1 Presentation
This is the data preparation code for the paper “Perceptions of Inequality and Meritocracy: Their Interplay in Shaping Preferences for Market Justice in Chile (2016-2023)”. The prepared dataset is ELSOC_Long_2016_2023_1.00.RData.
2 Libraries
3 Data
4 Processing
4.1 Select
db_proc <- elsoc_long_2016_2023 %>%
select(idencuesta, ola,
ponderador_long_total,segmento, estrato,
just_educ = d02_02, just_pension = d02_01,
just_health = d02_03, merit_effort = c18_09,
merit_talent = c18_10, perc_sal_gerente = d03_01,
perc_sal_obrero = d03_02, just_sal_gerente = d04_01,
just_sal_obrero = d04_02, age = m0_edad, m01,
sex = m0_sexo, ess = d01_01, ideo = c15
) %>%
as_tibble()4.2 Filter
4.3 Recode and transform
# general na's
db_proc[db_proc ==-999] <- NA
db_proc[db_proc ==-888] <- NA
db_proc[db_proc ==-777] <- NA
db_proc[db_proc ==-666] <- NA
# mjp
frq(db_proc$just_health)Grado de acuerdo: Justicia distributiva en salud (x) <numeric>
# total N=18021 valid N=17986 mean=1.86 sd=0.91
Value | Label | N | Raw % | Valid % | Cum. %
-------------------------------------------------------------------------------
-999 | No Responde | 0 | 0.00 | 0.00 | 0.00
-888 | No Sabe | 0 | 0.00 | 0.00 | 0.00
-777 | Valor perdido por error tecnico | 0 | 0.00 | 0.00 | 0.00
-666 | Valor perdido por encuesta incompleta | 0 | 0.00 | 0.00 | 0.00
1 | Totalmente en desacuerdo | 6820 | 37.84 | 37.92 | 37.92
2 | En desacuerdo | 8630 | 47.89 | 47.98 | 85.90
3 | Ni de acuerdo ni en desacuerdo | 949 | 5.27 | 5.28 | 91.18
4 | De acuerdo | 1355 | 7.52 | 7.53 | 98.71
5 | Totalmente de acuerdo | 232 | 1.29 | 1.29 | 100.00
<NA> | <NA> | 35 | 0.19 | <NA> | <NA>frq(db_proc$just_pension)Grado de acuerdo: Justicia distributiva en pensiones (x) <numeric>
# total N=18021 valid N=17966 mean=2.24 sd=1.11
Value | Label | N | Raw % | Valid % | Cum. %
-------------------------------------------------------------------------------
-999 | No Responde | 0 | 0.00 | 0.00 | 0.00
-888 | No Sabe | 0 | 0.00 | 0.00 | 0.00
-777 | Valor perdido por error tecnico | 0 | 0.00 | 0.00 | 0.00
-666 | Valor perdido por encuesta incompleta | 0 | 0.00 | 0.00 | 0.00
1 | Totalmente en desacuerdo | 4889 | 27.13 | 27.21 | 27.21
2 | En desacuerdo | 7802 | 43.29 | 43.43 | 70.64
3 | Ni de acuerdo ni en desacuerdo | 1740 | 9.66 | 9.68 | 80.32
4 | De acuerdo | 3087 | 17.13 | 17.18 | 97.51
5 | Totalmente de acuerdo | 448 | 2.49 | 2.49 | 100.00
<NA> | <NA> | 55 | 0.31 | <NA> | <NA>frq(db_proc$just_educ)Grado de acuerdo: Justicia distributiva en educacion (x) <numeric>
# total N=18021 valid N=17982 mean=1.91 sd=0.92
Value | Label | N | Raw % | Valid % | Cum. %
-------------------------------------------------------------------------------
-999 | No Responde | 0 | 0.00 | 0.00 | 0.00
-888 | No Sabe | 0 | 0.00 | 0.00 | 0.00
-777 | Valor perdido por error tecnico | 0 | 0.00 | 0.00 | 0.00
-666 | Valor perdido por encuesta incompleta | 0 | 0.00 | 0.00 | 0.00
1 | Totalmente en desacuerdo | 6299 | 34.95 | 35.03 | 35.03
2 | En desacuerdo | 8909 | 49.44 | 49.54 | 84.57
3 | Ni de acuerdo ni en desacuerdo | 1104 | 6.13 | 6.14 | 90.71
4 | De acuerdo | 1444 | 8.01 | 8.03 | 98.74
5 | Totalmente de acuerdo | 226 | 1.25 | 1.26 | 100.00
<NA> | <NA> | 39 | 0.22 | <NA> | <NA>labels1 <- c("Strongly desagree" = 1,
"Desagree" = 2,
"Neither agree nor desagree" = 3,
"Agree" = 4,
"Strongly agree" = 5)
db_proc <- db_proc %>%
mutate(
across(
.cols = c(just_health, just_pension, just_educ),
.fns = ~ sjlabelled::set_labels(., labels = labels1)
)
)
db_proc <- cbind(db_proc, "mjp" = rowMeans(db_proc %>% select(just_health, just_pension, just_educ), na.rm=TRUE))
summary(db_proc$mjp) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.000 1.333 2.000 2.006 2.333 5.000 23 db_proc <- db_proc %>%
mutate(
across(
.cols = c(just_health, just_pension, just_educ),
.fns = ~ car::recode(., recodes = c("1='Strongly desagree'; 2='Desagree';
3='Neither agree nor desagree'; 4='Agree';
5='Strongly agree'"),
levels = c("Strongly desagree", "Desagree", "Neither agree nor desagree", "Agree", "Strongly agree"),
as.factor = T)
)
)
db_proc$just_health <- sjlabelled::set_label(db_proc$just_health,
label = "Health distributive justice")
db_proc$just_pension <- sjlabelled::set_label(db_proc$just_pension,
label = "Pension distributive justice")
db_proc$just_educ <- sjlabelled::set_label(db_proc$just_educ,
label = "Education distributive justice")
db_proc$mjp <- sjlabelled::set_label(db_proc$mjp,
label = "Market justice preferences")
# merit
frq(db_proc$merit_effort)Grado de acuerdo: Las personas son recompensadas por sus esfuerzos (x) <numeric>
# total N=18021 valid N=17917 mean=2.61 sd=1.03
Value | Label | N | Raw % | Valid % | Cum. %
-------------------------------------------------------------------------------
-999 | No Responde | 0 | 0.00 | 0.00 | 0.00
-888 | No Sabe | 0 | 0.00 | 0.00 | 0.00
-777 | Valor perdido por error tecnico | 0 | 0.00 | 0.00 | 0.00
-666 | Valor perdido por encuesta incompleta | 0 | 0.00 | 0.00 | 0.00
1 | Totalmente en desacuerdo | 1936 | 10.74 | 10.81 | 10.81
2 | En desacuerdo | 8027 | 44.54 | 44.80 | 55.61
3 | Ni en desacuerdo ni de acuerdo | 3615 | 20.06 | 20.18 | 75.78
4 | De acuerdo | 3812 | 21.15 | 21.28 | 97.06
5 | Totalmente de acuerdo | 527 | 2.92 | 2.94 | 100.00
<NA> | <NA> | 104 | 0.58 | <NA> | <NA>frq(db_proc$merit_talent)Grado de acuerdo: Las personas son recompensada por su inteligencia (x) <numeric>
# total N=18021 valid N=17917 mean=2.77 sd=1.04
Value | Label | N | Raw % | Valid % | Cum. %
-------------------------------------------------------------------------------
-999 | No Responde | 0 | 0.00 | 0.00 | 0.00
-888 | No Sabe | 0 | 0.00 | 0.00 | 0.00
-777 | Valor perdido por error tecnico | 0 | 0.00 | 0.00 | 0.00
-666 | Valor perdido por encuesta incompleta | 0 | 0.00 | 0.00 | 0.00
1 | Totalmente en desacuerdo | 1598 | 8.87 | 8.92 | 8.92
2 | En desacuerdo | 6818 | 37.83 | 38.05 | 46.97
3 | Ni en desacuerdo ni de acuerdo | 4076 | 22.62 | 22.75 | 69.72
4 | De acuerdo | 4883 | 27.10 | 27.25 | 96.97
5 | Totalmente de acuerdo | 542 | 3.01 | 3.03 | 100.00
<NA> | <NA> | 104 | 0.58 | <NA> | <NA>db_proc <- db_proc %>%
mutate(
across(
.cols = c(merit_effort, merit_talent),
.fns = ~ sjlabelled::set_labels(., labels = labels1)
)
)
db_proc <- db_proc %>%
mutate(
across(
.cols = c(merit_effort, merit_talent),
.fns = ~ car::recode(., recodes = c("1='Strongly desagree'; 2='Desagree';
3='Neither agree nor desagree'; 4='Agree';
5='Strongly agree'"),
levels = c("Strongly desagree", "Desagree", "Neither agree nor desagree", "Agree", "Strongly agree"),
as.factor = T)
)
)
db_proc$merit_effort <- sjlabelled::set_label(db_proc$merit_effort,
label = "People are rewarded for their efforts")
db_proc$merit_talent <- sjlabelled::set_label(db_proc$merit_talent,
label = "People are rewarded for their intelligence")
# perc inequality gap
frq(db_proc$perc_sal_gerente)Salario percibido: Gerente gran empresa (x) <numeric>
# total N=18021 valid N=16988 mean=12420591048250076.00 sd=1087732675709961984.00
Value | N | Raw % | Valid % | Cum. %
------------------------------------------
0.00 | 10 | 0.06 | 0.06 | 0.06
1.00 | 1 | 0.01 | 0.01 | 0.06
2.00 | 1 | 0.01 | 0.01 | 0.07
208.00 | 1 | 0.01 | 0.01 | 0.08
556.00 | 1 | 0.01 | 0.01 | 0.08
568.00 | 1 | 0.01 | 0.01 | 0.09
889.00 | 1 | 0.01 | 0.01 | 0.09
999.00 | 2 | 0.01 | 0.01 | 0.11
5828.00 | 1 | 0.01 | 0.01 | 0.11
6582.00 | 1 | 0.01 | 0.01 | 0.12
10000.00 | 2 | 0.01 | 0.01 | 0.13
12000.00 | 1 | 0.01 | 0.01 | 0.14
15088.00 | 1 | 0.01 | 0.01 | 0.14
20000.00 | 2 | 0.01 | 0.01 | 0.15
35568.00 | 1 | 0.01 | 0.01 | 0.16
35652.00 | 1 | 0.01 | 0.01 | 0.16
38383.00 | 1 | 0.01 | 0.01 | 0.17
40000.00 | 1 | 0.01 | 0.01 | 0.18
50000.00 | 2 | 0.01 | 0.01 | 0.19
60000.00 | 1 | 0.01 | 0.01 | 0.19
65863.00 | 1 | 0.01 | 0.01 | 0.20
88888.00 | 1 | 0.01 | 0.01 | 0.21
1.00e+05 | 13 | 0.07 | 0.08 | 0.28
1.20e+05 | 1 | 0.01 | 0.01 | 0.29
1.50e+05 | 2 | 0.01 | 0.01 | 0.30
1.80e+05 | 2 | 0.01 | 0.01 | 0.31
2.00e+05 | 6 | 0.03 | 0.04 | 0.35
2.40e+05 | 1 | 0.01 | 0.01 | 0.35
2.50e+05 | 2 | 0.01 | 0.01 | 0.36
2.60e+05 | 1 | 0.01 | 0.01 | 0.37
2.70e+05 | 1 | 0.01 | 0.01 | 0.38
3.00e+05 | 9 | 0.05 | 0.05 | 0.43
3.80e+05 | 2 | 0.01 | 0.01 | 0.44
4.00e+05 | 4 | 0.02 | 0.02 | 0.47
4.50e+05 | 1 | 0.01 | 0.01 | 0.47
5.00e+05 | 21 | 0.12 | 0.12 | 0.59
6.00e+05 | 16 | 0.09 | 0.09 | 0.69
6.50e+05 | 1 | 0.01 | 0.01 | 0.69
6.80e+05 | 1 | 0.01 | 0.01 | 0.70
7.00e+05 | 18 | 0.10 | 0.11 | 0.81
8.00e+05 | 21 | 0.12 | 0.12 | 0.93
8.50e+05 | 1 | 0.01 | 0.01 | 0.94
8.60e+05 | 1 | 0.01 | 0.01 | 0.94
9.00e+05 | 14 | 0.08 | 0.08 | 1.02
9.60e+05 | 1 | 0.01 | 0.01 | 1.03
1.00e+06 | 314 | 1.74 | 1.85 | 2.88
1.10e+06 | 2 | 0.01 | 0.01 | 2.89
1.20e+06 | 42 | 0.23 | 0.25 | 3.14
1.30e+06 | 6 | 0.03 | 0.04 | 3.17
1.40e+06 | 8 | 0.04 | 0.05 | 3.22
1.50e+06 | 207 | 1.15 | 1.22 | 4.44
1.60e+06 | 4 | 0.02 | 0.02 | 4.46
1.70e+06 | 6 | 0.03 | 0.04 | 4.50
1.80e+06 | 31 | 0.17 | 0.18 | 4.68
1.90e+06 | 4 | 0.02 | 0.02 | 4.70
2.00e+06 | 741 | 4.11 | 4.36 | 9.07
2.00e+06 | 2 | 0.01 | 0.01 | 9.08
2.20e+06 | 1 | 0.01 | 0.01 | 9.08
2.30e+06 | 1 | 0.01 | 0.01 | 9.09
2.40e+06 | 1 | 0.01 | 0.01 | 9.09
2.45e+06 | 1 | 0.01 | 0.01 | 9.10
2.50e+06 | 142 | 0.79 | 0.84 | 9.94
2.60e+06 | 4 | 0.02 | 0.02 | 9.96
2.70e+06 | 3 | 0.02 | 0.02 | 9.98
2.80e+06 | 13 | 0.07 | 0.08 | 10.05
3.00e+06 | 900 | 4.99 | 5.30 | 15.35
3.20e+06 | 3 | 0.02 | 0.02 | 15.37
3.50e+06 | 93 | 0.52 | 0.55 | 15.92
3.60e+06 | 1 | 0.01 | 0.01 | 15.92
3.70e+06 | 2 | 0.01 | 0.01 | 15.93
3.80e+06 | 3 | 0.02 | 0.02 | 15.95
4.00e+06 | 703 | 3.90 | 4.14 | 20.09
4.50e+06 | 32 | 0.18 | 0.19 | 20.28
4.80e+06 | 1 | 0.01 | 0.01 | 20.28
5.00e+06 | 1762 | 9.78 | 10.37 | 30.66
5.00e+06 | 1 | 0.01 | 0.01 | 30.66
5.00e+06 | 2 | 0.01 | 0.01 | 30.67
5.20e+06 | 1 | 0.01 | 0.01 | 30.68
5.40e+06 | 1 | 0.01 | 0.01 | 30.69
5.50e+06 | 16 | 0.09 | 0.09 | 30.78
5.70e+06 | 1 | 0.01 | 0.01 | 30.79
5.80e+06 | 1 | 0.01 | 0.01 | 30.79
6.00e+06 | 712 | 3.95 | 4.19 | 34.98
6.20e+06 | 1 | 0.01 | 0.01 | 34.99
6.50e+06 | 13 | 0.07 | 0.08 | 35.07
6.80e+06 | 2 | 0.01 | 0.01 | 35.08
7.00e+06 | 523 | 2.90 | 3.08 | 38.16
7.40e+06 | 1 | 0.01 | 0.01 | 38.16
7.50e+06 | 7 | 0.04 | 0.04 | 38.20
7.60e+06 | 1 | 0.01 | 0.01 | 38.21
8.00e+06 | 822 | 4.56 | 4.84 | 43.05
8.00e+06 | 2 | 0.01 | 0.01 | 43.06
8.00e+06 | 1 | 0.01 | 0.01 | 43.07
8.50e+06 | 8 | 0.04 | 0.05 | 43.11
9.00e+06 | 135 | 0.75 | 0.79 | 43.91
9.50e+06 | 2 | 0.01 | 0.01 | 43.92
9.65e+06 | 1 | 0.01 | 0.01 | 43.93
9.80e+06 | 1 | 0.01 | 0.01 | 43.93
1.00e+07 | 2231 | 12.38 | 13.13 | 57.06
1.00e+07 | 1 | 0.01 | 0.01 | 57.07
1.05e+07 | 2 | 0.01 | 0.01 | 57.08
1.10e+07 | 52 | 0.29 | 0.31 | 57.39
1.20e+07 | 490 | 2.72 | 2.88 | 60.27
1.20e+07 | 1 | 0.01 | 0.01 | 60.28
1.28e+07 | 1 | 0.01 | 0.01 | 60.28
1.30e+07 | 46 | 0.26 | 0.27 | 60.55
1.40e+07 | 76 | 0.42 | 0.45 | 61.00
