Code
knitr::opts_chunk$set(warning = FALSE, message = FALSE)Exit Models
Code
library(sjPlot)
library(sjmisc)
library(sjlabelled)
library(lme4)
library(regclass)
library(stargazer)
library(dplyr)
library(tidyr)
library(effects)
library(lavaan)
library(ggplot2)
library(stringr)
library(labelled)
library(modelsummary)Code
data <- read.csv("data_for_analysis_extended_17.4.csv", header = TRUE)
new_data <- read.csv("new_data.csv", header = TRUE)A Distribution of Perceptions of Civil Service Exit
A Distribution of Perceptions of Influence (Future)
Code
Q5 <- as.data.frame(
new_data %>%
group_by(Q5) %>%
filter(!is.na(Q5))%>%
summarize(N = n()))
Q5_merged <-
Q5 %>%
mutate(Q5 = case_when(Q5 %in% c("1","2") ~ "1",
Q5 %in% "3" ~ "2",
Q5 %in% c("4","5") ~ "3"))
Q5_new <- Q5_merged %>%
type.convert(as.is=TRUE) %>%
group_by(Q5) %>%
summarise(N=sum(N, na.rm = TRUE))%>%
mutate(freq = N / sum(N))%>% round(2)%>%
mutate(freq.lab=str_c(100*freq,"%"))
ggplot(Q5_new) +
geom_bar(aes(x=factor(Q5), y=freq), stat="identity")+
geom_text(aes(x=factor(Q5), y=freq, label = freq.lab),vjust=-0.3,size=4)+
scale_x_discrete(labels = c("החלשות והחלשות משמעותית", "ללא שינוי" ,"התחזקות והתחזקות משמעותית"))+
scale_y_continuous(labels = function(x) paste0(x*100, "%"), limits=c(0,1))+
theme(
text = element_text(family="Optima"),
strip.text = element_text(size=24),
axis.text = element_text(size =12),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.title=element_blank(),
legend.text=element_text(size=14),
legend.position='bottom',
legend.direction ='horizontal',
legend.key=element_blank(),
legend.margin = unit(0.2, "line"),
legend.key.height=unit(0.6,"line"),
plot.title=element_text(size=12,face="bold", hjust = 0.5),
axis.title.x=element_text(size=24),
axis.title.y=element_text(size=24))+
xlab("")+
ylab("")+
ggtitle("במבט קדימה על חמש השנים הבאות, באיזו מידה אתה צופה שתהיה התחזקות או החלשות \n ?במידת ההשפעה של הדרג המקצועי ביחידה שלך על מדיניות המשרד בתחום הפעילות של היחידה")
A Distribution of Perceptions of Meritiocracy (Future)
Code
Q11 <- as.data.frame(
new_data %>%
group_by(Q11) %>%
filter(!is.na(Q11)) %>%
summarize(N = n()))
Q11_merged <-
Q11 %>%
mutate(Q11 = case_when(Q11 %in% c("1","2") ~ "1",
Q11 %in% "3" ~ "2",
Q11 %in% c("4","5") ~ "3"))
Q11_new <- Q11_merged %>%
type.convert(as.is=TRUE) %>%
group_by(Q11) %>%
summarise(N=sum(N, na.rm = TRUE))%>%
mutate(freq = N / sum(N))%>% round(2)%>%
mutate(freq.lab=str_c(100*freq,"%"))
ggplot(Q11_new) +
geom_bar(aes(factor(x=Q11), y=freq), stat="identity")+
geom_text(aes(x=factor(Q11), y=freq, label = freq.lab),vjust=-0.3,size=4)+
scale_x_discrete(labels = c("שינוי לרעה ושינוי משמעותי לרעה", "ללא שינוי" ,"שינוי לטובה ושינוי משמעותי לטובה"))+
scale_y_continuous(labels = function(x) paste0(x*100, "%"), limits=c(0,1))+
theme(strip.text = element_text(size=24),
axis.text = element_text(size =12),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.title=element_blank(),
legend.text=element_text(size=20),
legend.position='bottom',
legend.direction ='horizontal',
legend.key=element_blank(),
legend.margin = unit(0.2, "line"),
legend.key.height=unit(0.6,"line"),
