1 Presentation
This is the data preparation code for the paper “Changes in the Justification of Pension Inequality in Chile (2016–2023) and its Relationship to Social Class and Beliefs in Meritocracy”. The prepared dataset is ELSOC_Long_2016_2023_1.00.RData.
2 Libraries
3 Data
4 Processing
elsoc_long_2016_2023[elsoc_long_2016_2023 ==-999] <- NA
elsoc_long_2016_2023[elsoc_long_2016_2023 ==-888] <- NA
elsoc_long_2016_2023[elsoc_long_2016_2023 ==-777] <- NA
elsoc_long_2016_2023[elsoc_long_2016_2023 ==-666] <- NA
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
mutate(just_pension = d02_01,
merit_effort = c18_09,
merit_talent = c18_10,
age = m0_edad, m01,
sex = m0_sexo,
ideo = c15) %>%
as_tibble() %>%
sjlabelled::drop_labels(., drop.na = FALSE)# Market Justice Preferences
frq(elsoc_long_2016_2023$just_pension)Grado de acuerdo: Justicia distributiva en pensiones (x) <numeric>
# total N=20761 valid N=17966 mean=2.24 sd=1.11
Value | Label | N | Raw % | Valid % | Cum. %
------------------------------------------------------------------------
1 | Totalmente en desacuerdo | 4889 | 23.55 | 27.21 | 27.21
2 | En desacuerdo | 7802 | 37.58 | 43.43 | 70.64
3 | Ni de acuerdo ni en desacuerdo | 1740 | 8.38 | 9.68 | 80.32
4 | De acuerdo | 3087 | 14.87 | 17.18 | 97.51
5 | Totalmente de acuerdo | 448 | 2.16 | 2.49 | 100.00
<NA> | <NA> | 2795 | 13.46 | <NA> | <NA>elsoc_long_2016_2023$just_pension <- car::recode(elsoc_long_2016_2023$just_pension,
recodes = c("1='Strongly disagree'; 2='Disagree'; 3='Neither agree nor disagree'; 4='Agree'; 5='Strongly agree'"),
levels = c("Strongly disagree", "Disagree", "Neither agree nor disagree", "Agree", "Strongly agree"),
as.factor = T)
elsoc_long_2016_2023$just_pension <- sjlabelled::set_label(elsoc_long_2016_2023$just_pension,
label = "Pension distributive justice")# Social class scheme EGP
# ISCO 08
frq(elsoc_long_2016_2023$ciuo88_m03)CIUO (1988) del entrevistado (x) <numeric>
# total N=20761 valid N=1801 mean=5720.01 sd=2420.11
Value | N | Raw % | Valid % | Cum. %
----------------------------------------
1229 | 1 | 0.00 | 0.06 | 0.06
1232 | 1 | 0.00 | 0.06 | 0.11
1239 | 1 | 0.00 | 0.06 | 0.17
1252 | 1 | 0.00 | 0.06 | 0.22
1312 | 3 | 0.01 | 0.17 | 0.39
1313 | 2 | 0.01 | 0.11 | 0.50
1314 | 52 | 0.25 | 2.89 | 3.39
1315 | 8 | 0.04 | 0.44 | 3.83
1316 | 2 | 0.01 | 0.11 | 3.94
1317 | 7 | 0.03 | 0.39 | 4.33
1318 | 1 | 0.00 | 0.06 | 4.39
1319 | 3 | 0.01 | 0.17 | 4.55
2114 | 2 | 0.01 | 0.11 | 4.66
2122 | 1 | 0.00 | 0.06 | 4.72
2131 | 5 | 0.02 | 0.28 | 5.00
2132 | 1 | 0.00 | 0.06 | 5.05
2141 | 5 | 0.02 | 0.28 | 5.33
2142 | 4 | 0.02 | 0.22 | 5.55
2143 | 3 | 0.01 | 0.17 | 5.72
2146 | 1 | 0.00 | 0.06 | 5.77
2147 | 2 | 0.01 | 0.11 | 5.89
2149 | 19 | 0.09 | 1.05 | 6.94
2211 | 2 | 0.01 | 0.11 | 7.05
2213 | 2 | 0.01 | 0.11 | 7.16
2221 | 3 | 0.01 | 0.17 | 7.33
2222 | 2 | 0.01 | 0.11 | 7.44
2223 | 1 | 0.00 | 0.06 | 7.50
2229 | 2 | 0.01 | 0.11 | 7.61
2230 | 8 | 0.04 | 0.44 | 8.05
2310 | 10 | 0.05 | 0.56 | 8.61
2320 | 10 | 0.05 | 0.56 | 9.16
2331 | 32 | 0.15 | 1.78 | 10.94
2332 | 7 | 0.03 | 0.39 | 11.33
2340 | 3 | 0.01 | 0.17 | 11.49
2359 | 3 | 0.01 | 0.17 | 11.66
2411 | 12 | 0.06 | 0.67 | 12.33
2412 | 1 | 0.00 | 0.06 | 12.38
2419 | 6 | 0.03 | 0.33 | 12.72
2421 | 5 | 0.02 | 0.28 | 12.99
2429 | 2 | 0.01 | 0.11 | 13.10
2432 | 5 | 0.02 | 0.28 | 13.38
2441 | 1 | 0.00 | 0.06 | 13.44
2442 | 1 | 0.00 | 0.06 | 13.49
2445 | 10 | 0.05 | 0.56 | 14.05
2446 | 8 | 0.04 | 0.44 | 14.49
2451 | 4 | 0.02 | 0.22 | 14.71
2452 | 1 | 0.00 | 0.06 | 14.77
2455 | 1 | 0.00 | 0.06 | 14.83
2460 | 1 | 0.00 | 0.06 | 14.88
3112 | 7 | 0.03 | 0.39 | 15.27
3114 | 2 | 0.01 | 0.11 | 15.38
3118 | 1 | 0.00 | 0.06 | 15.44
3121 | 6 | 0.03 | 0.33 | 15.77
3131 | 2 | 0.01 | 0.11 | 15.88
3132 | 1 | 0.00 | 0.06 | 15.94
3152 | 9 | 0.04 | 0.50 | 16.44
3212 | 4 | 0.02 | 0.22 | 16.66
3213 | 2 | 0.01 | 0.11 | 16.77
3221 | 5 | 0.02 | 0.28 | 17.05
3222 | 2 | 0.01 | 0.11 | 17.16
3223 | 4 | 0.02 | 0.22 | 17.38
3225 | 3 | 0.01 | 0.17 | 17.55
3226 | 3 | 0.01 | 0.17 | 17.71
3229 | 2 | 0.01 | 0.11 | 17.82
3231 | 17 | 0.08 | 0.94 | 18.77
3310 | 1 | 0.00 | 0.06 | 18.82
3320 | 10 | 0.05 | 0.56 | 19.38
3330 | 2 | 0.01 | 0.11 | 19.49
3340 | 4 | 0.02 | 0.22 | 19.71
3411 | 10 | 0.05 | 0.56 | 20.27
3412 | 4 | 0.02 | 0.22 | 20.49
3413 | 5 | 0.02 | 0.28 | 20.77
3415 | 13 | 0.06 | 0.72 | 21.49
3416 | 2 | 0.01 | 0.11 | 21.60
3417 | 1 | 0.00 | 0.06 | 21.65
3419 | 2 | 0.01 | 0.11 | 21.77
3422 | 2 | 0.01 | 0.11 | 21.88
3423 | 8 | 0.04 | 0.44 | 22.32
3432 | 1 | 0.00 | 0.06 | 22.38
3433 | 14 | 0.07 | 0.78 | 23.15
3441 | 1 | 0.00 | 0.06 | 23.21
3449 | 1 | 0.00 | 0.06 | 23.26
3452 | 2 | 0.01 | 0.11 | 23.38
3471 | 4 | 0.02 | 0.22 | 23.60
3472 | 2 | 0.01 | 0.11 | 23.71
3473 | 5 | 0.02 | 0.28 | 23.99
3474 | 1 | 0.00 | 0.06 | 24.04
3475 | 4 | 0.02 | 0.22 | 24.26
4113 | 2 | 0.01 | 0.11 | 24.38
4115 | 49 | 0.24 | 2.72 | 27.10
4121 | 4 | 0.02 | 0.22 | 27.32
4122 | 5 | 0.02 | 0.28 | 27.60
4131 | 27 | 0.13 | 1.50 | 29.09
4132 | 6 | 0.03 | 0.33 | 29.43
4133 | 4 | 0.02 | 0.22 | 29.65
4142 | 2 | 0.01 | 0.11 | 29.76
4190 | 56 | 0.27 | 3.11 | 32.87
4211 | 22 | 0.11 | 1.22 | 34.09
4215 | 2 | 0.01 | 0.11 | 34.20
4222 | 12 | 0.06 | 0.67 | 34.87
4223 | 4 | 0.02 | 0.22 | 35.09
5112 | 1 | 0.00 | 0.06 | 35.15
5121 | 3 | 0.01 | 0.17 | 35.31
5122 | 56 | 0.27 | 3.11 | 38.42
5123 | 20 | 0.10 | 1.11 | 39.53
5131 | 39 | 0.19 | 2.17 | 41.70
5132 | 6 | 0.03 | 0.33 | 42.03
5133 | 10 | 0.05 | 0.56 | 42.59
5141 | 21 | 0.10 | 1.17 | 43.75
5142 | 1 | 0.00 | 0.06 | 43.81
5162 | 12 | 0.06 | 0.67 | 44.48
5163 | 2 | 0.01 | 0.11 | 44.59
5164 | 6 | 0.03 | 0.33 | 44.92
5169 | 4 | 0.02 | 0.22 | 45.14
5220 | 141 | 0.68 | 7.83 | 52.97
5230 | 36 | 0.17 | 2.00 | 54.97
6111 | 5 | 0.02 | 0.28 | 55.25
6112 | 11 | 0.05 | 0.61 | 55.86
6113 | 17 | 0.08 | 0.94 | 56.80
6124 | 1 | 0.00 | 0.06 | 56.86
6141 | 6 | 0.03 | 0.33 | 57.19
6152 | 1 | 0.00 | 0.06 | 57.25
7111 | 5 | 0.02 | 0.28 | 57.52
7112 | 1 | 0.00 | 0.06 | 57.58
7122 | 27 | 0.13 | 1.50 | 59.08
7123 | 5 | 0.02 | 0.28 | 59.36
7124 | 41 | 0.20 | 2.28 | 61.63
7129 | 11 | 0.05 | 0.61 | 62.24
7134 | 2 | 0.01 | 0.11 | 62.35
7135 | 1 | 0.00 | 0.06 | 62.41
7136 | 10 | 0.05 | 0.56 | 62.97
7137 | 18 | 0.09 | 1.00 | 63.96
7141 | 14 | 0.07 | 0.78 | 64.74
7142 | 8 | 0.04 | 0.44 | 65.19
7212 | 23 | 0.11 | 1.28 | 66.46
7213 | 10 | 0.05 | 0.56 | 67.02
7221 | 1 | 0.00 | 0.06 | 67.07
7224 | 1 | 0.00 | 0.06 | 67.13
7231 | 27 | 0.13 | 1.50 | 68.63
7233 | 15 | 0.07 | 0.83 | 69.46
7241 | 3 | 0.01 | 0.17 | 69.63
7242 | 4 | 0.02 | 0.22 | 69.85
7243 | 1 | 0.00 | 0.06 | 69.91
7244 | 2 | 0.01 | 0.11 | 70.02
7245 | 1 | 0.00 | 0.06 | 70.07
7311 | 3 | 0.01 | 0.17 | 70.24
7313 | 1 | 0.00 | 0.06 | 70.29
7322 | 1 | 0.00 | 0.06 | 70.35
7343 | 1 | 0.00 | 0.06 | 70.41
7344 | 1 | 0.00 | 0.06 | 70.46
7345 | 1 | 0.00 | 0.06 | 70.52
7411 | 12 | 0.06 | 0.67 | 71.18
7412 | 27 | 0.13 | 1.50 | 72.68
7413 | 1 | 0.00 | 0.06 | 72.74
7415 | 3 | 0.01 | 0.17 | 72.90
7421 | 2 | 0.01 | 0.11 | 73.01
7422 | 16 | 0.08 | 0.89 | 73.90
7432 | 3 | 0.01 | 0.17 | 74.07
7433 | 16 | 0.08 | 0.89 | 74.96
7436 | 16 | 0.08 | 0.89 | 75.85
7437 | 1 | 0.00 | 0.06 | 75.90
7442 | 3 | 0.01 | 0.17 | 76.07
8112 | 1 | 0.00 | 0.06 | 76.12
8121 | 1 | 0.00 | 0.06 | 76.18
8124 | 1 | 0.00 | 0.06 | 76.24
8141 | 4 | 0.02 | 0.22 | 76.46
8143 | 1 | 0.00 | 0.06 | 76.51
8159 | 1 | 0.00 | 0.06 | 76.57
8163 | 2 | 0.01 | 0.11 | 76.68
8211 | 4 | 0.02 | 0.22 | 76.90
8221 | 2 | 0.01 | 0.11 | 77.01
8232 | 4 | 0.02 | 0.22 | 77.23
8240 | 1 | 0.00 | 0.06 | 77.29
8251 | 3 | 0.01 | 0.17 | 77.46
8264 | 1 | 0.00 | 0.06 | 77.51
8272 | 1 | 0.00 | 0.06 | 77.57
8275 | 1 | 0.00 | 0.06 | 77.62
8276 | 1 | 0.00 | 0.06 | 77.68
8311 | 2 | 0.01 | 0.11 | 77.79
8322 | 35 | 0.17 | 1.94 | 79.73
8323 | 23 | 0.11 | 1.28 | 81.01
8324 | 26 | 0.13 | 1.44 | 82.45
8331 | 9 | 0.04 | 0.50 | 82.95
8332 | 10 | 0.05 | 0.56 | 83.51
8333 | 1 | 0.00 | 0.06 | 83.56
8334 | 5 | 0.02 | 0.28 | 83.84
8340 | 3 | 0.01 | 0.17 | 84.01
9111 | 3 | 0.01 | 0.17 | 84.18
9112 | 14 | 0.07 | 0.78 | 84.95
9131 | 80 | 0.39 | 4.44 | 89.39
9132 | 71 | 0.34 | 3.94 | 93.34
9133 | 2 | 0.01 | 0.11 | 93.45
9141 | 8 | 0.04 | 0.44 | 93.89
9142 | 1 | 0.00 | 0.06 | 93.95
9151 | 4 | 0.02 | 0.22 | 94.17
9152 | 47 | 0.23 | 2.61 | 96.78
9153 | 3 | 0.01 | 0.17 | 96.95
9161 | 1 | 0.00 | 0.06 | 97.00
9162 | 2 | 0.01 | 0.11 | 97.11
9211 | 15 | 0.07 | 0.83 | 97.95
9312 | 1 | 0.00 | 0.06 | 98.00
9313 | 14 | 0.07 | 0.78 | 98.78
9322 | 3 | 0.01 | 0.17 | 98.95
9333 | 9 | 0.04 | 0.50 | 99.44
9955 | 1 | 0.00 | 0.06 | 99.50
9988 | 1 | 0.00 | 0.06 | 99.56
9999 | 8 | 0.04 | 0.44 | 100.00
<NA> | 18960 | 91.33 | <NA> | <NA>elsoc_long_2016_2023 %>%
group_by(ola) %>%
summarise(
solo_NA = all(is.na(ciuo88_m03))
) # only wave 2016# A tibble: 7 × 2
ola solo_NA
<dbl> <lgl>
1 1 FALSE
2 2 TRUE
3 3 TRUE
4 4 TRUE
5 5 TRUE
6 6 TRUE
7 7 TRUE frq(elsoc_long_2016_2023$ciuo08_m03)CIUO (2008) del entrevistado (x) <numeric>
# total N=20761 valid N=5530 mean=5745.24 sd=2469.60
Value | N | Raw % | Valid % | Cum. %
----------------------------------------
110 | 9 | 0.04 | 0.16 | 0.16
210 | 9 | 0.04 | 0.16 | 0.33
310 | 6 | 0.03 | 0.11 | 0.43
1112 | 6 | 0.03 | 0.11 | 0.54
1113 | 1 | 0.00 | 0.02 | 0.56
1114 | 4 | 0.02 | 0.07 | 0.63
1120 | 6 | 0.03 | 0.11 | 0.74
