, and after that you should save the dataset.

 

. gen byte receives_welfare=0

 

. replace receives_welfare=1 if incwelfr>0 & incwelfr~=.

(1289 real changes made)

 

. tabulate receives_welfare

 

receives_we |

      lfare |      Freq.     Percent        Cum.

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

          0 |    132,421       99.04       99.04

          1 |      1,289        0.96      100.00

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

      Total |    133,710      100.00

 

. label define receives_welfare_lbl 0 "no" 1 "yes"

 

. label val receives_welfare receives_welfare_lbl

 

. tabulate receives_welfare

 

receives_we |

      lfare |      Freq.     Percent        Cum.

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

         no |    132,421       99.04       99.04

        yes |      1,289        0.96      100.00

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

      Total |    133,710      100.00

 

. label var receives_welfare "does respondent receive welfare"

 

. tabulate receives_welfare

 

       does |

 respondent |

    receive |

    welfare |      Freq.     Percent        Cum.

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

         no |    132,421       99.04       99.04

        yes |      1,289        0.96      100.00

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

      Total |    133,710      100.00

 

. sort receives_welfare

 

. by receives_welfare: summarize yrsed

 

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

-> receives_welfare = no

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

       yrsed |    101937    12.79583    3.153618          0         17

 

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

-> receives_welfare = yes

 

    Variable |       Obs        Mean    Std. Dev.       Min        Max

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

       yrsed |      1289     10.9903    2.817995          0         17

 

 

. table receives_welfare sex, contents(freq mean yrsed)

 

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

does      |

responden |

t receive |        Sex       

welfare   |     Male    Female

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

       no |   64,603    67,818

          | 12.80415  12.78808

          |

      yes |      188     1,101

          |    10.75  11.03133

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

 

. table receives_welfare sex, contents(freq mean yrsed)

 

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

does      |

responden |

t receive |        Sex       

welfare   |     Male    Female

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

       no |   64,603    67,818

          | 12.80415  12.78808

          |

      yes |      188     1,101

          |    10.75  11.03133

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

 

. table receives_welfare sex, contents(freq mean yrsed) row col

 

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

does      |

responden |

t receive |             Sex            

welfare   |     Male    Female     Total

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

       no |   64,603    67,818   132,421

          | 12.80415  12.78808  12.79583

          |

      yes |      188     1,101     1,289

          |    10.75  11.03133   10.9903

          |

    Total |   64,791    68,919   133,710

          | 12.79632  12.75218  12.77328

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

 

. table receives_welfare sex, contents(freq mean yrsed mean incwage) row col

 

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

does      |

responden |

t receive |                  Sex                

welfare   |        Male       Female        Total

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

       no |      64,603       67,818      132,421

          |    12.80415     12.78808     12.79583

          | 26027.96038  13735.28356  19664.13624

          |

      yes |         188        1,101        1,289

          |       10.75     11.03133      10.9903

          | 3934.324468  3453.988193  3524.044996

          |

    Total |      64,791       68,919      133,710

          |    12.79632     12.75218     12.77328

          | 25943.79926   13525.1652  19462.59227

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

 

. table receives_welfare sex if age>=20 & age<=35, contents(freq mean yrsed mean incwage) row col

 

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

does      |

responden |

t receive |                  Sex                

welfare   |        Male       Female        Total

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

       no |      14,121       14,207       28,328

          |    13.14121     13.50852     13.32542

          | 25300.65888  15972.03836   20622.1884

          |

      yes |          55          620          675

          |    11.51818     11.42016     11.42815

          | 4802.290909  3887.974194  3962.474074

          |

    Total |      14,176       14,827       29,003

          |    13.13491     13.42119     13.28126

          | 25221.12937  15466.73589   20234.4593

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

 

. tabulate incwage

 

   Wage and |

     salary |

     income |      Freq.     Percent        Cum.

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

          0 |     35,825       34.71       34.71

          1 |          7        0.01       34.71

          5 |         15        0.01       34.73

          7 |          1        0.00       34.73

          8 |          1        0.00       34.73

         10 |          1        0.00       34.73

         12 |          2        0.00       34.73

         18 |          1        0.00       34.73

         20 |         10        0.01       34.74

         21 |          2        0.00       34.74

         28 |          2        0.00       34.75

         30 |          5        0.00       34.75

         31 |          1        0.00       34.75

         34 |          4        0.00       34.76

         35 |          5        0.00       34.76

         36 |          1        0.00       34.76

         40 |          8        0.01       34.77

         44 |          1        0.00       34.77

         45 |          4        0.00       34.77

         46 |          3        0.00       34.78

--Break--

r(1);

 

* you never want to tabulate the continuous variables, unless you want to print the phone book. You can hit the red x to interrupt Stata commands that turn out to be mistakes.