1.50e+07 | 1106 | 6.14 | 6.51 | 67.51
1.50e+07 | 2 | 0.01 | 0.01 | 67.52
1.55e+07 | 1 | 0.01 | 0.01 | 67.53
1.60e+07 | 35 | 0.19 | 0.21 | 67.74
1.70e+07 | 28 | 0.16 | 0.16 | 67.90
1.80e+07 | 153 | 0.85 | 0.90 | 68.80
1.85e+07 | 1 | 0.01 | 0.01 | 68.81
1.89e+07 | 1 | 0.01 | 0.01 | 68.81
1.90e+07 | 11 | 0.06 | 0.06 | 68.88
2.00e+07 | 1695 | 9.41 | 9.98 | 78.86
2.10e+07 | 10 | 0.06 | 0.06 | 78.91
2.20e+07 | 28 | 0.16 | 0.16 | 79.08
2.22e+07 | 1 | 0.01 | 0.01 | 79.09
2.30e+07 | 11 | 0.06 | 0.06 | 79.15
2.40e+07 | 15 | 0.08 | 0.09 | 79.24
2.50e+07 | 423 | 2.35 | 2.49 | 81.73
2.60e+07 | 9 | 0.05 | 0.05 | 81.78
2.70e+07 | 10 | 0.06 | 0.06 | 81.84
2.80e+07 | 11 | 0.06 | 0.06 | 81.90
3.00e+07 | 882 | 4.89 | 5.19 | 87.10
3.20e+07 | 9 | 0.05 | 0.05 | 87.15
3.30e+07 | 2 | 0.01 | 0.01 | 87.16
3.40e+07 | 5 | 0.03 | 0.03 | 87.19
3.50e+07 | 77 | 0.43 | 0.45 | 87.64
3.60e+07 | 4 | 0.02 | 0.02 | 87.67
3.70e+07 | 2 | 0.01 | 0.01 | 87.68
3.80e+07 | 7 | 0.04 | 0.04 | 87.72
4.00e+07 | 321 | 1.78 | 1.89 | 89.61
4.20e+07 | 4 | 0.02 | 0.02 | 89.63
4.30e+07 | 2 | 0.01 | 0.01 | 89.65
4.40e+07 | 1 | 0.01 | 0.01 | 89.65
4.50e+07 | 28 | 0.16 | 0.16 | 89.82
4.70e+07 | 1 | 0.01 | 0.01 | 89.82
4.80e+07 | 1 | 0.01 | 0.01 | 89.83
4.90e+07 | 1 | 0.01 | 0.01 | 89.83
5.00e+07 | 528 | 2.93 | 3.11 | 92.94
5.00e+07 | 1 | 0.01 | 0.01 | 92.95
5.30e+07 | 1 | 0.01 | 0.01 | 92.95
5.40e+07 | 3 | 0.02 | 0.02 | 92.97
5.50e+07 | 11 | 0.06 | 0.06 | 93.04
5.70e+07 | 1 | 0.01 | 0.01 | 93.04
6.00e+07 | 145 | 0.80 | 0.85 | 93.90
6.00e+07 | 1 | 0.01 | 0.01 | 93.90
6.40e+07 | 1 | 0.01 | 0.01 | 93.91
6.50e+07 | 3 | 0.02 | 0.02 | 93.93
6.60e+07 | 1 | 0.01 | 0.01 | 93.93
6.80e+07 | 1 | 0.01 | 0.01 | 93.94
6.90e+07 | 1 | 0.01 | 0.01 | 93.94
7.00e+07 | 50 | 0.28 | 0.29 | 94.24
7.30e+07 | 1 | 0.01 | 0.01 | 94.24
7.50e+07 | 2 | 0.01 | 0.01 | 94.25
7.80e+07 | 1 | 0.01 | 0.01 | 94.26
8.00e+07 | 127 | 0.70 | 0.75 | 95.01
8.50e+07 | 1 | 0.01 | 0.01 | 95.01
8.60e+07 | 1 | 0.01 | 0.01 | 95.02
8.80e+07 | 1 | 0.01 | 0.01 | 95.03
9.00e+07 | 16 | 0.09 | 0.09 | 95.12
9.40e+07 | 1 | 0.01 | 0.01 | 95.13
1.00e+08 | 1 | 0.01 | 0.01 | 95.13
1.00e+08 | 400 | 2.22 | 2.35 | 97.49
1.10e+08 | 1 | 0.01 | 0.01 | 97.49
1.14e+08 | 1 | 0.01 | 0.01 | 97.50
1.15e+08 | 1 | 0.01 | 0.01 | 97.50
1.20e+08 | 19 | 0.11 | 0.11 | 97.62
1.30e+08 | 2 | 0.01 | 0.01 | 97.63
1.34e+08 | 1 | 0.01 | 0.01 | 97.63
1.40e+08 | 2 | 0.01 | 0.01 | 97.65
1.50e+08 | 34 | 0.19 | 0.20 | 97.85
1.70e+08 | 1 | 0.01 | 0.01 | 97.85
1.80e+08 | 5 | 0.03 | 0.03 | 97.88
2.00e+08 | 104 | 0.58 | 0.61 | 98.49
2.24e+08 | 1 | 0.01 | 0.01 | 98.50
2.30e+08 | 3 | 0.02 | 0.02 | 98.52
2.41e+08 | 1 | 0.01 | 0.01 | 98.52
2.50e+08 | 9 | 0.05 | 0.05 | 98.58
2.53e+08 | 1 | 0.01 | 0.01 | 98.58
2.80e+08 | 1 | 0.01 | 0.01 | 98.59
3.00e+08 | 63 | 0.35 | 0.37 | 98.96
3.20e+08 | 1 | 0.01 | 0.01 | 98.96
3.30e+08 | 1 | 0.01 | 0.01 | 98.97
3.40e+08 | 1 | 0.01 | 0.01 | 98.98
3.50e+08 | 2 | 0.01 | 0.01 | 98.99
3.60e+08 | 1 | 0.01 | 0.01 | 98.99
3.80e+08 | 2 | 0.01 | 0.01 | 99.01
3.90e+08 | 1 | 0.01 | 0.01 | 99.01
4.00e+08 | 41 | 0.23 | 0.24 | 99.25
4.26e+08 | 1 | 0.01 | 0.01 | 99.26
4.50e+08 | 1 | 0.01 | 0.01 | 99.26
5.00e+08 | 53 | 0.29 | 0.31 | 99.58
5.00e+08 | 1 | 0.01 | 0.01 | 99.58
6.00e+08 | 3 | 0.02 | 0.02 | 99.60
6.50e+08 | 1 | 0.01 | 0.01 | 99.61
7.00e+08 | 5 | 0.03 | 0.03 | 99.64
8.00e+08 | 7 | 0.04 | 0.04 | 99.68
8.00e+08 | 1 | 0.01 | 0.01 | 99.68
9.00e+08 | 2 | 0.01 | 0.01 | 99.69
1.00e+09 | 25 | 0.14 | 0.15 | 99.84
1.20e+09 | 1 | 0.01 | 0.01 | 99.85
1.50e+09 | 1 | 0.01 | 0.01 | 99.85
2.00e+09 | 1 | 0.01 | 0.01 | 99.86
2.50e+09 | 1 | 0.01 | 0.01 | 99.86
3.00e+09 | 2 | 0.01 | 0.01 | 99.88
4.00e+09 | 2 | 0.01 | 0.01 | 99.89
5.00e+09 | 7 | 0.04 | 0.04 | 99.93
6.00e+09 | 2 | 0.01 | 0.01 | 99.94
7.00e+09 | 1 | 0.01 | 0.01 | 99.95
9.00e+09 | 1 | 0.01 | 0.01 | 99.95
1.50e+10 | 1 | 0.01 | 0.01 | 99.96
2.00e+10 | 1 | 0.01 | 0.01 | 99.96
2.00e+11 | 1 | 0.01 | 0.01 | 99.97
1.00e+15 | 1 | 0.01 | 0.01 | 99.98
1.00e+18 | 1 | 0.01 | 0.01 | 99.98
1.00e+19 | 1 | 0.01 | 0.01 | 99.99
1.00e+20 | 2 | 0.01 | 0.01 | 100.00
<NA> | 1033 | 5.73 | <NA> | <NA>frq(db_proc$perc_sal_obrero)Salario percibido: Obrero no calificado (x) <numeric>
# total N=18021 valid N=17615 mean=5676979847104211.00 sd=753457354249442944.00
Value | N | Raw % | Valid % | Cum. %
------------------------------------------
0.00 | 6 | 0.03 | 0.03 | 0.03
1.00 | 1 | 0.01 | 0.01 | 0.04
66.00 | 1 | 0.01 | 0.01 | 0.05
99.00 | 1 | 0.01 | 0.01 | 0.05
247.00 | 1 | 0.01 | 0.01 | 0.06
300.00 | 2 | 0.01 | 0.01 | 0.07
310.00 | 1 | 0.01 | 0.01 | 0.07
358.00 | 1 | 0.01 | 0.01 | 0.08
400.00 | 1 | 0.01 | 0.01 | 0.09
555.00 | 1 | 0.01 | 0.01 | 0.09
595.00 | 1 | 0.01 | 0.01 | 0.10
683.00 | 1 | 0.01 | 0.01 | 0.10
2500.00 | 1 | 0.01 | 0.01 | 0.11
5383.00 | 1 | 0.01 | 0.01 | 0.11
5999.00 | 1 | 0.01 | 0.01 | 0.12
6586.00 | 1 | 0.01 | 0.01 | 0.12
8668.00 | 1 | 0.01 | 0.01 | 0.13
9858.00 | 1 | 0.01 | 0.01 | 0.14
20000.00 | 1 | 0.01 | 0.01 | 0.14
22000.00 | 1 | 0.01 | 0.01 | 0.15
24000.00 | 2 | 0.01 | 0.01 | 0.16
25000.00 | 6 | 0.03 | 0.03 | 0.19
25300.00 | 1 | 0.01 | 0.01 | 0.20
27000.00 | 1 | 0.01 | 0.01 | 0.20
28000.00 | 1 | 0.01 | 0.01 | 0.21
29000.00 | 1 | 0.01 | 0.01 | 0.22
30000.00 | 5 | 0.03 | 0.03 | 0.24
38383.00 | 1 | 0.01 | 0.01 | 0.25
40000.00 | 1 | 0.01 | 0.01 | 0.26
50000.00 | 4 | 0.02 | 0.02 | 0.28
60000.00 | 1 | 0.01 | 0.01 | 0.28
70000.00 | 3 | 0.02 | 0.02 | 0.30
80000.00 | 5 | 0.03 | 0.03 | 0.33
85000.00 | 1 | 0.01 | 0.01 | 0.33
90000.00 | 1 | 0.01 | 0.01 | 0.34
1.00e+05 | 33 | 0.18 | 0.19 | 0.53
1.05e+05 | 1 | 0.01 | 0.01 | 0.53
1.20e+05 | 27 | 0.15 | 0.15 | 0.69
1.25e+05 | 2 | 0.01 | 0.01 | 0.70
1.30e+05 | 9 | 0.05 | 0.05 | 0.75
1.37e+05 | 1 | 0.01 | 0.01 | 0.76
1.40e+05 | 5 | 0.03 | 0.03 | 0.78
1.50e+05 | 83 | 0.46 | 0.47 | 1.25
1.52e+05 | 1 | 0.01 | 0.01 | 1.26
1.55e+05 | 1 | 0.01 | 0.01 | 1.27
1.60e+05 | 5 | 0.03 | 0.03 | 1.29
1.70e+05 | 12 | 0.07 | 0.07 | 1.36
1.75e+05 | 1 | 0.01 | 0.01 | 1.37
1.78e+05 | 1 | 0.01 | 0.01 | 1.37
1.79e+05 | 1 | 0.01 | 0.01 | 1.38
1.80e+05 | 109 | 0.60 | 0.62 | 2.00
1.84e+05 | 1 | 0.01 | 0.01 | 2.00
1.85e+05 | 1 | 0.01 | 0.01 | 2.01
1.88e+05 | 1 | 0.01 | 0.01 | 2.02
1.89e+05 | 1 | 0.01 | 0.01 | 2.02
1.90e+05 | 12 | 0.07 | 0.07 | 2.09
1.98e+05 | 4 | 0.02 | 0.02 | 2.11
2.00e+05 | 633 | 3.51 | 3.59 | 5.71
2.00e+05 | 1 | 0.01 | 0.01 | 5.71
2.07e+05 | 2 | 0.01 | 0.01 | 5.72
2.09e+05 | 1 | 0.01 | 0.01 | 5.73
2.10e+05 | 58 | 0.32 | 0.33 | 6.06
2.11e+05 | 1 | 0.01 | 0.01 | 6.06
2.12e+05 | 2 | 0.01 | 0.01 | 6.07
2.13e+05 | 1 | 0.01 | 0.01 | 6.08
2.15e+05 | 7 | 0.04 | 0.04 | 6.12
2.16e+05 | 1 | 0.01 | 0.01 | 6.13
2.17e+05 | 3 | 0.02 | 0.02 | 6.14
2.20e+05 | 116 | 0.64 | 0.66 | 6.80
2.22e+05 | 1 | 0.01 | 0.01 | 6.81
2.23e+05 | 1 | 0.01 | 0.01 | 6.81
2.24e+05 | 1 | 0.01 | 0.01 | 6.82
2.25e+05 | 11 | 0.06 | 0.06 | 6.88
2.27e+05 | 1 | 0.01 | 0.01 | 6.89
2.30e+05 | 104 | 0.58 | 0.59 | 7.48
2.33e+05 | 1 | 0.01 | 0.01 | 7.48
2.35e+05 | 5 | 0.03 | 0.03 | 7.51
2.36e+05 | 2 | 0.01 | 0.01 | 7.52
2.37e+05 | 1 | 0.01 | 0.01 | 7.53
2.38e+05 | 1 | 0.01 | 0.01 | 7.53
2.39e+05 | 3 | 0.02 | 0.02 | 7.55
2.40e+05 | 240 | 1.33 | 1.36 | 8.91
2.41e+05 | 6 | 0.03 | 0.03 | 8.95
2.42e+05 | 3 | 0.02 | 0.02 | 8.96
2.44e+05 | 1 | 0.01 | 0.01 | 8.97
2.45e+05 | 10 | 0.06 | 0.06 | 9.03
2.46e+05 | 2 | 0.01 | 0.01 | 9.04
2.47e+05 | 1 | 0.01 | 0.01 | 9.04
2.48e+05 | 1 | 0.01 | 0.01 | 9.05
2.49e+05 | 2 | 0.01 | 0.01 | 9.06
2.50e+05 | 1724 | 9.57 | 9.79 | 18.85
2.51e+05 | 1 | 0.01 | 0.01 | 18.85
2.52e+05 | 4 | 0.02 | 0.02 | 18.88
2.53e+05 | 1 | 0.01 | 0.01 | 18.88
2.54e+05 | 2 | 0.01 | 0.01 | 18.89
2.55e+05 | 10 | 0.06 | 0.06 | 18.95
2.56e+05 | 5 | 0.03 | 0.03 | 18.98
2.57e+05 | 43 | 0.24 | 0.24 | 19.22
2.58e+05 | 7 | 0.04 | 0.04 | 19.26
2.58e+05 | 3 | 0.02 | 0.02 | 19.28
2.59e+05 | 3 | 0.02 | 0.02 | 19.30
2.60e+05 | 244 | 1.35 | 1.39 | 20.68
2.61e+05 | 1 | 0.01 | 0.01 | 20.69
2.62e+05 | 2 | 0.01 | 0.01 | 20.70
2.64e+05 | 14 | 0.08 | 0.08 | 20.78
2.65e+05 | 23 | 0.13 | 0.13 | 20.91
2.66e+05 | 1 | 0.01 | 0.01 | 20.91
2.66e+05 | 1 | 0.01 | 0.01 | 20.92
2.67e+05 | 14 | 0.08 | 0.08 | 21.00
2.68e+05 | 3 | 0.02 | 0.02 | 21.02
2.69e+05 | 2 | 0.01 | 0.01 | 21.03
2.70e+05 | 944 | 5.24 | 5.36 | 26.39
2.70e+05 | 2 | 0.01 | 0.01 | 26.40
2.71e+05 | 5 | 0.03 | 0.03 | 26.43
2.72e+05 | 15 | 0.08 | 0.09 | 26.51
2.73e+05 | 5 | 0.03 | 0.03 | 26.54
2.74e+05 | 5 | 0.03 | 0.03 | 26.57
2.75e+05 | 105 | 0.58 | 0.60 | 27.16
2.76e+05 | 1 | 0.01 | 0.01 | 27.17
2.76e+05 | 118 | 0.65 | 0.67 | 27.84
2.76e+05 | 1 | 0.01 | 0.01 | 27.85
2.77e+05 | 5 | 0.03 | 0.03 | 27.87
2.78e+05 | 34 | 0.19 | 0.19 | 28.07
2.79e+05 | 2 | 0.01 | 0.01 | 28.08
2.80e+05 | 765 | 4.25 | 4.34 | 32.42
2.81e+05 | 3 | 0.02 | 0.02 | 32.44
2.82e+05 | 4 | 0.02 | 0.02 | 32.46
2.83e+05 | 3 | 0.02 | 0.02 | 32.48
2.84e+05 | 5 | 0.03 | 0.03 | 32.51
2.85e+05 | 33 | 0.18 | 0.19 | 32.69
2.86e+05 | 14 | 0.08 | 0.08 | 32.77
2.87e+05 | 15 | 0.08 | 0.09 | 32.86
2.88e+05 | 44 | 0.24 | 0.25 | 33.11
2.89e+05 | 2 | 0.01 | 0.01 | 33.12
2.89e+05 | 2 | 0.01 | 0.01 | 33.13
2.90e+05 | 105 | 0.58 | 0.60 | 33.73
2.91e+05 | 1 | 0.01 | 0.01 | 33.73
2.92e+05 | 1 | 0.01 | 0.01 | 33.74
2.93e+05 | 1 | 0.01 | 0.01 | 33.74
2.94e+05 | 1 | 0.01 | 0.01 | 33.75
2.95e+05 | 3 | 0.02 | 0.02 | 33.77
2.97e+05 | 1 | 0.01 | 0.01 | 33.77
2.99e+05 | 1 | 0.01 | 0.01 | 33.78
3.00e+05 | 2988 | 16.58 | 16.96 | 50.74
3.00e+05 | 2 | 0.01 | 0.01 | 50.75
3.01e+05 | 208 | 1.15 | 1.18 | 51.93
3.02e+05 | 3 | 0.02 | 0.02 | 51.95
3.04e+05 | 1 | 0.01 | 0.01 | 51.96
3.05e+05 | 26 | 0.14 | 0.15 | 52.10
3.07e+05 | 1 | 0.01 | 0.01 | 52.11
3.09e+05 | 1 | 0.01 | 0.01 | 52.11
3.10e+05 | 66 | 0.37 | 0.37 | 52.49
3.15e+05 | 2 | 0.01 | 0.01 | 52.50
3.17e+05 | 1 | 0.01 | 0.01 | 52.51
3.18e+05 | 1 | 0.01 | 0.01 | 52.51
3.20e+05 | 154 | 0.85 | 0.87 | 53.39
3.25e+05 | 5 | 0.03 | 0.03 | 53.41
3.27e+05 | 2 | 0.01 | 0.01 | 53.43
3.30e+05 | 24 | 0.13 | 0.14 | 53.56
3.31e+05 | 1 | 0.01 | 0.01 | 53.57
3.37e+05 | 1 | 0.01 | 0.01 | 53.57
3.40e+05 | 21 | 0.12 | 0.12 | 53.69
3.45e+05 | 1 | 0.01 | 0.01 | 53.70
3.50e+05 | 1307 | 7.25 | 7.42 | 61.12
3.55e+05 | 1 | 0.01 | 0.01 | 61.12
3.58e+05 | 1 | 0.01 | 0.01 | 61.13
3.60e+05 | 47 | 0.26 | 0.27 | 61.40
3.65e+05 | 1 | 0.01 | 0.01 | 61.40
3.70e+05 | 35 | 0.19 | 0.20 | 61.60
3.75e+05 | 3 | 0.02 | 0.02 | 61.62
3.76e+05 | 1 | 0.01 | 0.01 | 61.62
3.80e+05 | 273 | 1.51 | 1.55 | 63.17
3.83e+05 | 2 | 0.01 | 0.01 | 63.18
3.85e+05 | 12 | 0.07 | 0.07 | 63.25
3.88e+05 | 1 | 0.01 | 0.01 | 63.26
3.90e+05 | 22 | 0.12 | 0.12 | 63.38
4.00e+05 | 2606 | 14.46 | 14.79 | 78.18
4.06e+05 | 1 | 0.01 | 0.01 | 78.18
4.08e+05 | 1 | 0.01 | 0.01 | 78.19
4.10e+05 | 22 | 0.12 | 0.12 | 78.31
4.20e+05 | 93 | 0.52 | 0.53 | 78.84
4.20e+05 | 1 | 0.01 | 0.01 | 78.85
4.25e+05 | 2 | 0.01 | 0.01 | 78.86
4.26e+05 | 1 | 0.01 | 0.01 | 78.86
4.29e+05 | 1 | 0.01 | 0.01 | 78.87
4.30e+05 | 13 | 0.07 | 0.07 | 78.94
