Data analysis 2

Changes in the Justification of Pension Inequality in Chile (2016–2023) and its Relationship to Social Class and Beliefs in Meritocracy

Author

Andreas Laffert & René Canales

Published

December 8, 2025

1 Presentation

This is the analysis 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 dataset used is df_balanced.RData.

2 Libraries

Show the code
if (! require("pacman")) install.packages("pacman")

pacman::p_load(tidyverse, 
               sjmisc, 
               sjPlot, 
               lme4, 
               here, 
               performance,
               influence.ME, 
               srvyr,
               ordinal,
               texreg, 
               ggdist,
               misty,
               kableExtra,
               ggalluvial, 
               shadowtext,
               MetBrewer,
               patchwork,
               sjlabelled)


options(scipen=999)
rm(list = ls())

3 Data

Show the code
load(file = here("input/data/proc/df_balanced.RData"))

glimpse(df_balanced)
Rows: 7,968
Columns: 17
$ idencuesta            <dbl> 1101011, 1101011, 1101011, 1101011, 1101011, 110…
$ ola                   <dbl> 1, 2, 3, 4, 6, 7, 1, 2, 3, 4, 6, 7, 1, 2, 3, 4, …
$ muestra               <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ponderador_long_total <dbl> 0.11821742, 0.16716225, 0.15261954, 0.33262422, …
$ segmento              <dbl> 110101, 110101, 110101, 110101, 110101, 110101, …
$ estrato               <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, …
$ just_pension          <fct> Strongly disagree, Strongly disagree, Strongly d…
$ egp                   <fct> Retired or pensioner, Retired or pensioner, Reti…
$ oesch                 <fct> 'Retired or pensioner', 'Retired or pensioner', …
$ merit_effort          <fct> Agree, Disagree, Agree, Disagree, Disagree, Disa…
$ merit_talent          <fct> Agree, Disagree, Neither agree nor disagree, Dis…
$ educ                  <fct> Less than Universitary, Less than Universitary, …
$ educyear              <dbl> 4.30, 4.30, 4.30, 4.30, 4.30, 4.30, 9.80, 9.80, …
$ sex                   <fct> Female, Female, Female, Female, Female, Female, …
$ age                   <dbl> 64, 64, 64, 64, 64, 64, 60, 60, 60, 60, 60, 60, …
$ aget                  <fct> 50-64, 50-64, 50-64, 50-64, 50-64, 50-64, 50-64,…
$ ideo                  <fct> Does not identify, Does not identify, Does not i…
Show the code
# Generate analytical sample

df_study1 <- df_balanced %>%
  select(-muestra) %>% 
  na.omit() %>% 
  mutate(ola = case_when(ola == 1 ~ 1,
                         ola == 2 ~ 2, 
                         ola == 3 ~ 3,
                         ola == 4 ~ 4,
                         ola == 6 ~ 5,
                         ola == 7 ~ 6)) %>% 
  mutate(ola = as.factor(ola),
         ola_num = as.numeric(ola),
         ola_2=as.numeric(ola)^2)

df_study1 <- df_study1 %>%
  group_by(idencuesta) %>%             # Agrupar por el identificador del participante
  mutate(n_participaciones = n()) %>%  # Contar el número de filas (participaciones) por participante
  ungroup()

df_study1 <- df_study1 %>% filter(n_participaciones>1)

# Corregir etiquetas

df_study1$just_pension <- sjlabelled::set_label(df_study1$just_pension, 
                        label = "Pension distributive justice")

df_study1$egp <- sjlabelled::set_label(df_study1$egp, 
                        label = "Social class")

df_study1$merit_effort <- sjlabelled::set_label(df_study1$merit_effort, 
                        label = "People are rewarded for their efforts")

df_study1$merit_talent <- sjlabelled::set_label(df_study1$merit_talent, 
                        label = "People are rewarded for their intelligence")

4 Analysis

4.1 Descriptives

Show the code
datos.pension <- df_study1 %>% 
   mutate(just_pension = factor(just_pension, 
                              levels = c("Strongly agree",
                                         "Agree",
                                         "Neither agree nor disagree",
                                         "Disagree",
                                         "Strongly disagree"))) %>% 
  group_by(idencuesta, ola) %>% 
  count(just_pension) %>% 
  group_by(ola) %>% 
  mutate(porcentaje=n/sum(n)) %>% 
  ungroup() %>% 
  na.omit() %>% 
  mutate(wave = case_when(ola == 1 ~ "2016",
                          ola == 2 ~ "2017",
                          ola == 3 ~ "2018",
                          ola == 4 ~ "2019",
                          ola == 5 ~ "2022",
                          ola == 6 ~ "2023"),
         wave = factor(wave, levels = c("2016",
                                        "2017",
                                        "2018",
                                        "2019",
                                        "2022",
                                        "2023")))



etiquetas.pension <- df_study1 %>%
  mutate(just_pension = factor(just_pension, 
                              levels = c("Strongly agree",
                                         "Agree",
                                         "Neither agree nor disagree",
                                         "Disagree",
                                         "Strongly disagree"))) %>% 
  group_by(ola, just_pension) %>%
  summarise(count = n(), .groups = "drop") %>%
  group_by(ola) %>%
  mutate(porcentaje = count / sum(count)) %>% 
  na.omit() %>% 
  mutate(idencuesta = 1,
         wave = case_when(ola == 1 ~ "2016",
                          ola == 2 ~ "2017",
                          ola == 3 ~ "2018",
                          ola == 4 ~ "2019",
                          ola == 5 ~ "2022",
                          ola == 6 ~ "2023"),
         wave = factor(wave, levels = c("2016",
                                        "2017",
                                        "2018",
                                        "2019",
                                        "2022",
                                        "2023")))


datos.pension %>% 
  ggplot(aes(x = wave, fill = just_pension, stratum = just_pension,
             alluvium = idencuesta, y = porcentaje)) +
  ggalluvial::geom_flow(alpha = .4) + 
  ggalluvial::geom_stratum(linetype = 0) +
  scale_y_continuous(labels = scales::percent) + 
  scale_fill_manual(values =  c("#0571B0","#92C5DE","#b3b3b3ff","#F4A582","#CA0020")) +
  geom_shadowtext(data = etiquetas.pension,
                  aes(label = ifelse(porcentaje > 0 , scales::percent(porcentaje, accuracy = .1),"")),
                  position = position_stack(vjust = .5),
                  show.legend = FALSE,
                  size = 3,
                  color = rep('white'),
                  bg.colour='grey30')+
  labs(y = "%",
       x = NULL,
       fill = NULL,
       title = NULL) +
  theme_ggdist() +
  theme(legend.position = "bottom")
Figure 1: Changes in the justification of inequality in pensions over time (2016-2023)
Show the code
t2 <- df_study1 %>% 
  filter(ola == last(ola)) %>% 
  select(just_pension, egp, merit_effort, merit_talent) 

print(summarytools::dfSummary(t2), method="render")
Table 1: Estadísticos descriptivos variables independientes