plot.title=element_text(size=12,face="bold", hjust = 0.5),
axis.title.x=element_text(size=24),
axis.title.y=element_text(size=24))+
xlab("")+
ylab("")+
ggtitle("במבט קדימה על חמש השנים הבאות, באיזו מידה אתה צופה שיהיה שינוי לטובה\n ?או לרעה במידה שבה קידומים במשרד ייעשו בפועל על בסיס ניסיון רלוונטי, יכולת ועבודה קשה")
Code
t1<-new_data %>% group_by(Q11) %>%
drop_na(Q11) %>%
summarize(n=n())%>%
mutate(freq=(n/sum(n))%>%round(2))%>%
mutate(freq.lab=str_c(100*freq,"%"))
t1%>%
#drop_na(trust_in_gov_lab)%>%
ggplot(aes(x=Q11 %>% as_factor(),y=n))+geom_col(width=0.75,alpha=0.8,fill="dodgerblue2")+
geom_text(aes(label=freq.lab),colour="gray20",hjust=-0.1,size=3)+
scale_x_discrete(labels = c("שינוי משמעותי לרעה", "שינוי לרעה", "ללא שינוי", "שינוי לטובה", "שינוי משמעותי לטובה"))+
theme_classic()+
labs(title=var_label(new_data$Q11) %>% stringr::str_wrap(),x="",y="")+
coord_flip() +
theme_classic()+
theme(
plot.title = element_text(hjust = 1),
plot.subtitle = element_text(hjust = 1),
plot.caption = element_text(hjust = 0)
)+
ylab("כמות משיבים")+
ggtitle("במבט קדימה על חמש השנים הבאות, באיזו מידה אתה צופה שיהיה שינוי לטובה\n ?או לרעה במידה שבה קידומים במשרד ייעשו בפועל על בסיס ניסיון רלוונטי, יכולת ועבודה קשה")
A Distribution of Perceptions of Backsliding Democracy
Code
data=mutate(data,demo_backsliding_original = (Q16_1 + Q16_2 + Q16_3 + Q16_4 + Q16_5)/5) # without Q15 for graph
ggplot(data = data,aes(x=demo_backsliding_original))+geom_histogram(binwidth = 0.3)+
scale_x_continuous(breaks = 1:7)+
xlab("אמונה שהדמוקרטיה הישראלית בסכנה עקב הרפורמה המשפטית (1 = סכנה נמוכה; 7 = סכנה גבוהה)")+
ylab("כמות משיבים")
Models
Code
data_1 <- select(data, c(PAST_INFLUENCE, PAST_MERITOCRATIC,
PROJECT_MERITOCRATIC,BACKSLIDING_2,PROJECT_INFLUENCE,
INTENT_EXIT_2,position_type,ranking,tenure,ministry,age,gender,religiosity,education))
data_2 <- na.omit(data_1)Descriptive Statistics
# A tibble: 2 x 2
gender N
<chr> <int>
1 female 206
2 male 155# A tibble: 4 x 2
ranking N
<chr> <int>
1 junior 56
2 middle 180
3 senior 107
4 very senior 18# A tibble: 5 x 2
education N
<chr> <int>
1 bachelor 51
2 high-school 3
3 master 274
4 other 6
5 phd 27# A tibble: 5 x 2
tenure N
<chr> <int>
1 1- 10
2 1-5 103
3 11-20 91
4 20+ 61
5 6-10 96# A tibble: 4 x 2
position_type N
<chr> <int>
1 competitive tender 310
2 other 28
3 replacement 12
4 trust based 11Multi-level Models
Code
m1 <- lme4::lmer(INTENT_EXIT_2 ~
tenure +
position_type +
ranking +
gender +
age +
factor(education)+
factor(religiosity) +
(1|ministry),
data_2)
m2 <- lme4::lmer(INTENT_EXIT_2 ~
BACKSLIDING_2+
tenure +
position_type +
ranking +
gender +
age +
factor(education)+
factor(religiosity) +
(1|ministry),
data_2)
m3 <- lme4::lmer(INTENT_EXIT_2 ~
PROJECT_INFLUENCE +
PAST_INFLUENCE+
tenure +
position_type +
ranking +
gender +
age +
factor(education)+
factor(religiosity) +
(1|ministry),
data_2)
m4 <- lme4::lmer(INTENT_EXIT_2 ~
PROJECT_MERITOCRATIC +
PAST_MERITOCRATIC +
tenure +
position_type +
ranking +
gender +
age +
factor(education)+
factor(religiosity) +
(1|ministry),
data_2)
m5 <- lme4::lmer(INTENT_EXIT_2 ~