 

* To ingest the new data, first put the uncompressed data file and the do file in one folder. Then set the home folder for stata to the folder with the data and the do file, using the cd command. Then run the do file.

 

. cd "C:\Users\Michael\Documents\current class files\intro soc methods\2005 data again"

C:\Users\Michael\Documents\current class files\intro soc methods\2005 data again

 

. clear all

 

. do "C:\Users\Michael\Documents\current class files\intro soc methods\2005 data again\cps_00010.do"

 

. * NOTE: You need to set the Stata working directory to the path

. * where the data file is located.

.

. set more off

 

.

. clear

 

. quietly infix             ///

>   int     year     1-4    ///

>   long    serial   5-9    ///

>   float   hwtsupp  10-19  ///

>   byte    month    20-21  ///

>   float   wtsupp   22-31  ///

>   float   wtfinl   32-41  ///

>   byte    age      42-43  ///

>   byte    sex      44-44  ///

>   double  inctot   45-52  ///

>   using `"cps_00010.dat"'

 

.

. replace hwtsupp = hwtsupp / 10000

(210648 real changes made)

 

. replace wtsupp  = wtsupp  / 10000

(210648 real changes made)

 

. replace wtfinl  = wtfinl  / 10000

(0 real changes made)

 

.

. format hwtsupp %10.4f

 

. format wtsupp  %10.4f

 

. format wtfinl  %10.4f

 

. format inctot  %8.0f

 

.

. label var year    `"Survey year"'

 

. label var serial  `"Household serial number"'

 

. label var hwtsupp `"Household weight, Supplement"'

 

. label var month   `"Month"'

 

. label var wtsupp  `"Supplement Weight"'

 

. label var wtfinl  `"Final Basic Weight"'

 

. label var age     `"Age"'

 

. label var sex     `"Sex"'

 

. label var inctot  `"Total personal income"'

 

.

. label define hwtsupp_lbl 0000000000 `"0000000000"'

 

. label values hwtsupp hwtsupp_lbl

 

.

. label define month_lbl 01 `"January"'

 

. label define month_lbl 02 `"February"', add

 

. label define month_lbl 03 `"March"', add

 

. label define month_lbl 04 `"April"', add

 

. label define month_lbl 05 `"May"', add

 

. label define month_lbl 06 `"June"', add

 

. label define month_lbl 07 `"July"', add

 

. label define month_lbl 08 `"August"', add

 

. label define month_lbl 09 `"September"', add

 

. label define month_lbl 10 `"October"', add

 

. label define month_lbl 11 `"November"', add

 

. label define month_lbl 12 `"December"', add

 

. label values month month_lbl

 

.

. label define wtfinl_lbl 0000000000 `"0"'

 

. label values wtfinl wtfinl_lbl

 

.

. label define age_lbl 00 `"Under 1 year"'

 

. label define age_lbl 01 `"1"', add

 

. label define age_lbl 02 `"2"', add

 

. label define age_lbl 03 `"3"', add

 

. label define age_lbl 04 `"4"', add

 

. label define age_lbl 05 `"5"', add

 

. label define age_lbl 06 `"6"', add

 

. label define age_lbl 07 `"7"', add

 

. label define age_lbl 08 `"8"', add

 

. label define age_lbl 09 `"9"', add

 

. label define age_lbl 10 `"10"', add

 

. label define age_lbl 11 `"11"', add

 

. label define age_lbl 12 `"12"', add

 

. label define age_lbl 13 `"13"', add

 

. label define age_lbl 14 `"14"', add

 

. label define age_lbl 15 `"15"', add

 

. label define age_lbl 16 `"16"', add

 

. label define age_lbl 17 `"17"', add

 

. label define age_lbl 18 `"18"', add

 

. label define age_lbl 19 `"19"', add

 

. label define age_lbl 20 `"20"', add

 

. label define age_lbl 21 `"21"', add

 

. label define age_lbl 22 `"22"', add

 

. label define age_lbl 23 `"23"', add

 

. label define age_lbl 24 `"24"', add

 

. label define age_lbl 25 `"25"', add

 

. label define age_lbl 26 `"26"', add

 