Data Frame Summary

t2

Dimensions: 1299 x 4
Duplicates: 1008
No Variable Label Stats / Values Freqs (% of Valid) Graph Valid Missing
1 just_pension [factor] Pension distributive justice
1. Strongly disagree
2. Disagree
3. Neither agree nor disagre
4. Agree
5. Strongly agree
224 ( 17.2% )
537 ( 41.3% )
168 ( 12.9% )
324 ( 24.9% )
46 ( 3.5% )
1299 (100.0%) 0 (0.0%)
2 egp [factor] Social class
1. Service class (I+II)
2. Intermediate class (III+I
3. Working class (V+VI+VII)
4. Retired or pensioner
5. Unemployed
6. Performs unpaid tasks
246 ( 18.9% )
165 ( 12.7% )
401 ( 30.9% )
192 ( 14.8% )
86 ( 6.6% )
209 ( 16.1% )
1299 (100.0%) 0 (0.0%)
3 merit_effort [factor] People are rewarded for their efforts
1. Strongly disagree
2. Disagree
3. Neither agree nor disagre
4. Agree
5. Strongly agree
124 ( 9.5% )
594 ( 45.7% )
297 ( 22.9% )
258 ( 19.9% )
26 ( 2.0% )
1299 (100.0%) 0 (0.0%)
4 merit_talent [factor] People are rewarded for their intelligence
1. Strongly disagree
2. Disagree
3. Neither agree nor disagre
4. Agree
5. Strongly agree
104 ( 8.0% )
500 ( 38.5% )
335 ( 25.8% )
333 ( 25.6% )
27 ( 2.1% )
1299 (100.0%) 0 (0.0%)

Generated by summarytools 1.1.4 (R version 4.2.3)
2025-12-08

Figure 2: Changes in the standarized mean of market justice preferences in pensions and meritocracy by social class (2016-2023)

4.2 Longitudinal multilevel models

4.3 ICC

Show the code
m0 <- clmm(just_pension ~ 1 + (1 | idencuesta), 
           link = "logit",
  Hess = TRUE, # calcula explícitamente la matriz varianza-covarianza de estimadores
                data = df_study1)

performance::icc(m0, by_group = T) # 0.24 es between, 0.76 within
# ICC by Group

Group      |   ICC
------------------
idencuesta | 0.237

4.4 Time effects

Show the code
#m1.1 <- clmm(just_pension ~ 1 + ola + (1 | idencuesta),
#                link = "logit",
#  Hess = TRUE,
#  data = df_study1)
#
#m1.2 <- clmm(just_pension ~ 1 + ola_num + (1 | idencuesta),
#                link = "logit",
#  Hess = TRUE, data = df_study1)
#
#m1.3 <- clmm(just_pension ~ 1 + ola_num + ola_2 + (1| idencuesta),
#                link = "logit",
#  Hess = TRUE, data = df_study1)
#
#m1.4 <- clmm(just_pension ~ 1 + ola_num + ola_2 + (1 + ola_num | idencuesta),
#                link = "logit",
#  Hess = TRUE, data = df_study1)
#
#save(m1.1,m1.2,m1.3,m1.4, file = here("output/time_effects.RData"))
#
load(file = here("output/time_effects.RData"))

ccoef <- list(
  "Strongly disagree|Disagree" = "Strongly disagree|Disagree",
  "Disagree|Neither agree nor disagree" = "Disagree|Neither agree nor disagree", 
  "Neither agree nor disagree|Agree" = "Neither agree nor disagree|Agree", 
  "Agree|Strongly agree" = "Agree|Strongly agree", 
  "ola2017" = "Wave 2017",
  "ola2018" = "Wave 2018",
  "ola2019" = "Wave 2019",
  "ola2022" = "Wave 2022",
  "ola2023" = "Wave 2023",
  ola_num = "Wave",
  ola_2 = "Wave^2")

texreg::htmlreg(list(m1.1,m1.2,m1.3,m1.4),
               caption.above = T,
               caption = NULL,
               stars = c(0.05, 0.01, 0.001),
               custom.coef.map = ccoef,
               digits = 3,
               groups = list("Wave (Ref.= 2016)" = 5:9),
               custom.note = "Note: Cells contain regression coefficients with standard errors in parentheses. %stars.",
               leading.zero = T,
               use.packages = F,
               booktabs = F,
               scalebox = 0.80,
               include.loglik = FALSE,
               include.aic = FALSE,
               center = T)
  Model 1 Model 2 Model 3 Model 4
Strongly disagree|Disagree -1.062*** -0.480*** -1.049*** -1.057***
  (0.066) (0.061) (0.105) (0.106)
Disagree|Neither agree nor disagree 1.327*** 1.877*** 1.320*** 1.342***
  (0.067) (0.066) (0.106) (0.108)
Neither agree nor disagree|Agree 1.948*** 2.492*** 1.938*** 1.968***
  (0.070) (0.069) (0.107) (0.110)
Agree|Strongly agree 4.582*** 5.105*** 4.559*** 4.616***
  (0.103) (0.103) (0.131) (0.136)
Wave (Ref.= 2016)        
         
     Wave 2017 -0.337***      
  (0.079)      
     Wave 2018 -0.013      
  (0.078)      
     Wave 2019 0.086      
  (0.077)      
     Wave 2022 0.879***      
  (0.078)      
     Wave 2023 0.854***      
  (0.077)      
Wave   0.231*** -0.184** -0.182**
    (0.013) (0.064) (0.064)
Wave^2     0.059*** 0.059***
      (0.009) (0.009)
BIC 19107.264 19187.247 19151.965 19166.031
Num. obs. 7522 7522 7522 7522
Groups (idencuesta) 1317 1317 1317 1317
Variance: idencuesta: (Intercept) 1.151 1.117 1.126 1.334
Variance: idencuesta: ola_num       0.018
Note: Cells contain regression coefficients with standard errors in parentheses. ***p < 0.001; **p < 0.01; *p < 0.05.