4.35e+05 | 1 | 0.01 | 0.01 | 78.95
4.40e+05 | 13 | 0.07 | 0.07 | 79.02
4.46e+05 | 5 | 0.03 | 0.03 | 79.05
4.47e+05 | 1 | 0.01 | 0.01 | 79.06
4.50e+05 | 628 | 3.48 | 3.57 | 82.62
4.60e+05 | 157 | 0.87 | 0.89 | 83.51
4.66e+05 | 2 | 0.01 | 0.01 | 83.53
4.68e+05 | 1 | 0.01 | 0.01 | 83.53
4.70e+05 | 39 | 0.22 | 0.22 | 83.75
4.75e+05 | 1 | 0.01 | 0.01 | 83.76
4.80e+05 | 92 | 0.51 | 0.52 | 84.28
4.85e+05 | 1 | 0.01 | 0.01 | 84.29
4.90e+05 | 11 | 0.06 | 0.06 | 84.35
4.97e+05 | 1 | 0.01 | 0.01 | 84.35
5.00e+05 | 1531 | 8.50 | 8.69 | 93.05
5.00e+05 | 1 | 0.01 | 0.01 | 93.05
5.20e+05 | 1 | 0.01 | 0.01 | 93.06
5.25e+05 | 1 | 0.01 | 0.01 | 93.06
5.40e+05 | 4 | 0.02 | 0.02 | 93.09
5.50e+05 | 72 | 0.40 | 0.41 | 93.49
5.60e+05 | 4 | 0.02 | 0.02 | 93.52
5.70e+05 | 1 | 0.01 | 0.01 | 93.52
5.80e+05 | 8 | 0.04 | 0.05 | 93.57
6.00e+05 | 562 | 3.12 | 3.19 | 96.76
6.39e+05 | 1 | 0.01 | 0.01 | 96.76
6.50e+05 | 32 | 0.18 | 0.18 | 96.95
6.70e+05 | 1 | 0.01 | 0.01 | 96.95
6.80e+05 | 1 | 0.01 | 0.01 | 96.96
7.00e+05 | 192 | 1.07 | 1.09 | 98.05
7.50e+05 | 5 | 0.03 | 0.03 | 98.08
8.00e+05 | 179 | 0.99 | 1.02 | 99.09
8.50e+05 | 2 | 0.01 | 0.01 | 99.10
9.00e+05 | 18 | 0.10 | 0.10 | 99.21
1.00e+06 | 64 | 0.36 | 0.36 | 99.57
1.00e+06 | 1 | 0.01 | 0.01 | 99.57
1.10e+06 | 1 | 0.01 | 0.01 | 99.58
1.20e+06 | 6 | 0.03 | 0.03 | 99.61
1.50e+06 | 9 | 0.05 | 0.05 | 99.67
1.80e+06 | 1 | 0.01 | 0.01 | 99.67
1.90e+06 | 1 | 0.01 | 0.01 | 99.68
2.00e+06 | 7 | 0.04 | 0.04 | 99.72
2.20e+06 | 1 | 0.01 | 0.01 | 99.72
2.40e+06 | 1 | 0.01 | 0.01 | 99.73
2.50e+06 | 4 | 0.02 | 0.02 | 99.75
2.57e+06 | 2 | 0.01 | 0.01 | 99.76
2.60e+06 | 1 | 0.01 | 0.01 | 99.77
2.70e+06 | 1 | 0.01 | 0.01 | 99.77
2.76e+06 | 1 | 0.01 | 0.01 | 99.78
2.80e+06 | 4 | 0.02 | 0.02 | 99.80
2.80e+06 | 1 | 0.01 | 0.01 | 99.81
2.90e+06 | 1 | 0.01 | 0.01 | 99.81
2.95e+06 | 1 | 0.01 | 0.01 | 99.82
3.00e+06 | 15 | 0.08 | 0.09 | 99.90
3.50e+06 | 1 | 0.01 | 0.01 | 99.91
4.00e+06 | 3 | 0.02 | 0.02 | 99.93
4.60e+06 | 1 | 0.01 | 0.01 | 99.93
5.00e+06 | 4 | 0.02 | 0.02 | 99.95
6.00e+06 | 2 | 0.01 | 0.01 | 99.97
1.00e+07 | 1 | 0.01 | 0.01 | 99.97
1.50e+07 | 1 | 0.01 | 0.01 | 99.98
2.10e+07 | 1 | 0.01 | 0.01 | 99.98
3.50e+07 | 1 | 0.01 | 0.01 | 99.99
2.80e+08 | 1 | 0.01 | 0.01 | 99.99
1.00e+20 | 1 | 0.01 | 0.01 | 100.00
<NA> | 406 | 2.25 | <NA> | <NA>db_proc <- db_proc %>%
mutate(perc_sal_obrero=replace(perc_sal_obrero, perc_sal_obrero <= 40000 | perc_sal_obrero>= 1000000, NA)) %>%
mutate(perc_sal_gerente=replace(perc_sal_gerente, perc_sal_gerente <= 250000 | perc_sal_gerente>= 100000001, NA))
db_proc$perc_inequality <- SciViews::ln(db_proc$perc_sal_gerente/db_proc$perc_sal_obrero)
summary(db_proc$perc_inequality) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-0.9904 2.6391 3.3524 3.3630 4.0943 6.9078 1702 db_proc$perc_inequality <- sjlabelled::set_label(db_proc$perc_inequality,
label = "Inequality gap perception")
# just inequality gap
frq(db_proc$just_sal_gerente)Salario justo: Gerente gran empresa (x) <numeric>
# total N=18021 valid N=16905 mean=5921443374470978.00 sd=769117341165130752.00
Value | N | Raw % | Valid % | Cum. %
------------------------------------------
0.00 | 16 | 0.09 | 0.09 | 0.09
1.00 | 1 | 0.01 | 0.01 | 0.10
888.00 | 1 | 0.01 | 0.01 | 0.11
999.00 | 2 | 0.01 | 0.01 | 0.12
10000.00 | 3 | 0.02 | 0.02 | 0.14
50000.00 | 2 | 0.01 | 0.01 | 0.15
80000.00 | 2 | 0.01 | 0.01 | 0.16
88888.00 | 1 | 0.01 | 0.01 | 0.17
90000.00 | 1 | 0.01 | 0.01 | 0.17
1.00e+05 | 14 | 0.08 | 0.08 | 0.25
1.20e+05 | 1 | 0.01 | 0.01 | 0.26
1.50e+05 | 5 | 0.03 | 0.03 | 0.29
1.80e+05 | 1 | 0.01 | 0.01 | 0.30
2.00e+05 | 23 | 0.13 | 0.14 | 0.43
2.10e+05 | 1 | 0.01 | 0.01 | 0.44
2.41e+05 | 1 | 0.01 | 0.01 | 0.44
2.45e+05 | 1 | 0.01 | 0.01 | 0.45
2.50e+05 | 32 | 0.18 | 0.19 | 0.64
2.57e+05 | 3 | 0.02 | 0.02 | 0.66
2.60e+05 | 5 | 0.03 | 0.03 | 0.69
2.66e+05 | 2 | 0.01 | 0.01 | 0.70
2.70e+05 | 10 | 0.06 | 0.06 | 0.76
2.75e+05 | 1 | 0.01 | 0.01 | 0.76
2.76e+05 | 6 | 0.03 | 0.04 | 0.80
2.78e+05 | 3 | 0.02 | 0.02 | 0.82
2.80e+05 | 10 | 0.06 | 0.06 | 0.88
2.82e+05 | 1 | 0.01 | 0.01 | 0.88
2.86e+05 | 1 | 0.01 | 0.01 | 0.89
2.88e+05 | 4 | 0.02 | 0.02 | 0.91
2.90e+05 | 1 | 0.01 | 0.01 | 0.92
3.00e+05 | 55 | 0.31 | 0.33 | 1.24
3.01e+05 | 2 | 0.01 | 0.01 | 1.25
3.10e+05 | 1 | 0.01 | 0.01 | 1.26
3.20e+05 | 1 | 0.01 | 0.01 | 1.27
3.50e+05 | 16 | 0.09 | 0.09 | 1.36
3.60e+05 | 2 | 0.01 | 0.01 | 1.37
3.80e+05 | 3 | 0.02 | 0.02 | 1.39
4.00e+05 | 64 | 0.36 | 0.38 | 1.77
4.20e+05 | 1 | 0.01 | 0.01 | 1.77
4.50e+05 | 10 | 0.06 | 0.06 | 1.83
4.60e+05 | 1 | 0.01 | 0.01 | 1.84
4.70e+05 | 1 | 0.01 | 0.01 | 1.85
4.80e+05 | 1 | 0.01 | 0.01 | 1.85
5.00e+05 | 238 | 1.32 | 1.41 | 3.26
5.50e+05 | 1 | 0.01 | 0.01 | 3.27
6.00e+05 | 119 | 0.66 | 0.70 | 3.97
6.50e+05 | 2 | 0.01 | 0.01 | 3.98
6.80e+05 | 1 | 0.01 | 0.01 | 3.99
7.00e+05 | 72 | 0.40 | 0.43 | 4.41
7.50e+05 | 8 | 0.04 | 0.05 | 4.46
8.00e+05 | 212 | 1.18 | 1.25 | 5.71
8.50e+05 | 2 | 0.01 | 0.01 | 5.73
9.00e+05 | 62 | 0.34 | 0.37 | 6.09
9.50e+05 | 1 | 0.01 | 0.01 | 6.10
1.00e+06 | 1 | 0.01 | 0.01 | 6.10
1.00e+06 | 1191 | 6.61 | 7.05 | 13.15
1.00e+06 | 1 | 0.01 | 0.01 | 13.16
1.01e+06 | 1 | 0.01 | 0.01 | 13.16
1.10e+06 | 2 | 0.01 | 0.01 | 13.17
1.20e+06 | 108 | 0.60 | 0.64 | 13.81
1.25e+06 | 2 | 0.01 | 0.01 | 13.82
1.30e+06 | 13 | 0.07 | 0.08 | 13.90
1.40e+06 | 10 | 0.06 | 0.06 | 13.96
1.45e+06 | 1 | 0.01 | 0.01 | 13.97
1.50e+06 | 762 | 4.23 | 4.51 | 18.47
1.50e+06 | 2 | 0.01 | 0.01 | 18.49
1.60e+06 | 8 | 0.04 | 0.05 | 18.53
1.65e+06 | 1 | 0.01 | 0.01 | 18.54
1.70e+06 | 7 | 0.04 | 0.04 | 18.58
1.75e+06 | 1 | 0.01 | 0.01 | 18.59
1.80e+06 | 48 | 0.27 | 0.28 | 18.87
1.90e+06 | 4 | 0.02 | 0.02 | 18.89
2.00e+06 | 1925 | 10.68 | 11.39 | 30.28
2.00e+06 | 1 | 0.01 | 0.01 | 30.29
2.00e+06 | 1 | 0.01 | 0.01 | 30.29
2.10e+06 | 2 | 0.01 | 0.01 | 30.30
2.20e+06 | 2 | 0.01 | 0.01 | 30.32
2.30e+06 | 2 | 0.01 | 0.01 | 30.33
2.40e+06 | 2 | 0.01 | 0.01 | 30.34
2.45e+06 | 1 | 0.01 | 0.01 | 30.35
2.50e+06 | 392 | 2.18 | 2.32 | 32.66
2.60e+06 | 1 | 0.01 | 0.01 | 32.67
2.70e+06 | 2 | 0.01 | 0.01 | 32.68
2.80e+06 | 5 | 0.03 | 0.03 | 32.71
3.00e+06 | 1804 | 10.01 | 10.67 | 43.38
3.20e+06 | 1 | 0.01 | 0.01 | 43.39
3.25e+06 | 1 | 0.01 | 0.01 | 43.40
3.40e+06 | 2 | 0.01 | 0.01 | 43.41
3.50e+06 | 129 | 0.72 | 0.76 | 44.17
3.60e+06 | 1 | 0.01 | 0.01 | 44.18
3.70e+06 | 2 | 0.01 | 0.01 | 44.19
3.80e+06 | 3 | 0.02 | 0.02 | 44.21
4.00e+06 | 885 | 4.91 | 5.24 | 49.44
4.20e+06 | 1 | 0.01 | 0.01 | 49.45
4.30e+06 | 1 | 0.01 | 0.01 | 49.45
4.50e+06 | 37 | 0.21 | 0.22 | 49.67
4.80e+06 | 2 | 0.01 | 0.01 | 49.68
4.88e+06 | 1 | 0.01 | 0.01 | 49.69
5.00e+06 | 2576 | 14.29 | 15.24 | 64.93
5.01e+06 | 1 | 0.01 | 0.01 | 64.93
5.50e+06 | 10 | 0.06 | 0.06 | 64.99
6.00e+06 | 627 | 3.48 | 3.71 | 68.70
6.50e+06 | 10 | 0.06 | 0.06 | 68.76
6.80e+06 | 1 | 0.01 | 0.01 | 68.77
7.00e+06 | 417 | 2.31 | 2.47 | 71.23
7.50e+06 | 15 | 0.08 | 0.09 | 71.32
8.00e+06 | 633 | 3.51 | 3.74 | 75.07
8.50e+06 | 7 | 0.04 | 0.04 | 75.11
9.00e+06 | 99 | 0.55 | 0.59 | 75.69
9.50e+06 | 1 | 0.01 | 0.01 | 75.70
9.62e+06 | 1 | 0.01 | 0.01 | 75.71
1.00e+07 | 1672 | 9.28 | 9.89 | 85.60
1.00e+07 | 1 | 0.01 | 0.01 | 85.60
1.00e+07 | 1 | 0.01 | 0.01 | 85.61
1.08e+07 | 1 | 0.01 | 0.01 | 85.61
1.10e+07 | 14 | 0.08 | 0.08 | 85.70
1.20e+07 | 214 | 1.19 | 1.27 | 86.96
1.25e+07 | 4 | 0.02 | 0.02 | 86.99
1.30e+07 | 14 | 0.08 | 0.08 | 87.07
1.35e+07 | 1 | 0.01 | 0.01 | 87.07
1.40e+07 | 19 | 0.11 | 0.11 | 87.19
1.50e+07 | 603 | 3.35 | 3.57 | 90.75
1.60e+07 | 14 | 0.08 | 0.08 | 90.84
1.70e+07 | 13 | 0.07 | 0.08 | 90.91
1.80e+07 | 50 | 0.28 | 0.30 | 91.21
1.90e+07 | 4 | 0.02 | 0.02 | 91.23
2.00e+07 | 577 | 3.20 | 3.41 | 94.65
2.10e+07 | 1 | 0.01 | 0.01 | 94.65
2.20e+07 | 10 | 0.06 | 0.06 | 94.71
2.25e+07 | 1 | 0.01 | 0.01 | 94.72
2.30e+07 | 2 | 0.01 | 0.01 | 94.73
2.40e+07 | 2 | 0.01 | 0.01 | 94.74
2.50e+07 | 141 | 0.78 | 0.83 | 95.58
2.75e+07 | 1 | 0.01 | 0.01 | 95.58
2.80e+07 | 2 | 0.01 | 0.01 | 95.59
2.90e+07 | 2 | 0.01 | 0.01 | 95.60
3.00e+07 | 208 | 1.15 | 1.23 | 96.84
3.20e+07 | 2 | 0.01 | 0.01 | 96.85
3.50e+07 | 11 | 0.06 | 0.07 | 96.91
3.80e+07 | 1 | 0.01 | 0.01 | 96.92
3.90e+07 | 1 | 0.01 | 0.01 | 96.92
4.00e+07 | 61 | 0.34 | 0.36 | 97.28
4.30e+07 | 1 | 0.01 | 0.01 | 97.29
4.50e+07 | 7 | 0.04 | 0.04 | 97.33
4.90e+07 | 1 | 0.01 | 0.01 | 97.34
5.00e+07 | 190 | 1.05 | 1.12 | 98.46
5.50e+07 | 2 | 0.01 | 0.01 | 98.47
5.80e+07 | 1 | 0.01 | 0.01 | 98.48
6.00e+07 | 32 | 0.18 | 0.19 | 98.67
7.00e+07 | 15 | 0.08 | 0.09 | 98.76
7.00e+07 | 1 | 0.01 | 0.01 | 98.76
8.00e+07 | 24 | 0.13 | 0.14 | 98.91
9.00e+07 | 3 | 0.02 | 0.02 | 98.92
1.00e+08 | 65 | 0.36 | 0.38 | 99.31
1.10e+08 | 1 | 0.01 | 0.01 | 99.31
1.20e+08 | 4 | 0.02 | 0.02 | 99.34
1.40e+08 | 1 | 0.01 | 0.01 | 99.34
1.50e+08 | 15 | 0.08 | 0.09 | 99.43
2.00e+08 | 18 | 0.10 | 0.11 | 99.54
2.50e+08 | 4 | 0.02 | 0.02 | 99.56
3.00e+08 | 10 | 0.06 | 0.06 | 99.62
3.80e+08 | 1 | 0.01 | 0.01 | 99.63
4.00e+08 | 4 | 0.02 | 0.02 | 99.65
5.00e+08 | 23 | 0.13 | 0.14 | 99.79
6.00e+08 | 4 | 0.02 | 0.02 | 99.81
7.00e+08 | 2 | 0.01 | 0.01 | 99.82
1.00e+09 | 9 | 0.05 | 0.05 | 99.88
2.00e+09 | 2 | 0.01 | 0.01 | 99.89
3.00e+09 | 4 | 0.02 | 0.02 | 99.91
4.00e+09 | 1 | 0.01 | 0.01 | 99.92
5.00e+09 | 2 | 0.01 | 0.01 | 99.93
6.00e+09 | 1 | 0.01 | 0.01 | 99.93
7.00e+09 | 4 | 0.02 | 0.02 | 99.96
8.00e+09 | 3 | 0.02 | 0.02 | 99.98
1.00e+15 | 1 | 0.01 | 0.01 | 99.98
1.00e+15 | 1 | 0.01 | 0.01 | 99.99
1.00e+17 | 1 | 0.01 | 0.01 | 99.99
1.00e+20 | 1 | 0.01 | 0.01 | 100.00
<NA> | 1116 | 6.19 | <NA> | <NA>frq(db_proc$just_sal_obrero)Salario justo: Obrero no calificado (x) <numeric>
# total N=18021 valid N=17598 mean=56824639164516384.00 sd=7538211934104444928.00
Value | N | Raw % | Valid % | Cum. %
------------------------------------------
0.00 | 7 | 0.04 | 0.04 | 0.04
1.00 | 1 | 0.01 | 0.01 | 0.05
20000.00 | 1 | 0.01 | 0.01 | 0.05
25000.00 | 1 | 0.01 | 0.01 | 0.06
40000.00 | 1 | 0.01 | 0.01 | 0.06
50000.00 | 5 | 0.03 | 0.03 | 0.09
60000.00 | 2 | 0.01 | 0.01 | 0.10
80000.00 | 1 | 0.01 | 0.01 | 0.11
1.00e+05 | 10 | 0.06 | 0.06 | 0.16
1.30e+05 | 1 | 0.01 | 0.01 | 0.17
1.50e+05 | 5 | 0.03 | 0.03 | 0.20
1.60e+05 | 1 | 0.01 | 0.01 | 0.20
1.70e+05 | 1 | 0.01 | 0.01 | 0.21
1.80e+05 | 6 | 0.03 | 0.03 | 0.24
1.90e+05 | 2 | 0.01 | 0.01 | 0.26
2.00e+05 | 51 | 0.28 | 0.29 | 0.55
2.10e+05 | 1 | 0.01 | 0.01 | 0.55
2.12e+05 | 2 | 0.01 | 0.01 | 0.56
2.20e+05 | 8 | 0.04 | 0.05 | 0.61
2.24e+05 | 1 | 0.01 | 0.01 | 0.61
2.25e+05 | 1 | 0.01 | 0.01 | 0.62
2.30e+05 | 5 | 0.03 | 0.03 | 0.65
2.40e+05 | 20 | 0.11 | 0.11 | 0.76
2.45e+05 | 2 | 0.01 | 0.01 | 0.77
2.50e+05 | 137 | 0.76 | 0.78 | 1.55
2.56e+05 | 2 | 0.01 | 0.01 | 1.56
2.57e+05 | 3 | 0.02 | 0.02 | 1.58
2.58e+05 | 1 | 0.01 | 0.01 | 1.59
2.60e+05 | 9 | 0.05 | 0.05 | 1.64
2.67e+05 | 1 | 0.01 | 0.01 | 1.64
2.70e+05 | 41 | 0.23 | 0.23 | 1.88
2.71e+05 | 1 | 0.01 | 0.01 | 1.88