1211 | 3 | 0.01 | 0.05 | 0.80
1212 | 9 | 0.04 | 0.16 | 0.96
1213 | 1 | 0.00 | 0.02 | 0.98
1219 | 1 | 0.00 | 0.02 | 0.99
1221 | 4 | 0.02 | 0.07 | 1.07
1222 | 2 | 0.01 | 0.04 | 1.10
1311 | 2 | 0.01 | 0.04 | 1.14
1321 | 5 | 0.02 | 0.09 | 1.23
1322 | 1 | 0.00 | 0.02 | 1.25
1323 | 3 | 0.01 | 0.05 | 1.30
1324 | 9 | 0.04 | 0.16 | 1.46
1330 | 1 | 0.00 | 0.02 | 1.48
1341 | 1 | 0.00 | 0.02 | 1.50
1342 | 3 | 0.01 | 0.05 | 1.56
1344 | 1 | 0.00 | 0.02 | 1.57
1345 | 4 | 0.02 | 0.07 | 1.65
1346 | 4 | 0.02 | 0.07 | 1.72
1349 | 3 | 0.01 | 0.05 | 1.77
1411 | 4 | 0.02 | 0.07 | 1.84
1412 | 20 | 0.10 | 0.36 | 2.21
1420 | 27 | 0.13 | 0.49 | 2.69
1431 | 3 | 0.01 | 0.05 | 2.75
1439 | 8 | 0.04 | 0.14 | 2.89
2113 | 4 | 0.02 | 0.07 | 2.97
2114 | 3 | 0.01 | 0.05 | 3.02
2131 | 7 | 0.03 | 0.13 | 3.15
2132 | 8 | 0.04 | 0.14 | 3.29
2133 | 4 | 0.02 | 0.07 | 3.36
2139 | 1 | 0.00 | 0.02 | 3.38
2141 | 4 | 0.02 | 0.07 | 3.45
2142 | 15 | 0.07 | 0.27 | 3.73
2143 | 3 | 0.01 | 0.05 | 3.78
2144 | 7 | 0.03 | 0.13 | 3.91
2145 | 4 | 0.02 | 0.07 | 3.98
2146 | 10 | 0.05 | 0.18 | 4.16
2149 | 28 | 0.13 | 0.51 | 4.67
2151 | 5 | 0.02 | 0.09 | 4.76
2152 | 3 | 0.01 | 0.05 | 4.81
2153 | 2 | 0.01 | 0.04 | 4.85
2161 | 15 | 0.07 | 0.27 | 5.12
2162 | 1 | 0.00 | 0.02 | 5.14
2163 | 2 | 0.01 | 0.04 | 5.17
2164 | 2 | 0.01 | 0.04 | 5.21
2165 | 1 | 0.00 | 0.02 | 5.23
2166 | 4 | 0.02 | 0.07 | 5.30
2211 | 2 | 0.01 | 0.04 | 5.33
2212 | 11 | 0.05 | 0.20 | 5.53
2213 | 1 | 0.00 | 0.02 | 5.55
2221 | 26 | 0.13 | 0.47 | 6.02
2222 | 1 | 0.00 | 0.02 | 6.04
2230 | 4 | 0.02 | 0.07 | 6.11
2240 | 4 | 0.02 | 0.07 | 6.18
2241 | 4 | 0.02 | 0.07 | 6.26
2242 | 2 | 0.01 | 0.04 | 6.29
2243 | 8 | 0.04 | 0.14 | 6.44
2244 | 6 | 0.03 | 0.11 | 6.55
2245 | 3 | 0.01 | 0.05 | 6.60
2250 | 3 | 0.01 | 0.05 | 6.65
2261 | 7 | 0.03 | 0.13 | 6.78
2262 | 1 | 0.00 | 0.02 | 6.80
2263 | 4 | 0.02 | 0.07 | 6.87
2264 | 4 | 0.02 | 0.07 | 6.94
2265 | 5 | 0.02 | 0.09 | 7.03
2266 | 5 | 0.02 | 0.09 | 7.12
2269 | 1 | 0.00 | 0.02 | 7.14
2300 | 1 | 0.00 | 0.02 | 7.16
2310 | 25 | 0.12 | 0.45 | 7.61
2320 | 6 | 0.03 | 0.11 | 7.72
2330 | 59 | 0.28 | 1.07 | 8.79
2341 | 78 | 0.38 | 1.41 | 10.20
2342 | 41 | 0.20 | 0.74 | 10.94
2351 | 7 | 0.03 | 0.13 | 11.07
2352 | 22 | 0.11 | 0.40 | 11.46
2353 | 1 | 0.00 | 0.02 | 11.48
2356 | 2 | 0.01 | 0.04 | 11.52
2359 | 33 | 0.16 | 0.60 | 12.12
2411 | 44 | 0.21 | 0.80 | 12.91
2412 | 3 | 0.01 | 0.05 | 12.97
2413 | 8 | 0.04 | 0.14 | 13.11
2421 | 7 | 0.03 | 0.13 | 13.24
2422 | 9 | 0.04 | 0.16 | 13.40
2423 | 11 | 0.05 | 0.20 | 13.60
2424 | 1 | 0.00 | 0.02 | 13.62
2431 | 9 | 0.04 | 0.16 | 13.78
2433 | 8 | 0.04 | 0.14 | 13.92
2434 | 6 | 0.03 | 0.11 | 14.03
2511 | 3 | 0.01 | 0.05 | 14.09
2512 | 12 | 0.06 | 0.22 | 14.30
2513 | 4 | 0.02 | 0.07 | 14.38
2514 | 1 | 0.00 | 0.02 | 14.39
2519 | 3 | 0.01 | 0.05 | 14.45
2521 | 3 | 0.01 | 0.05 | 14.50
2522 | 3 | 0.01 | 0.05 | 14.56
2523 | 3 | 0.01 | 0.05 | 14.61
2529 | 7 | 0.03 | 0.13 | 14.74
2611 | 22 | 0.11 | 0.40 | 15.14
2619 | 3 | 0.01 | 0.05 | 15.19
2621 | 1 | 0.00 | 0.02 | 15.21
2622 | 5 | 0.02 | 0.09 | 15.30
2630 | 1 | 0.00 | 0.02 | 15.32
2631 | 4 | 0.02 | 0.07 | 15.39
2632 | 6 | 0.03 | 0.11 | 15.50
2634 | 35 | 0.17 | 0.63 | 16.13
2635 | 23 | 0.11 | 0.42 | 16.55
2636 | 1 | 0.00 | 0.02 | 16.56
2641 | 2 | 0.01 | 0.04 | 16.60
2642 | 12 | 0.06 | 0.22 | 16.82
2652 | 6 | 0.03 | 0.11 | 16.93
2654 | 1 | 0.00 | 0.02 | 16.94
2655 | 2 | 0.01 | 0.04 | 16.98
2656 | 1 | 0.00 | 0.02 | 17.00
2659 | 3 | 0.01 | 0.05 | 17.05
3111 | 5 | 0.02 | 0.09 | 17.14
3112 | 10 | 0.05 | 0.18 | 17.32
3113 | 2 | 0.01 | 0.04 | 17.36
3114 | 9 | 0.04 | 0.16 | 17.52
3116 | 2 | 0.01 | 0.04 | 17.56
3117 | 2 | 0.01 | 0.04 | 17.59
3118 | 4 | 0.02 | 0.07 | 17.67
3119 | 3 | 0.01 | 0.05 | 17.72
3121 | 6 | 0.03 | 0.11 | 17.83
3122 | 8 | 0.04 | 0.14 | 17.97
3123 | 30 | 0.14 | 0.54 | 18.52
3132 | 8 | 0.04 | 0.14 | 18.66
3133 | 1 | 0.00 | 0.02 | 18.68
3134 | 2 | 0.01 | 0.04 | 18.72
3135 | 1 | 0.00 | 0.02 | 18.73
3139 | 3 | 0.01 | 0.05 | 18.79
3141 | 1 | 0.00 | 0.02 | 18.81
3142 | 3 | 0.01 | 0.05 | 18.86
3143 | 3 | 0.01 | 0.05 | 18.92
3144 | 1 | 0.00 | 0.02 | 18.93
3152 | 1 | 0.00 | 0.02 | 18.95
3154 | 3 | 0.01 | 0.05 | 19.01
3155 | 1 | 0.00 | 0.02 | 19.02
3211 | 12 | 0.06 | 0.22 | 19.24
3212 | 2 | 0.01 | 0.04 | 19.28
3213 | 5 | 0.02 | 0.09 | 19.37
3214 | 1 | 0.00 | 0.02 | 19.39
3215 | 2 | 0.01 | 0.04 | 19.42
3221 | 63 | 0.30 | 1.14 | 20.56
3231 | 1 | 0.00 | 0.02 | 20.58
3251 | 16 | 0.08 | 0.29 | 20.87
3252 | 1 | 0.00 | 0.02 | 20.89
3253 | 5 | 0.02 | 0.09 | 20.98
3254 | 1 | 0.00 | 0.02 | 20.99
3255 | 2 | 0.01 | 0.04 | 21.03
3256 | 4 | 0.02 | 0.07 | 21.10
3257 | 10 | 0.05 | 0.18 | 21.28
3259 | 7 | 0.03 | 0.13 | 21.41
3311 | 7 | 0.03 | 0.13 | 21.54
3312 | 6 | 0.03 | 0.11 | 21.65
3313 | 10 | 0.05 | 0.18 | 21.83
3314 | 5 | 0.02 | 0.09 | 21.92
3315 | 3 | 0.01 | 0.05 | 21.97
3321 | 16 | 0.08 | 0.29 | 22.26
3322 | 11 | 0.05 | 0.20 | 22.46
3323 | 5 | 0.02 | 0.09 | 22.55
3324 | 1 | 0.00 | 0.02 | 22.57
3331 | 3 | 0.01 | 0.05 | 22.62
3332 | 7 | 0.03 | 0.13 | 22.75
3333 | 9 | 0.04 | 0.16 | 22.91
3334 | 19 | 0.09 | 0.34 | 23.25
3339 | 3 | 0.01 | 0.05 | 23.31
3341 | 7 | 0.03 | 0.13 | 23.44
3342 | 4 | 0.02 | 0.07 | 23.51
3343 | 41 | 0.20 | 0.74 | 24.25
3344 | 8 | 0.04 | 0.14 | 24.39
3352 | 7 | 0.03 | 0.13 | 24.52
3353 | 3 | 0.01 | 0.05 | 24.58
3354 | 4 | 0.02 | 0.07 | 24.65
3359 | 8 | 0.04 | 0.14 | 24.79
3411 | 8 | 0.04 | 0.14 | 24.94
3412 | 5 | 0.02 | 0.09 | 25.03
3422 | 9 | 0.04 | 0.16 | 25.19
3423 | 2 | 0.01 | 0.04 | 25.23
3431 | 6 | 0.03 | 0.11 | 25.33
3432 | 5 | 0.02 | 0.09 | 25.42
3434 | 7 | 0.03 | 0.13 | 25.55
3435 | 3 | 0.01 | 0.05 | 25.61
3511 | 1 | 0.00 | 0.02 | 25.62
3512 | 5 | 0.02 | 0.09 | 25.71
3513 | 10 | 0.05 | 0.18 | 25.90
3521 | 1 | 0.00 | 0.02 | 25.91
3522 | 2 | 0.01 | 0.04 | 25.95
3611 | 22 | 0.11 | 0.40 | 26.35
3612 | 1 | 0.00 | 0.02 | 26.37
4110 | 64 | 0.31 | 1.16 | 27.52
4120 | 42 | 0.20 | 0.76 | 28.28
4121 | 1 | 0.00 | 0.02 | 28.30
4131 | 2 | 0.01 | 0.04 | 28.34
4132 | 2 | 0.01 | 0.04 | 28.37
4190 | 1 | 0.00 | 0.02 | 28.39
4211 | 18 | 0.09 | 0.33 | 28.72
4212 | 1 | 0.00 | 0.02 | 28.73
4214 | 8 | 0.04 | 0.14 | 28.88
4221 | 1 | 0.00 | 0.02 | 28.90
4222 | 2 | 0.01 | 0.04 | 28.93
4223 | 16 | 0.08 | 0.29 | 29.22
4224 | 3 | 0.01 | 0.05 | 29.28
4225 | 2 | 0.01 | 0.04 | 29.31
4226 | 18 | 0.09 | 0.33 | 29.64
4227 | 6 | 0.03 | 0.11 | 29.75
4229 | 6 | 0.03 | 0.11 | 29.86
4310 | 1 | 0.00 | 0.02 | 29.87
4311 | 45 | 0.22 | 0.81 | 30.69
4312 | 6 | 0.03 | 0.11 | 30.80
4313 | 5 | 0.02 | 0.09 | 30.89
4321 | 69 | 0.33 | 1.25 | 32.13
4322 | 6 | 0.03 | 0.11 | 32.24
4323 | 19 | 0.09 | 0.34 | 32.59
4411 | 2 | 0.01 | 0.04 | 32.62
4412 | 3 | 0.01 | 0.05 | 32.68
4415 | 2 | 0.01 | 0.04 | 32.71
4416 | 13 | 0.06 | 0.24 | 32.95
4419 | 41 | 0.20 | 0.74 | 33.69
4791 | 1 | 0.00 | 0.02 | 33.71
5112 | 3 | 0.01 | 0.05 | 33.76
5120 | 123 | 0.59 | 2.22 | 35.99
5131 | 48 | 0.23 | 0.87 | 36.85
5132 | 4 | 0.02 | 0.07 | 36.93
5141 | 49 | 0.24 | 0.89 | 37.81
5142 | 20 | 0.10 | 0.36 | 38.17
5151 | 5 | 0.02 | 0.09 | 38.26
5152 | 4 | 0.02 | 0.07 | 38.34
5153 | 29 | 0.14 | 0.52 | 38.86
5161 | 2 | 0.01 | 0.04 | 38.90
5163 | 3 | 0.01 | 0.05 | 38.95
5164 | 2 | 0.01 | 0.04 | 38.99
5211 | 98 | 0.47 | 1.77 | 40.76
5212 | 31 | 0.15 | 0.56 | 41.32
5221 | 192 | 0.92 | 3.47 | 44.79
5222 | 23 | 0.11 | 0.42 | 45.21
5223 | 202 | 0.97 | 3.65 | 48.86
5230 | 67 | 0.32 | 1.21 | 50.07
5242 | 12 | 0.06 | 0.22 | 50.29
5243 | 7 | 0.03 | 0.13 | 50.42
5244 | 16 | 0.08 | 0.29 | 50.71
5245 | 6 | 0.03 | 0.11 | 50.81
5246 | 5 | 0.02 | 0.09 | 50.90
5249 | 16 | 0.08 | 0.29 | 51.19
5311 | 35 | 0.17 | 0.63 | 51.83
5312 | 84 | 0.40 | 1.52 | 53.35
5321 | 13 | 0.06 | 0.24 | 53.58
5322 | 44 | 0.21 | 0.80 | 54.38
5329 | 6 | 0.03 | 0.11 | 54.48
5411 | 6 | 0.03 | 0.11 | 54.59
5412 | 14 | 0.07 | 0.25 | 54.85
5413 | 5 | 0.02 | 0.09 | 54.94
5414 | 127 | 0.61 | 2.30 | 57.23
5419 | 5 | 0.02 | 0.09 | 57.32
6110 | 1 | 0.00 | 0.02 | 57.34
6111 | 10 | 0.05 | 0.18 | 57.52
6112 | 5 | 0.02 | 0.09 | 57.61
6113 | 33 | 0.16 | 0.60 | 58.21
6122 | 2 | 0.01 | 0.04 | 58.25
6123 | 1 | 0.00 | 0.02 | 58.26
6129 | 1 | 0.00 | 0.02 | 58.28
6151 | 1 | 0.00 | 0.02 | 58.30
6152 | 1 | 0.00 | 0.02 | 58.32
6210 | 6 | 0.03 | 0.11 | 58.43
6221 | 5 | 0.02 | 0.09 | 58.52
6222 | 4 | 0.02 | 0.07 | 58.59
6223 | 1 | 0.00 | 0.02 | 58.61
6310 | 4 | 0.02 | 0.07 | 58.68
6340 | 1 | 0.00 | 0.02 | 58.70
7111 | 35 | 0.17 | 0.63 | 59.33
7112 | 52 | 0.25 | 0.94 | 60.27
7113 | 3 | 0.01 | 0.05 | 60.33
7114 | 9 | 0.04 | 0.16 | 60.49
7115 | 82 | 0.39 | 1.48 | 61.97
7119 | 8 | 0.04 | 0.14 | 62.12
7121 | 1 | 0.00 | 0.02 | 62.13
7122 | 5 | 0.02 | 0.09 | 62.22
7123 | 4 | 0.02 | 0.07 | 62.30
7124 | 1 | 0.00 | 0.02 | 62.31
7125 | 1 | 0.00 | 0.02 | 62.33
7126 | 36 | 0.17 | 0.65 | 62.98
7127 | 3 | 0.01 | 0.05 | 63.04
7130 | 1 | 0.00 | 0.02 | 63.06
7131 | 23 | 0.11 | 0.42 | 63.47
7132 | 11 | 0.05 | 0.20 | 63.67
7211 | 1 | 0.00 | 0.02 | 63.69
7212 | 60 | 0.29 | 1.08 | 64.77
7213 | 20 | 0.10 | 0.36 | 65.14
7214 | 9 | 0.04 | 0.16 | 65.30
7215 | 1 | 0.00 | 0.02 | 65.32
7221 | 1 | 0.00 | 0.02 | 65.33
7222 | 1 | 0.00 | 0.02 | 65.35
7223 | 3 | 0.01 | 0.05 | 65.41
7224 | 1 | 0.00 | 0.02 | 65.42
7231 | 59 | 0.28 | 1.07 | 66.49
7232 | 2 | 0.01 | 0.04 | 66.53
7233 | 27 | 0.13 | 0.49 | 67.02
7234 | 1 | 0.00 | 0.02 | 67.03