4.4.1 Anova

Show the code
anova(m1.3, m1.4) # quedarse con tiempo continua y con pendiente aleatoria
Likelihood ratio tests of cumulative link models:
 
     formula:                                                        link:
m1.3 just_pension ~ 1 + ola_num + ola_2 + (1 | idencuesta)           logit
m1.4 just_pension ~ 1 + ola_num + ola_2 + (1 + ola_num | idencuesta) logit
     threshold:
m1.3 flexible  
m1.4 flexible  

     no.par   AIC  logLik LR.stat df Pr(>Chisq)
m1.3      7 19103 -9544.7                      
m1.4      9 19104 -9542.9  3.7849  2     0.1507

4.5 WE and BE main effects

Show the code
## WE and BE main effects
#m3 <- clmm(just_pension ~ 1 + ola_num + ola_2 + egp + 
#             (1 + ola_num | idencuesta), 
#           link = "logit",
#           Hess = TRUE,
#           data = df_study1)
#
#m4 <- clmm(just_pension ~ 1 + ola_num +  ola_2 + egp +  
#             merit_effort_cwc + merit_talent_cwc + (1 + ola_num | idencuesta), 
#            link = "logit",
#           Hess = TRUE,
#           data = df_study1)
#
#m5 <- clmm(just_pension ~ 1 + ola_num +  ola_2 + egp +  
#             merit_effort_cwc + merit_talent_cwc + 
#             merit_effort_mean + merit_talent_mean + 
#             (1 + ola_num| idencuesta), 
#            link = "logit",
#           Hess = TRUE,
#           data = df_study1)
#
#m6 <- clmm(just_pension ~ 1 + ola_num +  ola_2 + egp + 
#              merit_effort_cwc + merit_talent_cwc + 
#              merit_effort_mean + merit_talent_mean + 
#              educ + ideo + sex + age + 
#              (1 + ola_num| idencuesta), 
#            link = "logit",
#            Hess = TRUE,
#           data = df_study1)
#
#save(m3,m4,m5,m6, file = here("output/main_effects.RData"))

load(file = here("output/main_effects.RData"))

htmlreg(list(m3,m4,m5,m6))
Statistical models
  Model 1 Model 2 Model 3 Model 4
ola_num -0.18** -0.19** -0.19** -0.19**
  (0.06) (0.06) (0.06) (0.06)
ola_2 0.06*** 0.06*** 0.06*** 0.06***
  (0.01) (0.01) (0.01) (0.01)
egpIntermediate class (III+IV) 0.10 0.10 0.15 0.18
  (0.12) (0.12) (0.12) (0.12)
egpService class (I+II) 0.17 0.17 0.16 0.02
  (0.11) (0.11) (0.11) (0.11)
egpRetired or pensioner 0.10 0.10 0.03 0.11
  (0.12) (0.12) (0.11) (0.13)
egpUnemployed 0.10 0.10 0.15 0.12
  (0.16) (0.16) (0.16) (0.15)
egpPerforms unpaid tasks -0.20 -0.20 -0.21 0.03
  (0.11) (0.11) (0.11) (0.11)
Strongly disagree|Disagree -1.02*** -1.03*** 0.20 -0.06
  (0.12) (0.12) (0.21) (0.25)
Disagree|Neither agree nor disagree 1.38*** 1.37*** 2.60*** 2.34***
  (0.12) (0.12) (0.21) (0.25)
Neither agree nor disagree|Agree 2.00*** 2.00*** 3.23*** 2.97***
  (0.12) (0.12) (0.21) (0.25)
Agree|Strongly agree 4.65*** 4.65*** 5.88*** 5.62***
  (0.15) (0.15) (0.23) (0.27)
merit_effort_cwc   0.11** 0.12*** 0.12**
    (0.04) (0.04) (0.04)
merit_talent_cwc   0.07 0.07 0.07
    (0.03) (0.03) (0.03)
merit_effort_mean     0.39*** 0.39***
      (0.10) (0.09)
merit_talent_mean     0.08 0.03
      (0.10) (0.09)
educUniversitary       0.54***
        (0.10)
ideoCenter       0.26*
        (0.11)
ideoRight       0.60***
        (0.12)
ideoDoes not identify       0.15
        (0.09)
sexFemale       -0.49***
        (0.08)
age       -0.00
        (0.00)
Log Likelihood -9537.45 -9519.91 -9491.85 -9441.48
AIC 19102.91 19071.82 19019.70 18930.97
BIC 19199.86 19182.63 19144.36 19097.18
Num. obs. 7522 7522 7522 7522
Groups (idencuesta) 1317 1317 1317 1317
Variance: idencuesta: (Intercept) 1.30 1.29 1.15 1.01
Variance: idencuesta: ola_num 0.02 0.02 0.02 0.02
***p < 0.001; **p < 0.01; *p < 0.05
Show the code
## WE and BE main effects
#m3_oesch <- clmm(just_pension ~ 1 + ola_num + ola_2 + oesch + 
#             (1 + ola_num | idencuesta), 
#           link = "logit",
#           Hess = TRUE,
#           data = df_study1)
#
#m4_oesch <- clmm(just_pension ~ 1 + ola_num +  ola_2 + oesch +  
#             merit_effort_cwc + merit_talent_cwc + (1 + ola_num | idencuesta), 
#            link = "logit",
#           Hess = TRUE,
#           data = df_study1)
#
#m5_oesch <- clmm(just_pension ~ 1 + ola_num +  ola_2 + oesch +  
#             merit_effort_cwc + merit_talent_cwc + 
#             merit_effort_mean + merit_talent_mean + 
#             (1 + ola_num| idencuesta), 
#            link = "logit",
#           Hess = TRUE,
#           data = df_study1)
#
#m6_oesch <- clmm(just_pension ~ 1 + ola_num +  ola_2 + oesch + 
#              merit_effort_cwc + merit_talent_cwc + 
#              merit_effort_mean + merit_talent_mean + 
#              educ + ideo + sex + age + 
#              (1 + ola_num| idencuesta), 
#            link = "logit",
#            Hess = TRUE,
#           data = df_study1)
#
#save(m3_oesch,m4_oesch,m5_oesch,m6_oesch, file = here("output/oesch_main_effects.RData"))
#
load(file = here("output/oesch_main_effects.RData"))