2.72e+05 | 1 | 0.01 | 0.01 | 1.89
2.75e+05 | 4 | 0.02 | 0.02 | 1.91
2.76e+05 | 1 | 0.01 | 0.01 | 1.91
2.76e+05 | 1 | 0.01 | 0.01 | 1.92
2.78e+05 | 1 | 0.01 | 0.01 | 1.93
2.80e+05 | 31 | 0.17 | 0.18 | 2.10
2.82e+05 | 1 | 0.01 | 0.01 | 2.11
2.85e+05 | 2 | 0.01 | 0.01 | 2.12
2.86e+05 | 1 | 0.01 | 0.01 | 2.13
2.90e+05 | 7 | 0.04 | 0.04 | 2.17
2.96e+05 | 1 | 0.01 | 0.01 | 2.17
3.00e+05 | 412 | 2.29 | 2.34 | 4.51
3.00e+05 | 1 | 0.01 | 0.01 | 4.52
3.01e+05 | 5 | 0.03 | 0.03 | 4.55
3.10e+05 | 5 | 0.03 | 0.03 | 4.57
3.15e+05 | 1 | 0.01 | 0.01 | 4.58
3.20e+05 | 11 | 0.06 | 0.06 | 4.64
3.25e+05 | 1 | 0.01 | 0.01 | 4.65
3.30e+05 | 4 | 0.02 | 0.02 | 4.67
3.40e+05 | 5 | 0.03 | 0.03 | 4.70
3.45e+05 | 1 | 0.01 | 0.01 | 4.71
3.50e+05 | 470 | 2.61 | 2.67 | 7.38
3.50e+05 | 1 | 0.01 | 0.01 | 7.38
3.55e+05 | 1 | 0.01 | 0.01 | 7.39
3.58e+05 | 1 | 0.01 | 0.01 | 7.39
3.60e+05 | 8 | 0.04 | 0.05 | 7.44
3.70e+05 | 11 | 0.06 | 0.06 | 7.50
3.75e+05 | 1 | 0.01 | 0.01 | 7.51
3.80e+05 | 57 | 0.32 | 0.32 | 7.83
3.85e+05 | 1 | 0.01 | 0.01 | 7.84
3.90e+05 | 3 | 0.02 | 0.02 | 7.85
4.00e+05 | 1502 | 8.33 | 8.54 | 16.39
4.15e+05 | 1 | 0.01 | 0.01 | 16.39
4.20e+05 | 15 | 0.08 | 0.09 | 16.48
4.25e+05 | 1 | 0.01 | 0.01 | 16.48
4.30e+05 | 5 | 0.03 | 0.03 | 16.51
4.40e+05 | 1 | 0.01 | 0.01 | 16.52
4.46e+05 | 1 | 0.01 | 0.01 | 16.52
4.47e+05 | 1 | 0.01 | 0.01 | 16.53
4.50e+05 | 595 | 3.30 | 3.38 | 19.91
4.60e+05 | 17 | 0.09 | 0.10 | 20.01
4.70e+05 | 2 | 0.01 | 0.01 | 20.02
4.80e+05 | 34 | 0.19 | 0.19 | 20.21
4.90e+05 | 2 | 0.01 | 0.01 | 20.22
5.00e+05 | 4593 | 25.49 | 26.10 | 46.32
5.10e+05 | 4 | 0.02 | 0.02 | 46.35
5.20e+05 | 5 | 0.03 | 0.03 | 46.37
5.40e+05 | 3 | 0.02 | 0.02 | 46.39
5.50e+05 | 281 | 1.56 | 1.60 | 47.99
5.60e+05 | 11 | 0.06 | 0.06 | 48.05
5.70e+05 | 2 | 0.01 | 0.01 | 48.06
5.80e+05 | 18 | 0.10 | 0.10 | 48.16
5.90e+05 | 3 | 0.02 | 0.02 | 48.18
5.99e+05 | 1 | 0.01 | 0.01 | 48.19
6.00e+05 | 2714 | 15.06 | 15.42 | 63.61
6.20e+05 | 3 | 0.02 | 0.02 | 63.63
6.30e+05 | 1 | 0.01 | 0.01 | 63.63
6.40e+05 | 1 | 0.01 | 0.01 | 63.64
6.50e+05 | 185 | 1.03 | 1.05 | 64.69
6.70e+05 | 1 | 0.01 | 0.01 | 64.69
6.80e+05 | 5 | 0.03 | 0.03 | 64.72
6.89e+05 | 1 | 0.01 | 0.01 | 64.73
7.00e+05 | 1337 | 7.42 | 7.60 | 72.33
7.20e+05 | 1 | 0.01 | 0.01 | 72.33
7.50e+05 | 114 | 0.63 | 0.65 | 72.98
7.80e+05 | 1 | 0.01 | 0.01 | 72.99
8.00e+05 | 1858 | 10.31 | 10.56 | 83.54
8.01e+05 | 1 | 0.01 | 0.01 | 83.55
8.20e+05 | 1 | 0.01 | 0.01 | 83.55
8.50e+05 | 64 | 0.36 | 0.36 | 83.92
8.80e+05 | 1 | 0.01 | 0.01 | 83.92
8.90e+05 | 1 | 0.01 | 0.01 | 83.93
9.00e+05 | 286 | 1.59 | 1.63 | 85.56
9.20e+05 | 1 | 0.01 | 0.01 | 85.56
9.50e+05 | 9 | 0.05 | 0.05 | 85.61
9.80e+05 | 1 | 0.01 | 0.01 | 85.62
1.00e+06 | 1681 | 9.33 | 9.55 | 95.17
1.08e+06 | 1 | 0.01 | 0.01 | 95.18
1.10e+06 | 5 | 0.03 | 0.03 | 95.20
1.20e+06 | 167 | 0.93 | 0.95 | 96.15
1.25e+06 | 1 | 0.01 | 0.01 | 96.16
1.30e+06 | 8 | 0.04 | 0.05 | 96.20
1.40e+06 | 9 | 0.05 | 0.05 | 96.26
1.45e+06 | 1 | 0.01 | 0.01 | 96.26
1.50e+06 | 289 | 1.60 | 1.64 | 97.90
1.60e+06 | 2 | 0.01 | 0.01 | 97.91
1.75e+06 | 1 | 0.01 | 0.01 | 97.92
1.80e+06 | 11 | 0.06 | 0.06 | 97.98
1.90e+06 | 1 | 0.01 | 0.01 | 97.99
2.00e+06 | 163 | 0.90 | 0.93 | 98.91
2.50e+06 | 12 | 0.07 | 0.07 | 98.98
2.60e+06 | 1 | 0.01 | 0.01 | 98.99
2.80e+06 | 1 | 0.01 | 0.01 | 98.99
3.00e+06 | 40 | 0.22 | 0.23 | 99.22
4.00e+06 | 18 | 0.10 | 0.10 | 99.32
4.50e+06 | 3 | 0.02 | 0.02 | 99.34
4.80e+06 | 1 | 0.01 | 0.01 | 99.35
5.00e+06 | 34 | 0.19 | 0.19 | 99.54
5.50e+06 | 2 | 0.01 | 0.01 | 99.55
6.00e+06 | 9 | 0.05 | 0.05 | 99.60
7.00e+06 | 6 | 0.03 | 0.03 | 99.64
7.50e+06 | 1 | 0.01 | 0.01 | 99.64
8.00e+06 | 12 | 0.07 | 0.07 | 99.71
8.25e+06 | 1 | 0.01 | 0.01 | 99.72
9.00e+06 | 3 | 0.02 | 0.02 | 99.73
1.00e+07 | 15 | 0.08 | 0.09 | 99.82
1.20e+07 | 1 | 0.01 | 0.01 | 99.82
1.50e+07 | 4 | 0.02 | 0.02 | 99.85
2.00e+07 | 5 | 0.03 | 0.03 | 99.87
3.00e+07 | 2 | 0.01 | 0.01 | 99.89
6.00e+07 | 1 | 0.01 | 0.01 | 99.89
7.00e+07 | 1 | 0.01 | 0.01 | 99.90
1.00e+08 | 3 | 0.02 | 0.02 | 99.91
2.50e+08 | 4 | 0.02 | 0.02 | 99.94
3.00e+08 | 2 | 0.01 | 0.01 | 99.95
3.20e+08 | 3 | 0.02 | 0.02 | 99.97
3.50e+08 | 3 | 0.02 | 0.02 | 99.98
3.60e+08 | 1 | 0.01 | 0.01 | 99.99
4.80e+08 | 1 | 0.01 | 0.01 | 99.99
1.00e+21 | 1 | 0.01 | 0.01 | 100.00
<NA> | 423 | 2.35 | <NA> | <NA>db_proc <- db_proc %>%
mutate(just_sal_obrero=replace(just_sal_obrero, just_sal_obrero <= 40000 | just_sal_obrero>=999999999999999, NA)) %>%
mutate(just_sal_gerente=replace(just_sal_gerente, just_sal_gerente <= 100000 | just_sal_gerente>=999999999999999, NA))
db_proc$just_inequality <- SciViews::ln(db_proc$just_sal_gerente/db_proc$just_sal_obrero)
summary(db_proc$just_inequality) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-6.551 1.204 1.897 1.932 2.639 10.373 1191 db_proc$just_inequality <- sjlabelled::set_label(db_proc$just_inequality,
label = "Inequality gap justification")
# sex
db_proc$sex <- car::recode(db_proc$sex,
recodes = c("1='Male'; 2='Female'"),
levels = c("Male", "Female"),
as.factor = T)
db_proc$sex <- sjlabelled::set_label(db_proc$sex,
label = "Gender")
# wave
db_proc <- db_proc %>%
rename(wave = ola) %>%
mutate(wave = case_when(wave == 1 ~ 1,
wave == 2 ~ 2,
wave == 3 ~ 3,
wave == 4 ~ 4,
wave == 6 ~ 5,
wave == 7 ~ 6,
TRUE ~ NA_real_))
db_proc$wave <- sjlabelled::set_label(db_proc$wave,
label = "Wave")
# age
frq(db_proc$age)Edad del entrevistado (x) <numeric>
# total N=18021 valid N=18021 mean=48.75 sd=15.47
Value | N | Raw % | Valid % | Cum. %
--------------------------------------
18 | 34 | 0.19 | 0.19 | 0.19
19 | 72 | 0.40 | 0.40 | 0.59
20 | 108 | 0.60 | 0.60 | 1.19
21 | 138 | 0.77 | 0.77 | 1.95
22 | 170 | 0.94 | 0.94 | 2.90
23 | 200 | 1.11 | 1.11 | 4.01
24 | 227 | 1.26 | 1.26 | 5.27
25 | 290 | 1.61 | 1.61 | 6.88
26 | 273 | 1.51 | 1.51 | 8.39
27 | 267 | 1.48 | 1.48 | 9.87
28 | 319 | 1.77 | 1.77 | 11.64
29 | 302 | 1.68 | 1.68 | 13.32
30 | 316 | 1.75 | 1.75 | 15.07
31 | 321 | 1.78 | 1.78 | 16.85
32 | 323 | 1.79 | 1.79 | 18.64
33 | 332 | 1.84 | 1.84 | 20.49
34 | 323 | 1.79 | 1.79 | 22.28
35 | 321 | 1.78 | 1.78 | 24.06
36 | 388 | 2.15 | 2.15 | 26.21
37 | 329 | 1.83 | 1.83 | 28.04
38 | 353 | 1.96 | 1.96 | 30.00
39 | 303 | 1.68 | 1.68 | 31.68
40 | 335 | 1.86 | 1.86 | 33.54
41 | 333 | 1.85 | 1.85 | 35.39
42 | 346 | 1.92 | 1.92 | 37.31
43 | 336 | 1.86 | 1.86 | 39.17
44 | 311 | 1.73 | 1.73 | 40.90
45 | 316 | 1.75 | 1.75 | 42.65
46 | 354 | 1.96 | 1.96 | 44.61
47 | 364 | 2.02 | 2.02 | 46.63
48 | 352 | 1.95 | 1.95 | 48.59
49 | 347 | 1.93 | 1.93 | 50.51
50 | 386 | 2.14 | 2.14 | 52.66
51 | 372 | 2.06 | 2.06 | 54.72
52 | 402 | 2.23 | 2.23 | 56.95
53 | 354 | 1.96 | 1.96 | 58.91
54 | 404 | 2.24 | 2.24 | 61.16
55 | 427 | 2.37 | 2.37 | 63.53
56 | 440 | 2.44 | 2.44 | 65.97
57 | 389 | 2.16 | 2.16 | 68.13
58 | 401 | 2.23 | 2.23 | 70.35
59 | 387 | 2.15 | 2.15 | 72.50
60 | 406 | 2.25 | 2.25 | 74.75
61 | 356 | 1.98 | 1.98 | 76.73
62 | 331 | 1.84 | 1.84 | 78.56
63 | 334 | 1.85 | 1.85 | 80.42
64 | 300 | 1.66 | 1.66 | 82.08
65 | 310 | 1.72 | 1.72 | 83.80
66 | 289 | 1.60 | 1.60 | 85.41
67 | 287 | 1.59 | 1.59 | 87.00
68 | 215 | 1.19 | 1.19 | 88.19
69 | 226 | 1.25 | 1.25 | 89.45
70 | 248 | 1.38 | 1.38 | 90.82
71 | 228 | 1.27 | 1.27 | 92.09
72 | 196 | 1.09 | 1.09 | 93.17
73 | 205 | 1.14 | 1.14 | 94.31
74 | 221 | 1.23 | 1.23 | 95.54
75 | 216 | 1.20 | 1.20 | 96.74
76 | 164 | 0.91 | 0.91 | 97.65
77 | 117 | 0.65 | 0.65 | 98.30
78 | 80 | 0.44 | 0.44 | 98.74
79 | 85 | 0.47 | 0.47 | 99.21
80 | 54 | 0.30 | 0.30 | 99.51
81 | 37 | 0.21 | 0.21 | 99.72
82 | 18 | 0.10 | 0.10 | 99.82
83 | 8 | 0.04 | 0.04 | 99.86
84 | 10 | 0.06 | 0.06 | 99.92
85 | 2 | 0.01 | 0.01 | 99.93
86 | 1 | 0.01 | 0.01 | 99.93
87 | 2 | 0.01 | 0.01 | 99.94
88 | 3 | 0.02 | 0.02 | 99.96
89 | 3 | 0.02 | 0.02 | 99.98
90 | 2 | 0.01 | 0.01 | 99.99
91 | 1 | 0.01 | 0.01 | 99.99
92 | 1 | 0.01 | 0.01 | 100.00
<NA> | 0 | 0.00 | <NA> | <NA>db_proc$age <-
factor(car::recode(db_proc$age,
"18:29=1;30:49=2;50:64=3;65:150=4"),
labels = c('18-29', '30-49', '50-64', '65 or more'))
db_proc$age <-
sjlabelled::set_label(db_proc$age,
label = c("Age groups"))
# political indentification
frq(db_proc$ideo)Autoubicacion escala izquierda-derecha (x) <numeric>
# total N=18021 valid N=17760 mean=7.53 sd=3.97
Value | Label | N | Raw % | Valid % | Cum. %
-------------------------------------------------------------------------------
-999 | No Responde | 0 | 0.00 | 0.00 | 0.00
-888 | No Sabe | 0 | 0.00 | 0.00 | 0.00
-777 | Valor perdido por error tecnico | 0 | 0.00 | 0.00 | 0.00
-666 | Valor perdido por encuesta incompleta | 0 | 0.00 | 0.00 | 0.00
0 | 0 Izquierda | 928 | 5.15 | 5.23 | 5.23
1 | 1 | 347 | 1.93 | 1.95 | 7.18
2 | 2 | 545 | 3.02 | 3.07 | 10.25
3 | 3 | 864 | 4.79 | 4.86 | 15.11
4 | 4 | 1024 | 5.68 | 5.77 | 20.88
5 | 5 Centro | 4408 | 24.46 | 24.82 | 45.70
6 | 6 | 637 | 3.53 | 3.59 | 49.28
7 | 7 | 637 | 3.53 | 3.59 | 52.87
8 | 8 | 543 | 3.01 | 3.06 | 55.93
9 | 9 | 166 | 0.92 | 0.93 | 56.86
10 | 10 Derecha | 937 | 5.20 | 5.28 | 62.14
11 | 11 Independiente | 670 | 3.72 | 3.77 | 65.91
12 | 12 Ninguno | 6054 | 33.59 | 34.09 | 100.00
<NA> | <NA> | 261 | 1.45 | <NA> | <NA>db_proc$ideo<-
factor(
car::recode(
db_proc$ideo,
"c(11,12,-888,-999)='Does not identify';c(0,1,2,3,4)='Left';
c(5)='Center';c(6,7,8,9,10)='Right'"
),
levels = c('Left', 'Center', 'Right', 'Does not identify')
)
db_proc$ideo<- factor(db_proc$ideo,levels = levels(db_proc$ideo))
db_proc$ideo <-
sjlabelled::set_label(x = db_proc$ideo,
label = "Political identification")
frq(db_proc$ideo)Political identification (x) <categorical>
# total N=18021 valid N=17760 mean=2.71 sd=1.17
Value | N | Raw % | Valid % | Cum. %
---------------------------------------------------
Left | 3708 | 20.58 | 20.88 | 20.88
Center | 4408 | 24.46 | 24.82 | 45.70
Right | 2920 | 16.20 | 16.44 | 62.14
Does not identify | 6724 | 37.31 | 37.86 | 100.00
<NA> | 261 | 1.45 | <NA> | <NA># ess
sjmisc::frq(db_proc$ess)Estatus Social Subjetivo: Donde se ubicaria ud. en la sociedad chilena (x) <numeric>
# total N=18021 valid N=17926 mean=4.41 sd=1.60
Value | Label | N | Raw % | Valid % | Cum. %
-------------------------------------------------------------------------------
-999 | No Responde | 0 | 0.00 | 0.00 | 0.00
-888 | No Sabe | 0 | 0.00 | 0.00 | 0.00
-777 | Valor perdido por error tecnico | 0 | 0.00 | 0.00 | 0.00
-666 | Valor perdido por encuesta incompleta | 0 | 0.00 | 0.00 | 0.00
0 | 0 El nivel mas bajo | 369 | 2.05 | 2.06 | 2.06
1 | 1 | 488 | 2.71 | 2.72 | 4.78
2 | 2 | 1004 | 5.57 | 5.60 | 10.38
3 | 3 | 2435 | 13.51 | 13.58 | 23.97
4 | 4 | 3996 | 22.17 | 22.29 | 46.26
5 | 5 | 6765 | 37.54 | 37.74 | 84.00
6 | 6 | 1656 | 9.19 | 9.24 | 93.23
7 | 7 | 571 | 3.17 | 3.19 | 96.42
8 | 8 | 440 | 2.44 | 2.45 | 98.87
9 | 9 | 66 | 0.37 | 0.37 | 99.24
10 | 10 El nivel mas alto | 136 | 0.75 | 0.76 | 100.00
<NA> | <NA> | 95 | 0.53 | <NA> | <NA>db_proc$ess <- as.numeric(db_proc$ess)