PROJECT_INFLUENCE +
PROJECT_MERITOCRATIC +
PAST_MERITOCRATIC +
PAST_INFLUENCE+
tenure +
position_type +
ranking +
gender +
age +
factor(education)+
factor(religiosity) +
(1|ministry),
data_2)
m6 <- lme4::lmer(INTENT_EXIT_2 ~
BACKSLIDING_2+
PROJECT_INFLUENCE +
PROJECT_MERITOCRATIC+
PAST_INFLUENCE +
PAST_MERITOCRATIC +
tenure +
position_type +
ranking +
gender +
age +
factor(education)+
factor(religiosity) +
(1|ministry),
data_2)
m7 <- lme4::lmer(INTENT_EXIT_2 ~
BACKSLIDING_2+
PROJECT_MERITOCRATIC+
PAST_MERITOCRATIC +
tenure +
position_type +
ranking +
gender +
age +
factor(education)+
factor(religiosity) +
(1|ministry),
data_2)
#Predictions from model 6
data_2$religiosity <- as.factor(data_2$religiosity)
data_2$education <- as.factor(data_2$education)
m6 <- lme4::lmer(INTENT_EXIT_2 ~
BACKSLIDING_2+
PROJECT_INFLUENCE +
PROJECT_MERITOCRATIC+
PAST_INFLUENCE +
PAST_MERITOCRATIC +
tenure +
position_type +
ranking +
gender +
age +
education+
religiosity +
(1|ministry),
data_2)
models <- list(m1, m2, m3, m4, m5, m6, m7)
modelsummary(models, stars = TRUE)| (1) | (2) | (3) | (4) | (5) | (6) | (7) | |
|---|---|---|---|---|---|---|---|
| (Intercept) | 0.092 | 0.047 | 0.040 | -0.043 | -0.060 | -0.084 | -0.067 |
| (0.151) | (0.149) | (0.160) | (0.150) | (0.164) | (0.163) | (0.149) | |
| tenure1-5 | -0.112 | -0.110 | -0.135 | -0.125 | -0.127 | -0.121 | -0.122 |
| (0.108) | (0.106) | (0.107) | (0.104) | (0.104) | (0.104) | (0.103) | |
| tenure11-20 | -0.039 | -0.048 | -0.068 | -0.088 | -0.089 | -0.085 | -0.086 |
| (0.112) | (0.110) | (0.111) | (0.108) | (0.109) | (0.108) | (0.107) | |
| tenure20+ | -0.128 | -0.133 | -0.150 | -0.169 | -0.170 | -0.166 | -0.166 |
| (0.118) | (0.116) | (0.117) | (0.114) | (0.114) | (0.113) | (0.113) | |
| tenure6-10 | -0.092 | -0.101 | -0.115 | -0.131 | -0.132 | -0.130 | -0.131 |
| (0.111) | (0.109) | (0.110) | (0.107) | (0.107) | (0.106) | (0.106) | |
| position_typeother | 0.053 | 0.037 | 0.044 | 0.041 | 0.042 | 0.036 | 0.034 |
| (0.066) | (0.065) | (0.065) | (0.063) | (0.064) | (0.063) | (0.063) | |
| position_typereplacement | 0.016 | -0.013 | -0.015 | -0.018 | -0.021 | -0.032 | -0.033 |
| (0.095) | (0.094) | (0.095) | (0.092) | (0.093) | (0.092) | (0.092) | |
| position_typetrust based | 0.094 | 0.133 | 0.106 | 0.107 | 0.106 | 0.130 | 0.132 |
| (0.100) | (0.099) | (0.099) | (0.096) | (0.097) | (0.097) | (0.096) | |
| rankingmiddle | 0.045 | 0.034 | 0.050 | 0.040 | 0.038 | 0.031 | 0.035 |
| (0.055) | (0.054) | (0.055) | (0.053) | (0.054) | (0.053) | (0.052) | |
| rankingsenior | 0.026 | 0.018 | 0.029 | 0.037 | 0.034 | 0.033 | 0.036 |
| (0.062) | (0.060) | (0.062) | (0.060) | (0.061) | (0.061) | (0.060) | |
| rankingvery senior | 0.066 | 0.027 | 0.053 | 0.052 | 0.048 | 0.036 | 0.040 |
| (0.096) | (0.095) | (0.097) | (0.094) | (0.096) | (0.095) | (0.094) | |
| gendermale | 0.014 | 0.033 | 0.021 | 0.022 | 0.021 | 0.034 | 0.035 |
| (0.034) | (0.034) | (0.034) | (0.033) | (0.033) | (0.033) | (0.033) | |
| age31-40 | 0.134+ | 0.131+ | 0.118 | 0.127+ | 0.128+ | 0.124+ | 0.121+ |
| (0.072) | (0.071) | (0.072) | (0.070) | (0.071) | (0.070) | (0.070) | |