htmlreg(list(m3_oesch,m4_oesch,m5_oesch,m6_oesch))
Statistical models
  Model 1 Model 2 Model 3 Model 4
ola_num -0.18** -0.19** -0.19** -0.19**
  (0.06) (0.06) (0.06) (0.06)
ola_2 0.06*** 0.06*** 0.06*** 0.06***
  (0.01) (0.01) (0.01) (0.01)
oesch’Lower-grade service class’ -0.27 -0.27 -0.28 0.00
  (0.22) (0.22) (0.22) (0.22)
oesch’Skilled workers’ -0.50** -0.50** -0.50** -0.11
  (0.18) (0.18) (0.18) (0.19)
oesch’Small business owners’ -0.40* -0.40* -0.46* 0.02
  (0.19) (0.19) (0.19) (0.20)
oesch’Unskilled workers’ -0.58** -0.58** -0.57** -0.05
  (0.19) (0.19) (0.19) (0.20)
oesch’Retired or pensioner’ -0.41* -0.41* -0.50** 0.02
  (0.19) (0.19) (0.19) (0.21)
oesch’Unemployed’ -0.41 -0.41 -0.38 0.02
  (0.22) (0.22) (0.22) (0.22)
oesch’Performs unpaid tasks’ -0.71*** -0.71*** -0.74*** -0.07
  (0.19) (0.19) (0.19) (0.20)
Strongly disagree|Disagree -1.53*** -1.54*** -0.34 -0.20
  (0.20) (0.20) (0.26) (0.30)
Disagree|Neither agree nor disagree 0.87*** 0.86*** 2.06*** 2.21***
  (0.19) (0.20) (0.26) (0.30)
Neither agree nor disagree|Agree 1.49*** 1.49*** 2.69*** 2.83***
  (0.20) (0.20) (0.26) (0.31)
Agree|Strongly agree 4.14*** 4.14*** 5.34*** 5.49***
  (0.21) (0.21) (0.27) (0.32)
merit_effort_cwc   0.11** 0.12*** 0.12**
    (0.04) (0.04) (0.04)
merit_talent_cwc   0.07 0.07 0.07
    (0.03) (0.03) (0.03)
merit_effort_mean     0.39*** 0.39***
      (0.10) (0.09)
merit_talent_mean     0.07 0.02
      (0.10) (0.09)
educUniversitary       0.52***
        (0.11)
ideoCenter       0.27*
        (0.11)
ideoRight       0.61***
        (0.12)
ideoDoes not identify       0.15
        (0.09)
sexFemale       -0.48***
        (0.08)
age       -0.00
        (0.00)
Log Likelihood -9533.19 -9515.68 -9487.77 -9442.00
AIC 19098.38 19067.37 19015.53 18936.01
BIC 19209.19 19192.03 19154.04 19116.07
Num. obs. 7522 7522 7522 7522
Groups (idencuesta) 1317 1317 1317 1317
Variance: idencuesta: (Intercept) 1.28 1.26 1.12 1.01
Variance: idencuesta: ola_num 0.02 0.02 0.02 0.02
***p < 0.001; **p < 0.01; *p < 0.05

4.6 Interactions without controls (total effect)

Show the code
## WE and BE Interactions without controls

#m7 <- clmm(just_pension ~ 1 + ola_num +  ola_2 + egp + 
#             merit_effort_cwc + merit_talent_cwc + 
#             merit_effort_mean + merit_talent_mean + 
#             egp*merit_effort_cwc + (1 + ola_num + merit_effort_cwc| idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
#m8 <- clmm(just_pension ~ 1 + ola_num +  ola_2 + egp + 
#             merit_effort_cwc + merit_talent_cwc + 
#             merit_effort_mean + merit_talent_mean + 
#             egp*merit_talent_cwc + (1 + ola_num + merit_talent_cwc| idencuesta), 
#           link = "logit",
# Hess = TRUE,
#          data = df_study1)
#
#
#m9 <- clmm(just_pension ~ 1 + ola_num +  ola_2 + egp + 
#             merit_effort_cwc + merit_talent_cwc + 
#             merit_effort_mean + merit_talent_mean + 
#             egp*merit_effort_mean + (1 + ola_num + merit_effort_mean| idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
#m10 <- clmm(just_pension ~ 1 + ola_num +  ola_2 + egp + 
#              merit_effort_cwc + merit_talent_cwc + 
#              merit_effort_mean + merit_talent_mean +
#              egp*merit_talent_mean + (1 + ola_num + merit_talent_mean| idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
#save(m7,m8,m9,m10, file = here("output/interactions_total.RData"))
load(file = here("output/interactions_total.RData"))