db_proc$ess <- sjlabelled::set_label(x = db_proc$ess,
label = "Subjective Social Status")
# educ
frq(db_proc$m01)Nivel educacional (x) <numeric>
# total N=18021 valid N=18006 mean=5.28 sd=2.23
Value | Label | N | Raw % | Valid % | Cum. %
-------------------------------------------------------------------------------------
-999 | No Responde | 0 | 0.00 | 0.00 | 0.00
-888 | No Sabe | 0 | 0.00 | 0.00 | 0.00
-777 | Valor perdido por error tecnico | 0 | 0.00 | 0.00 | 0.00
-666 | Valor perdido por encuesta incompleta | 0 | 0.00 | 0.00 | 0.00
1 | Sin estudios | 182 | 1.01 | 1.01 | 1.01
2 | Educacion Basica o Preparatoria incompleta | 2132 | 11.83 | 11.84 | 12.85
3 | Educacion Basica o Preparatoria completa | 1749 | 9.71 | 9.71 | 22.56
4 | Educacion Media o Humanidades incompleta | 2338 | 12.97 | 12.98 | 35.55
5 | Educacion Media o Humanidades completa | 5303 | 29.43 | 29.45 | 65.00
6 | Tecnica Superior incompleta | 656 | 3.64 | 3.64 | 68.64
7 | Tecnica Superior completa | 2264 | 12.56 | 12.57 | 81.22
8 | Universitaria incompleta | 1030 | 5.72 | 5.72 | 86.94
9 | Universitaria completa | 2053 | 11.39 | 11.40 | 98.34
10 | Estudios de posgrado (magister o doctorado) | 299 | 1.66 | 1.66 | 100.00
<NA> | <NA> | 15 | 0.08 | <NA> | <NA>db_proc$educ <-
car::recode(db_proc$m01,
"c(1,2,3,4,5,6,7)=1;c(8,9,10)=2; c(-888,-999)=NA")
db_proc$educ <-
factor(db_proc$educ,
labels = c("Less than Universitary","Universitary"))
db_proc$educ <-
sjlabelled::set_label(x = db_proc$educ,
label = "Education")
#Recoding of education to years based on casen 2017.
db_proc$educyear<- as.numeric(
car::recode(db_proc$m01,
"1=0;2=4.3;3=7.5;4=9.8;5=12.02;6=13.9;
7=14.8;8=14.9;9=16.9;10=19.07;c(-888,-999)=NA",
as.numeric = T))
db_proc$educyear <-
sjlabelled::set_label(x = db_proc$educyear,
label = "Education in years")
class(db_proc$educyear)[1] "numeric"frq(db_proc$educyear)Education in years (x) <numeric>
# total N=18021 valid N=18006 mean=11.51 sd=4.00
Value | N | Raw % | Valid % | Cum. %
---------------------------------------
0.00 | 182 | 1.01 | 1.01 | 1.01
4.30 | 2132 | 11.83 | 11.84 | 12.85
7.50 | 1749 | 9.71 | 9.71 | 22.56
9.80 | 2338 | 12.97 | 12.98 | 35.55
12.02 | 5303 | 29.43 | 29.45 | 65.00
13.90 | 656 | 3.64 | 3.64 | 68.64
14.80 | 2264 | 12.56 | 12.57 | 81.22
14.90 | 1030 | 5.72 | 5.72 | 86.94
16.90 | 2053 | 11.39 | 11.40 | 98.34
19.07 | 299 | 1.66 | 1.66 | 100.00
<NA> | 15 | 0.08 | <NA> | <NA># Household income----------------------------------------------------------------
# Ltop is the lower limit of the top category, Ltop-1 is the lower limit of the category before the
# top one, ftop is the frequency in the top category, and ftop-1 is the frequency in the
# category before the top one.
sjmisc::frq( elsoc_long_2016_2023$m30)Ingreso total del hogar (en 20 tramos) (x) <numeric>
# total N=20761 valid N=4825 mean=-120.34 sd=330.86
Value | Label | N | Raw % | Valid % | Cum. %
----------------------------------------------------------------------------------------
-999 | No Responde | 424 | 2.04 | 8.79 | 8.79
-888 | No Sabe | 221 | 1.06 | 4.58 | 13.37
-777 | Valor perdido por error tecnico | 0 | 0.00 | 0.00 | 13.37
-666 | Valor perdido por encuesta incompleta | 0 | 0.00 | 0.00 | 13.37
1 | Menos de $220.000 mensuales liquidos | 332 | 1.60 | 6.88 | 20.25
2 | De $220.001 a $280.000 mensuales liquidos | 259 | 1.25 | 5.37 | 25.62
3 | De $280.001 a $330.000 mensuales liquidos | 267 | 1.29 | 5.53 | 31.15
4 | De $330.001 a $380.000 mensuales liquidos | 219 | 1.05 | 4.54 | 35.69
5 | De $380.001 a $420.000 mensuales liquidos | 296 | 1.43 | 6.13 | 41.82
6 | De $420.001 a $470.000 mensuales liquidos | 225 | 1.08 | 4.66 | 46.49
7 | De $470.001 a $510.000 mensuales liquidos | 326 | 1.57 | 6.76 | 53.24
8 | De $510.001 a $560.000 mensuales liquidos | 145 | 0.70 | 3.01 | 56.25
9 | De $560.001 a $610.000 mensuales liquidos | 253 | 1.22 | 5.24 | 61.49
10 | De $610.001 a $670.000 mensuales liquidos | 125 | 0.60 | 2.59 | 64.08
11 | De $670.001 a $730.000 mensuales liquidos | 184 | 0.89 | 3.81 | 67.90
12 | De $730.001 a $800.000 mensuales liquidos | 196 | 0.94 | 4.06 | 71.96
13 | De $800.001 a $890.000 mensuales liquidos | 129 | 0.62 | 2.67 | 74.63
14 | De $890.001 a $980.000 mensuales liquidos | 118 | 0.57 | 2.45 | 77.08
15 | De $980.001 a $1.100.000 mensuales liquidos | 249 | 1.20 | 5.16 | 82.24
16 | De $1.100.001 a $1.260.000 mensuales liquidos | 175 | 0.84 | 3.63 | 85.87
17 | De $1.260.001 a $1.490.000 mensuales liquidos | 124 | 0.60 | 2.57 | 88.44
18 | De $1.490.001 a $1.850.000 mensuales liquidos | 219 | 1.05 | 4.54 | 92.97
19 | De $1.850.001 a $2.700.000 mensuales liquidos | 191 | 0.92 | 3.96 | 96.93
20 | Mas de $2.700.000 a mensuales liquidos | 148 | 0.71 | 3.07 | 100.00
<NA> | <NA> | 15936 | 76.76 | <NA> | <NA>Ltop_1<- 1850001
Ltop <- 2700000
ftop_1 <- 42
ftop <- 34
V = (log(ftop_1 + ftop) - log(ftop)) / (log(Ltop) -log(Ltop_1))
M_top = 0.5* Ltop *(1+(V/(V-1)));M_top # = $3.897.232[1] 3897232#Impute midpoint of income ranges
elsoc_long_2016_2023$m30_rec <-
as.numeric(car::recode( elsoc_long_2016_2023$m30,
"1=110000;2=251000;3=305000;4=355000;5=400000;
6=445000;7=490000;8=535000;9=585000;10=640000;11=700000;12=765000;
13=845000;14=935000;15=1040000;16=1180000;17=1375000;18=1670000;
19=2275000;20=3897232;NA=NA;c(-888,-999)=NA"))
summary( elsoc_long_2016_2023$m30_rec) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
110000 355000 585000 826707 1040000 3897232 16581 #Impute midpoint of income ranges (2021)
frq(elsoc_long_2016_2023$m30b)Ingreso total del hogar (en 5 tramos) (x) <numeric>
# total N=20761 valid N=248 mean=-228.60 sd=406.42
Value | Label | N | Raw %
--------------------------------------------------------------------------
-999 | No Responde | 27 | 0.13
-888 | No Sabe | 34 | 0.16
-777 | Valor perdido por error tecnico | 0 | 0.00
-666 | Valor perdido por encuesta incompleta | 0 | 0.00
1 | Menos de $250.000 pesos mensuales liquidos | 58 | 0.28
2 | Entre $250.000 y $350.000 pesos mensuales liquidos | 45 | 0.22
3 | Entre $350.000 y $450.000 pesos mensuales liquidos | 39 | 0.19
4 | Entre $450.000 y $700.000 pesos mensuales liquidos | 18 | 0.09
5 | Mas de 700.000 pesos mensuales liquidos | 27 | 0.13
<NA> | <NA> | 20513 | 98.81
Value | Valid % | Cum. %
------------------------
-999 | 10.89 | 10.89
-888 | 13.71 | 24.60
-777 | 0.00 | 24.60
-666 | 0.00 | 24.60
1 | 23.39 | 47.98
2 | 18.15 | 66.13
3 | 15.73 | 81.85
4 | 7.26 | 89.11
5 | 10.89 | 100.00
<NA> | <NA> | <NA>Ltop_1<- 450000
Ltop <- 700000
ftop_1 <- 18
ftop <- 27
V = (log(ftop_1 + ftop) - log(ftop)) / (log(Ltop) -log(Ltop_1))
M_top = 0.5* Ltop *(1+(V/(V-1))) # = $2.941.412
elsoc_long_2016_2023$m30b_rec <-
as.numeric(car::recode( elsoc_long_2016_2023$m30b,
"1=125000;2=300000;3=400000;4=575000;5=2941412;NA=NA;c(-888,-999)=NA"))
summary( elsoc_long_2016_2023$m30b_rec) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
125000 125000 300000 674428 400000 2941412 20574 # Combine m30_rec with m30b_rec
elsoc_long_2016_2023$m30_rec <-
ifelse(is.na( elsoc_long_2016_2023$m30_rec),
yes = elsoc_long_2016_2023$m30b_rec,
no = elsoc_long_2016_2023$m30_rec)
summary( elsoc_long_2016_2023$m30_rec) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
110000 355000 535000 820186 1040000 3897232 16394 #Recode DK/DA of Income to NA
elsoc_long_2016_2023$m29_rec <-
as.numeric(car::recode( elsoc_long_2016_2023$m29,"c(-888,-999)=NA"))
#replace NA of income with new imputed variable
elsoc_long_2016_2023$m29_imp <-
ifelse(test = !is.na( elsoc_long_2016_2023$m29_rec),
yes = elsoc_long_2016_2023$m29_rec,
no = elsoc_long_2016_2023$m30_rec)
summary( elsoc_long_2016_2023$m29_imp) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-777 300000 500000 960751 800000 4000000000 690 # elsoc_long_2016_2023$m29_imp <-
# ifelse(test = is.na( elsoc_long_2016_2023$m29_imp),
# yes = elsoc_long_2016_2023$m30b_rec,
# no = elsoc_long_2016_2023$m29_imp)
summary( elsoc_long_2016_2023$m29_imp) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-777 300000 500000 960751 800000 4000000000 690 # N Household:
elsoc_long_2016_2023 <-
elsoc_long_2016_2023 %>%
mutate(n_hogar =
dplyr::case_when(ola == 1 ~ nhogar1,
ola == 2 ~ m46_nhogar,
ola == 3 ~ m54,
ola == 4 ~ m54,
ola == 5 ~ m54,
ola == 6 ~ m54,
ola == 7 ~ m54))
summary( elsoc_long_2016_2023$n_hogar) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-999.000 2.000 3.000 1.746 4.000 15.000 3054 #imputar tamanio del hogar de ola 5 a ola 6
elsoc_long_2016_2023 <-
elsoc_long_2016_2023 %>%
group_by(idencuesta) %>%
mutate(n_hogar = if_else(ola == 6 & is.na(n_hogar), last(n_hogar[ola == 5]), n_hogar)) %>%
ungroup()
sjmisc::frq( elsoc_long_2016_2023$n_hogar)x <numeric>
# total N=20761 valid N=19969 mean=1.87 sd=35.14
Value | N | Raw % | Valid % | Cum. %
---------------------------------------
-999 | 19 | 0.09 | 0.10 | 0.10
-888 | 7 | 0.03 | 0.04 | 0.13
1 | 2854 | 13.75 | 14.29 | 14.42
2 | 4926 | 23.73 | 24.67 | 39.09
3 | 4664 | 22.47 | 23.36 | 62.45
4 | 4066 | 19.58 | 20.36 | 82.81
5 | 2029 | 9.77 | 10.16 | 92.97
6 | 826 | 3.98 | 4.14 | 97.11
7 | 327 | 1.58 | 1.64 | 98.74
8 | 151 | 0.73 | 0.76 | 99.50
9 | 47 | 0.23 | 0.24 | 99.73
10 | 29 | 0.14 | 0.15 | 99.88
11 | 10 | 0.05 | 0.05 | 99.93
12 | 8 | 0.04 | 0.04 | 99.97
13 | 2 | 0.01 | 0.01 | 99.98
14 | 2 | 0.01 | 0.01 | 99.99
15 | 2 | 0.01 | 0.01 | 100.00
<NA> | 792 | 3.81 | <NA> | <NA>table( elsoc_long_2016_2023$n_hogar, elsoc_long_2016_2023$ola)
1 2 3 4 5 6 7
-999 0 0 3 6 1 1 8
-888 0 0 4 0 0 0 3
1 429 580 556 396 299 252 342
2 749 576 852 808 652 550 739
3 690 549 872 782 641 516 614
4 584 288 791 750 608 495 550
5 295 95 415 385 314 263 262
6 111 44 148 183 125 102 113
7 38 4 69 52 62 50 52
8 23 8 20 33 24 19 24
9 5 3 8 10 7 7 7
10 1 2 7 6 3 3 7
11 0 0 1 5 1 1 2
12 0 0 1 0 2 2 3
13 1 0 0 1 0 0 0
14 1 0 1 0 0 0 0
15 0 0 0 0 1 1 0#Recode DK/DA to NA
elsoc_long_2016_2023$n_hogar_r<-
car::recode( elsoc_long_2016_2023$n_hogar,"c(-888,-999)=NA")
table( elsoc_long_2016_2023$m13, elsoc_long_2016_2023$n_hogar_r)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
-999 87 123 128 127 35 16 8 2 1 0 0 1 0 0 0
-888 63 105 139 108 59 14 9 1 1 1 0 0 0 0 0
0 4 8 6 8 1 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
10 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
256 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
1500 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0
10000 1 1 0 2 0 0 0 0 0 0 0 0 0 0 0
11000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
12000 3 0 0 1 0 0 1 0 0 0 0 0 0 0 0
15000 0 1 2 1 0 0 0 0 0 0 0 0 0 0 0
20000 2 6 2 8 1 0 0 0 0 0 0 0 0 0 0
21000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
25000 0 4 2 1 0 0 1 0 0 0 0 0 0 0 0
30000 3 4 10 5 2 1 2 1 0 0 0 0 0 0 0
33000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