| age41-50 | 0.199* | 0.206** | 0.195* | 0.192* | 0.193* | 0.190* | 0.188* |
| (0.080) | (0.078) | (0.079) | (0.078) | (0.079) | (0.078) | (0.078) | |
| age51-60 | 0.256** | 0.268** | 0.252** | 0.243** | 0.245** | 0.243** | 0.241** |
| (0.091) | (0.089) | (0.091) | (0.090) | (0.091) | (0.090) | (0.090) | |
| age60+ | 0.194 | 0.225+ | 0.201 | 0.207+ | 0.213+ | 0.217+ | 0.210+ |
| (0.127) | (0.125) | (0.130) | (0.125) | (0.128) | (0.127) | (0.124) | |
| factor(education)high-school | 0.431* | 0.395* | 0.409* | 0.369+ | 0.369+ | 0.358+ | |
| (0.202) | (0.198) | (0.200) | (0.195) | (0.195) | (0.193) | ||
| factor(education)master | 0.055 | 0.057 | 0.063 | 0.080 | 0.080 | 0.081 | |
| (0.053) | (0.052) | (0.052) | (0.051) | (0.051) | (0.051) | ||
| factor(education)other | -0.038 | -0.041 | -0.028 | -0.036 | -0.037 | -0.037 | |
| (0.146) | (0.144) | (0.145) | (0.141) | (0.141) | (0.140) | ||
| factor(education)phd | -0.049 | -0.048 | -0.059 | -0.041 | -0.041 | -0.037 | |
| (0.080) | (0.079) | (0.079) | (0.078) | (0.078) | (0.077) | ||
| factor(religiosity)other | 0.036 | 0.001 | 0.002 | 0.048 | 0.045 | 0.034 | |
| (0.176) | (0.173) | (0.175) | (0.170) | (0.171) | (0.169) | ||
| factor(religiosity)religious | 0.084 | 0.050 | 0.045 | -0.002 | -0.005 | -0.008 | |
| (0.092) | (0.090) | (0.092) | (0.089) | (0.090) | (0.089) | ||
| factor(religiosity)secular | 0.209* | 0.036 | 0.132 | 0.058 | 0.054 | -0.026 | |
| (0.086) | (0.095) | (0.089) | (0.087) | (0.088) | (0.094) | ||
| factor(religiosity)traditional-religious | 0.197+ | 0.107 | 0.150 | 0.122 | 0.118 | 0.076 | |
| (0.107) | (0.108) | (0.108) | (0.104) | (0.105) | (0.105) | ||
| factor(religiosity)traditional-unsecular | 0.174+ | 0.054 | 0.118 | 0.047 | 0.045 | -0.008 | |
| (0.097) | (0.100) | (0.098) | (0.096) | (0.097) | (0.099) | ||
| BACKSLIDING_2 | 0.258*** | 0.172* | 0.165* | ||||
| (0.067) | (0.073) | (0.071) | |||||
| PROJECT_INFLUENCE | 0.224** | 0.013 | -0.029 | ||||
| (0.074) | (0.087) | (0.088) | |||||
| PAST_INFLUENCE | -0.020 | 0.020 | 0.027 | ||||
| (0.081) | (0.083) | (0.082) | |||||
| PROJECT_MERITOCRATIC | 0.398*** | 0.393*** | 0.350*** | 0.330*** | |||
| (0.073) | (0.089) | (0.090) | (0.077) | ||||
| PAST_MERITOCRATIC | 0.056 | 0.060 | 0.091 | 0.083 | |||
| (0.063) | (0.066) | (0.067) | (0.064) | ||||
| educationhigh-school | 0.357+ | ||||||
| (0.194) | |||||||
| educationmaster | 0.081 | ||||||
| (0.051) | |||||||
| educationother | -0.040 | ||||||
| (0.140) | |||||||
| educationphd | -0.034 | ||||||
| (0.077) | |||||||
| religiosityother | 0.035 | ||||||
| (0.170) | |||||||
| religiosityreligious | -0.010 | ||||||
| (0.090) | |||||||
| religiositysecular | -0.029 | ||||||
| (0.094) | |||||||
| religiositytraditional-religious | 0.075 | ||||||
| (0.106) | |||||||
| religiositytraditional-unsecular | -0.011 | ||||||
| (0.099) | |||||||
| SD (Intercept ministry) | 0.000 | 0.000 | 0.000 | 0.023 | 0.023 | 0.020 | 0.018 |
| SD (Observations) | 0.309 | 0.303 | 0.305 | 0.296 | 0.297 | 0.295 | 0.294 |
| Num.Obs. | 361 | 361 | 361 | 361 | 361 | 361 | 361 |
