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name: <unnamed>
log: C:\Users\Michael\Documents\newer web pages\soc_meth_proj3\fall_2012_381_logs\class2.log
log type: text
opened on: 27 Sep 2012, 13:22:14
. cd "C:\Users\Michael\Documents\current class files\intro soc methods\new march 2005 incorrect year CPS for HW1"
C:\Users\Michael\Documents\current class files\intro soc methods\new march 2005 incorrect year CPS for HW1
*Let’s take a look at welfare income.
. summarize incwelfr
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incwelfr | 103226 40.62242 478.8231 0 25000
* This suggests that the average welfare cost per US adult in the CPS is $40 per year, see ipums.org for variable descriptions.
. summarize incwelfr if age <15
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incwelfr | 0
* all the incwelfr values are missing for ages under 15. If you check ipums.org you will see that the universe for incwelfr is persons age 15+, meaning persons under 15 are not asked the question, i.e. are not in the universe for the question (so their values are missing).
. by sex: summarize incwelfr if age >20 & incwelfr>0
---------------------------------------------------------------------------------------
-> sex = Male
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incwelfr | 142 3065.908 2711.885 1 13800
---------------------------------------------------------------------------------------
-> sex = Female
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incwelfr | 972 3430.868 2885.764 1 25000
* Women are more likely to receive welfare, and the average welfare income for people on welfare is about $3400 per year.
. by sex: summarize incwelfr if age >20 & incwelfr>0 [fweight= perwt_rounded]
------------------------------------------------------------------------------------
-> sex = Male
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incwelfr | 256209 2972.657 2636.861 1 13800
------------------------------------------------------------------------------------
-> sex = Female
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incwelfr | 1886278 3237.094 2906.139 1 25000
* It looks like there were 2.1 million people in the US on welfare in 2000 older than 20 years of age (there are some additional welfare recipients age 15-20).
. gen byte receives_welfare=0
. replace receives_welfare=1 if incwelfr>0 & incwelfr~=.
(1289 real changes made)
. label var receives_welfare "does respondent receive welfare"
* This above command attaches a label to the variable itself.
. label define receives_welfare_lbl 0 "no" 1 "yes"
. label val receives_welfare receives_welfare_lbl
* The above 2 commands first define labels that we want to attach to the values of receives_welfare, then we attach those labels to the values of the variable.
. tabulate receives_welfare [fweight= perwt_rounded]
does |
respondent |
receive |
welfare | Freq. Percent Cum.
------------+-----------------------------------
no |271,536,575 99.07 99.07
yes | 2,551,246 0.93 100.00
------------+-----------------------------------
Total |274,087,821 100.00
. table educrec sex if age>20 [fweight= perwt_rounded], contents(freq mean incwelfr mean receives_welfare)
--------------------------------------------------
Educational attainment | Sex
recode | Male Female
------------------------+-------------------------
None or preschool | 409,822 463,962
| 0 201.8166229
| 0 .04848
|
Grades 1, 2, 3, or 4 | 988,458 959,869
| 21.2051377 186.4335592
| .011155 .039831
|
Grades 5, 6, 7, or 8 | 4792742 5028804
| 10.72959028 119.6578288
| .005356 .032857
|
Grade 9 | 1926372 2028431
| 20.88420617 134.0259969
| .007086 .046607
|
Grade 10 | 2498378 2892776
| 22.49344775 214.192737
| .008675 .0635
|
Grade 11 | 2607008 3013104
| 23.15243145 216.6690639
| .007129 .073434
|
Grade 12 | 3.01e+07 3.47e+07
| 11.72673341 67.85343211
| .003832 .021749
|
1 to 3 years of college | 2.35e+07 2.70e+07
| 7.269855825 44.67187372
| .002034 .01304
|
4+ years of college | 2.40e+07 2.28e+07
| .3599692853 5.49143018
| .000103 .002347
--------------------------------------------------
* Along with gender predicting welfare receipt, higher education, especially college education cuts welfare receipt drastically.