35000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
39000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
40000 7 10 7 8 1 1 1 0 0 0 0 0 0 0 0
45000 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0
48000 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
50000 11 20 9 12 7 7 2 0 0 0 0 0 0 0 0
52000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
55000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
60000 4 12 12 12 7 1 1 0 0 0 0 0 0 0 0
65000 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0
66000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
67000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
68000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
70000 4 8 7 5 2 2 1 0 0 0 0 0 0 0 0
75000 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0
78000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
80000 11 21 14 11 10 4 0 1 0 0 0 0 0 0 0
84000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
85000 0 2 1 1 0 0 0 0 0 0 0 0 0 0 0
90000 1 7 4 6 1 1 2 1 0 0 0 0 0 0 0
92000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
95000 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0
96000 1 0 1 5 0 0 0 0 0 0 0 0 0 0 0
97000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
100000 23 48 40 39 18 11 0 1 0 0 0 0 0 0 0
101000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
102000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
105000 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0
107000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
109000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
110000 2 0 2 0 0 1 0 0 0 0 1 0 0 0 0
111000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
112000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
114000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
114444 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
115000 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
120000 11 34 20 29 13 3 5 0 0 0 0 0 0 0 0
120008 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
122000 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0
123000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
125000 0 2 2 4 0 0 0 0 0 0 0 0 0 0 0
127000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
128000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
130000 3 5 6 8 2 1 1 0 0 0 0 0 0 0 0
135000 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0
136000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
137000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
140000 5 4 4 3 3 0 0 0 0 0 0 0 0 0 0
144000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
145000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
146000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
150000 21 46 51 41 27 6 3 2 1 0 0 0 0 0 0
155000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
156000 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
160000 6 15 10 10 4 3 0 0 0 0 0 0 0 0 0
162000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
165000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
168000 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0
170000 2 8 3 4 1 2 0 0 0 0 0 0 0 0 0
176000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
179000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
180000 11 24 21 24 4 5 2 1 0 0 0 0 0 0 0
182000 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0
183000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
185000 0 1 2 0 1 0 0 0 0 0 0 0 0 0 0
190000 4 3 3 5 3 0 1 0 0 0 0 0 0 0 0
194000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
198000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
200000 58 105 115 92 45 14 4 6 0 0 0 0 0 0 0
203000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
206000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
207000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
208000 0 0 1 2 0 0 0 0 0 0 0 0 0 0 0
210000 4 2 2 3 4 0 0 0 0 0 0 0 0 0 0
212000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
213000 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0
215000 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0
216000 0 1 1 2 0 0 0 0 0 0 0 0 0 0 0
219000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
220000 6 9 13 6 3 3 0 0 0 0 0 0 0 0 0
222000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
224000 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0
225000 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0
228000 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
230000 2 7 14 5 5 2 2 0 0 0 0 0 0 0 0
233000 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
235000 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0
238000 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
239000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
240000 10 18 23 21 6 3 1 0 0 0 0 0 0 0 0
241000 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0
245000 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0
246000 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
249000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
250000 44 94 89 69 34 14 2 2 1 0 0 0 0 0 0
251000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
252000 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0
255000 1 2 1 1 0 0 0 0 0 0 0 0 0 0 0
257000 0 2 2 4 0 1 1 0 0 0 0 0 0 0 0
257500 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
258000 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0
260000 9 16 15 5 2 4 1 1 0 0 0 0 0 0 0
261000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
263000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
264000 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
265000 2 1 1 1 0 0 0 0 0 0 0 0 0 0 0
266000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
267000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
268000 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0
270000 23 36 43 22 8 6 0 2 1 0 0 0 0 0 0
272000 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
273000 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
275000 2 4 5 2 3 0 0 0 0 0 0 0 0 0 0
276000 3 4 6 3 3 0 0 0 0 0 0 0 0 0 0
277000 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0
278000 1 2 0 1 0 0 0 0 0 0 0 0 0 0 0
279000 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0
280000 20 40 35 25 15 4 0 1 1 0 0 0 0 0 0
283000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
284000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
285000 1 1 6 3 0 1 0 0 0 0 0 0 0 0 0
286000 0 2 0 1 1 0 0 0 0 0 0 0 0 0 0
287000 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0
288000 1 0 1 4 1 0 0 0 0 0 0 0 0 0 0
290000 4 2 10 9 2 0 0 1 0 0 0 0 0 0 0
291000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
295000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
297000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
298000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
300000 117 200 191 149 80 43 10 7 2 2 0 0 0 0 1
300001 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
300008 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
301000 1 9 11 7 3 3 1 1 0 0 0 0 0 0 0
302000 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0
303000 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0
304000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
305000 3 1 1 0 1 0 0 0 0 0 0 0 0 0 0
306000 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0
307000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
308000 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
310000 1 7 5 8 4 2 0 0 0 0 0 0 0 0 0
312000 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0
313000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
314000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
315000 0 3 4 2 0 0 0 0 0 0 0 0 0 0 0
316000 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
318500 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
320000 18 35 45 21 9 5 3 2 2 0 0 0 1 0 0
320500 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
321000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
322000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
324000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
325000 0 3 2 1 3 0 0 0 0 0 0 0 0 0 0
326000 1 1 2 1 1 1 0 0 0 1 0 0 0 0 0
326400 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
326500 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0
327000 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0
327500 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
328000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
329000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
330000 4 10 11 9 5 0 1 0 0 0 0 0 0 0 0
332000 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
333000 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0
333333 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
335000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
338000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
340000 5 10 6 9 3 1 1 0 1 0 0 0 0 0 0
344000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
345000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
346000 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
347000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
348000 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0
349000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
350000 62 117 120 89 57 21 12 1 0 0 1 0 0 0 0
350080 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
351000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
352000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
356000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
357000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
358000 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
360000 7 18 17 15 8 1 1 0 0 0 0 0 0 0 0
361000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
362000 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0
365000 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0
367000 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0
368000 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0
369000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
370000 4 11 12 7 5 3 2 0 0 0 0 0 0 0 1
372000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
373000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
375000 0 2 3 3 0 0 0 0 0 0 0 0 0 0 0
377000 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0
378000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
380000 18 35 39 40 21 10 3 2 0 0 0 0 0 0 0
382000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
382500 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
384000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
385000 0 1 1 2 0 0 0 0 0 0 0 0 0 0 0
388000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