7311 | 1 | 0.00 | 0.02 | 67.05
7313 | 1 | 0.00 | 0.02 | 67.07
7316 | 1 | 0.00 | 0.02 | 67.09
7317 | 3 | 0.01 | 0.05 | 67.14
7318 | 7 | 0.03 | 0.13 | 67.27
7319 | 9 | 0.04 | 0.16 | 67.43
7322 | 14 | 0.07 | 0.25 | 67.69
7323 | 2 | 0.01 | 0.04 | 67.72
7411 | 52 | 0.25 | 0.94 | 68.66
7412 | 8 | 0.04 | 0.14 | 68.81
7413 | 8 | 0.04 | 0.14 | 68.95
7421 | 5 | 0.02 | 0.09 | 69.04
7422 | 12 | 0.06 | 0.22 | 69.26
7511 | 25 | 0.12 | 0.45 | 69.71
7512 | 93 | 0.45 | 1.68 | 71.39
7513 | 6 | 0.03 | 0.11 | 71.50
7514 | 8 | 0.04 | 0.14 | 71.65
7515 | 2 | 0.01 | 0.04 | 71.68
7521 | 6 | 0.03 | 0.11 | 71.79
7522 | 40 | 0.19 | 0.72 | 72.51
7523 | 1 | 0.00 | 0.02 | 72.53
7531 | 35 | 0.17 | 0.63 | 73.16
7533 | 53 | 0.26 | 0.96 | 74.12
7534 | 7 | 0.03 | 0.13 | 74.25
7536 | 4 | 0.02 | 0.07 | 74.32
7542 | 3 | 0.01 | 0.05 | 74.38
7543 | 2 | 0.01 | 0.04 | 74.41
7544 | 5 | 0.02 | 0.09 | 74.50
8111 | 10 | 0.05 | 0.18 | 74.68
8112 | 5 | 0.02 | 0.09 | 74.77
8113 | 4 | 0.02 | 0.07 | 74.85
8114 | 2 | 0.01 | 0.04 | 74.88
8121 | 2 | 0.01 | 0.04 | 74.92
8122 | 3 | 0.01 | 0.05 | 74.97
8131 | 1 | 0.00 | 0.02 | 74.99
8141 | 3 | 0.01 | 0.05 | 75.05
8142 | 13 | 0.06 | 0.24 | 75.28
8143 | 2 | 0.01 | 0.04 | 75.32
8151 | 1 | 0.00 | 0.02 | 75.33
8152 | 1 | 0.00 | 0.02 | 75.35
8153 | 3 | 0.01 | 0.05 | 75.41
8156 | 1 | 0.00 | 0.02 | 75.42
8157 | 5 | 0.02 | 0.09 | 75.52
8160 | 25 | 0.12 | 0.45 | 75.97
8171 | 4 | 0.02 | 0.07 | 76.04
8172 | 15 | 0.07 | 0.27 | 76.31
8182 | 5 | 0.02 | 0.09 | 76.40
8183 | 7 | 0.03 | 0.13 | 76.53
8189 | 1 | 0.00 | 0.02 | 76.55
8219 | 2 | 0.01 | 0.04 | 76.58
8312 | 1 | 0.00 | 0.02 | 76.60
8321 | 1 | 0.00 | 0.02 | 76.62
8322 | 132 | 0.64 | 2.39 | 79.01
8331 | 40 | 0.19 | 0.72 | 79.73
8332 | 82 | 0.39 | 1.48 | 81.21
8341 | 14 | 0.07 | 0.25 | 81.46
8342 | 30 | 0.14 | 0.54 | 82.01
8343 | 16 | 0.08 | 0.29 | 82.30
8344 | 11 | 0.05 | 0.20 | 82.50
8350 | 7 | 0.03 | 0.13 | 82.62
9111 | 304 | 1.46 | 5.50 | 88.12
9112 | 221 | 1.06 | 4.00 | 92.12
9121 | 2 | 0.01 | 0.04 | 92.15
9129 | 2 | 0.01 | 0.04 | 92.19
9211 | 64 | 0.31 | 1.16 | 93.35
9212 | 5 | 0.02 | 0.09 | 93.44
9213 | 1 | 0.00 | 0.02 | 93.45
9214 | 18 | 0.09 | 0.33 | 93.78
9215 | 14 | 0.07 | 0.25 | 94.03
9216 | 3 | 0.01 | 0.05 | 94.09
9311 | 1 | 0.00 | 0.02 | 94.10
9312 | 6 | 0.03 | 0.11 | 94.21
9313 | 46 | 0.22 | 0.83 | 95.05
9321 | 11 | 0.05 | 0.20 | 95.24
9329 | 6 | 0.03 | 0.11 | 95.35
9333 | 23 | 0.11 | 0.42 | 95.77
9334 | 58 | 0.28 | 1.05 | 96.82
9411 | 13 | 0.06 | 0.24 | 97.05
9412 | 30 | 0.14 | 0.54 | 97.59
9510 | 2 | 0.01 | 0.04 | 97.63
9520 | 43 | 0.21 | 0.78 | 98.41
9529 | 1 | 0.00 | 0.02 | 98.43
9611 | 4 | 0.02 | 0.07 | 98.50
9612 | 4 | 0.02 | 0.07 | 98.57
9613 | 6 | 0.03 | 0.11 | 98.68
9621 | 12 | 0.06 | 0.22 | 98.90
9622 | 1 | 0.00 | 0.02 | 98.92
9623 | 3 | 0.01 | 0.05 | 98.97
9629 | 3 | 0.01 | 0.05 | 99.02
9999 | 54 | 0.26 | 0.98 | 100.00
<NA> | 15231 | 73.36 | <NA> | <NA>elsoc_long_2016_2023 %>%
group_by(ola) %>%
summarise(
solo_NA = all(is.na(ciuo08_m03))
) # only wave 2018, 2021 and 2023# A tibble: 7 × 2
ola solo_NA
<dbl> <lgl>
1 1 TRUE
2 2 TRUE
3 3 FALSE
4 4 TRUE
5 5 FALSE
6 6 TRUE
7 7 FALSE elsoc_long_2016_2023$isco88 <- NA
elsoc_long_2016_2023$ciuo88_m03 <- as.character(elsoc_long_2016_2023$ciuo88_m03)
elsoc_long_2016_2023$ciuo08_m03 <- as.character(elsoc_long_2016_2023$ciuo08_m03)
elsoc_long_2016_2023$isco88[elsoc_long_2016_2023$ola %in% c(1)] <- elsoc_long_2016_2023$ciuo88_m03[elsoc_long_2016_2023$ola %in% c(1)]
elsoc_long_2016_2023$isco88[elsoc_long_2016_2023$ola %in% c(3,5,7)] <- DIGCLASS::isco08_to_isco88(elsoc_long_2016_2023$ciuo08_m03[elsoc_long_2016_2023$ola %in% c(3,5,7)]
)
elsoc_long_2016_2023 %>%
group_by(ola) %>%
summarise(
solo_NA = all(is.na(isco88))
)# A tibble: 7 × 2
ola solo_NA
<dbl> <lgl>
1 1 FALSE
2 2 TRUE
3 3 FALSE
4 4 TRUE
5 5 FALSE
6 6 TRUE
7 7 FALSE # Crear una columna con la variable "isco08" adelantada una ola
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
group_by(idencuesta) %>% # Agrupa por id para trabajar en cada individuo
mutate(isco88_lagged=lag(isco88,n=1)) %>% # Desplaza isco08 a la siguiente ola
ungroup()
# Rellenar los valores NA en la variable original
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
mutate(isco88=ifelse(!is.na(isco88),isco88,isco88_lagged)) %>% # Si isco08 es NA, sustituir con el valor de la ola anterior
select(-isco88_lagged) # Elimina la columna temporal
elsoc_long_2016_2023 %>%
group_by(ola) %>%
summarise(
solo_NA = all(is.na(isco88))
)# A tibble: 7 × 2
ola solo_NA
<dbl> <lgl>
1 1 FALSE
2 2 FALSE
3 3 FALSE
4 4 FALSE
5 5 FALSE
6 6 FALSE
7 7 FALSE # SELF EMPLOYMENT -- EMPLOYMENT RELATION
frq(elsoc_long_2016_2023$m07)Relacion de empleo (x) <numeric>
# total N=20761 valid N=7288 mean=2.35 sd=1.90
Value
-----
1
2
3
4
5
6
7
<NA>
Label
-----------------------------------------------------------------------------------
Empleado u obrero en empresa privada
Empleado u obrero del sector publico (incluso empresa publica o municipalidad)
Miembro de las Fuerzas Armadas y de Orden
Patron/a o empleador/a (contrata o paga a honorarios a uno/o o mas trabajadores/as)
Trabaja solo, no tiene empleados/as
Familiar no remunerado
Servicio domestico
<NA>
N | Raw % | Valid % | Cum. %
--------------------------------
4160 | 20.04 | 57.08 | 57.08
964 | 4.64 | 13.23 | 70.31
80 | 0.39 | 1.10 | 71.41
329 | 1.58 | 4.51 | 75.92
1379 | 6.64 | 18.92 | 94.84
15 | 0.07 | 0.21 | 95.05
361 | 1.74 | 4.95 | 100.00
13473 | 64.90 | <NA> | <NA>labs_sj <- c(
`1` = "Empleado u obrero en empresa privada",
`2` = "Empleado u obrero del sector público",
`3` = "Miembro de las Fuerzas Armadas y de Orden",
`4` = "Patrón/a o empleador/a",
`5` = "Trabaja solo, no tiene empleados",
`6` = "Familiar no remunerado",
`7` = "Servicio doméstico"
)
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
mutate(m07 = suppressWarnings(as.integer(m07)),
m07 = if_else(m07 %in% c(1,2,4,5,7), m07, NA_integer_)) %>%
group_by(idencuesta) %>%
arrange(idencuesta, ola, .by_group = TRUE) %>%
mutate(rel_empleo = coalesce(m07, lag(m07))) %>%
ungroup() %>%
mutate(
rel_empleo = set_labels(rel_empleo, labels = labs_sj),
rel_empleo = set_label(rel_empleo, label = "Relación de empleo (1–7)")
)
frq(elsoc_long_2016_2023$rel_empleo)Relación de empleo (1–7) (x) <integer>
# total N=20761 valid N=12067 mean=2.35 sd=1.91
Value | Label | N | Raw % | Valid % | Cum. %
-----------------------------------------------------------------------------------
1 | Empleado u obrero en empresa privada | 6979 | 33.62 | 57.84 | 57.84
2 | Empleado u obrero del sector público | 1583 | 7.62 | 13.12 | 70.95
3 | Miembro de las Fuerzas Armadas y de Orden | 0 | 0.00 | 0.00 | 70.95
4 | Patrón/a o empleador/a | 566 | 2.73 | 4.69 | 75.64
5 | Trabaja solo, no tiene empleados | 2330 | 11.22 | 19.31 | 94.95
6 | Familiar no remunerado | 0 | 0.00 | 0.00 | 94.95
7 | Servicio doméstico | 609 | 2.93 | 5.05 | 100.00
<NA> | <NA> | 8694 | 41.88 | <NA> | <NA>sjt.xtab(elsoc_long_2016_2023$rel_empleo,elsoc_long_2016_2023$ola,
show.col.prc=TRUE,
var.labels=c("Relación de empleo","Ola"),
show.summary=FALSE, title=NULL)| Relación de empleo | Ola | Total | ||||||
| 2016 | 2017 | 2018 | 2019 | 2021 | 2022 | 2023 | ||
| Empleado u obrero en empresa privada |
1091 62.1 % |
898 61.1 % |
1349 59.1 % |
1147 58.5 % |
897 54.8 % |
731 53.3 % |
866 54.5 % |
6979 57.8 % |
| Empleado u obrero del sector público |
186 10.6 % |
155 10.6 % |
291 12.8 % |
262 13.4 % |
230 14 % |
195 14.2 % |
264 16.6 % |
1583 13.1 % |
| Patrón/a o empleador/a |
85 4.8 % |
72 4.9 % |
109 4.8 % |
92 4.7 % |
86 5.3 % |
67 4.9 % |
55 3.5 % |
566 4.7 % |
| Trabaja solo, no tiene empleados |
321 18.3 % |
277 18.9 % |
415 18.2 % |
354 18.1 % |
347 21.2 % |
307 22.4 % |
309 19.5 % |
2330 19.3 % |
| Servicio doméstico |
75 4.3 % |
67 4.6 % |
118 5.2 % |
106 5.4 % |
78 4.8 % |
71 5.2 % |
94 5.9 % |
609 5 % |
| Total |
1758 100 % |
1469 100 % |
2282 100 % |
1961 100 % |
1638 100 % |
1371 100 % |
1588 100 % |
12067 100 % |
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
rowwise() %>%
mutate(self_employed = case_when(rel_empleo %in% c(4:5) ~ 1,
rel_empleo %in% c(1,2,7) ~ 0,
TRUE ~ NA)) %>%
ungroup()
frq(elsoc_long_2016_2023$self_employed)x <numeric>
# total N=20761 valid N=12067 mean=0.24 sd=0.43
Value | N | Raw % | Valid % | Cum. %
---------------------------------------
0 | 9171 | 44.17 | 76 | 76
1 | 2896 | 13.95 | 24 | 100
<NA> | 8694 | 41.88 | <NA> | <NA># SUPERVISION
frq(elsoc_long_2016_2023$m06)Cantidad de personas supervisadas (x) <numeric>
# total N=20761 valid N=6858 mean=6.00 sd=123.49
Value | N | Raw % | Valid % | Cum. %
----------------------------------------
0 | 4952 | 23.85 | 72.21 | 72.21
1 | 267 | 1.29 | 3.89 | 76.10
2 | 299 | 1.44 | 4.36 | 80.46
3 | 225 | 1.08 | 3.28 | 83.74
4 | 161 | 0.78 | 2.35 | 86.09
5 | 133 | 0.64 | 1.94 | 88.03
6 | 90 | 0.43 | 1.31 | 89.34
7 | 60 | 0.29 | 0.87 | 90.22
8 | 76 | 0.37 | 1.11 | 91.32
9 | 20 | 0.10 | 0.29 | 91.62
10 | 109 | 0.53 | 1.59 | 93.21
11 | 14 | 0.07 | 0.20 | 93.41
12 | 35 | 0.17 | 0.51 | 93.92
13 | 11 | 0.05 | 0.16 | 94.08
14 | 7 | 0.03 | 0.10 | 94.18
15 | 40 | 0.19 | 0.58 | 94.77
16 | 8 | 0.04 | 0.12 | 94.88
17 | 2 | 0.01 | 0.03 | 94.91
18 | 8 | 0.04 | 0.12 | 95.03