htmlreg(list(m7, m8, m9, m10))
Statistical models
  Model 1 Model 2 Model 3 Model 4
ola_num -0.19** -0.19** -0.19** -0.19**
  (0.07) (0.07) (0.06) (0.06)
ola_2 0.06*** 0.06*** 0.06*** 0.06***
  (0.01) (0.01) (0.01) (0.01)
egpIntermediate class (III+IV) 0.15 0.15 -0.74 -0.80
  (0.12) (0.12) (0.55) (0.56)
egpService class (I+II) 0.16 0.16 -0.05 -0.36
  (0.11) (0.11) (0.50) (0.52)
egpRetired or pensioner 0.03 0.03 -0.35 -0.10
  (0.12) (0.12) (0.54) (0.57)
egpUnemployed 0.14 0.15 -0.38 -0.39
  (0.16) (0.16) (0.68) (0.75)
egpPerforms unpaid tasks -0.21 -0.21 0.20 0.40
  (0.11) (0.11) (0.48) (0.53)
merit_effort_cwc 0.07 0.12*** 0.12*** 0.12***
  (0.06) (0.04) (0.04) (0.04)
merit_talent_cwc 0.07 -0.03 0.06 0.06
  (0.04) (0.06) (0.03) (0.03)
merit_effort_mean 0.38*** 0.41*** 0.33* 0.40***
  (0.10) (0.10) (0.14) (0.10)
merit_talent_mean 0.08 0.07 0.07 -0.00
  (0.10) (0.10) (0.10) (0.14)
egpIntermediate class (III+IV):merit_effort_cwc 0.11      
  (0.10)      
egpService class (I+II):merit_effort_cwc 0.08      
  (0.09)      
egpRetired or pensioner:merit_effort_cwc 0.02      
  (0.09)      
egpUnemployed:merit_effort_cwc 0.09      
  (0.13)      
egpPerforms unpaid tasks:merit_effort_cwc 0.03      
  (0.09)      
Strongly disagree|Disagree 0.17 0.19 0.02 0.01
  (0.21) (0.21) (0.32) (0.33)
Disagree|Neither agree nor disagree 2.62*** 2.64*** 2.42*** 2.41***
  (0.22) (0.22) (0.33) (0.34)
Neither agree nor disagree|Agree 3.25*** 3.28*** 3.04*** 3.04***
  (0.22) (0.22) (0.33) (0.34)
Agree|Strongly agree 5.93*** 5.96*** 5.70*** 5.69***
  (0.24) (0.24) (0.34) (0.35)
egpIntermediate class (III+IV):merit_talent_cwc   0.17    
    (0.10)    
egpService class (I+II):merit_talent_cwc   0.09    
    (0.09)    
egpRetired or pensioner:merit_talent_cwc   0.05    
    (0.09)    
egpUnemployed:merit_talent_cwc   0.26*    
    (0.13)    
egpPerforms unpaid tasks:merit_talent_cwc   0.18    
    (0.09)    
egpIntermediate class (III+IV):merit_effort_mean     0.36  
      (0.21)  
egpService class (I+II):merit_effort_mean     0.08  
      (0.19)  
egpRetired or pensioner:merit_effort_mean     0.14  
      (0.20)  
egpUnemployed:merit_effort_mean     0.21  
      (0.26)  
egpPerforms unpaid tasks:merit_effort_mean     -0.15  
      (0.18)  
egpIntermediate class (III+IV):merit_talent_mean       0.36
        (0.20)
egpService class (I+II):merit_talent_mean       0.18
        (0.18)
egpRetired or pensioner:merit_talent_mean       0.05
        (0.19)
egpUnemployed:merit_talent_mean       0.20
        (0.27)
egpPerforms unpaid tasks:merit_talent_mean       -0.21
        (0.18)
Log Likelihood -9482.36 -9475.05 -9483.36 -9483.01
AIC 19016.72 19002.10 19018.71 19018.02
BIC 19196.78 19182.16 19198.78 19198.09
Num. obs. 7522 7522 7522 7522
Groups (idencuesta) 1317 1317 1317 1317
Variance: idencuesta: (Intercept) 1.18 1.18 2.84 2.12
Variance: idencuesta: ola_num 0.01 0.01 0.01 0.02
Variance: idencuesta: merit_effort_cwc 0.11      
Variance: idencuesta: merit_talent_cwc   0.14    
Variance: idencuesta: merit_effort_mean     0.38  
Variance: idencuesta: merit_talent_mean       0.27
***p < 0.001; **p < 0.01; *p < 0.05
Show the code
 ## WE and BE Interactions without controls

#m7_oesch <- clmm(just_pension ~ 1 + ola_num +  ola_2 + oesch + 
#             merit_effort_cwc + merit_talent_cwc + 
#             merit_effort_mean + merit_talent_mean + 
#             oesch*merit_effort_cwc + (1 + ola_num + merit_effort_cwc| idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
#m8_oesch <- clmm(just_pension ~ 1 + ola_num +  ola_2 + oesch + 
#             merit_effort_cwc + merit_talent_cwc + 
#             merit_effort_mean + merit_talent_mean + 
#             oesch*merit_talent_cwc + (1 + ola_num + merit_talent_cwc| idencuesta), 
#           link = "logit",
# Hess = TRUE,
#          data = df_study1)
#
#
#m9_oesch <- clmm(just_pension ~ 1 + ola_num +  ola_2 + oesch + 
#             merit_effort_cwc + merit_talent_cwc + 
#             merit_effort_mean + merit_talent_mean + 
#             oesch*merit_effort_mean + (1 + ola_num + merit_effort_mean| idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
#m10_oesch <- clmm(just_pension ~ 1 + ola_num +  ola_2 + oesch + 
#              merit_effort_cwc + merit_talent_cwc + 
#              merit_effort_mean + merit_talent_mean +
#              oesch*merit_talent_mean + (1 + ola_num + merit_talent_mean| idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
#save(m7_oesch,m8_oesch,m9_oesch,m10_oesch, file = here("output/oesch_interactions_total.RData"))

load(file = here("output/oesch_interactions_total.RData"))