389000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
390000 2 6 8 7 3 0 0 0 0 0 0 0 0 0 0
391000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
392000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
394000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
395000 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0
396000 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0
397000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
398000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
400000 103 208 207 198 98 45 9 8 1 1 1 0 0 0 0
400020 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
403000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
405000 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0
408000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
410000 0 6 1 1 1 1 0 0 0 0 0 0 0 0 0
412000 1 2 0 1 1 0 0 0 0 0 0 0 0 0 0
413000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
414000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
415000 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0
418000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
420000 7 14 19 9 8 7 1 2 1 0 0 0 0 0 0
421000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
422000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
430000 5 8 4 14 5 2 0 0 1 0 0 0 0 0 0
435000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
436000 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
440000 0 5 4 8 0 1 0 0 0 0 0 0 0 0 0
445000 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
446000 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0
448000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
450000 48 83 90 72 43 20 5 3 1 1 0 0 0 0 0
450008 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
452000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
455000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
458000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
460000 4 10 13 17 5 2 1 0 1 0 0 0 0 0 0
464000 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0
465000 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
468000 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
470000 1 8 8 6 3 1 0 0 0 0 0 0 0 0 0
474000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
475000 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
476000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
477000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
478000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
480000 16 17 27 29 9 1 3 3 0 1 0 0 0 0 0
485000 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
490000 3 3 6 3 2 3 0 0 0 0 0 0 0 0 0
492000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
495000 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
496000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
497000 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
500000 118 173 204 174 85 26 18 6 1 2 1 0 0 0 0
504000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
508000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
510000 0 2 2 2 1 0 0 0 0 0 0 0 0 0 0
512000 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0
513000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
515000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
517000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
520000 4 4 7 13 7 2 1 0 1 0 0 0 0 0 0
525000 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0
530000 4 7 1 2 0 0 0 0 0 0 0 0 0 0 0
531000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
535000 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0
540000 0 7 1 3 0 1 0 0 0 0 0 0 0 0 0
543000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
545000 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
547000 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0
549000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
550000 24 36 42 40 21 9 0 2 1 0 1 0 0 0 0
552000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
558000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
560000 2 8 3 6 5 1 0 0 0 1 0 0 0 0 0
563000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
570000 1 8 1 4 4 0 1 0 0 0 0 0 0 0 0
571000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
575000 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0
579000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
580000 3 9 11 9 1 0 0 0 0 0 0 0 0 0 0
582000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
583000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
590000 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0
600000 68 138 126 136 75 22 16 10 0 0 0 2 0 0 0
605000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
610000 0 4 2 0 0 0 0 0 0 0 0 0 0 0 0
620000 2 6 6 2 4 1 0 0 0 0 1 0 0 0 0
625000 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0
630000 1 3 5 3 0 0 0 0 0 0 0 0 0 0 0
632000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
640000 0 1 3 0 1 0 0 0 0 0 0 0 0 0 0
650000 18 28 37 35 18 1 0 2 0 1 0 1 0 0 0
651000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
658000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
660000 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0
665000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
670000 0 2 1 1 1 0 0 0 0 0 0 0 0 0 0
680000 2 8 7 9 4 1 0 0 0 0 0 0 0 0 0
690000 0 2 3 3 0 0 0 0 0 0 0 0 0 0 0
694000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
695000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
700000 47 84 99 88 45 19 11 1 1 0 2 1 0 0 0
701000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
702000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
710000 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0
715000 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0
720000 1 3 2 2 2 0 1 0 0 0 0 0 0 0 0
730000 1 1 3 1 1 0 0 0 0 0 0 0 0 0 0
732000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
735000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
740000 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0
750000 14 29 35 21 16 2 1 0 0 0 0 0 0 0 0
760000 1 0 4 1 0 0 0 0 0 0 0 0 0 0 0
765000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
770000 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0
775000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
779000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
780000 1 5 1 3 1 2 0 0 0 0 0 0 0 0 0
785000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
790000 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0
800000 49 90 111 73 54 17 4 3 0 1 0 0 0 0 0
809000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
810000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
813000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
815000 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0
820000 2 0 1 2 1 0 0 0 0 0 0 0 0 0 0
830000 0 2 0 3 0 0 1 0 0 0 0 0 0 0 0
835000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
840000 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0
850000 11 22 26 27 9 3 3 1 0 0 0 0 0 0 0
850001 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
855000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
860000 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
870000 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0
875000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
880000 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0
890000 1 1 4 2 1 0 1 0 1 0 0 0 0 0 0
900000 30 41 48 34 14 9 1 0 0 1 0 0 0 0 0
910000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
920000 1 1 5 2 0 0 1 1 0 1 0 0 0 0 0
930000 2 1 0 1 0 2 0 0 0 0 0 0 0 0 0
936000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
940000 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
945000 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0
946000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
950000 6 5 12 10 4 0 1 0 0 0 0 0 0 0 0
956000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
960000 0 3 2 2 0 0 0 0 0 0 0 0 0 0 0
970000 1 0 2 1 1 0 0 0 0 0 0 0 0 0 0
980000 0 3 3 4 1 0 0 0 0 0 0 0 0 0 0
984000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
990000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
1000000 38 55 101 64 35 10 5 0 1 1 0 0 0 0 0
1000500 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
1025000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
1030000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1049000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
1050000 0 6 4 1 1 0 0 0 0 0 0 0 0 0 0
1060000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
1065000 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
1066000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
1070000 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0
1078000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
1100000 11 13 17 17 8 4 1 0 0 0 0 0 0 0 0
1120000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
1150000 2 3 1 3 0 0 0 0 0 1 0 0 0 0 0
1200000 29 40 56 49 17 12 2 1 0 0 1 1 0 0 0
1250000 0 2 4 1 0 0 0 0 0 0 0 0 0 0 0
1260000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
1280000 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0
1300000 4 19 21 27 8 4 3 0 0 0 0 0 0 0 0
1340000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
1350000 2 1 3 2 1 0 1 0 0 0 0 0 0 0 0
1400000 9 6 27 15 4 1 0 0 0 0 0 0 0 0 0
1450000 1 3 3 1 0 0 0 0 0 0 0 0 0 0 0
1470000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
1480000 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
1490000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
1500000 31 46 44 40 19 4 1 0 0 0 0 0 0 0 0
1600000 10 10 13 6 4 0 0 1 0 0 0 0 0 0 0
1650000 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0
1700000 9 7 10 8 2 1 0 0 0 0 0 0 0 0 0
1750000 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0
1800000 9 4 13 13 5 0 0 0 0 0 0 0 0 0 0
1836000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
1850000 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
1870000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
1874000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
1900000 2 5 4 5 0 0 1 1 0 0 0 0 0 0 0
1950000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1990000 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
2000000 14 21 34 26 5 5 3 0 1 0 0 0 0 0 0
2050000 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
2100000 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0
2160000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
2200000 2 3 3 1 0 1 0 0 0 0 0 0 0 0 0
2250000 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0
2300000 1 2 3 1 2 0 0 0 0 0 0 0 0 0 0
2400000 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0
2500000 10 12 8 3 2 1 1 0 0 0 0 0 0 0 0
2600000 0 1 1 2 2 0 0 0 0 0 0 0 0 0 0