19 | 2 | 0.01 | 0.03 | 95.06
20 | 65 | 0.31 | 0.95 | 96.00
21 | 3 | 0.01 | 0.04 | 96.05
22 | 6 | 0.03 | 0.09 | 96.14
23 | 3 | 0.01 | 0.04 | 96.18
24 | 3 | 0.01 | 0.04 | 96.22
25 | 22 | 0.11 | 0.32 | 96.54
26 | 1 | 0.00 | 0.01 | 96.56
27 | 3 | 0.01 | 0.04 | 96.60
28 | 4 | 0.02 | 0.06 | 96.66
29 | 1 | 0.00 | 0.01 | 96.68
30 | 40 | 0.19 | 0.58 | 97.26
31 | 2 | 0.01 | 0.03 | 97.29
32 | 3 | 0.01 | 0.04 | 97.33
35 | 10 | 0.05 | 0.15 | 97.48
38 | 1 | 0.00 | 0.01 | 97.49
40 | 29 | 0.14 | 0.42 | 97.91
41 | 1 | 0.00 | 0.01 | 97.93
44 | 1 | 0.00 | 0.01 | 97.94
45 | 8 | 0.04 | 0.12 | 98.06
47 | 1 | 0.00 | 0.01 | 98.08
48 | 2 | 0.01 | 0.03 | 98.10
49 | 1 | 0.00 | 0.01 | 98.12
50 | 33 | 0.16 | 0.48 | 98.60
52 | 1 | 0.00 | 0.01 | 98.61
53 | 1 | 0.00 | 0.01 | 98.63
55 | 2 | 0.01 | 0.03 | 98.66
60 | 10 | 0.05 | 0.15 | 98.80
68 | 1 | 0.00 | 0.01 | 98.82
70 | 6 | 0.03 | 0.09 | 98.91
75 | 1 | 0.00 | 0.01 | 98.92
80 | 5 | 0.02 | 0.07 | 98.99
85 | 1 | 0.00 | 0.01 | 99.01
90 | 2 | 0.01 | 0.03 | 99.04
100 | 16 | 0.08 | 0.23 | 99.27
102 | 1 | 0.00 | 0.01 | 99.29
104 | 1 | 0.00 | 0.01 | 99.30
105 | 1 | 0.00 | 0.01 | 99.31
120 | 3 | 0.01 | 0.04 | 99.36
130 | 1 | 0.00 | 0.01 | 99.37
135 | 1 | 0.00 | 0.01 | 99.39
150 | 4 | 0.02 | 0.06 | 99.45
155 | 1 | 0.00 | 0.01 | 99.46
160 | 1 | 0.00 | 0.01 | 99.48
180 | 2 | 0.01 | 0.03 | 99.50
195 | 1 | 0.00 | 0.01 | 99.52
200 | 13 | 0.06 | 0.19 | 99.71
238 | 1 | 0.00 | 0.01 | 99.72
250 | 2 | 0.01 | 0.03 | 99.75
270 | 1 | 0.00 | 0.01 | 99.77
300 | 5 | 0.02 | 0.07 | 99.84
340 | 2 | 0.01 | 0.03 | 99.87
350 | 1 | 0.00 | 0.01 | 99.88
500 | 5 | 0.02 | 0.07 | 99.96
700 | 1 | 0.00 | 0.01 | 99.97
720 | 1 | 0.00 | 0.01 | 99.99
10000 | 1 | 0.00 | 0.01 | 100.00
<NA> | 13903 | 66.97 | <NA> | <NA>elsoc_long_2016_2023$is_supervisor <- if_else(elsoc_long_2016_2023$m06 >= 1, 1, 0)
frq(elsoc_long_2016_2023$is_supervisor)x <numeric>
# total N=20761 valid N=6858 mean=0.28 sd=0.45
Value | N | Raw % | Valid % | Cum. %
----------------------------------------
0 | 4952 | 23.85 | 72.21 | 72.21
1 | 1906 | 9.18 | 27.79 | 100.00
<NA> | 13903 | 66.97 | <NA> | <NA>elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
group_by(idencuesta) %>%
mutate(is_supervisor_lagged=lag(is_supervisor,n=1)) %>%
ungroup()
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
mutate(is_supervisor=ifelse(!is.na(is_supervisor),is_supervisor,is_supervisor_lagged)) %>%
select(-is_supervisor_lagged)
sjt.xtab(elsoc_long_2016_2023$is_supervisor,elsoc_long_2016_2023$ola,
show.col.prc=TRUE,
var.labels=c("Supervisor","Ola"),
show.summary=FALSE, title=NULL)| Supervisor | Ola | Total | ||||||
| 2016 | 2017 | 2018 | 2019 | 2021 | 2022 | 2023 | ||
| 0 |
1339 74.3 % |
1124 74.8 % |
1788 77 % |
1532 76.9 % |
778 62.2 % |
643 62.7 % |
1112 71.1 % |
8316 72.6 % |
| 1 |
463 25.7 % |
378 25.2 % |
534 23 % |
461 23.1 % |
473 37.8 % |
383 37.3 % |
452 28.9 % |
3144 27.4 % |
| Total |
1802 100 % |
1502 100 % |
2322 100 % |
1993 100 % |
1251 100 % |
1026 100 % |
1564 100 % |
11460 100 % |
# N EMPLOYEES
frq(elsoc_long_2016_2023$m06)Cantidad de personas supervisadas (x) <numeric>
# total N=20761 valid N=6858 mean=6.00 sd=123.49
Value | N | Raw % | Valid % | Cum. %
----------------------------------------
0 | 4952 | 23.85 | 72.21 | 72.21
1 | 267 | 1.29 | 3.89 | 76.10
2 | 299 | 1.44 | 4.36 | 80.46
3 | 225 | 1.08 | 3.28 | 83.74
4 | 161 | 0.78 | 2.35 | 86.09
5 | 133 | 0.64 | 1.94 | 88.03
6 | 90 | 0.43 | 1.31 | 89.34
7 | 60 | 0.29 | 0.87 | 90.22
8 | 76 | 0.37 | 1.11 | 91.32
9 | 20 | 0.10 | 0.29 | 91.62
10 | 109 | 0.53 | 1.59 | 93.21
11 | 14 | 0.07 | 0.20 | 93.41
12 | 35 | 0.17 | 0.51 | 93.92
13 | 11 | 0.05 | 0.16 | 94.08
14 | 7 | 0.03 | 0.10 | 94.18
15 | 40 | 0.19 | 0.58 | 94.77
16 | 8 | 0.04 | 0.12 | 94.88
17 | 2 | 0.01 | 0.03 | 94.91
18 | 8 | 0.04 | 0.12 | 95.03
19 | 2 | 0.01 | 0.03 | 95.06
20 | 65 | 0.31 | 0.95 | 96.00
21 | 3 | 0.01 | 0.04 | 96.05
22 | 6 | 0.03 | 0.09 | 96.14
23 | 3 | 0.01 | 0.04 | 96.18
24 | 3 | 0.01 | 0.04 | 96.22
25 | 22 | 0.11 | 0.32 | 96.54
26 | 1 | 0.00 | 0.01 | 96.56
27 | 3 | 0.01 | 0.04 | 96.60
28 | 4 | 0.02 | 0.06 | 96.66
29 | 1 | 0.00 | 0.01 | 96.68
30 | 40 | 0.19 | 0.58 | 97.26
31 | 2 | 0.01 | 0.03 | 97.29
32 | 3 | 0.01 | 0.04 | 97.33
35 | 10 | 0.05 | 0.15 | 97.48
38 | 1 | 0.00 | 0.01 | 97.49
40 | 29 | 0.14 | 0.42 | 97.91
41 | 1 | 0.00 | 0.01 | 97.93
44 | 1 | 0.00 | 0.01 | 97.94
45 | 8 | 0.04 | 0.12 | 98.06
47 | 1 | 0.00 | 0.01 | 98.08
48 | 2 | 0.01 | 0.03 | 98.10
49 | 1 | 0.00 | 0.01 | 98.12
50 | 33 | 0.16 | 0.48 | 98.60
52 | 1 | 0.00 | 0.01 | 98.61
53 | 1 | 0.00 | 0.01 | 98.63
55 | 2 | 0.01 | 0.03 | 98.66
60 | 10 | 0.05 | 0.15 | 98.80
68 | 1 | 0.00 | 0.01 | 98.82
70 | 6 | 0.03 | 0.09 | 98.91
75 | 1 | 0.00 | 0.01 | 98.92
80 | 5 | 0.02 | 0.07 | 98.99
85 | 1 | 0.00 | 0.01 | 99.01
90 | 2 | 0.01 | 0.03 | 99.04
100 | 16 | 0.08 | 0.23 | 99.27
102 | 1 | 0.00 | 0.01 | 99.29
104 | 1 | 0.00 | 0.01 | 99.30
105 | 1 | 0.00 | 0.01 | 99.31
120 | 3 | 0.01 | 0.04 | 99.36
130 | 1 | 0.00 | 0.01 | 99.37
135 | 1 | 0.00 | 0.01 | 99.39
150 | 4 | 0.02 | 0.06 | 99.45
155 | 1 | 0.00 | 0.01 | 99.46
160 | 1 | 0.00 | 0.01 | 99.48
180 | 2 | 0.01 | 0.03 | 99.50
195 | 1 | 0.00 | 0.01 | 99.52
200 | 13 | 0.06 | 0.19 | 99.71
238 | 1 | 0.00 | 0.01 | 99.72
250 | 2 | 0.01 | 0.03 | 99.75
270 | 1 | 0.00 | 0.01 | 99.77
300 | 5 | 0.02 | 0.07 | 99.84
340 | 2 | 0.01 | 0.03 | 99.87
350 | 1 | 0.00 | 0.01 | 99.88
500 | 5 | 0.02 | 0.07 | 99.96
700 | 1 | 0.00 | 0.01 | 99.97
720 | 1 | 0.00 | 0.01 | 99.99
10000 | 1 | 0.00 | 0.01 | 100.00
<NA> | 13903 | 66.97 | <NA> | <NA>elsoc_long_2016_2023$n_employees <- if_else(elsoc_long_2016_2023$is_supervisor == 0, 0, elsoc_long_2016_2023$m06)
frq(elsoc_long_2016_2023$n_employees)x <numeric>
# total N=20761 valid N=10222 mean=4.03 sd=101.19
Value | N | Raw % | Valid % | Cum. %
----------------------------------------
0 | 8316 | 40.06 | 81.35 | 81.35
1 | 267 | 1.29 | 2.61 | 83.97
2 | 299 | 1.44 | 2.93 | 86.89
3 | 225 | 1.08 | 2.20 | 89.09
4 | 161 | 0.78 | 1.58 | 90.67
5 | 133 | 0.64 | 1.30 | 91.97
6 | 90 | 0.43 | 0.88 | 92.85
7 | 60 | 0.29 | 0.59 | 93.44
8 | 76 | 0.37 | 0.74 | 94.18
9 | 20 | 0.10 | 0.20 | 94.37
10 | 109 | 0.53 | 1.07 | 95.44
11 | 14 | 0.07 | 0.14 | 95.58
12 | 35 | 0.17 | 0.34 | 95.92
13 | 11 | 0.05 | 0.11 | 96.03
14 | 7 | 0.03 | 0.07 | 96.10
15 | 40 | 0.19 | 0.39 | 96.49
16 | 8 | 0.04 | 0.08 | 96.57
17 | 2 | 0.01 | 0.02 | 96.59
18 | 8 | 0.04 | 0.08 | 96.66
19 | 2 | 0.01 | 0.02 | 96.68
20 | 65 | 0.31 | 0.64 | 97.32
21 | 3 | 0.01 | 0.03 | 97.35
22 | 6 | 0.03 | 0.06 | 97.41
23 | 3 | 0.01 | 0.03 | 97.44
24 | 3 | 0.01 | 0.03 | 97.47
25 | 22 | 0.11 | 0.22 | 97.68
26 | 1 | 0.00 | 0.01 | 97.69
27 | 3 | 0.01 | 0.03 | 97.72
28 | 4 | 0.02 | 0.04 | 97.76
29 | 1 | 0.00 | 0.01 | 97.77
30 | 40 | 0.19 | 0.39 | 98.16
31 | 2 | 0.01 | 0.02 | 98.18
32 | 3 | 0.01 | 0.03 | 98.21
35 | 10 | 0.05 | 0.10 | 98.31
38 | 1 | 0.00 | 0.01 | 98.32
40 | 29 | 0.14 | 0.28 | 98.60
41 | 1 | 0.00 | 0.01 | 98.61
44 | 1 | 0.00 | 0.01 | 98.62
45 | 8 | 0.04 | 0.08 | 98.70
47 | 1 | 0.00 | 0.01 | 98.71
48 | 2 | 0.01 | 0.02 | 98.73
49 | 1 | 0.00 | 0.01 | 98.74
50 | 33 | 0.16 | 0.32 | 99.06
52 | 1 | 0.00 | 0.01 | 99.07
53 | 1 | 0.00 | 0.01 | 99.08
55 | 2 | 0.01 | 0.02 | 99.10
60 | 10 | 0.05 | 0.10 | 99.20
68 | 1 | 0.00 | 0.01 | 99.21
70 | 6 | 0.03 | 0.06 | 99.27
75 | 1 | 0.00 | 0.01 | 99.28
80 | 5 | 0.02 | 0.05 | 99.32
85 | 1 | 0.00 | 0.01 | 99.33
90 | 2 | 0.01 | 0.02 | 99.35
100 | 16 | 0.08 | 0.16 | 99.51
102 | 1 | 0.00 | 0.01 | 99.52
104 | 1 | 0.00 | 0.01 | 99.53
105 | 1 | 0.00 | 0.01 | 99.54
120 | 3 | 0.01 | 0.03 | 99.57
130 | 1 | 0.00 | 0.01 | 99.58
135 | 1 | 0.00 | 0.01 | 99.59
150 | 4 | 0.02 | 0.04 | 99.63
155 | 1 | 0.00 | 0.01 | 99.64
160 | 1 | 0.00 | 0.01 | 99.65
180 | 2 | 0.01 | 0.02 | 99.67
195 | 1 | 0.00 | 0.01 | 99.68
200 | 13 | 0.06 | 0.13 | 99.80
238 | 1 | 0.00 | 0.01 | 99.81
250 | 2 | 0.01 | 0.02 | 99.83
270 | 1 | 0.00 | 0.01 | 99.84
300 | 5 | 0.02 | 0.05 | 99.89
340 | 2 | 0.01 | 0.02 | 99.91
350 | 1 | 0.00 | 0.01 | 99.92
500 | 5 | 0.02 | 0.05 | 99.97
700 | 1 | 0.00 | 0.01 | 99.98
720 | 1 | 0.00 | 0.01 | 99.99
10000 | 1 | 0.00 | 0.01 | 100.00
<NA> | 10539 | 50.76 | <NA> | <NA>elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
group_by(idencuesta) %>%
mutate(n_employees_lagged=lag(n_employees,n=1)) %>%
ungroup()
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
mutate(n_employees=ifelse(!is.na(n_employees),n_employees,n_employees_lagged)) %>%
select(-n_employees_lagged)
sjt.xtab(elsoc_long_2016_2023$n_employees,elsoc_long_2016_2023$ola,
show.col.prc=TRUE,
var.labels=c("N employees","Ola"),
show.summary=FALSE, title=NULL)| N employees | Ola | Total | ||||||
| 2016 | 2017 | 2018 | 2019 | 2021 | 2022 | 2023 | ||
| 0 |
1339 74.3 % |
1124 74.8 % |
1962 78.6 % |
1608 77.7 % |
1332 73.8 % |
829 68.4 % |
1231 73.1 % |
9425 75 % |
| 1 |
62 3.4 % |
54 3.6 % |
66 2.6 % |
55 2.7 % |
77 4.3 % |
68 5.6 % |
66 3.9 % |
448 3.6 % |
| 2 |
72 4 % |
61 4.1 % |
71 2.8 % |
62 3 % |
80 4.4 % |
60 5 % |
76 4.5 % |
482 3.8 % |
| 3 |
49 2.7 % |
39 2.6 % |
66 2.6 % |
62 3 % |
59 3.3 % |
50 4.1 % |
52 3.1 % |
377 3 % |
| 4 |
40 2.2 % |
32 2.1 % |
51 2 % |
45 2.2 % |
43 2.4 % |
35 2.9 % |
31 1.8 % |
277 2.2 % |
| 5 |
39 2.2 % |
30 2 % |
35 1.4 % |
30 1.4 % |
26 1.4 % |