| R2 Marg. | 0.117 | 0.152 | 0.140 | 0.186 | 0.185 | 0.197 | 0.198 |
| R2 Cond. | 0.191 | 0.190 | 0.201 | 0.201 | |||
| AIC | 297.5 | 288.5 | 298.1 | 279.3 | 289.5 | 289.4 | 279.5 |
| BIC | 402.5 | 397.3 | 410.9 | 392.1 | 410.1 | 413.9 | 396.1 |
| ICC | 0.0 | 0.0 | 0.0 | 0.0 | |||
| RMSE | 0.30 | 0.29 | 0.29 | 0.28 | 0.28 | 0.28 | 0.28 |
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 |
Plot of Intention to Leave and Politization of Appointment System
Code
predict <- as.data.frame(effect("BACKSLIDING_2",m6))
predict <- rename(predict,Intention_to_leave ="fit")
predict <- rename(predict,democratic_backsliding ="BACKSLIDING_2")
ggplot(data=predict, aes(x=democratic_backsliding, y=Intention_to_leave))+
geom_pointrange(aes(ymin=Intention_to_leave-se, ymax=Intention_to_leave+se))+
scale_x_continuous(breaks = c(0,0.2, 0.5, 0.8,1))+
xlab("democratic backsliding")+
ylab("intention to leave")
Plot intention to leave
Code
predict <- as.data.frame(effect("PROJECT_MERITOCRATIC",m6))
predict <- rename(predict,Intention_to_leave ="fit")
predict <- rename(predict,expectation_of_future_meritocracy ="PROJECT_MERITOCRATIC")
ggplot(data=predict, aes(x=expectation_of_future_meritocracy, y=Intention_to_leave))+
geom_pointrange(aes(ymin=Intention_to_leave-se, ymax=Intention_to_leave+se))+
scale_x_continuous(breaks = c(0,0.2, 0.5, 0.8,1)) +
xlab("expectation of future politicization of appointments and recruitments in the civil service")+
ylab("intention to leave")
#FE modelling without demographics and ministry affilliations (including regression table as html)
Code
m1 <- lm(INTENT_EXIT_2 ~
tenure +
position_type +
ranking +
gender +
age +
factor(religiosity) +
factor(education)+
factor(ministry),
data=data_2)
m2 <-lm(INTENT_EXIT_2 ~
BACKSLIDING_2+
tenure +
position_type +
ranking +
gender +
age +
factor(education)+
factor(religiosity) +
factor(ministry),
data=data_2)
m3 <- lm(INTENT_EXIT_2 ~
PROJECT_INFLUENCE +
PAST_INFLUENCE+
tenure +
position_type +
ranking +
gender +
age +
factor(education)+
factor(religiosity) +
factor(ministry),
data=data_2)
m4 <- lm(INTENT_EXIT_2 ~
PROJECT_MERITOCRATIC +
PAST_MERITOCRATIC +
tenure +
position_type +
ranking +
gender +
age +
factor(education)+
factor(religiosity) +
factor(ministry),
data=data_2)
m5 <- lm(INTENT_EXIT_2 ~
PROJECT_INFLUENCE +
PROJECT_MERITOCRATIC +
PAST_MERITOCRATIC +
PAST_INFLUENCE+
tenure +
position_type +
ranking +
gender +
age +
factor(education)+
factor(religiosity) +
factor(ministry),
data=data_2)
m6 <- lm(INTENT_EXIT_2 ~
BACKSLIDING_2+
PROJECT_INFLUENCE +
PROJECT_MERITOCRATIC+
PAST_INFLUENCE +
PAST_MERITOCRATIC +
tenure +
position_type +
ranking +
gender +
age +
factor(education)+
factor(religiosity) +
factor(ministry),
data=data_2)
m7 <- lm(INTENT_EXIT_2 ~
BACKSLIDING_2+
PROJECT_MERITOCRATIC+
PAST_MERITOCRATIC +
tenure +
position_type +
ranking +
gender +
age +
factor(education)+
factor(religiosity) +
factor(ministry),
data=data_2)
models <- list(m1, m2, m3, m4, m5, m6, m7)
modelsummary(models, stars = TRUE)| (1) | (2) | (3) | (4) | (5) | (6) | (7) | |
|---|---|---|---|---|---|---|---|
| (Intercept) | 0.127 | 0.013 | 0.057 | -0.018 | -0.042 | -0.105 | -0.079 |