. label define age_lbl 27 `"27"', add

 

. label define age_lbl 28 `"28"', add

 

. label define age_lbl 29 `"29"', add

 

. label define age_lbl 30 `"30"', add

 

. label define age_lbl 31 `"31"', add

 

. label define age_lbl 32 `"32"', add

 

. label define age_lbl 33 `"33"', add

 

. label define age_lbl 34 `"34"', add

 

. label define age_lbl 35 `"35"', add

 

. label define age_lbl 36 `"36"', add

 

. label define age_lbl 37 `"37"', add

 

. label define age_lbl 38 `"38"', add

 

. label define age_lbl 39 `"39"', add

 

. label define age_lbl 40 `"40"', add

 

. label define age_lbl 41 `"41"', add

 

. label define age_lbl 42 `"42"', add

 

. label define age_lbl 43 `"43"', add

 

. label define age_lbl 44 `"44"', add

 

. label define age_lbl 45 `"45"', add

 

. label define age_lbl 46 `"46"', add

 

. label define age_lbl 47 `"47"', add

 

. label define age_lbl 48 `"48"', add

 

. label define age_lbl 49 `"49"', add

 

. label define age_lbl 50 `"50"', add

 

. label define age_lbl 51 `"51"', add

 

. label define age_lbl 52 `"52"', add

 

. label define age_lbl 53 `"53"', add

 

. label define age_lbl 54 `"54"', add

 

. label define age_lbl 55 `"55"', add

 

. label define age_lbl 56 `"56"', add

 

. label define age_lbl 57 `"57"', add

 

. label define age_lbl 58 `"58"', add

 

. label define age_lbl 59 `"59"', add

 

. label define age_lbl 60 `"60"', add

 

. label define age_lbl 61 `"61"', add

 

. label define age_lbl 62 `"62"', add

 

. label define age_lbl 63 `"63"', add

 

. label define age_lbl 64 `"64"', add

 

. label define age_lbl 65 `"65"', add

 

. label define age_lbl 66 `"66"', add

 

. label define age_lbl 67 `"67"', add

 

. label define age_lbl 68 `"68"', add

 

. label define age_lbl 69 `"69"', add

 

. label define age_lbl 70 `"70"', add

 

. label define age_lbl 71 `"71"', add

 

. label define age_lbl 72 `"72"', add

 

. label define age_lbl 73 `"73"', add

 

. label define age_lbl 74 `"74"', add

 

. label define age_lbl 75 `"75"', add

 

. label define age_lbl 76 `"76"', add

 

. label define age_lbl 77 `"77"', add

 

. label define age_lbl 78 `"78"', add

 

. label define age_lbl 79 `"79"', add

 

. label define age_lbl 80 `"80"', add

 

. label define age_lbl 81 `"81"', add

 

. label define age_lbl 82 `"82"', add

 

. label define age_lbl 83 `"83"', add

 

. label define age_lbl 84 `"84"', add

 

. label define age_lbl 85 `"85"', add

 

. label define age_lbl 86 `"86"', add

 

. label define age_lbl 87 `"87"', add

 

. label define age_lbl 88 `"88"', add

 

. label define age_lbl 89 `"89"', add

 

. label define age_lbl 90 `"90 (90+, 1988-2002)"', add

 

. label define age_lbl 91 `"91"', add

 

. label define age_lbl 92 `"92"', add

 

. label define age_lbl 93 `"93"', add

 

. label define age_lbl 94 `"94"', add

 

. label define age_lbl 95 `"95"', add

 

. label define age_lbl 96 `"96"', add

 

. label define age_lbl 97 `"97"', add

 

. label define age_lbl 98 `"98"', add

 

. label define age_lbl 99 `"99+"', add

 

. label values age age_lbl

 

.

. label define sex_lbl 1 `"Male"'

 

. label define sex_lbl 2 `"Female"', add

 

. label define sex_lbl 9 `"NIU"', add

 

. label values sex sex_lbl

 

.

. label define inctot_lbl 00999997 `"00999997"'

 

. label define inctot_lbl 99999997 `"99999997"', add

 

. label define inctot_lbl 99999999 `"99999999"', add

 

. label values inctot inctot_lbl

 

.

.

.

end of do-file

 

. log close

      name:  <unnamed>

       log:  C:\Users\Michael\Documents\newer web pages\soc_meth_proj3\fall_2015_381_logs\class1.log

  log type:  text

 closed on:  21 Sep 2015, 12:56:38

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