22 1.8 % |
33 2 % |
215 1.7 % |
| 6 |
22 1.2 % |
18 1.2 % |
28 1.1 % |
26 1.3 % |
15 0.8 % |
11 0.9 % |
26 1.5 % |
146 1.2 % |
| 7 |
13 0.7 % |
9 0.6 % |
19 0.8 % |
18 0.9 % |
14 0.8 % |
13 1.1 % |
14 0.8 % |
100 0.8 % |
| 8 |
17 0.9 % |
12 0.8 % |
29 1.2 % |
24 1.2 % |
15 0.8 % |
13 1.1 % |
16 1 % |
126 1 % |
| 9 |
3 0.2 % |
3 0.2 % |
6 0.2 % |
4 0.2 % |
2 0.1 % |
2 0.2 % |
9 0.5 % |
29 0.2 % |
| 10 |
27 1.5 % |
19 1.3 % |
29 1.2 % |
21 1 % |
31 1.7 % |
24 2 % |
25 1.5 % |
176 1.4 % |
| 11 |
3 0.2 % |
3 0.2 % |
3 0.1 % |
2 0.1 % |
4 0.2 % |
3 0.2 % |
4 0.2 % |
22 0.2 % |
| 12 |
11 0.6 % |
8 0.5 % |
7 0.3 % |
7 0.3 % |
7 0.4 % |
7 0.6 % |
10 0.6 % |
57 0.5 % |
| 13 |
5 0.3 % |
5 0.3 % |
4 0.2 % |
2 0.1 % |
2 0.1 % |
1 0.1 % |
0 0 % |
19 0.2 % |
| 14 |
1 0.1 % |
1 0.1 % |
1 0 % |
1 0 % |
4 0.2 % |
2 0.2 % |
2 0.1 % |
12 0.1 % |
| 15 |
12 0.7 % |
9 0.6 % |
10 0.4 % |
10 0.5 % |
15 0.8 % |
13 1.1 % |
3 0.2 % |
72 0.6 % |
| 16 |
3 0.2 % |
3 0.2 % |
1 0 % |
1 0 % |
1 0.1 % |
0 0 % |
3 0.2 % |
12 0.1 % |
| 17 |
2 0.1 % |
2 0.1 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
4 0 % |
| 18 |
3 0.2 % |
2 0.1 % |
4 0.2 % |
4 0.2 % |
0 0 % |
0 0 % |
1 0.1 % |
14 0.1 % |
| 19 |
1 0.1 % |
1 0.1 % |
0 0 % |
0 0 % |
1 0.1 % |
0 0 % |
0 0 % |
3 0 % |
| 20 |
12 0.7 % |
8 0.5 % |
26 1 % |
23 1.1 % |
10 0.6 % |
9 0.7 % |
17 1 % |
105 0.8 % |
| 21 |
1 0.1 % |
1 0.1 % |
0 0 % |
0 0 % |
2 0.1 % |
1 0.1 % |
0 0 % |
5 0 % |
| 22 |
1 0.1 % |
1 0.1 % |
1 0 % |
0 0 % |
2 0.1 % |
1 0.1 % |
2 0.1 % |
8 0.1 % |
| 23 |
1 0.1 % |
1 0.1 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
1 0.1 % |
5 0 % |
| 24 |
1 0.1 % |
1 0.1 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
1 0.1 % |
5 0 % |
| 25 |
4 0.2 % |
3 0.2 % |
4 0.2 % |
4 0.2 % |
9 0.5 % |
7 0.6 % |
5 0.3 % |
36 0.3 % |
| 26 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
1 0.1 % |
0 0 % |
2 0 % |
| 27 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
2 0.1 % |
1 0.1 % |
0 0 % |
5 0 % |
| 28 |
1 0.1 % |
1 0.1 % |
1 0 % |
0 0 % |
1 0.1 % |
1 0.1 % |
1 0.1 % |
6 0 % |
| 29 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
2 0 % |
| 30 |
10 0.6 % |
8 0.5 % |
12 0.5 % |
7 0.3 % |
8 0.4 % |
6 0.5 % |
11 0.7 % |
62 0.5 % |
| 31 |
1 0.1 % |
1 0.1 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
4 0 % |
| 32 |
0 0 % |
0 0 % |
1 0 % |
0 0 % |
1 0.1 % |
1 0.1 % |
1 0.1 % |
4 0 % |
| 35 |
2 0.1 % |
1 0.1 % |
4 0.2 % |
3 0.1 % |
1 0.1 % |
1 0.1 % |
3 0.2 % |
15 0.1 % |
| 38 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
0 0 % |
0 0 % |
1 0 % |
| 40 |
12 0.7 % |
12 0.8 % |
6 0.2 % |
6 0.3 % |
7 0.4 % |
7 0.6 % |
4 0.2 % |
54 0.4 % |
| 41 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
0 0 % |
0 0 % |
1 0 % |
| 44 |
1 0.1 % |
1 0.1 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
2 0 % |
| 45 |
4 0.2 % |
4 0.3 % |
0 0 % |
0 0 % |
2 0.1 % |
2 0.2 % |
2 0.1 % |
14 0.1 % |
| 47 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
1 0.1 % |
0 0 % |
2 0 % |
| 48 |
1 0.1 % |
1 0.1 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
3 0 % |
| 49 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
1 0 % |
| 50 |
5 0.3 % |
3 0.2 % |
9 0.4 % |
7 0.3 % |
6 0.3 % |
4 0.3 % |
13 0.8 % |
47 0.4 % |
| 52 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
2 0 % |
| 53 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
1 0.1 % |
0 0 % |
2 0 % |
| 55 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
1 0.1 % |
0 0 % |
0 0 % |
3 0 % |
| 60 |
4 0.2 % |
4 0.3 % |
2 0.1 % |
2 0.1 % |
3 0.2 % |
3 0.2 % |
1 0.1 % |
19 0.2 % |
| 68 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
1 0 % |
| 70 |
3 0.2 % |
3 0.2 % |
1 0 % |
1 0 % |
2 0.1 % |
2 0.2 % |
0 0 % |
12 0.1 % |
| 75 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
2 0 % |
| 80 |
1 0.1 % |
1 0.1 % |
2 0.1 % |
2 0.1 % |
1 0.1 % |
1 0.1 % |
1 0.1 % |
9 0.1 % |
| 85 |
1 0.1 % |
1 0.1 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
2 0 % |
| 90 |
1 0.1 % |
0 0 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
3 0 % |
| 100 |
4 0.2 % |
4 0.3 % |
7 0.3 % |
7 0.3 % |
4 0.2 % |
3 0.2 % |
1 0.1 % |
30 0.2 % |
| 102 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
1 0.1 % |
0 0 % |
2 0 % |
| 104 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
1 0.1 % |
0 0 % |
2 0 % |
| 105 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
2 0 % |
| 120 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
2 0.1 % |
4 0 % |
| 130 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
0 0 % |
0 0 % |
1 0 % |
| 135 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
1 0 % |
| 150 |
1 0.1 % |
1 0.1 % |
2 0.1 % |
2 0.1 % |
0 0 % |
0 0 % |
1 0.1 % |
7 0.1 % |
| 155 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
2 0 % |
| 160 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
1 0 % |
| 180 |
1 0.1 % |
1 0.1 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
4 0 % |
| 195 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
1 0 % |
| 200 |
2 0.1 % |
2 0.1 % |
3 0.1 % |
3 0.1 % |
5 0.3 % |
4 0.3 % |
3 0.2 % |
22 0.2 % |
| 238 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
2 0 % |
| 250 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
1 0.1 % |
3 0 % |
| 270 |
0 0 % |
0 0 % |
0 0 % |
0 0 % |
1 0.1 % |
0 0 % |
0 0 % |
1 0 % |
| 300 |
1 0.1 % |
1 0.1 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
3 0.2 % |
6 0 % |
| 340 |
1 0.1 % |
1 0.1 % |
0 0 % |
0 0 % |
1 0.1 % |
0 0 % |
0 0 % |
3 0 % |
| 350 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
2 0 % |
| 500 |
1 0.1 % |
1 0.1 % |
2 0.1 % |
1 0 % |
0 0 % |
0 0 % |
2 0.1 % |
7 0.1 % |
| 700 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
2 0 % |
| 720 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
2 0 % |
| 10000 |
0 0 % |
0 0 % |
1 0 % |
1 0 % |
0 0 % |
0 0 % |
0 0 % |
2 0 % |
| Total |
1802 100 % |
1502 100 % |
2496 100 % |
2069 100 % |
1805 100 % |
1212 100 % |
1683 100 % |
12569 100 % |
## EGP SCHEME
elsoc_long_2016_2023 %>%
select(isco88, self_employed, n_employees, is_supervisor) %>%
na.omit()# A tibble: 11,098 × 4
isco88 self_employed n_employees is_supervisor
<chr> <dbl> <dbl> <dbl>
1 9112 1 0 0
2 9112 1 0 0
3 7231 0 1 1
4 7231 0 1 1
5 7231 0 0 0
6 7231 0 0 0
7 8232 0 5 1
8 7422 1 0 0
9 7422 1 0 0
10 7422 0 0 0
# ℹ 11,088 more rowselsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
mutate(
isco88 = repair_isco(isco88))
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
mutate(
egp11 = isco88_to_egp(isco88, self_employed, n_employees, n_classes = 11, label = T),
egp11_mp = isco88_to_egp_mp(isco88, is_supervisor, self_employed, n_employees, label = TRUE),
oesch5 = isco88_to_oesch(isco88, self_employed, n_employees, n_classes = 5, label = T))
frq(elsoc_long_2016_2023$egp11)x <character>
# total N=20761 valid N=11364 mean=5.74 sd=3.67
Value | N | Raw % | Valid % | Cum. %
------------------------------------------------------------------------
'I: Higher Controllers' | 1626 | 7.83 | 14.31 | 14.31
'II: Lower Controllers' | 2285 | 11.01 | 20.11 | 34.42
'IIIa: Routine Nonmanual' | 583 | 2.81 | 5.13 | 39.55
'IIIb: Lower Sales-Service' | 1095 | 5.27 | 9.64 | 49.18
'IVa: Self-employed with employees' | 31 | 0.15 | 0.27 | 49.45
'IVb: Self-employed with no employees' | 358 | 1.72 | 3.15 | 52.60
'IVc: Self-employed Farmer' | 111 | 0.53 | 0.98 | 53.58
'V: Manual Supervisors' | 1104 | 5.32 | 9.71 | 63.30
'VI: Skilled Worker' | 1073 | 5.17 | 9.44 | 72.74
'VIIa: Unskilled Worker' | 2777 | 13.38 | 24.44 | 97.18
'VIIb: Farm Labor' | 321 | 1.55 | 2.82 | 100.00
<NA> | 9397 | 45.26 | <NA> | <NA>frq(elsoc_long_2016_2023$egp11_mp)x <character>
# total N=20761 valid N=11364 mean=7.35 sd=4.16
Value | N | Raw % | Valid % | Cum. %
---------------------------------------------------------------------
Ia Higher Managers | 1355 | 6.53 | 11.92 | 11.92
Ib Lower Managers | 271 | 1.31 | 2.38 | 14.31
IIa Higher Professionals | 1436 | 6.92 | 12.64 | 26.94
IIb Lower Professionals | 849 | 4.09 | 7.47 | 34.42
IIIa Routine Nonmanual | 583 | 2.81 | 5.13 | 39.55
IIIb Lower Sales-Service | 1095 | 5.27 | 9.64 | 49.18
IVa Self-employed with employees | 31 | 0.15 | 0.27 | 49.45
IVb Self-employed with no employees | 358 | 1.72 | 3.15 | 52.60
IVc Self-employed Farmer | 111 | 0.53 | 0.98 | 53.58
V Manual Supervisors | 1104 | 5.32 | 9.71 | 63.30
VI Skilled Worker | 1073 | 5.17 | 9.44 | 72.74
VIIa Unskilled Worker | 2777 | 13.38 | 24.44 | 97.18
VIIb Farm Labor | 321 | 1.55 | 2.82 | 100.00
<NA> | 9397 | 45.26 | <NA> | <NA>frq(elsoc_long_2016_2023$oesch5)x <character>
# total N=20761 valid N=11531 mean=3.34 sd=1.26
Value | N | Raw % | Valid % | Cum. %
--------------------------------------------------------------
'Higher-grade service class' | 1284 | 6.18 | 11.14 | 11.14
'Lower-grade service class' | 1289 | 6.21 | 11.18 | 22.31
'Skilled workers' | 3938 | 18.97 | 34.15 | 56.47
'Small business owners' | 2288 | 11.02 | 19.84 | 76.31
'Unskilled workers' | 2732 | 13.16 | 23.69 | 100.00
<NA> | 9230 | 44.46 | <NA> | <NA>sjt.xtab(elsoc_long_2016_2023$egp11_mp,elsoc_long_2016_2023$ola,
show.col.prc=TRUE,
show.na=TRUE,
var.labels=c("EGP","Ola"),
show.summary=FALSE, title=NULL)| EGP | Ola | Total | |||||||