| (0.196) | (0.194) | (0.199) | (0.192) | (0.199) | (0.201) | (0.193) | |
| tenure1-5 | -0.144 | -0.141 | -0.166 | -0.143 | -0.146 | -0.140 | -0.142 |
| (0.112) | (0.110) | (0.111) | (0.107) | (0.108) | (0.107) | (0.107) | |
| tenure11-20 | -0.049 | -0.069 | -0.076 | -0.096 | -0.097 | -0.100 | -0.100 |
| (0.117) | (0.114) | (0.116) | (0.112) | (0.112) | (0.112) | (0.111) | |
| tenure20+ | -0.128 | -0.145 | -0.150 | -0.164 | -0.165 | -0.167 | -0.167 |
| (0.125) | (0.122) | (0.123) | (0.120) | (0.120) | (0.119) | (0.119) | |
| tenure6-10 | -0.102 | -0.114 | -0.125 | -0.134 | -0.136 | -0.136 | -0.137 |
| (0.115) | (0.112) | (0.113) | (0.110) | (0.110) | (0.110) | (0.109) | |
| position_typeother | 0.065 | 0.051 | 0.054 | 0.041 | 0.042 | 0.039 | 0.037 |
| (0.072) | (0.071) | (0.071) | (0.069) | (0.069) | (0.069) | (0.069) | |
| position_typereplacement | 0.042 | 0.007 | 0.004 | 0.002 | -0.002 | -0.014 | -0.014 |
| (0.099) | (0.097) | (0.098) | (0.095) | (0.095) | (0.095) | (0.094) | |
| position_typetrust based | 0.124 | 0.159 | 0.125 | 0.108 | 0.104 | 0.128 | 0.132 |
| (0.106) | (0.104) | (0.105) | (0.101) | (0.102) | (0.102) | (0.101) | |
| rankingmiddle | 0.066 | 0.054 | 0.065 | 0.058 | 0.055 | 0.048 | 0.052 |
| (0.057) | (0.056) | (0.057) | (0.054) | (0.055) | (0.055) | (0.054) | |
| rankingsenior | 0.029 | 0.023 | 0.025 | 0.042 | 0.037 | 0.038 | 0.042 |
| (0.065) | (0.063) | (0.065) | (0.063) | (0.064) | (0.063) | (0.062) | |
| rankingvery senior | 0.069 | 0.038 | 0.058 | 0.060 | 0.053 | 0.046 | 0.053 |
| (0.100) | (0.098) | (0.100) | (0.097) | (0.099) | (0.098) | (0.096) | |
| gendermale | 0.043 | 0.064+ | 0.049 | 0.041 | 0.041 | 0.054 | 0.055 |
| (0.036) | (0.036) | (0.036) | (0.035) | (0.035) | (0.036) | (0.035) | |
| age31-40 | 0.083 | 0.085 | 0.072 | 0.084 | 0.086 | 0.083 | 0.079 |
| (0.075) | (0.074) | (0.075) | (0.073) | (0.073) | (0.073) | (0.072) | |
| age41-50 | 0.148+ | 0.159+ | 0.145+ | 0.148+ | 0.150+ | 0.148+ | 0.146+ |
| (0.084) | (0.083) | (0.084) | (0.082) | (0.083) | (0.082) | (0.082) | |
| age51-60 | 0.193+ | 0.213* | 0.186+ | 0.188+ | 0.191* | 0.194* | 0.191* |
| (0.099) | (0.097) | (0.098) | (0.096) | (0.097) | (0.096) | (0.096) | |
| age60+ | 0.150 | 0.186 | 0.162 | 0.171 | 0.181 | 0.186 | 0.175 |
| (0.135) | (0.132) | (0.136) | (0.131) | (0.134) | (0.133) | (0.131) | |
| factor(religiosity)other | -0.033 | -0.050 | -0.044 | 0.028 | 0.022 | 0.019 | 0.020 |
| (0.189) | (0.185) | (0.187) | (0.182) | (0.183) | (0.182) | (0.181) | |
| factor(religiosity)religious | 0.063 | 0.030 | 0.028 | -0.013 | -0.019 | -0.024 | -0.019 |
| (0.101) | (0.100) | (0.101) | (0.098) | (0.099) | (0.099) | (0.097) | |
| factor(religiosity)secular | 0.198* | 0.022 | 0.122 | 0.052 | 0.045 | -0.033 | -0.026 |
| (0.095) | (0.104) | (0.097) | (0.095) | (0.096) | (0.103) | (0.102) | |
| factor(religiosity)traditional-religious | 0.196+ | 0.091 | 0.149 | 0.124 | 0.116 | 0.068 | 0.075 |
| (0.116) | (0.117) | (0.116) | (0.112) | (0.114) | (0.115) | (0.114) | |
| factor(religiosity)traditional-unsecular | 0.154 | 0.030 | 0.097 | 0.029 | 0.023 | -0.029 | -0.022 |