. table educrec sex if age>20 [fweight= perwt_rounded], contents(freq mean incwelfr mean receives_welfare) row col
---------------------------------------------------------------
Educational attainment | Sex
recode | Male Female Total
------------------------+--------------------------------------
None or preschool | 409,822 463,962 873,784
| 0 201.8166229 107.1606301
| 0 .04848 .025742
|
Grades 1, 2, 3, or 4 | 988,458 959,869 1948327
| 21.2051377 186.4335592 102.6070993
| .011155 .039831 .025283
|
Grades 5, 6, 7, or 8 | 4792742 5028804 9821546
| 10.72959028 119.6578288 66.50276097
| .005356 .032857 .019437
|
Grade 9 | 1926372 2028431 3954803
| 20.88420617 134.0259969 78.91498944
| .007086 .046607 .027357
|
Grade 10 | 2498378 2892776 5391154
| 22.49344775 214.192737 125.3551177
| .008675 .0635 .038093
|
Grade 11 | 2607008 3013104 5620112
| 23.15243145 216.6690639 126.9022747
| .007129 .073434 .042677
|
Grade 12 | 3.01e+07 3.47e+07 6.48e+07
| 11.72673341 67.85343211 41.8001018
| .003832 .021749 .013432
|
1 to 3 years of college | 2.35e+07 2.70e+07 5.05e+07
| 7.269855825 44.67187372 27.25585651
| .002034 .01304 .007915
|
4+ years of college | 2.40e+07 2.28e+07 4.68e+07
| .3599692853 5.49143018 2.858781299
| .000103 .002347 .001196
|
Total | 9.09e+07 9.89e+07 1.90e+08
| 8.382322858 61.73025686 36.18842854
| .00282 .01907 .01129
---------------------------------------------------------------
* the row and col options add nice summary and total data to the table. Of people older than 20 years of age in the US, 1.9% of women and 0.28% of men were on welfare.
. 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: Every single one of the 133,710 cases has a race. By default, Stata leaves missing value cases out of the tables, but you can force Stata to show you the missing value cases if you invoke the “missing” option. How can there be no missing values? The census bureau must be imputing the missing values.
. summarize age
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
age | 133710 35.17964 22.21722 0 90
* And note, 90 is the highest recorded age. How can this be? Age is topcoded, to protect confidentiality of individuals with outlying values (see ipums variable documentation for confirmation of this).
. 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
. clear all
*Now on to the ingestion of the fresh ipums data:
* If you are downloading a stata file, there are no extra steps needed. If you are downloading an old fashioned ASCII file, first put the files all in one folder, and unzip the data file.
* Then cd (change directory) to the name of the directory you put the files in.
. cd "C:\Users\Michael\Documents\current class files\intro soc methods\new march 2005 incorrect year CPS for HW1"
C:\Users\Michael\Documents\current class files\intro soc methods\new march 2005 incorrect year CPS for HW1
* Then on the File menu, pick “do,” and the do-file in your folder should be listed. Pick it to run it.
. do "C:\Users\Michael\Documents\current class files\intro soc methods\new march 2005 incorrect year CPS for HW1\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 22-29 ///
> float wtfinl 30-39 ///
> byte age 40-41 ///
> byte sex 42-42 ///
> long inctot 43-48 ///
> using `"cps_00010.dat"'
.
. replace hwtsupp = hwtsupp / 10000
(210648 real changes made)
. replace wtsupp = wtsupp / 100
(210648 real changes made)
. replace wtfinl = wtfinl / 10000
(177664 real changes made)
.
. format hwtsupp %10.4f
. format wtsupp %8.2f
. format wtfinl %10.4f
.
. 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 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 values sex sex_lbl
.
. label define inctot_lbl 999999 `"999999"'
. label values inctot inctot_lbl
* Don’t forget to save your STATA file when you are done.
.
.
.
end of do-file
. clear all
. exit, clear