| 2016 | 2017 | 2018 | 2019 | 2021 | 2022 | 2023 | NA | ||
| Ia Higher Managers |
194 6.6 % |
164 6.6 % |
299 8 % |
249 7.3 % |
162 5.9 % |
75 2.7 % |
212 7.8 % |
0 0 % |
1355 6.5 % |
| Ib Lower Managers |
36 1.2 % |
29 1.2 % |
48 1.3 % |
41 1.2 % |
40 1.5 % |
33 1.2 % |
44 1.6 % |
0 0 % |
271 1.3 % |
| IIa Higher Professionals |
211 7.2 % |
168 6.8 % |
244 6.5 % |
209 6.1 % |
221 8.1 % |
176 6.4 % |
207 7.6 % |
0 0 % |
1436 6.9 % |
| IIb Lower Professionals |
119 4.1 % |
89 3.6 % |
173 4.6 % |
145 4.2 % |
116 4.2 % |
90 3.3 % |
117 4.3 % |
0 0 % |
849 4.1 % |
| IIIa Routine Nonmanual |
100 3.4 % |
84 3.4 % |
123 3.3 % |
104 3 % |
57 2.1 % |
45 1.6 % |
70 2.6 % |
0 0 % |
583 2.8 % |
| IIIb Lower Sales-Service |
185 6.3 % |
144 5.8 % |
218 5.8 % |
191 5.6 % |
137 5 % |
114 4.2 % |
106 3.9 % |
0 0 % |
1095 5.3 % |
| IVa Self-employed with employees |
9 0.3 % |
5 0.2 % |
5 0.1 % |
4 0.1 % |
3 0.1 % |
3 0.1 % |
2 0.1 % |
0 0 % |
31 0.1 % |
| IVb Self-employed with no employees |
57 1.9 % |
51 2.1 % |
54 1.4 % |
49 1.4 % |
61 2.2 % |
47 1.7 % |
39 1.4 % |
0 0 % |
358 1.7 % |
| IVc Self-employed Farmer |
16 0.5 % |
15 0.6 % |
21 0.6 % |
17 0.5 % |
19 0.7 % |
11 0.4 % |
12 0.4 % |
0 0 % |
111 0.5 % |
| V Manual Supervisors |
183 6.3 % |
160 6.5 % |
192 5.1 % |
175 5.1 % |
155 5.7 % |
67 2.5 % |
172 6.3 % |
0 0 % |
1104 5.3 % |
| VI Skilled Worker |
188 6.4 % |
163 6.6 % |
206 5.5 % |
177 5.2 % |
124 4.5 % |
101 3.7 % |
114 4.2 % |
0 0 % |
1073 5.2 % |
| VIIa Unskilled Worker |
401 13.7 % |
348 14.1 % |
590 15.7 % |
503 14.7 % |
348 12.7 % |
208 7.6 % |
379 13.9 % |
0 0 % |
2777 13.4 % |
| VIIb Farm Labor |
49 1.7 % |
41 1.7 % |
54 1.4 % |
47 1.4 % |
47 1.7 % |
41 1.5 % |
42 1.5 % |
0 0 % |
321 1.5 % |
| NA |
1179 40.3 % |
1012 40.9 % |
1521 40.6 % |
1506 44.1 % |
1250 45.6 % |
1719 63 % |
1210 44.4 % |
0 0 % |
9397 45.3 % |
| Total |
2927 100 % |
2473 100 % |
3748 100 % |
3417 100 % |
2740 100 % |
2730 100 % |
2726 100 % |
0 100 % |
20761 100 % |
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
mutate(egp3_mp = case_when(egp11_mp %in% c("Ia Higher Managers",
"Ib Lower Managers",
"IIa Higher Professionals",
"IIb Lower Professionals") ~ "Service class (I+II)",
egp11_mp %in% c("IIIa Routine Nonmanual",
"IIIb Lower Sales-Service",
"IVa Self-employed with employees",
"IVb Self-employed with no employees",
"IVc Self-employed Farmer") ~ "Intermediate class (III+IV)",
egp11_mp %in% c("V Manual Supervisors",
"VI Skilled Worker",
"VIIa Unskilled Worker",
"VIIb Farm Labor") ~ "Working class (V+VI+VII)"),
egp3_mp = factor(egp3_mp, levels = c("Service class (I+II)",
"Intermediate class (III+IV)",
"Working class (V+VI+VII)")),
egp3 = case_when(egp11 %in% c("'I: Higher Controllers'",
"'II: Lower Controllers'") ~ "Service class (I+II)",
egp11 %in% c("'IIIa: Routine Nonmanual'",
"'IIIb: Lower Sales-Service'",
"'IVa: Self-employed with employees'",
"'IVb: Self-employed with no employees'",
"'IVc: Self-employed Farmer'") ~ "Intermediate class (III+IV)",
egp11 %in% c("'V: Manual Supervisors'",
"'VI: Skilled Worker'",
"'VIIa: Unskilled Worker'",
"'VIIb: Farm Labor'") ~ "Working class (V+VI+VII)"),
egp3 = factor(egp3, levels = c("Service class (I+II)",
"Intermediate class (III+IV)",
"Working class (V+VI+VII)")),
)
frq(elsoc_long_2016_2023$egp3)x <categorical>
# total N=20761 valid N=11364 mean=2.12 sd=0.89
Value | N | Raw % | Valid % | Cum. %
-------------------------------------------------------------
Service class (I+II) | 3911 | 18.84 | 34.42 | 34.42
Intermediate class (III+IV) | 2178 | 10.49 | 19.17 | 53.58
Working class (V+VI+VII) | 5275 | 25.41 | 46.42 | 100.00
<NA> | 9397 | 45.26 | <NA> | <NA>frq(elsoc_long_2016_2023$egp3_mp)x <categorical>
# total N=20761 valid N=11364 mean=2.12 sd=0.89
Value | N | Raw % | Valid % | Cum. %
-------------------------------------------------------------
Service class (I+II) | 3911 | 18.84 | 34.42 | 34.42
Intermediate class (III+IV) | 2178 | 10.49 | 19.17 | 53.58
Working class (V+VI+VII) | 5275 | 25.41 | 46.42 | 100.00
<NA> | 9397 | 45.26 | <NA> | <NA>frq(elsoc_long_2016_2023$oesch5)x <character>
# total N=20761 valid N=11531 mean=3.34 sd=1.26
Value | N | Raw % | Valid % | Cum. %
--------------------------------------------------------------
'Higher-grade service class' | 1284 | 6.18 | 11.14 | 11.14
'Lower-grade service class' | 1289 | 6.21 | 11.18 | 22.31
'Skilled workers' | 3938 | 18.97 | 34.15 | 56.47
'Small business owners' | 2288 | 11.02 | 19.84 | 76.31
'Unskilled workers' | 2732 | 13.16 | 23.69 | 100.00
<NA> | 9230 | 44.46 | <NA> | <NA>sjt.xtab(elsoc_long_2016_2023$egp3_mp,elsoc_long_2016_2023$ola,
show.col.prc=TRUE,
var.labels=c("EGP","Ola"),
show.summary=FALSE, title=NULL)| EGP | Ola | Total | ||||||
| 2016 | 2017 | 2018 | 2019 | 2021 | 2022 | 2023 | ||
| Service class (I+II) |
560 32 % |
450 30.8 % |
764 34.3 % |
644 33.7 % |
539 36.2 % |
374 37 % |
580 38.3 % |
3911 34.4 % |
| Intermediate class (III+IV) |
367 21 % |
299 20.5 % |
421 18.9 % |
365 19.1 % |
277 18.6 % |
220 21.8 % |
229 15.1 % |
2178 19.2 % |
| Working class (V+VI+VII) |
821 47 % |
712 48.7 % |
1042 46.8 % |
902 47.2 % |
674 45.2 % |
417 41.2 % |
707 46.6 % |
5275 46.4 % |
| Total |
1748 100 % |
1461 100 % |
2227 100 % |
1911 100 % |
1490 100 % |
1011 100 % |
1516 100 % |
11364 100 % |
sjt.xtab(elsoc_long_2016_2023$oesch5,elsoc_long_2016_2023$ola,
show.col.prc=TRUE,
var.labels=c("Oesch","Ola"),
show.summary=FALSE, title=NULL)| Oesch | Ola | Total | ||||||
| 2016 | 2017 | 2018 | 2019 | 2021 | 2022 | 2023 | ||
| 'Higher-grade service class' |
144 8.2 % |
115 7.9 % |
248 11.1 % |
205 10.7 % |
205 13.4 % |
157 14.2 % |
210 13.7 % |
1284 11.1 % |
| 'Lower-grade service class' |
199 11.4 % |
150 10.3 % |
233 10.4 % |
194 10.1 % |
186 12.2 % |
139 12.5 % |
188 12.2 % |
1289 11.2 % |
| 'Skilled workers' |
677 38.7 % |
562 38.5 % |
714 32 % |
626 32.6 % |
492 32.2 % |
412 37.2 % |
455 29.6 % |
3938 34.2 % |
| 'Small business owners' |
365 20.9 % |
321 22 % |
472 21.1 % |
405 21.1 % |
295 19.3 % |
104 9.4 % |
326 21.2 % |
2288 19.8 % |
| 'Unskilled workers' |
363 20.8 % |
313 21.4 % |
567 25.4 % |
488 25.4 % |
348 22.8 % |
297 26.8 % |
356 23.2 % |
2732 23.7 % |
| Total |
1748 100 % |
1461 100 % |
2234 100 % |
1918 100 % |
1526 100 % |
1109 100 % |
1535 100 % |
11531 100 % |
# Exploración de NA en EGP y OESCH
# Distribución general de NA en EGP por ola
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
mutate(egp_na = is.na(egp3_mp))
sjt.xtab(elsoc_long_2016_2023$egp_na, elsoc_long_2016_2023$ola,
show.col.prc = TRUE,
var.labels = c("EGP es NA", "Ola"),
show.summary = FALSE,
title = NULL) # ok| EGP es NA | Ola | Total | ||||||
| 2016 | 2017 | 2018 | 2019 | 2021 | 2022 | 2023 | ||
| FALSE |
1748 59.7 % |
1461 59.1 % |
2227 59.4 % |
1911 55.9 % |
1490 54.4 % |
1011 37 % |
1516 55.6 % |
11364 54.7 % |
| TRUE |
1179 40.3 % |
1012 40.9 % |
1521 40.6 % |
1506 44.1 % |
1250 45.6 % |
1719 63 % |
1210 44.4 % |
9397 45.3 % |
| Total |
2927 100 % |
2473 100 % |
3748 100 % |
3417 100 % |
2740 100 % |
2730 100 % |
2726 100 % |
20761 100 % |
# Análisis de componentes ocupacionales en casos NA
elsoc_long_2016_2023 %>%
filter(is.na(egp3_mp)) %>%
summarise(
total_na = n(),
con_isco88 = sum(!is.na(isco88)),
pct_con_isco88 = round(100 * con_isco88 / total_na, 2),
con_rel_empleo = sum(!is.na(rel_empleo)),
pct_con_rel_empleo = round(100 * con_rel_empleo / total_na, 2)
) %>%
print()# A tibble: 1 × 5
total_na con_isco88 pct_con_isco88 con_rel_empleo pct_con_rel_empleo
<int> <int> <dbl> <int> <dbl>
1 9397 771 8.2 703 7.48# Situación laboral (m02) en casos NA de EGP
frq(elsoc_long_2016_2023$m02)Actividad principal (x) <numeric>
# total N=20761 valid N=20713 mean=3.15 sd=2.48
Value
-----
1
2
3
4
5
6
7
8
9
<NA>
Label
--------------------------------------------------------------------------------------
Trabaja de manera remunerada con jornada completa
Trabaja de manera remunerada a tiempo parcial o hace trabajos ocasionales
Estudia y trabaja
Solo estudia
Jubilado o pensionado
Desempleado, buscando trabajo
Realiza tareas no remuneradas (quehaceres del hogar, cuidando ninnos u otras personas)
Esta enfermo o tiene una discapacidad
No estudia, no trabaja y no busca trabajo
<NA>
N | Raw % | Valid % | Cum. %
-------------------------------
9094 | 43.80 | 43.90 | 43.90
3125 | 15.05 | 15.09 | 58.99
434 | 2.09 | 2.10 | 61.09
475 | 2.29 | 2.29 | 63.38
3303 | 15.91 | 15.95 | 79.33
1038 | 5.00 | 5.01 | 84.34
2486 | 11.97 | 12.00 | 96.34
198 | 0.95 | 0.96 | 97.30
560 | 2.70 | 2.70 | 100.00
48 | 0.23 | <NA> | <NA>na_summary <- elsoc_long_2016_2023 %>%
filter(is.na(egp3_mp)) %>%
count(m02) %>%
mutate(
situacion = case_when(
m02 == 4 ~ "Solo estudia",
m02 == 5 ~ "Jubilado o pensionado",
m02 == 6 ~ "Desempleado, buscando trabajo",
m02 == 7 ~ "Realiza tareas no remuneradas",
m02 == 8 ~ "Enfermo o con discapacidad",
m02 == 9 ~ "No estudia, no trabaja, no busca trabajo",
m02 %in% c(1, 2, 3) ~ "Otra situación laboral",
is.na(m02) ~ "Sin respuesta en m02",
TRUE ~ "Otro"
),
porcentaje = round(100 * n / sum(n), 2)
) %>%
arrange(desc(n))
print(na_summary)# A tibble: 10 × 4
m02 n situacion porcentaje
<dbl> <int> <chr> <dbl>
1 5 3142 Jubilado o pensionado 33.4