| (0.105) | (0.108) | (0.106) | (0.103) | (0.104) | (0.107) | (0.106) | |
| factor(education)high-school | 0.415* | 0.367+ | 0.397+ | 0.373+ | 0.370+ | 0.353+ | 0.356+ |
| (0.210) | (0.206) | (0.207) | (0.201) | (0.202) | (0.201) | (0.200) | |
| factor(education)master | 0.054 | 0.055 | 0.057 | 0.069 | 0.069 | 0.072 | 0.072 |
| (0.054) | (0.053) | (0.054) | (0.053) | (0.053) | (0.053) | (0.052) | |
| factor(education)other | 0.049 | 0.032 | 0.043 | 0.024 | 0.020 | 0.015 | 0.020 |
| (0.152) | (0.149) | (0.150) | (0.146) | (0.146) | (0.146) | (0.145) | |
| factor(education)phd | -0.046 | -0.046 | -0.057 | -0.048 | -0.049 | -0.042 | -0.043 |
| (0.085) | (0.083) | (0.084) | (0.081) | (0.082) | (0.081) | (0.081) | |
| factor(ministry)Aliyah and Integration | 0.154 | 0.240 | 0.187 | 0.228 | 0.219 | 0.273 | 0.281 |
| (0.347) | (0.340) | (0.343) | (0.333) | (0.335) | (0.334) | (0.332) | |
| factor(ministry)Communications | -0.087 | -0.025 | -0.108 | -0.085 | -0.095 | -0.052 | -0.047 |
| (0.336) | (0.330) | (0.333) | (0.322) | (0.323) | (0.322) | (0.321) | |
| factor(ministry)Construction and Housing | 0.056 | 0.107 | 0.049 | 0.054 | 0.049 | 0.078 | 0.080 |
| (0.145) | (0.143) | (0.144) | (0.139) | (0.140) | (0.140) | (0.139) | |
| factor(ministry)Culture and Sports | 0.420 | 0.480+ | 0.456+ | 0.294 | 0.296 | 0.320 | 0.331 |
| (0.255) | (0.250) | (0.252) | (0.247) | (0.249) | (0.248) | (0.246) | |
| factor(ministry)Defense | 0.038 | 0.115 | 0.016 | 0.038 | 0.036 | 0.091 | 0.086 |
| (0.178) | (0.175) | (0.176) | (0.170) | (0.171) | (0.172) | (0.171) | |
| factor(ministry)Economy | 0.008 | 0.056 | 0.000 | 0.019 | 0.012 | 0.041 | 0.045 |
| (0.131) | (0.128) | (0.130) | (0.125) | (0.126) | (0.126) | (0.125) | |
| factor(ministry)Education | -0.031 | 0.049 | -0.027 | 0.020 | 0.018 | 0.060 | 0.057 |
| (0.146) | (0.145) | (0.144) | (0.140) | (0.141) | (0.142) | (0.141) | |
| factor(ministry)Energy | 0.222 | 0.286+ | 0.224 | 0.235 | 0.232 | 0.275+ | 0.276+ |
| (0.158) | (0.156) | (0.156) | (0.152) | (0.152) | (0.153) | (0.152) | |
| factor(ministry)Environmental Protection | 0.187 | 0.234 | 0.160 | 0.115 | 0.108 | 0.144 | 0.150 |
| (0.163) | (0.160) | (0.161) | (0.156) | (0.157) | (0.157) | (0.156) | |
| factor(ministry)Foreign Affairs | 0.079 | 0.054 | 0.002 | -0.019 | -0.023 | -0.022 | -0.025 |
| (0.226) | (0.221) | (0.224) | (0.217) | (0.219) | (0.217) | (0.216) | |
| factor(ministry)Health | -0.006 | 0.053 | -0.018 | -0.010 | -0.010 | 0.024 | 0.021 |
| (0.132) | (0.130) | (0.131) | (0.127) | (0.127) | (0.128) | (0.127) | |
| factor(ministry)Independent Authorities | -0.147 | -0.089 | -0.179 | -0.193 | -0.202 | -0.148 | -0.144 |
| (0.169) | (0.166) | (0.168) | (0.162) | (0.164) | (0.165) | (0.163) | |
| factor(ministry)Interior | -0.046 | 0.014 | -0.052 | 0.002 | -0.004 | 0.029 | 0.029 |
| (0.152) | (0.150) | (0.150) | (0.146) | (0.147) | (0.147) | (0.146) | |
| factor(ministry)Law | 0.069 | 0.151 | 0.041 | 0.077 | 0.071 | 0.124 | 0.123 |
| (0.128) | (0.127) | (0.127) | (0.122) | (0.123) | (0.125) | (0.123) | |