2700000 2 1 0 1 0 0 0 0 0 0 0 0 0 0 0
2700040 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
2800000 1 1 2 3 1 0 0 0 0 0 0 0 0 0 0
2900000 0 1 1 1 0 2 0 0 0 0 0 0 0 0 0
3000000 4 8 10 10 1 2 1 0 0 0 0 0 0 0 0
3100000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3200000 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0
3400000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
3500000 0 4 3 5 1 1 0 0 0 0 0 0 0 0 0
3600000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
4000000 1 4 2 6 2 1 0 0 0 1 0 0 0 0 0
4500000 2 1 0 0 1 0 0 0 0 0 0 0 0 0 0
4800000 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
5000000 3 1 5 1 2 0 0 0 0 0 0 0 0 0 0
5500000 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0
6000000 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0
6500000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7000000 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0
9000000 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0
9491286 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
10000000 0 0 2 1 1 0 0 0 0 0 0 0 0 0 0
15000000 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0
20000000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
999999999 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0#ingresos individuales para hogares de 1 persona
elsoc_long_2016_2023 %>% sjmisc::find_var('Ingreso') col.nr var.name var.label
1 587 m13 Ingreso mensual entrevistado (monto)
2 588 m14 Ingreso mensual del entrevistado (en 16 tramos)
3 589 m14b Ingreso mensual del entrevistado (en 5 tramos)
4 609 m29 Ingreso total del hogar (monto)
5 610 m30 Ingreso total del hogar (en 20 tramos)
6 611 m30b Ingreso total del hogar (en 5 tramos) elsoc_long_2016_2023$inc_ind <- ifelse( elsoc_long_2016_2023$n_hogar_r==1, elsoc_long_2016_2023$m13,NA)
summary( elsoc_long_2016_2023$inc_ind) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-999 250000 400000 540774 650000 9000000 19176 #
# c(40000,
# (40001 + 85000) / 2,
# (85001 + 125000) / 2,
# (125001 + 170000) / 2,
# (170001 + 210000) / 2,
# (210001 + 230000) / 2,
# (230001 + 280000) / 2,
# (280001 + 320000) / 2,
# (320001 + 360000) / 2,
# (360001 + 400000) / 2,
# (400001 + 465000) / 2,
# (465001 + 540000) / 2,
# (540001 + 665000) / 2,
# (665001 + 850000) / 2,
# (850001 + 1300000) / 2,
# 1300001)
frq( elsoc_long_2016_2023$m14)Ingreso mensual del entrevistado (en 16 tramos) (x) <numeric>
# total N=20761 valid N=990 mean=-256.91 sd=440.12
Value | Label | N | Raw % | Valid % | Cum. %
--------------------------------------------------------------------------------------
-999 | No Responde | 221 | 1.06 | 22.32 | 22.32
-888 | No Sabe | 46 | 0.22 | 4.65 | 26.97
-777 | Valor perdido por error tecnico | 0 | 0.00 | 0.00 | 26.97
-666 | Valor perdido por encuesta incompleta | 0 | 0.00 | 0.00 | 26.97
1 | Menos de $40.000 mensuales liquidos | 18 | 0.09 | 1.82 | 28.79
2 | De $40.001 a $85.000 mensuales liquidos | 26 | 0.13 | 2.63 | 31.41
3 | De $85.001 a $125.000 mensuales liquidos | 27 | 0.13 | 2.73 | 34.14
4 | De $125.001 a $170.000 mensuales liquidos | 34 | 0.16 | 3.43 | 37.58
5 | De $170.001 a $210.000 mensuales liquidos | 36 | 0.17 | 3.64 | 41.21
6 | De $210.001 a $230.000 mensuales liquidos | 16 | 0.08 | 1.62 | 42.83
7 | De $230.001 a $280.000 mensuales liquidos | 37 | 0.18 | 3.74 | 46.57
8 | De $280.001 a $320.000 mensuales liquidos | 55 | 0.26 | 5.56 | 52.12
9 | De $320.001 a $360.000 mensuales liquidos | 57 | 0.27 | 5.76 | 57.88
10 | De $360.001 a $400.000 mensuales liquidos | 43 | 0.21 | 4.34 | 62.22
11 | De $400.001 a $465.000 mensuales liquidos | 58 | 0.28 | 5.86 | 68.08
12 | De $465.001 a $540.000 mensuales liquidos | 47 | 0.23 | 4.75 | 72.83
13 | De $540.001 a $665.000 mensuales liquidos | 61 | 0.29 | 6.16 | 78.99
14 | De $665.001 a $850.000 mensuales liquidos | 84 | 0.40 | 8.48 | 87.47
15 | De $850.001 a $1.300.000 mensuales liquidos | 70 | 0.34 | 7.07 | 94.55
16 | Mas de $1.300.001 a mensuales liquidos | 54 | 0.26 | 5.45 | 100.00
<NA> | <NA> | 19771 | 95.23 | <NA> | <NA>Ltop_1<- 850001
Ltop <- 1300001
ftop_1 <- 65
ftop <- 40
V = (log(ftop_1 + ftop) - log(ftop)) / (log(Ltop) -log(Ltop_1))
M_top = 0.5* Ltop *(1+(V/(V-1))) # = $1.811.247
elsoc_long_2016_2023$m14_rec<-
car::recode( elsoc_long_2016_2023$m14,
"1=40000.0;2=62500.5;3=105000.5;4=147500.5;5=190000.5;6=220000.5;
7=255000.5;8=300000.5;9=340000.5;10=380000.5;11=432500.5;12=502500.5;
13=602500.5;14=757500.5;15=1075000.5;16=1811247"
)
elsoc_long_2016_2023$inc_ind <- ifelse( elsoc_long_2016_2023$n_hogar_r==1 & is.na( elsoc_long_2016_2023$inc_ind), elsoc_long_2016_2023$m14_rec, elsoc_long_2016_2023$inc_ind)
summary( elsoc_long_2016_2023$inc_ind) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-999 250000 400000 540774 650000 9000000 19176 # Ingreso del hogar pegarle ingreso del hogar unipersonal
summary( elsoc_long_2016_2023$m29_imp) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-777 300000 500000 960751 800000 4000000000 690 elsoc_long_2016_2023$m29_imp <-
ifelse(is.na( elsoc_long_2016_2023$m29_imp) & elsoc_long_2016_2023$n_hogar_r==1,
elsoc_long_2016_2023$inc_ind,
elsoc_long_2016_2023$m29_imp) #recupero 14 casos
summary( elsoc_long_2016_2023$m29_imp) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-999 300000 500000 958629 800000 4000000000 642 # Household Equivalent income
# 1.0 to the first adult;
# 0.5 to the second and each subsequent person aged 14 and over;
# 0.3 to each child aged under 14.
elsoc_long_2016_2023$n_hogar_r_eq <- ( elsoc_long_2016_2023$n_hogar_r + 1) /2
table( elsoc_long_2016_2023$n_hogar_r_eq)
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8
2854 4926 4664 4066 2029 826 327 151 47 29 10 8 2 2 2 frq( elsoc_long_2016_2023$n_hogar_r_eq)x <numeric>
# total N=20761 valid N=19943 mean=2.07 sd=0.79
Value | N | Raw % | Valid % | Cum. %
---------------------------------------
1.00 | 2854 | 13.75 | 14.31 | 14.31
1.50 | 4926 | 23.73 | 24.70 | 39.01
2.00 | 4664 | 22.47 | 23.39 | 62.40
2.50 | 4066 | 19.58 | 20.39 | 82.79
3.00 | 2029 | 9.77 | 10.17 | 92.96
3.50 | 826 | 3.98 | 4.14 | 97.10
4.00 | 327 | 1.58 | 1.64 | 98.74
4.50 | 151 | 0.73 | 0.76 | 99.50
5.00 | 47 | 0.23 | 0.24 | 99.73
5.50 | 29 | 0.14 | 0.15 | 99.88
6.00 | 10 | 0.05 | 0.05 | 99.93
6.50 | 8 | 0.04 | 0.04 | 99.97
7.00 | 2 | 0.01 | 0.01 | 99.98
7.50 | 2 | 0.01 | 0.01 | 99.99
8.00 | 2 | 0.01 | 0.01 | 100.00
<NA> | 818 | 3.94 | <NA> | <NA># Per capita household income:
elsoc_long_2016_2023$ing_pc <-
(elsoc_long_2016_2023$m29_imp/ elsoc_long_2016_2023$n_hogar_r)
# Per capita household income equivalized (OECD):
elsoc_long_2016_2023$ing_pc_eq <-
( elsoc_long_2016_2023$m29_imp/ elsoc_long_2016_2023$n_hogar_r_eq)
descr( elsoc_long_2016_2023$ing_pc_eq)
## Basic descriptive statistics
var type label n NA.prc mean sd se md trimmed
dd numeric dd 19348 6.81 542096.7 19231103 138256.7 260000 300880
range iqr skew
2666667665.67 (-999-2666666666.67) 290000 137.78descr( elsoc_long_2016_2023$ing_pc)
## Basic descriptive statistics
var type label n NA.prc mean sd se md trimmed
dd numeric dd 19348 6.81 401651.1 14423877 103696.5 180000 215843.5
range iqr skew
2000000999 (-999-2000000000) 233333.3 137.77 elsoc_long_2016_2023$ing_pc <-
sjlabelled::set_label(x = elsoc_long_2016_2023$ing_pc,
label = "Household income per capita")
sjmisc::descr( elsoc_long_2016_2023$ing_pc)
## Basic descriptive statistics
var type label n NA.prc mean sd
dd numeric Household income per capita 19348 6.81 401651.1 14423877
se md trimmed range iqr skew
103696.5 180000 215843.5 2000000999 (-999-2000000000) 233333.3 137.77# Compute income quintiles
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
group_by(ola) %>%
mutate(quintil = ntile(-desc(ing_pc), 5)) %>%
ungroup()
elsoc_long_2016_2023$quintil <-
factor( elsoc_long_2016_2023$quintil,
levels = c(1, 2, 3, 4, 5),
labels = c('Q1', 'Q2', 'Q3', 'Q4', 'Q5')) # Quintiles as factors
elsoc_long_2016_2023$quintil <-
sjlabelled::set_label(x = elsoc_long_2016_2023$quintil,
label = "Household income quintile per capita")
sjmisc::frq( elsoc_long_2016_2023$quintil)Household income quintile per capita (x) <categorical>
# total N=20761 valid N=19348 mean=3.00 sd=1.41
Value | N | Raw % | Valid % | Cum. %
---------------------------------------
Q1 | 3873 | 18.66 | 20.02 | 20.02
Q2 | 3871 | 18.65 | 20.01 | 40.02
Q3 | 3870 | 18.64 | 20.00 | 60.03
Q4 | 3868 | 18.63 | 19.99 | 80.02
Q5 | 3866 | 18.62 | 19.98 | 100.00
<NA> | 1413 | 6.81 | <NA> | <NA>#include new quintile category with missing cases
elsoc_long_2016_2023$quintil1<-
car::recode( elsoc_long_2016_2023$quintil,
"'Q1'='Q1';'Q2'= 'Q2';'Q3'='Q3';'Q4'='Q4';'Q5'='Q5'; NA='QNA'")
# elsoc_long_2016_2023$quintil1 <- factor( elsoc_long_2016_2023$quintil1, c("Q1","Q2","Q3","Q4","Q5","QNA"))
elsoc_long_2016_2023$quintil1 <-
sjlabelled::set_label(x = elsoc_long_2016_2023$quintil1,
label = "Household income quintile per capita (NA)")
sjmisc::frq( elsoc_long_2016_2023$quintil1)Household income quintile per capita (NA) (x) <categorical>
# total N=20761 valid N=20761 mean=3.20 sd=1.56
Value | N | Raw % | Valid % | Cum. %
---------------------------------------
Q1 | 3873 | 18.66 | 18.66 | 18.66
Q2 | 3871 | 18.65 | 18.65 | 37.30
Q3 | 3870 | 18.64 | 18.64 | 55.94
Q4 | 3868 | 18.63 | 18.63 | 74.57
Q5 | 3866 | 18.62 | 18.62 | 93.19
QNA | 1413 | 6.81 | 6.81 | 100.00
<NA> | 0 | 0.00 | <NA> | <NA>4.4 Missing values
idencuesta wave ponderador_long_total
0 0 0
segmento estrato just_educ
0 0 39
just_pension just_health merit_effort
55 35 104
merit_talent perc_sal_gerente perc_sal_obrero
104 1522 591
just_sal_gerente just_sal_obrero age
1163 435 0
m01 sex ess
15 0 95
ideo mjp perc_inequality
261 23 1702
just_inequality educ educyear
1191 15 15
ing_pc quintil quintil1
4270 4270 3137 n_miss(db_proc)[1] 19042prop_miss(db_proc)*100[1] 3.913541miss_var_summary(db_proc)# A tibble: 27 × 3
variable n_miss pct_miss
<chr> <int> <num>
1 ing_pc 4270 23.7
2 quintil 4270 23.7
3 quintil1 3137 17.4
4 perc_inequality 1702 9.44
5 perc_sal_gerente 1522 8.45
6 just_inequality 1191 6.61
7 just_sal_gerente 1163 6.45
8 perc_sal_obrero 591 3.28
9 just_sal_obrero 435 2.41
10 ideo 261 1.45
# ℹ 17 more rowsmiss_var_table(db_proc)# A tibble: 17 × 3
n_miss_in_var n_vars pct_vars
<int> <int> <dbl>
1 0 7 25.9
2 15 3 11.1
3 23 1 3.70
4 35 1 3.70
5 39 1 3.70
6 55 1 3.70
7 95 1 3.70
8 104 2 7.41
9 261 1 3.70
10 435 1 3.70
11 591 1 3.70
12 1163 1 3.70
13 1191 1 3.70
14 1522 1 3.70
15 1702 1 3.70
16 3137 1 3.70
17 4270 2 7.41vis_miss(db_proc) + theme(axis.text.x = element_text(angle=80))
5 Save and export
db_proc <- db_proc %>%
select(-c(perc_sal_gerente, perc_sal_obrero, just_sal_gerente, just_sal_obrero, m01))
df1 <- db_proc %>% na.omit()
df1 <- sjlabelled::copy_labels(df_new = df1, df_origin = db_proc)
save(df1, file = here("input/data/proc/df1.RData"))