htmlreg(list(m7_oesch, m8_oesch, m9_oesch, m10_oesch))
Statistical models
  Model 1 Model 2 Model 3 Model 4
ola_num -0.18** -0.19** -0.19** -0.19**
  (0.07) (0.07) (0.06) (0.06)
ola_2 0.06*** 0.06*** 0.06*** 0.06***
  (0.01) (0.01) (0.01) (0.01)
oesch’Lower-grade service class’ -0.28 -0.28 -0.98 -1.41
  (0.22) (0.22) (1.01) (1.03)
oesch’Skilled workers’ -0.51** -0.51** -0.67 -0.45
  (0.18) (0.18) (0.78) (0.81)
oesch’Small business owners’ -0.47* -0.47* -0.59 0.56
  (0.19) (0.19) (0.85) (0.89)
oesch’Unskilled workers’ -0.59** -0.59** -1.05 -0.99
  (0.19) (0.19) (0.84) (0.87)
oesch’Retired or pensioner’ -0.51** -0.51** -0.89 -0.25
  (0.19) (0.19) (0.84) (0.87)
oesch’Unemployed’ -0.40 -0.39 -0.93 -0.56
  (0.22) (0.22) (0.93) (0.99)
oesch’Performs unpaid tasks’ -0.75*** -0.75*** -0.34 0.25
  (0.19) (0.19) (0.80) (0.84)
merit_effort_cwc 0.07 0.12*** 0.12*** 0.12***
  (0.15) (0.04) (0.04) (0.04)
merit_talent_cwc 0.06 -0.05 0.07 0.07
  (0.04) (0.15) (0.03) (0.03)
merit_effort_mean 0.39*** 0.41*** 0.32 0.41***
  (0.10) (0.10) (0.28) (0.10)
merit_talent_mean 0.07 0.06 0.07 0.13
  (0.10) (0.10) (0.10) (0.27)
oesch’Lower-grade service class’:merit_effort_cwc 0.03      
  (0.20)      
oesch’Skilled workers’:merit_effort_cwc 0.11      
  (0.16)      
oesch’Small business owners’:merit_effort_cwc 0.07      
  (0.17)      
oesch’Unskilled workers’:merit_effort_cwc -0.03      
  (0.17)      
oesch’Retired or pensioner’:merit_effort_cwc 0.03      
  (0.17)      
oesch’Unemployed’:merit_effort_cwc 0.10      
  (0.19)      
oesch’Performs unpaid tasks’:merit_effort_cwc 0.04      
  (0.17)      
Strongly disagree|Disagree -0.37 -0.36 -0.53 -0.15
  (0.26) (0.26) (0.72) (0.74)
Disagree|Neither agree nor disagree 2.07*** 2.10*** 1.87** 2.26**
  (0.26) (0.26) (0.72) (0.74)
Neither agree nor disagree|Agree 2.71*** 2.73*** 2.50*** 2.89***
  (0.26) (0.26) (0.72) (0.74)
Agree|Strongly agree 5.38*** 5.42*** 5.15*** 5.54***
  (0.28) (0.28) (0.72) (0.75)
oesch’Lower-grade service class’:merit_talent_cwc   0.12    
    (0.20)    
oesch’Skilled workers’:merit_talent_cwc   0.11    
    (0.16)    
oesch’Small business owners’:merit_talent_cwc   0.07    
    (0.17)    
oesch’Unskilled workers’:merit_talent_cwc   0.09    
    (0.17)    
oesch’Retired or pensioner’:merit_talent_cwc   0.08    
    (0.17)    
oesch’Unemployed’:merit_talent_cwc   0.29    
    (0.19)    
oesch’Performs unpaid tasks’:merit_talent_cwc   0.20    
    (0.17)    
oesch’Lower-grade service class’:merit_effort_mean     0.27  
      (0.39)  
oesch’Skilled workers’:merit_effort_mean     0.07  
      (0.30)  
oesch’Small business owners’:merit_effort_mean     0.05  
      (0.32)  
oesch’Unskilled workers’:merit_effort_mean     0.18  
      (0.32)  
oesch’Retired or pensioner’:merit_effort_mean     0.15  
      (0.31)  
oesch’Unemployed’:merit_effort_mean     0.21  
      (0.36)  
oesch’Performs unpaid tasks’:merit_effort_mean     -0.15  
      (0.30)  
oesch’Lower-grade service class’:merit_talent_mean       0.42
        (0.36)
oesch’Skilled workers’:merit_talent_mean       -0.01
        (0.29)
oesch’Small business owners’:merit_talent_mean       -0.36
        (0.31)
oesch’Unskilled workers’:merit_talent_mean       0.15
        (0.31)
oesch’Retired or pensioner’:merit_talent_mean       -0.08
        (0.30)
oesch’Unemployed’:merit_talent_mean       0.07
        (0.35)
oesch’Performs unpaid tasks’:merit_talent_mean       -0.35
        (0.30)
Log Likelihood -9478.09 -9472.43 -9480.36 -9476.92
AIC 19016.18 19004.86 19020.71 19013.84
BIC 19223.94 19212.63 19228.48 19221.61
Num. obs. 7522 7522 7522 7522
Groups (idencuesta) 1317 1317 1317 1317
Variance: idencuesta: (Intercept) 1.15 1.17 2.78 1.98
Variance: idencuesta: ola_num 0.01 0.01 0.01 0.02
Variance: idencuesta: merit_effort_cwc 0.12      
Variance: idencuesta: merit_talent_cwc   0.14    
Variance: idencuesta: merit_effort_mean     0.37  
Variance: idencuesta: merit_talent_mean       0.25
***p < 0.001; **p < 0.01; *p < 0.05

4.7 Interactions with controls (direct effect)

Show the code
 ## WE and BE Interactions with controls

# m11 <- clmm(just_pension ~ 1 + ola_num +  ola_2 + egp + 
#               merit_effort_cwc + merit_talent_cwc + 
#               merit_effort_mean + merit_talent_mean + 
#               ideo + sex + age + 
#               egp*merit_effort_cwc + (1 + ola_num + merit_effort_cwc| idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
# m12 <- clmm(just_pension ~ 1 + ola_num +  ola_2 + egp + 
#               merit_effort_cwc + merit_talent_cwc +
#               merit_effort_mean + merit_talent_mean + 
#               ideo + sex + age + 
#               egp*merit_talent_cwc + (1 + ola_num + merit_talent_cwc| idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
#
# m13 <- clmm(just_pension ~ 1 + ola_num +  ola_2 + egp + 
#               merit_effort_cwc + merit_talent_cwc + 
#               merit_effort_mean + merit_talent_mean + 
#               ideo + sex + age + 
#               egp*merit_effort_mean + (1 + ola_num | idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
# m14 <- clmm(just_pension ~ 1 + ola_num +  ola_2 + egp + 
#               merit_effort_cwc + merit_talent_cwc + 
#               merit_effort_mean + merit_talent_mean + 
#               ideo + sex + age + 
#               egp*merit_talent_mean + (1 + ola_num | idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
#save(m11,m12,m13,m14, file = here("output/interactions_direct.RData"))

load(file = here("output/interactions_direct.RData"))