2 7 2322 Realiza tareas no remuneradas 24.7
3 1 1064 Otra situación laboral 11.3
4 6 858 Desempleado, buscando trabajo 9.13
5 2 761 Otra situación laboral 8.1
6 9 531 No estudia, no trabaja, no busca trabajo 5.65
7 4 427 Solo estudia 4.54
8 8 173 Enfermo o con discapacidad 1.84
9 3 88 Otra situación laboral 0.94
10 NA 31 Sin respuesta en m02 0.33# Verificación: cobertura de m02 en casos NA de EGP
total_na_egp <- sum(is.na(elsoc_long_2016_2023$egp3_mp))
na_con_m02 <- elsoc_long_2016_2023 %>%
filter(is.na(egp3_mp) & !is.na(m02)) %>%
nrow()
cat(sprintf("Total NA en EGP: %d\n", total_na_egp))Total NA en EGP: 9397cat(sprintf("NA en EGP con respuesta en m02: %d (%.2f%%)\n",
na_con_m02, 100 * na_con_m02 / total_na_egp))NA en EGP con respuesta en m02: 9366 (99.67%)cat(sprintf("NA en EGP sin respuesta en m02: %d (%.2f%%)\n",
total_na_egp - na_con_m02, 100 * (total_na_egp - na_con_m02) / total_na_egp))NA en EGP sin respuesta en m02: 31 (0.33%)# 5. Categorización de casos NA según situación laboral
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
mutate(
situacion_na_egp = case_when(
!is.na(egp3_mp) ~ "Tiene EGP",
is.na(egp3_mp) & m02 == 4 ~ "Solo estudia",
is.na(egp3_mp) & m02 == 5 ~ "Jubilado o pensionado",
is.na(egp3_mp) & m02 == 6 ~ "Desempleado, buscando trabajo",
is.na(egp3_mp) & m02 == 7 ~ "Realiza tareas no remuneradas",
is.na(egp3_mp) & m02 == 8 ~ "Enfermo o con discapacidad",
is.na(egp3_mp) & m02 == 9 ~ "No estudia, no trabaja, no busca trabajo",
is.na(egp3_mp) & m02 %in% c(1, 2, 3) ~ "Otra situación laboral",
is.na(egp3_mp) & is.na(m02) ~ "Sin respuesta",
TRUE ~ "Otro"
)
)
# Tabla cruzada por ola
sjt.xtab(elsoc_long_2016_2023$situacion_na_egp, elsoc_long_2016_2023$ola,
show.col.prc = TRUE,
var.labels = c("Situación laboral (casos NA EGP)", "Ola"),
show.summary = FALSE,
title = NULL)| Situación laboral (casos NA EGP) |
Ola | Total | ||||||
| 2016 | 2017 | 2018 | 2019 | 2021 | 2022 | 2023 | ||
| Desempleado, buscando trabajo |
166 5.7 % |
33 1.3 % |
190 5.1 % |
73 2.1 % |
164 6 % |
87 3.2 % |
145 5.3 % |
858 4.1 % |
| Enfermo o con discapacidad |
24 0.8 % |
12 0.5 % |
29 0.8 % |
24 0.7 % |
43 1.6 % |
18 0.7 % |
23 0.8 % |
173 0.8 % |
| Jubilado o pensionado |
371 12.7 % |
305 12.3 % |
474 12.6 % |
432 12.6 % |
431 15.7 % |
507 18.6 % |
622 22.8 % |
3142 15.1 % |
| No estudia, no trabaja, no busca trabajo |
90 3.1 % |
65 2.6 % |
150 4 % |
59 1.7 % |
33 1.2 % |
52 1.9 % |
82 3 % |
531 2.6 % |
| Otra situación laboral |
54 1.8 % |
263 10.6 % |
110 2.9 % |
485 14.2 % |
169 6.2 % |
749 27.4 % |
83 3 % |
1913 9.2 % |
| Realiza tareas no remuneradas |
367 12.5 % |
276 11.2 % |
439 11.7 % |
374 10.9 % |
360 13.1 % |
273 10 % |
233 8.5 % |
2322 11.2 % |
| Sin respuesta |
6 0.2 % |
1 0 % |
14 0.4 % |
1 0 % |
0 0 % |
5 0.2 % |
4 0.1 % |
31 0.1 % |
| Solo estudia |
101 3.5 % |
57 2.3 % |
115 3.1 % |
58 1.7 % |
50 1.8 % |
28 1 % |
18 0.7 % |
427 2.1 % |
| Tiene EGP |
1748 59.7 % |
1461 59.1 % |
2227 59.4 % |
1911 55.9 % |
1490 54.4 % |
1011 37 % |
1516 55.6 % |
11364 54.7 % |
| Total |
2927 100 % |
2473 100 % |
3748 100 % |
3417 100 % |
2740 100 % |
2730 100 % |
2726 100 % |
20761 100 % |
# Esquema de clase ampliado: EGP + Outside the workforce
# Crear variable egp4 que incluye categoría residual
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
mutate(
egp4 = case_when(
# Mantener categorías EGP existentes
!is.na(egp3_mp) ~ as.character(egp3_mp),
# Categoría residual para quienes están fuera de la fuerza de trabajo
is.na(egp3_mp) & m02 == 5 ~ "Retired or pensioner",
is.na(egp3_mp) & m02 == 6 ~ "Unemployed",
is.na(egp3_mp) & m02 == 7 ~ "Performs unpaid tasks",
# NA para casos sin información
TRUE ~ NA_character_
),
egp4 = factor(egp4,
levels = c("Service class (I+II)",
"Intermediate class (III+IV)",
"Working class (V+VI+VII)",
"Retired or pensioner",
"Unemployed",
"Performs unpaid tasks"))
)
# Verificar distribución
frq(elsoc_long_2016_2023$egp4)x <categorical>
# total N=20761 valid N=17686 mean=3.10 sd=1.60
Value | N | Raw % | Valid % | Cum. %
-------------------------------------------------------------
Service class (I+II) | 3911 | 18.84 | 22.11 | 22.11
Intermediate class (III+IV) | 2178 | 10.49 | 12.31 | 34.43
Working class (V+VI+VII) | 5275 | 25.41 | 29.83 | 64.25
Retired or pensioner | 3142 | 15.13 | 17.77 | 82.02
Unemployed | 858 | 4.13 | 4.85 | 86.87
Performs unpaid tasks | 2322 | 11.18 | 13.13 | 100.00
<NA> | 3075 | 14.81 | <NA> | <NA># Tabla cruzada por ola
sjt.xtab(elsoc_long_2016_2023$egp4, elsoc_long_2016_2023$ola,
show.col.prc = TRUE,
var.labels = c("EGP Extended (with Out of labor force people)", "Wave"),
show.summary = FALSE,
title = NULL)| EGP Extended (with Out of labor force people) |
Wave | Total | ||||||
| 2016 | 2017 | 2018 | 2019 | 2021 | 2022 | 2023 | ||
| Service class (I+II) |
560 21.1 % |
450 21.7 % |
764 22.9 % |
644 23.1 % |
539 22 % |
374 19.9 % |
580 23.1 % |
3911 22.1 % |
| Intermediate class (III+IV) |
367 13.8 % |
299 14.4 % |
421 12.6 % |
365 13.1 % |
277 11.3 % |
220 11.7 % |
229 9.1 % |
2178 12.3 % |
| Working class (V+VI+VII) |
821 31 % |
712 34.3 % |
1042 31.3 % |
902 32.3 % |
674 27.6 % |
417 22.2 % |
707 28.1 % |
5275 29.8 % |
| Retired or pensioner |
371 14 % |
305 14.7 % |
474 14.2 % |
432 15.5 % |
431 17.6 % |
507 27 % |
622 24.7 % |
3142 17.8 % |
| Unemployed |
166 6.3 % |
33 1.6 % |
190 5.7 % |
73 2.6 % |
164 6.7 % |
87 4.6 % |
145 5.8 % |
858 4.9 % |
| Performs unpaid tasks |
367 13.8 % |
276 13.3 % |
439 13.2 % |
374 13.4 % |
360 14.7 % |
273 14.5 % |
233 9.3 % |
2322 13.1 % |
| Total |
2652 100 % |
2075 100 % |
3330 100 % |
2790 100 % |
2445 100 % |
1878 100 % |
2516 100 % |
17686 100 % |
# Comparación entre egp3 y egp4
comparison_table <- elsoc_long_2016_2023 %>%
count(egp3_mp, egp4) %>%
arrange(desc(n))
print(comparison_table)# A tibble: 7 × 3
egp3_mp egp4 n
<fct> <fct> <int>
1 Working class (V+VI+VII) Working class (V+VI+VII) 5275
2 Service class (I+II) Service class (I+II) 3911
3 <NA> Retired or pensioner 3142
4 <NA> <NA> 3075
5 <NA> Performs unpaid tasks 2322
6 Intermediate class (III+IV) Intermediate class (III+IV) 2178
7 <NA> Unemployed 858# Esquema de clase ampliado: OESCH + Outside the workforce
# Crear variable egp4 que incluye categoría residual
elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
mutate(
oesch5 = case_when(
# Mantener categorías OESCH existentes
!is.na(oesch5) ~ as.character(oesch5),
# Categoría residual para quienes están fuera de la fuerza de trabajo
is.na(oesch5) & m02 == 5 ~ "'Retired or pensioner'",
is.na(oesch5) & m02 == 6 ~ "'Unemployed'",
is.na(oesch5) & m02 == 7 ~ "'Performs unpaid tasks'",
# NA para casos sin información
TRUE ~ NA_character_
),
oesch5 = factor(oesch5,
levels = c("'Higher-grade service class'",
"'Lower-grade service class'",
"'Skilled workers'",
"'Small business owners'",
"'Unskilled workers'",
"'Retired or pensioner'",
"'Unemployed'",
"'Performs unpaid tasks'"))
)
# Verificar distribución
frq(elsoc_long_2016_2023$oesch5)x <categorical>
# total N=20761 valid N=17830 mean=4.59 sd=2.04
Value | N | Raw % | Valid % | Cum. %
--------------------------------------------------------------
'Higher-grade service class' | 1284 | 6.18 | 7.20 | 7.20
'Lower-grade service class' | 1289 | 6.21 | 7.23 | 14.43
'Skilled workers' | 3938 | 18.97 | 22.09 | 36.52
'Small business owners' | 2288 | 11.02 | 12.83 | 49.35
'Unskilled workers' | 2732 | 13.16 | 15.32 | 64.67
'Retired or pensioner' | 3134 | 15.10 | 17.58 | 82.25
'Unemployed' | 854 | 4.11 | 4.79 | 87.04
'Performs unpaid tasks' | 2311 | 11.13 | 12.96 | 100.00
<NA> | 2931 | 14.12 | <NA> | <NA># Meritocracy
frq(elsoc_long_2016_2023$merit_effort)Grado de acuerdo: Las personas son recompensadas por sus esfuerzos (x) <numeric>
# total N=20761 valid N=20629 mean=2.60 sd=1.02
Value | Label | N | Raw % | Valid % | Cum. %
------------------------------------------------------------------------
1 | Totalmente en desacuerdo | 2173 | 10.47 | 10.53 | 10.53
2 | En desacuerdo | 9442 | 45.48 | 45.77 | 56.30
3 | Ni en desacuerdo ni de acuerdo | 4037 | 19.45 | 19.57 | 75.87
4 | De acuerdo | 4379 | 21.09 | 21.23 | 97.10
5 | Totalmente de acuerdo | 598 | 2.88 | 2.90 | 100.00
<NA> | <NA> | 132 | 0.64 | <NA> | <NA>frq(elsoc_long_2016_2023$merit_talent)Grado de acuerdo: Las personas son recompensada por su inteligencia (x) <numeric>
# total N=20761 valid N=20631 mean=2.77 sd=1.03
Value | Label | N | Raw % | Valid % | Cum. %
------------------------------------------------------------------------
1 | Totalmente en desacuerdo | 1766 | 8.51 | 8.56 | 8.56
2 | En desacuerdo | 8108 | 39.05 | 39.30 | 47.86
3 | Ni en desacuerdo ni de acuerdo | 4571 | 22.02 | 22.16 | 70.02
4 | De acuerdo | 5577 | 26.86 | 27.03 | 97.05
5 | Totalmente de acuerdo | 609 | 2.93 | 2.95 | 100.00
<NA> | <NA> | 130 | 0.63 | <NA> | <NA>elsoc_long_2016_2023 <- elsoc_long_2016_2023 %>%
mutate(
across(
.cols = c(merit_effort, merit_talent),
.fns = ~ car::recode(., recodes = c("1='Strongly disagree'; 2='Disagree';
3='Neither agree nor disagree'; 4='Agree';
5='Strongly agree'"),
levels = c("Strongly disagree", "Disagree", "Neither agree nor disagree", "Agree", "Strongly agree"),
as.factor = T)
)
)
