-------------------------------------------------------------------------------------------
[STATA commands and my notes in
Bold]
log: C:\AAA Miker Files\newer web
pages\soc_meth_proj3\first class_2003.log
log type: text
opened on:
. use "C:\AAA Miker Files\newer web
pages\soc_meth_proj3\cps_y2k_numeric.dta", clear
[opening
the log and opening the dataset are best performed from the menus]
. describe
Contains
data from C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta
obs: 133,710
vars: 39
size: 9,493,410 (9.4% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format
label variable label
-------------------------------------------------------------------------------
phseq str5 %9s household sequence number
p2
pernum byte %8.0g
age byte %8.0g p15
maritl byte %26.0g
marlbl Marital Status p17
sex byte %8.0g
sexnm p20
vet byte %22.0g
vetnm veteran status p21
hga byte %8.0g Educational Attainment p22
race byte %11.0g
racenm p25
reorigin byte %8.0g Hispanic Origin p27
hrs1 byte %8.0g hours worked last week p76
clswkr byte %32.0g
cwrknm sector of worker p109
grswk int %9.0g gross weekly wages p135
unmem byte %13.0g
unnm labor union member p139
lfsr byte %28.0g
lfsrnm labor force status p145
ernval float %9.0g
main job last year
earnings p228
ssval long %12.0g last year soc security
payments
p291
pawval int %12.0g last year welfare payments
p305
wgt2 int %9.0g rounded weight based on p50
ernval2 float %9.0g main job earnings, losses
recoded to zero
htype byte %37.0g
htpnm household type h25
state byte %8.0g HG-ST60, or simply state of
residence h40
hpmsasz byte %8.0g metropolitan area size h56
hcccr byte %8.0g residence in central city h58
frelu18 byte %8.0g number of kids in fam under
18
f29
povll byte %8.0g ratio of fam income to poverty
level f38
fwsval float %9.0g family income f48
famwgt2 int %8.0g adjusted family weight f233
yrsed
float %9.0g years of education, from hga
citizen byte %33.0g
citnm citizenship p733
health byte %11.0g
hlthnm self reported health
status p800
occ int %8.0g occupation P 106
ptotr byte %8.0g total person income
categories
P466
penatvty int %8.0g country of birth P 722,
Appendix H
pemntvty int
%8.0g Mother's
country of birth,
P725, appendix H
pefntvty int %8.0g Father's country of birth,
P728, appendix H
peinusyr byte %8.0g time of immigration, P 731
pxnatvty byte %8.0g allocation flag for country
of
birth P 734
hgmsac int %8.0g
metropolitan area
code, h44,
appendix E
pppos2 byte %8.0g family sequence number within
each household p46
-------------------------------------------------------------------------------
Sorted
by: race
. tabulate
sex
p20 | Freq. Percent Cum.
------------+-----------------------------------
male | 64791 48.46 48.46
female | 68919 51.54 100.00
------------+-----------------------------------
Total | 133710
100.00
. tabulate
race
p25 | Freq. Percent Cum.
------------+-----------------------------------
White | 113475 84.87 84.87
Black | 13626 10.19 95.06
Amer
Indian | 1894 1.42 96.47
Asian | 4715 3.53 100.00
------------+-----------------------------------
Total | 133710
100.00
. tabulate
race [fweight=wgt2]
p25 | Freq. Percent Cum.