htmlreg(list(m11,m12,m13,m14))
Statistical models
  Model 1 Model 2 Model 3 Model 4
ola_num -0.19** -0.19** -0.19** -0.19**
  (0.07) (0.07) (0.06) (0.06)
ola_2 0.06*** 0.06*** 0.06*** 0.06***
  (0.01) (0.01) (0.01) (0.01)
egpIntermediate class (III+IV) 0.25* 0.25* -0.74 -0.83
  (0.12) (0.12) (0.52) (0.53)
egpService class (I+II) 0.21* 0.21* -0.21 -0.41
  (0.11) (0.11) (0.46) (0.50)
egpRetired or pensioner 0.20 0.20 -0.42 -0.12
  (0.13) (0.13) (0.51) (0.54)
egpUnemployed 0.17 0.18 -0.22 -0.17
  (0.16) (0.16) (0.64) (0.71)
egpPerforms unpaid tasks 0.06 0.06 0.31 0.49
  (0.12) (0.12) (0.45) (0.49)
merit_effort_cwc 0.07 0.12*** 0.12*** 0.12***
  (0.06) (0.04) (0.04) (0.04)
merit_talent_cwc 0.07 -0.02 0.07 0.07
  (0.04) (0.06) (0.03) (0.03)
merit_effort_mean 0.38*** 0.41*** 0.30* 0.41***
  (0.10) (0.10) (0.13) (0.09)
merit_talent_mean 0.01 -0.01 0.00 -0.09
  (0.10) (0.10) (0.10) (0.13)
ideoCenter 0.25* 0.26* 0.26* 0.26*
  (0.11) (0.11) (0.11) (0.11)
ideoRight 0.65*** 0.65*** 0.65*** 0.66***
  (0.12) (0.13) (0.12) (0.12)
ideoDoes not identify 0.12 0.12 0.12 0.12
  (0.09) (0.10) (0.09) (0.09)
sexFemale -0.54*** -0.54*** -0.52*** -0.51***
  (0.08) (0.08) (0.08) (0.08)
age -0.00 -0.00 -0.00 -0.00
  (0.00) (0.00) (0.00) (0.00)
egpIntermediate class (III+IV):merit_effort_cwc 0.11      
  (0.10)      
egpService class (I+II):merit_effort_cwc 0.08      
  (0.09)      
egpRetired or pensioner:merit_effort_cwc 0.02      
  (0.09)      
egpUnemployed:merit_effort_cwc 0.09      
  (0.13)      
egpPerforms unpaid tasks:merit_effort_cwc 0.03      
  (0.09)      
Strongly disagree|Disagree -0.30 -0.29 -0.51 -0.49
  (0.25) (0.25) (0.34) (0.35)
Disagree|Neither agree nor disagree 2.15*** 2.17*** 1.89*** 1.91***
  (0.26) (0.26) (0.34) (0.35)
Neither agree nor disagree|Agree 2.78*** 2.81*** 2.52*** 2.54***
  (0.26) (0.26) (0.34) (0.35)
Agree|Strongly agree 5.46*** 5.49*** 5.17*** 5.19***
  (0.27) (0.27) (0.35) (0.36)
egpIntermediate class (III+IV):merit_talent_cwc   0.17    
    (0.10)    
egpService class (I+II):merit_talent_cwc   0.09    
    (0.09)    
egpRetired or pensioner:merit_talent_cwc   0.05    
    (0.09)    
egpUnemployed:merit_talent_cwc   0.26*    
    (0.13)    
egpPerforms unpaid tasks:merit_talent_cwc   0.17    
    (0.09)    
egpIntermediate class (III+IV):merit_effort_mean     0.39  
      (0.20)  
egpService class (I+II):merit_effort_mean     0.16  
      (0.17)  
egpRetired or pensioner:merit_effort_mean     0.23  
      (0.18)  
egpUnemployed:merit_effort_mean     0.15  
      (0.25)  
egpPerforms unpaid tasks:merit_effort_mean     -0.10  
      (0.17)  
egpIntermediate class (III+IV):merit_talent_mean       0.40*
        (0.19)
egpService class (I+II):merit_talent_mean       0.22
        (0.17)
egpRetired or pensioner:merit_talent_mean       0.11
        (0.18)
egpUnemployed:merit_talent_mean       0.12
        (0.26)
egpPerforms unpaid tasks:merit_talent_mean       -0.16
        (0.17)
Log Likelihood -9445.38 -9437.87 -9451.15 -9450.32
AIC 18952.76 18937.74 18958.31 18956.63
BIC 19167.45 19152.43 19152.22 19150.55
Num. obs. 7522 7522 7522 7522
Groups (idencuesta) 1317 1317 1317 1317
Variance: idencuesta: (Intercept) 1.11 1.12 1.07 1.06
Variance: idencuesta: ola_num 0.01 0.01 0.02 0.02
Variance: idencuesta: merit_effort_cwc 0.11      
Variance: idencuesta: merit_talent_cwc   0.14    
***p < 0.001; **p < 0.01; *p < 0.05
Show the code
 ## WE and BE Interactions with controls

# m11_oesch <- clmm(just_pension ~ 1 + ola_num +  ola_2 + oesch + 
#               merit_effort_cwc + merit_talent_cwc + 
#               merit_effort_mean + merit_talent_mean + 
#               ideo + sex + age + 
#               oesch*merit_effort_cwc + (1 + ola_num + merit_effort_cwc| idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
# m12_oesch <- clmm(just_pension ~ 1 + ola_num +  ola_2 + oesch + 
#               merit_effort_cwc + merit_talent_cwc +
#               merit_effort_mean + merit_talent_mean + 
#               ideo + sex + age + 
#               oesch*merit_talent_cwc + (1 + ola_num + merit_talent_cwc| idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
#
# m13_oesch <- clmm(just_pension ~ 1 + ola_num +  ola_2 + oesch + 
#               merit_effort_cwc + merit_talent_cwc + 
#               merit_effort_mean + merit_talent_mean + 
#               ideo + sex + age + 
#               oesch*merit_effort_mean + (1 + ola_num | idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
# m14_oesch <- clmm(just_pension ~ 1 + ola_num +  ola_2 + oesch + 
#               merit_effort_cwc + merit_talent_cwc + 
#               merit_effort_mean + merit_talent_mean + 
#               ideo + sex + age + 
#               oesch*merit_talent_mean + (1 + ola_num | idencuesta), 
#            link = "logit",
#  Hess = TRUE,
#           data = df_study1)
#
#save(m11_oesch,m12_oesch,m13_oesch,m14_oesch, file = #here("output/oesch_interactions_direct.RData"))