elsoc_long_2016_2023$merit_effort <- sjlabelled::set_label(elsoc_long_2016_2023$merit_effort,
label = "People are rewarded for their efforts")
elsoc_long_2016_2023$merit_talent <- sjlabelled::set_label(elsoc_long_2016_2023$merit_talent,
label = "People are rewarded for their intelligence")# Controls
# sex
elsoc_long_2016_2023$sex <- car::recode(elsoc_long_2016_2023$sex,
recodes = c("1='Male'; 2='Female'"),
levels = c("Male", "Female"),
as.factor = T)
elsoc_long_2016_2023$sex <- sjlabelled::set_label(elsoc_long_2016_2023$sex,
label = "Gender")
# age
frq(elsoc_long_2016_2023$age)Edad del entrevistado (x) <numeric>
# total N=20761 valid N=20761 mean=48.84 sd=15.43
Value | N | Raw % | Valid % | Cum. %
--------------------------------------
18 | 34 | 0.16 | 0.16 | 0.16
19 | 72 | 0.35 | 0.35 | 0.51
20 | 113 | 0.54 | 0.54 | 1.05
21 | 156 | 0.75 | 0.75 | 1.81
22 | 187 | 0.90 | 0.90 | 2.71
23 | 231 | 1.11 | 1.11 | 3.82
24 | 262 | 1.26 | 1.26 | 5.08
25 | 324 | 1.56 | 1.56 | 6.64
26 | 318 | 1.53 | 1.53 | 8.17
27 | 312 | 1.50 | 1.50 | 9.68
28 | 361 | 1.74 | 1.74 | 11.42
29 | 345 | 1.66 | 1.66 | 13.08
30 | 365 | 1.76 | 1.76 | 14.84
31 | 364 | 1.75 | 1.75 | 16.59
32 | 369 | 1.78 | 1.78 | 18.37
33 | 377 | 1.82 | 1.82 | 20.18
34 | 384 | 1.85 | 1.85 | 22.03
35 | 373 | 1.80 | 1.80 | 23.83
36 | 444 | 2.14 | 2.14 | 25.97
37 | 373 | 1.80 | 1.80 | 27.76
38 | 411 | 1.98 | 1.98 | 29.74
39 | 365 | 1.76 | 1.76 | 31.50
40 | 386 | 1.86 | 1.86 | 33.36
41 | 391 | 1.88 | 1.88 | 35.24
42 | 395 | 1.90 | 1.90 | 37.15
43 | 379 | 1.83 | 1.83 | 38.97
44 | 361 | 1.74 | 1.74 | 40.71
45 | 370 | 1.78 | 1.78 | 42.49
46 | 410 | 1.97 | 1.97 | 44.47
47 | 411 | 1.98 | 1.98 | 46.45
48 | 405 | 1.95 | 1.95 | 48.40
49 | 403 | 1.94 | 1.94 | 50.34
50 | 451 | 2.17 | 2.17 | 52.51
51 | 417 | 2.01 | 2.01 | 54.52
52 | 462 | 2.23 | 2.23 | 56.75
53 | 413 | 1.99 | 1.99 | 58.74
54 | 463 | 2.23 | 2.23 | 60.97
55 | 495 | 2.38 | 2.38 | 63.35
56 | 498 | 2.40 | 2.40 | 65.75
57 | 449 | 2.16 | 2.16 | 67.91
58 | 468 | 2.25 | 2.25 | 70.17
59 | 461 | 2.22 | 2.22 | 72.39
60 | 481 | 2.32 | 2.32 | 74.70
61 | 397 | 1.91 | 1.91 | 76.61
62 | 393 | 1.89 | 1.89 | 78.51
63 | 386 | 1.86 | 1.86 | 80.37
64 | 355 | 1.71 | 1.71 | 82.08
65 | 362 | 1.74 | 1.74 | 83.82
66 | 316 | 1.52 | 1.52 | 85.34
67 | 335 | 1.61 | 1.61 | 86.96
68 | 253 | 1.22 | 1.22 | 88.17
69 | 260 | 1.25 | 1.25 | 89.43
70 | 282 | 1.36 | 1.36 | 90.79
71 | 268 | 1.29 | 1.29 | 92.08
72 | 230 | 1.11 | 1.11 | 93.18
73 | 235 | 1.13 | 1.13 | 94.32
74 | 241 | 1.16 | 1.16 | 95.48
75 | 245 | 1.18 | 1.18 | 96.66
76 | 180 | 0.87 | 0.87 | 97.52
77 | 152 | 0.73 | 0.73 | 98.26
78 | 105 | 0.51 | 0.51 | 98.76
79 | 98 | 0.47 | 0.47 | 99.23
80 | 62 | 0.30 | 0.30 | 99.53
81 | 40 | 0.19 | 0.19 | 99.73
82 | 19 | 0.09 | 0.09 | 99.82
83 | 9 | 0.04 | 0.04 | 99.86
84 | 11 | 0.05 | 0.05 | 99.91
85 | 4 | 0.02 | 0.02 | 99.93
86 | 2 | 0.01 | 0.01 | 99.94
87 | 2 | 0.01 | 0.01 | 99.95
88 | 3 | 0.01 | 0.01 | 99.97
89 | 3 | 0.01 | 0.01 | 99.98
90 | 2 | 0.01 | 0.01 | 99.99
91 | 1 | 0.00 | 0.00 | 100.00
92 | 1 | 0.00 | 0.00 | 100.00
<NA> | 0 | 0.00 | <NA> | <NA>elsoc_long_2016_2023$aget <-
factor(car::recode(elsoc_long_2016_2023$age,
"18:29=1;30:49=2;50:64=3;65:150=4"),
labels = c('18-29', '30-49', '50-64', '65 or more'))
elsoc_long_2016_2023$aget <-
sjlabelled::set_label(elsoc_long_2016_2023$aget,
label = c("Age groups"))
# political indentification
frq(elsoc_long_2016_2023$ideo)Autoubicacion escala izquierda-derecha (x) <numeric>
# total N=20761 valid N=20443 mean=7.39 sd=3.96
Value | Label | N | Raw % | Valid % | Cum. %
----------------------------------------------------------
0 | 0 Izquierda | 1146 | 5.52 | 5.61 | 5.61
1 | 1 | 386 | 1.86 | 1.89 | 7.49
2 | 2 | 625 | 3.01 | 3.06 | 10.55
3 | 3 | 1007 | 4.85 | 4.93 | 15.48
4 | 4 | 1193 | 5.75 | 5.84 | 21.31
5 | 5 Centro | 5360 | 25.82 | 26.22 | 47.53
6 | 6 | 749 | 3.61 | 3.66 | 51.20
7 | 7 | 707 | 3.41 | 3.46 | 54.65
8 | 8 | 601 | 2.89 | 2.94 | 57.59
9 | 9 | 177 | 0.85 | 0.87 | 58.46
10 | 10 Derecha | 1135 | 5.47 | 5.55 | 64.01
11 | 11 Independiente | 727 | 3.50 | 3.56 | 67.57
12 | 12 Ninguno | 6630 | 31.93 | 32.43 | 100.00
<NA> | <NA> | 318 | 1.53 | <NA> | <NA>elsoc_long_2016_2023$ideo<-
factor(
car::recode(
elsoc_long_2016_2023$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')
)
elsoc_long_2016_2023$ideo<- factor(elsoc_long_2016_2023$ideo,levels = levels(elsoc_long_2016_2023$ideo))
elsoc_long_2016_2023$ideo <-
sjlabelled::set_label(x = elsoc_long_2016_2023$ideo,
label = "Political identification")
frq(elsoc_long_2016_2023$ideo)Political identification (x) <categorical>
# total N=20761 valid N=20443 mean=2.67 sd=1.17
Value | N | Raw % | Valid % | Cum. %
---------------------------------------------------
Left | 4357 | 20.99 | 21.31 | 21.31
Center | 5360 | 25.82 | 26.22 | 47.53
Right | 3369 | 16.23 | 16.48 | 64.01
Does not identify | 7357 | 35.44 | 35.99 | 100.00
<NA> | 318 | 1.53 | <NA> | <NA># Socioeconomic_________________________________________________________________
# Education_______________________________________
elsoc_long_2016_2023$educ <-
car::recode(elsoc_long_2016_2023$m01,
"c(1,2,3,4,5,6,7)=1;c(8,9,10)=2; c(-888,-999)=NA")
elsoc_long_2016_2023$educ <-
factor(elsoc_long_2016_2023$educ,
labels = c("Less than Universitary","Universitary"))
#reverse education, reference level is the highest level
#elsoc_long_2016_2023$educ <- forcats::fct_rev(elsoc_long_2016_2023$educ)
elsoc_long_2016_2023$educ <-
sjlabelled::set_label(x = elsoc_long_2016_2023$educ,
label = "Education")
sjmisc::frq(elsoc_long_2016_2023$educ)Education (x) <categorical>
# total N=20761 valid N=20746 mean=1.19 sd=0.39
Value | N | Raw % | Valid % | Cum. %
---------------------------------------------------------
Less than Universitary | 16786 | 80.85 | 80.91 | 80.91
Universitary | 3960 | 19.07 | 19.09 | 100.00
<NA> | 15 | 0.07 | <NA> | <NA>#Recoding of education to years based on casen 2017.
elsoc_long_2016_2023$educyear<- as.numeric(
car::recode(elsoc_long_2016_2023$m01,
"1=0;2=4.3;3=7.5;4=9.8;5=12.02;6=13.9;
7=14.8;8=14.9;9=16.9;10=19.07;c(-888,-999)=NA",
as.numeric = T))
elsoc_long_2016_2023$educyear <-
sjlabelled::set_label(x = elsoc_long_2016_2023$educyear,
label = "Education in years")
class(elsoc_long_2016_2023$educyear)[1] "numeric"sjmisc::frq(elsoc_long_2016_2023$educyear)Education in years (x) <numeric>
# total N=20761 valid N=20746 mean=11.56 sd=3.99
Value | N | Raw % | Valid % | Cum. %
---------------------------------------
0.00 | 200 | 0.96 | 0.96 | 0.96
4.30 | 2412 | 11.62 | 11.63 | 12.59
7.50 | 1992 | 9.59 | 9.60 | 22.19
9.80 | 2692 | 12.97 | 12.98 | 35.17
12.02 | 6122 | 29.49 | 29.51 | 64.68
13.90 | 749 | 3.61 | 3.61 | 68.29
14.80 | 2619 | 12.61 | 12.62 | 80.91
14.90 | 1197 | 5.77 | 5.77 | 86.68
16.90 | 2413 | 11.62 | 11.63 | 98.31
19.07 | 350 | 1.69 | 1.69 | 100.00
<NA> | 15 | 0.07 | <NA> | <NA># Reshape long to wide
df_study1_long <- elsoc_long_2016_2023 %>%
select(idencuesta,
ola,
muestra,
ponderador_long_total,
segmento,
estrato,
just_pension,
egp = egp4,
oesch = oesch5,
merit_effort,
merit_talent,
educ,
educyear,
sex,
age,
aget,
ideo)
vars_invariantes <- c("egp", "oesch",
"age", "aget",
"sex",
"educ", "educyear",
"ideo")
df_study1_long_fix <- df_study1_long %>%
arrange(idencuesta, muestra, ola) %>%
group_by(idencuesta, muestra) %>%
mutate(
across(
all_of(vars_invariantes),
~ {
v1 <- .x[ola == 1 & !is.na(.x)]
if (length(v1) > 0) v1[1] else .x
}
)
) %>%
ungroup()
frq(df_study1_long_fix$ola) # okIdentificador de ola de encuesta (x) <numeric>
# total N=20761 valid N=20761 mean=3.95 sd=1.94
Value | Label | N | Raw % | Valid % | Cum. %
-----------------------------------------------
1 | 2016 | 2927 | 14.10 | 14.10 | 14.10
2 | 2017 | 2473 | 11.91 | 11.91 | 26.01
3 | 2018 | 3748 | 18.05 | 18.05 | 44.06
4 | 2019 | 3417 | 16.46 | 16.46 | 60.52
5 | 2021 | 2740 | 13.20 | 13.20 | 73.72
6 | 2022 | 2730 | 13.15 | 13.15 | 86.87
7 | 2023 | 2726 | 13.13 | 13.13 | 100.00
<NA> | <NA> | 0 | 0.00 | <NA> | <NA>df_unbalanced <- df_study1_long_fix
ids_t7 <- df_study1_long_fix %>%
filter(ola == 7) %>%
distinct(idencuesta) %>%
pull(idencuesta)
df_t7 <- df_study1_long_fix %>%
filter(idencuesta %in% ids_t7)
df_ids_balanced <- df_t7 %>%
group_by(idencuesta) %>%
summarise(
n_olas_distintas = n_distinct(ola),
.groups = "drop"
) %>%
filter(n_olas_distintas == length(waves_use)) %>% # tiene las 6 olas
pull(idencuesta)
df_balanced <- df_t7 %>%
filter(idencuesta %in% df_ids_balanced)
# Número de individuos
n_distinct(df_unbalanced$idencuesta)[1] 4447n_distinct(df_balanced$idencuesta)[1] 1328# Distribución por ola
df_unbalanced %>% count(ola)# A tibble: 6 × 2
ola n
<dbl> <int>
1 1 2927
2 2 2473
3 3 3748
4 4 3417
5 6 2730
6 7 2726df_balanced %>% count(ola)# A tibble: 6 × 2
ola n
<dbl> <int>
1 1 1328
2 2 1328
3 3 1328
4 4 1328
5 6 1328
6 7 13285 Save and export
save(df_study1_long,file = here::here("input/data/proc/df_study1_long_vf.RData")) # original
save(df_study1_long_fix,file = here::here("input/data/proc/df_study1_long_fix_vf.RData")) # fix orginal
save(df_unbalanced,file = here::here("input/data/proc/df_unbalanced.RData")) # Filter data by the idencuesta of t7
save(df_balanced,file = here::here("input/data/proc/df_balanced.RData"))