------------------------------------------------------------------------
log: C:\AAA Miker Files\newer web pages\soc_meth_proj3\first_class_2009.log
log type: text
opened on: 27 Jan 2009, 11:06:04
*Before you do anything else, start a log. I much prefer the .log format, pure text files, rather than the .smcl version which is marked up and only readable by Stata.
. use "C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_mar_2000_new.dta", clear
no room to add more observations
An attempt was made to increase the number of observations beyond
what is currently possible. You have the following alternatives:
1. Store your variables more efficiently; see help compress.
(Think of Stata's data area as the area of a rectangle; Stata
can trade off width and length.)
2. Drop some variables or observations; see help drop.
3. Increase the amount of memory allocated to the data area using
the set memory command; see help memory.
r(901);
. *this is the error you get when you try to open a dataset and you don't have room in memory. So add more memory.
. set mem 200m
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:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_mar_2000_new.dta", clear
* Use (open a dataset) and log are easiest to set from the Stata menus
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps
> _mar_2000_new.dta
obs: 133,710
vars: 55 21 Jan 2009 17:08
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 inctotlbl
Total personal income
incwage long %12.0g incwagelbl
Wage and salary income
incss long %12.0g incsslbl Social Security income
incwelfr long %12.0g incwelfrlbl
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 sex, missing
Sex | Freq. Percent Cum.
------------+-----------------------------------
Male | 64,791 48.46 48.46
Female | 68,919 51.54 100.00
------------+-----------------------------------
Total | 133,710 100.00
* There are no missing values here. all 133,710 individuals in the dataset have given themselves or have been assigned a gender code.
. tabulate sex [fweight= perwt_rounded}
invalid syntax
r(198);
*fweights go with the square brackets...
. 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
. summarize perwt_rounded
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
perwt_roun~d | 133710 2049.868 1083.244 93 14281
. 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
. *race is stored as a number
. 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
. summarize age
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
age | 133710 35.17964 22.21722 0 90
. *age is topcoded to protect confidentiality. See also the ipums documentation for information like 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
. summarize incwelfr
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incwelfr | 103226 40.62242 478.8231 0 25000
. 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
. summarize incwelfr if incwelfr>0, detail
Welfare (public assistance) income
-------------------------------------------------------------
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 incwelfr if incwelfr>0 [fweight= perwt_rounded], detail
Welfare (public assistance) income
-------------------------------------------------------------
Percentiles Smallest
1% 30 1
5% 200 1
10% 400 1 Obs 2551246
25% 960 1 Sum of Wgt. 2551246
50% 2400 Mean 3072.095
Largest Std. Dev. 2803.442
75% 4200 15600
90% 6276 19999 Variance 7859287
95% 8400 23292 Skewness 2.073514
99% 12648 25000 Kurtosis 11.33882
. summarize inctot
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
inctot | 103226 26011.4 32061.48 -24998 425510
. sort sex
. by sex: summarize inctot
---------------------------------------------------------------------------------
-> sex = Male
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
inctot | 49353 34066.95 38476.77 -24998 425510
---------------------------------------------------------------------------------
-> sex = Female
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
inctot | 53873 18631.72 22349.34 -18582 385068
. *maybe we should look at age groups who are most likely to be in the labor force
. by sex: summarize inctot if age>29 & age<50
---------------------------------------------------------------------------------
-> sex = Male
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
inctot | 19765 42955.84 40054.33 -9999 424770
---------------------------------------------------------------------------------
-> sex = Female
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
inctot | 20848 23340.47 24891.67 -12949 385068
. by sex: summarize inctot if age>29 & age<50 [fweight= perwt_rounded]
---------------------------------------------------------------------------------
-> sex = Male
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
inctot | 41410548 44016.05 40909.26 -9999 424770
---------------------------------------------------------------------------------
-> sex = Female
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
inctot | 42661051 24043.58 25408.11 -12949 385068
. tabulate empstat
Employment status | Freq. Percent Cum.
-------------------------------+-----------------------------------
NIU | 30,484 22.80 22.80
At work | 62,726 46.91 69.71
Has job, not at work last week | 2,340 1.75 71.46
Armed Forces | 420 0.31 71.77
Unemployed, experienced worker | 2,745 2.05 73.83
Unemployed, new worker | 202 0.15 73.98
Not in labor force | 30,755 23.00 96.98
NILF, unable to work | 4,038 3.02 100.00
-------------------------------+-----------------------------------
Total | 133,710 100.00
. summarize wkswork1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
wkswork1 | 133710 24.62875 24.69243 0 52
. by sex: summarize inctot if age>29 & age<50 & wkswork1> 40 [fweight= perwt_rounded]
-> sex = Male
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
inctot | 35318484 49066.08 41412.21 -9999 424770
-> sex = Female
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
inctot | 28589270 31985.83 25616.21 -12949 345179
. *let's just look at the lawyers
. tabulate occ1990 if occ1990==178
Occupation, 1990 basis | Freq. Percent Cum.
----------------------------------------+-----------------------------------
Lawyers | 441 100.00 100.00
----------------------------------------+-----------------------------------
Total | 441 100.00
*Notice the double equal signs signifying equal after the “if.” That is a peculiarity of Stata syntax.
. by sex: summarize inctot if age>29 & age<50 & wkswork1> 40& occ1990==178 [fweight= perwt_rounded]
-> sex = Male
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
inctot | 340036 107954 66792.21 1 294343
-> sex = Female
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
inctot | 168665 85354.7 53892.39 15200 229966
*I have not made any changes to the dataset, so I don’t need to save it. The log saves all my commands and results. As long as you start the log at the beginning of your session, you will have a nice text file containing all your results.
. clear all
. exit, clear