load(file = here("output/oesch_interactions_direct.RData"))

htmlreg(list(m11_oesch,m12_oesch,m13_oesch,m14_oesch))
Statistical models
  Model 1 Model 2 Model 3 Model 4
ola_num -0.18** -0.19** -0.19** -0.19**
  (0.07) (0.07) (0.06) (0.06)
ola_2 0.06*** 0.06*** 0.06*** 0.06***
  (0.01) (0.01) (0.01) (0.01)
oesch’Lower-grade service class’ -0.20 -0.19 -0.81 -0.88
  (0.22) (0.22) (0.93) (0.97)
oesch’Skilled workers’ -0.48** -0.48** -0.57 -0.15
  (0.18) (0.18) (0.71) (0.76)
oesch’Small business owners’ -0.35 -0.36 -0.41 0.83
  (0.19) (0.19) (0.77) (0.83)
oesch’Unskilled workers’ -0.45* -0.45* -0.73 -0.48
  (0.19) (0.19) (0.77) (0.82)
oesch’Retired or pensioner’ -0.30 -0.30 -0.78 0.05
  (0.20) (0.20) (0.77) (0.82)
oesch’Unemployed’ -0.32 -0.32 -0.59 -0.00
  (0.21) (0.22) (0.86) (0.94)
oesch’Performs unpaid tasks’ -0.45* -0.45* -0.06 0.67
  (0.19) (0.19) (0.73) (0.79)
merit_effort_cwc 0.06 0.12*** 0.12*** 0.12***
  (0.15) (0.04) (0.04) (0.04)
merit_talent_cwc 0.06 -0.05 0.07 0.07
  (0.04) (0.15) (0.03) (0.03)
merit_effort_mean 0.39*** 0.42*** 0.36 0.41***
  (0.10) (0.10) (0.25) (0.09)
merit_talent_mean -0.00 -0.02 -0.00 0.15
  (0.10) (0.10) (0.10) (0.25)
ideoCenter 0.27* 0.28* 0.28* 0.29**
  (0.11) (0.11) (0.11) (0.11)
ideoRight 0.66*** 0.66*** 0.65*** 0.66***
  (0.12) (0.13) (0.12) (0.12)
ideoDoes not identify 0.13 0.13 0.13 0.15
  (0.09) (0.10) (0.09) (0.09)
sexFemale -0.50*** -0.50*** -0.48*** -0.47***
  (0.08) (0.08) (0.08) (0.08)
age -0.00 -0.00 -0.00 -0.00
  (0.00) (0.00) (0.00) (0.00)
oesch’Lower-grade service class’:merit_effort_cwc 0.03      
  (0.20)      
oesch’Skilled workers’:merit_effort_cwc 0.11      
  (0.16)      
oesch’Small business owners’:merit_effort_cwc 0.07      
  (0.17)      
oesch’Unskilled workers’:merit_effort_cwc -0.03      
  (0.17)      
oesch’Retired or pensioner’:merit_effort_cwc 0.03      
  (0.17)      
oesch’Unemployed’:merit_effort_cwc 0.10      
  (0.19)      
oesch’Performs unpaid tasks’:merit_effort_cwc 0.04      
  (0.17)      
Strongly disagree|Disagree -0.77** -0.76** -0.82 -0.23
  (0.29) (0.29) (0.66) (0.70)
Disagree|Neither agree nor disagree 1.68*** 1.70*** 1.58* 2.17**
  (0.29) (0.29) (0.66) (0.70)
Neither agree nor disagree|Agree 2.31*** 2.34*** 2.21*** 2.80***
  (0.29) (0.29) (0.66) (0.70)
Agree|Strongly agree 4.99*** 5.02*** 4.86*** 5.45***
  (0.30) (0.30) (0.67) (0.71)
oesch’Lower-grade service class’:merit_talent_cwc   0.12    
    (0.20)    
oesch’Skilled workers’:merit_talent_cwc   0.11    
    (0.16)    
oesch’Small business owners’:merit_talent_cwc   0.06    
    (0.17)    
oesch’Unskilled workers’:merit_talent_cwc   0.09    
    (0.17)    
oesch’Retired or pensioner’:merit_talent_cwc   0.08    
    (0.17)    
oesch’Unemployed’:merit_talent_cwc   0.28    
    (0.19)    
oesch’Performs unpaid tasks’:merit_talent_cwc   0.20    
    (0.17)    
oesch’Lower-grade service class’:merit_effort_mean     0.24  
      (0.35)  
oesch’Skilled workers’:merit_effort_mean     0.04  
      (0.27)  
oesch’Small business owners’:merit_effort_mean     0.02  
      (0.29)  
oesch’Unskilled workers’:merit_effort_mean     0.11  
      (0.29)  
oesch’Retired or pensioner’:merit_effort_mean     0.18  
      (0.28)  
oesch’Unemployed’:merit_effort_mean     0.11  
      (0.33)  
oesch’Performs unpaid tasks’:merit_effort_mean     -0.15  
      (0.27)  
oesch’Lower-grade service class’:merit_talent_mean       0.25
        (0.34)
oesch’Skilled workers’:merit_talent_mean       -0.11
        (0.27)
oesch’Small business owners’:merit_talent_mean       -0.42
        (0.29)
oesch’Unskilled workers’:merit_talent_mean       0.01
        (0.29)
oesch’Retired or pensioner’:merit_talent_mean       -0.14
        (0.28)
oesch’Unemployed’:merit_talent_mean       -0.11
        (0.33)
oesch’Performs unpaid tasks’:merit_talent_mean       -0.41
        (0.28)
Log Likelihood -9443.80 -9437.87 -9451.23 -9448.05
AIC 18957.60 18945.74 18966.46 18960.10
BIC 19200.00 19188.13 19188.08 19181.72
Num. obs. 7522 7522 7522 7522
Groups (idencuesta) 1317 1317 1317 1317
Variance: idencuesta: (Intercept) 1.08 1.08 1.06 1.03
Variance: idencuesta: ola_num 0.01 0.01 0.02 0.02
Variance: idencuesta: merit_effort_cwc 0.11      
Variance: idencuesta: merit_talent_cwc   0.14    
***p < 0.001; **p < 0.01; *p < 0.05