------------+-----------------------------------
White | 224256269 82.07 82.07
Black |
35370557 12.95
95.02
Amer
Indian | 2837831 1.04 96.06
Asian |
10769164 3.94 100.00
------------+-----------------------------------
Total | 273233821 100.00
[Note the difference between
weighted and unweighted data]
. summarize
pawval
Variable | Obs
Mean Std. Dev. Min Max
-------------+-----------------------------------------------------
pawval | 133710
31.36108 421.0593 0
25000
[$31 dollars per person doesn't
seem like a lot of money, but then most people get zero]
. summarize
pawval, detail
last
year welfare payments p305
-------------------------------------------------------------
Percentiles Smallest
1%
0 0
5%
0 0
10% 0 0 Obs 133710
25%
0 0 Sum of Wgt. 133710
50% 0 Mean 31.36108
Largest Std. Dev. 421.0593
75% 0 15600
90% 0 19999 Variance 177290.9
95% 0 23292 Skewness 19.34523
99% 0 25000 Kurtosis 522.8002
.[99% of the population gets zero in welfare]
. summarize
pawval if pawval>0, detail
last
year welfare payments p305
-------------------------------------------------------------
Percentiles Smallest
1%
26 1
5%
214 1
10% 450 1 Obs 1289
25%
1026 1 Sum of Wgt. 1289
50% 2664 Mean 3253.134
Largest Std. Dev. 2813.505
75% 4668 15600
90% 7000 19999 Variance 7915809
95% 8400 23292 Skewness 1.79416
99% 12648 25000 Kurtosis 9.428488
. summarize
pawval if pawval>0 [fweight=wgt2], detail
last
year welfare payments p305
-------------------------------------------------------------
Percentiles Smallest
1%
26 1
5%
200 1
10% 400 1 Obs 2557520
25%
972 4 Sum of Wgt.
2557520
50% 2412 Mean 3088.576
Largest Std. Dev. 2815.483
75% 4200 14760
90% 6480 19999 Variance 7926943
95% 8400 23292 Skewness 2.080939
99% 12084 25000 Kurtosis 11.32371
. sort sex
. by sex:
summarize pawval if pawval>0, detail
__________________________________________________________________
>
_____________
-> sex = male
last
year welfare payments p305
-------------------------------------------------------------
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
last
year welfare payments p305
-------------------------------------------------------------
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 pawval if pawval>0 [fweight=wgt2], detail
__________________________________________________________________
>
_____________
-> sex = male
last year welfare
payments p305
-------------------------------------------------------------
Percentiles Smallest
1%
12 1
5%
113 4
10% 240 12 Obs 355923
25%
819 12 Sum of Wgt. 355923
50% 2514 Mean 2990.004
Largest Std. Dev. 2689.037
75% 4224 8892
90% 6960 11580 Variance 7230921
95% 8400 13200 Skewness 1.263455
99% 11580 13800 Kurtosis 4.509215
__________________________________________________________________
>
_____________
-> sex = female
last
year welfare payments p305
-------------------------------------------------------------
Percentiles Smallest
1%
53 1
5%
240 1
10% 402 12
Obs 2201597
25%
1000 26 Sum of Wgt.
2201597
50% 2412 Mean 3104.512
Largest Std. Dev. 2835.074
75% 4160 14760
90% 6318 19999 Variance 8037644
95% 8400 23292 Skewness 2.191611
99% 12648 25000 Kurtosis 12.17824
. summarize ernval2
Variable | Obs
Mean Std. Dev. Min Max
-------------+----------------------------------------------------
ernval2 | 133710 15373.05
26884.27 0 362302
. summarize ernval2
Variable | Obs
Mean Std. Dev. Min
Max
-------------+-----------------------------------------------------
ernval2 | 133710 15373.05
26884.27 0 362302
. summarize ernval2, detail
main job
earnings, losses recoded to zero
-------------------------------------------------------------
Percentiles Smallest
1%
0 0
5%
0 0
10% 0 0 Obs 133710
25%
0 0 Sum of Wgt. 133710
50% 1700 Mean 15373.05
Largest Std. Dev. 26884.27
75% 23565 362302
90% 45000 362302 Variance 7.23e+08
95% 60000 362302 Skewness 3.955539
99% 117000 362302 Kurtosis 28.44627
. summarize ernval2 if age>20 & age<65,
detail
main job
earnings, losses recoded to zero
-------------------------------------------------------------
Percentiles Smallest
1%
0 0
5%
0 0
10% 0 0 Obs 75914
25%
5000 0 Sum of Wgt. 75914
50% 19500 Mean 25874.98
Largest Std. Dev. 30901.43
75% 35000 362302
90% 56000 362302 Variance 9.55e+08
95% 75000 362302 Skewness 3.388729
99% 150000 362302 Kurtosis 21.81292
. summarize ernval2 if age>20 & age<65
& ernval2>0, detail
main job
earnings, losses recoded to zero
-------------------------------------------------------------
Percentiles Smallest
1%
360 1
5%
2500 1
10% 5000 1 Obs 62648
25%
12800 1 Sum of Wgt. 62648
50% 24960 Mean 31354.12
Largest Std. Dev. 31389.64
75% 40000 362302
90% 60000 362302 Variance 9.85e+08
95% 80000 362302 Skewness 3.507321
99% 197387 362302 Kurtosis 22.09524
. by sex:
summarize ernval2 if age>20 &
age<65 & ernval2>0 [fweight=wgt2], detail
________________________________________________________________________
>
_______
-> sex = male
main job
earnings, losses recoded to zero
-------------------------------------------------------------
Percentiles Smallest
1%
600 1
5%
4500 1
10% 8500 1
Obs 67894423
25%
18000 1 Sum of Wgt.