| factor(ministry)Local Authorities | -0.028 | 0.041 | 0.001 | 0.060 | 0.052 | 0.078 | 0.085 |
| (0.222) | (0.218) | (0.220) | (0.213) | (0.214) | (0.214) | (0.212) | |
| factor(ministry)Ministry of Labor | -0.001 | 0.054 | -0.006 | -0.023 | -0.028 | 0.006 | 0.010 |
| (0.144) | (0.141) | (0.142) | (0.138) | (0.139) | (0.139) | (0.138) | |
| factor(ministry)National Security | -0.162 | -0.077 | -0.128 | -0.123 | -0.127 | -0.094 | -0.087 |
| (0.169) | (0.167) | (0.167) | (0.162) | (0.163) | (0.163) | (0.162) | |
| factor(ministry)Other | -0.065 | 0.028 | -0.077 | -0.065 | -0.069 | -0.009 | -0.009 |
| (0.149) | (0.148) | (0.147) | (0.143) | (0.143) | (0.145) | (0.144) | |
| factor(ministry)Population and Immigration Authority | 0.413 | 0.483 | 0.394 | 0.515 | 0.512 | 0.542+ | 0.533+ |
| (0.336) | (0.330) | (0.332) | (0.322) | (0.324) | (0.323) | (0.321) | |
| factor(ministry)Prime Minister | 0.028 | 0.089 | 0.041 | 0.059 | 0.056 | 0.080 | 0.083 |
| (0.132) | (0.130) | (0.130) | (0.127) | (0.128) | (0.128) | (0.127) | |
| factor(ministry)Religious Services | -0.229 | -0.163 | -0.261 | -0.219 | -0.236 | -0.203 | -0.193 |
| (0.344) | (0.337) | (0.340) | (0.329) | (0.332) | (0.331) | (0.328) | |
| factor(ministry)Science and Technology | -0.159 | -0.111 | -0.166 | -0.119 | -0.126 | -0.093 | -0.091 |
| (0.168) | (0.165) | (0.167) | (0.161) | (0.163) | (0.162) | (0.161) | |
| factor(ministry)Social Equality | 0.234 | 0.290 | 0.253 | 0.304 | 0.299 | 0.324 | 0.328 |
| (0.256) | (0.251) | (0.253) | (0.246) | (0.247) | (0.246) | (0.245) | |
| factor(ministry)Transportation | 0.079 | 0.134 | 0.063 | 0.040 | 0.036 | 0.075 | 0.077 |
| (0.152) | (0.150) | (0.151) | (0.146) | (0.147) | (0.147) | (0.146) | |
| factor(ministry)Treasury | -0.077 | 0.008 | -0.095 | -0.108 | -0.113 | -0.052 | -0.051 |
| (0.133) | (0.132) | (0.131) | (0.127) | (0.128) | (0.130) | (0.129) | |
| factor(ministry)Welfare and Social Security | 0.050 | 0.129 | 0.054 | 0.043 | 0.037 | 0.079 | 0.085 |
| (0.134) | (0.132) | (0.132) | (0.128) | (0.129) | (0.130) | (0.129) | |
| BACKSLIDING_2 | 0.267*** | 0.164* | 0.156* | ||||
| (0.070) | (0.078) | (0.076) | |||||
| PROJECT_INFLUENCE | 0.246** | 0.018 | -0.028 | ||||
| (0.077) | (0.091) | (0.093) | |||||
| PAST_INFLUENCE | 0.005 | 0.038 | 0.041 | ||||
| (0.085) | (0.086) | (0.085) | |||||
| PROJECT_MERITOCRATIC | 0.424*** | 0.420*** | 0.378*** | 0.357*** | |||
| (0.076) | (0.095) | (0.096) | (0.083) | ||||
| PAST_MERITOCRATIC | 0.028 | 0.036 | 0.073 | 0.061 | |||
| (0.068) | (0.071) | (0.072) | (0.069) | ||||
| Num.Obs. | 361 | 361 | 361 | 361 | 361 | 361 | 361 |
| R2 | 0.190 | 0.227 | 0.217 | 0.264 | 0.265 | 0.275 | 0.274 |
| R2 Adj. | 0.060 | 0.099 | 0.085 | 0.140 | 0.135 | 0.145 | 0.149 |
| AIC | 226.0 | 211.3 | 217.7 | 195.4 | 199.1 | 196.0 | 192.4 |
| BIC | 428.2 | 417.4 | 427.7 | 405.4 | 416.9 | 417.6 | 406.3 |
| Log.Lik. | -61.003 | -52.648 | -54.865 | -43.687 | -43.557 | -40.982 | -41.198 |
| F | 1.457 | 1.779 | 1.645 | 2.129 | 2.042 | 2.107 | 2.191 |
| RMSE | 0.29 | 0.28 | 0.28 | 0.27 | 0.27 | 0.27 | 0.27 |
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 |