67894423
50% 31000 Mean 40027.19
Largest Std. Dev. 37704.15
75% 50000 362302
90% 75000 362302 Variance 1.42e+09
95% 100000 362302 Skewness 2.932063
99% 229339 362302 Kurtosis 14.6763
________________________________________________________________________
>
_______
-> sex = female
main job
earnings, losses recoded to zero
-------------------------------------------------------------
Percentiles Smallest
1%
280 1
5%
1680 1
10% 3600 1 Obs 61355837
25%
10000 1 Sum of Wgt.
61355837
50% 20000 Mean 23874.06
Largest Std. Dev. 21717.82
75% 32000 284133
90% 47000 284133 Variance 4.72e+08
95% 60000 333564 Skewness 3.286336
99% 100000 333564 Kurtosis 25.66604
. by sex:
summarize ernval2 if age>20 &
age<65 & ernval2>0 & occ== 178 [fweight=wgt2], detail
[occ==178
are the lawyers, see the documentation, esp the
________________________________________________________________________
>
_______
-> sex = male
main job
earnings, losses recoded to zero
-------------------------------------------------------------
Percentiles Smallest
1%
1 1
5%
12000 1
10% 25000 1 Obs 634637
25%
45000 1 Sum of Wgt. 634637
50% 85000 Mean 98741.13
Largest Std. Dev. 69309.71
75% 125000 229339
90% 229339 229339 Variance 4.80e+09
95% 229339 229339 Skewness .8406135
99% 229339 229339 Kurtosis 2.631127
________________________________________________________________________
>
_______
-> sex = female
main job
earnings, losses recoded to zero
-------------------------------------------------------------
Percentiles Smallest
1%
260 260
5%
3200 1000
10% 10400 2160 Obs 293371
25%
30000 3000 Sum of Wgt. 293371
50% 45000 Mean 62148.52
Largest Std. Dev. 48241.36
75% 90000 197387
90% 130000 197387 Variance 2.33e+09
95% 150000 197387 Skewness 1.181763
99% 197387 197387 Kurtosis 3.886878
. by sex:
summarize ernval2 if age>20 &
age<65 & ernval2>0 & occ== 178, detail
________________________________________________________________________
>
_______
-> sex = male
main job
earnings, losses recoded to zero
-------------------------------------------------------------
Percentiles Smallest
1%
1 1
5%
12000 1
10% 25000 1 Obs 282
25%
45000 1 Sum of Wgt. 282
50% 82000 Mean 97762.75
Largest Std. Dev. 70088.99
75% 120000
229339
90% 229339 229339 Variance 4.91e+09
95% 229339 229339 Skewness .9811787
99% 229339 362302 Kurtosis 3.175355
________________________________________________________________________
>
_______
-> sex = female
main job
earnings, losses recoded to zero
-------------------------------------------------------------
Percentiles Smallest
1%
1000 260
5%
4900 1000
10% 11115.5 2160 Obs 130
25%
34500 3000 Sum of Wgt. 130
50% 46000 Mean 62693.02
Largest Std. Dev. 46890.42
75% 90000 197387
90% 126500 197387 Variance 2.20e+09
95% 150000 197387 Skewness 1.17612
99% 197387 197387 Kurtosis 4.077507
. exit, clear