In [1]:
library(haven)
library(tidyverse)
library(readr)
library(broom)
library(ggplot2)
library(knitr)
library(scales)
data <- read_dta(
file = "/home/jovyan/Project/cen_ind_2021_pumf_v2.dta",
col_select = c(TotInc, CIP2021, agegrp, Gender, immstat, pr, wkswrk, NOC21)
)
data_main <- data %>%
group_by(CIP2021) %>%
sample_frac(0.50) %>%
ungroup() %>%
rename(
Income = TotInc,
field = CIP2021,
age = agegrp,
gender = Gender,
immigrant = immstat,
province = pr,
weeks = wkswrk,
occupation = NOC21
) %>%
mutate(
field = as.factor(field),
age = as.factor(age),
gender = as.factor(gender),
immigrant = as.factor(immigrant),
province = as.factor(province),
occupation = as.character(occupation),
Income_10k = Income / 10000,
log_income = log(pmax(Income, 1))
) %>%
filter(!is.na(Income) & Income > 0 & Income < 10000000)
data_main <- data_main %>%
filter(field != "99")
field_labels <- c(
"1" = "Education",
"2" = "Arts & Communications",
"3" = "Humanities",
"4" = "Social Sciences & Law",
"5" = "Business",
"6" = "Physical Sciences",
"7" = "Computer Science & Math",
"8" = "Engineering",
"9" = "Agriculture & Natural Resources",
"10" = "Health",
"11" = "Services",
"13" = "Other/Interdisciplinary",
"88" = "No Specialization"
)
data_main <- data_main %>%
mutate(field_name = recode(as.character(field), !!!field_labels))
high_skill_noc <- c("0", "1", "2", "3", "4")
data_main <- data_main %>%
mutate(
noc_prefix_1dig = substr(occupation, 1, 1),
high_skill_job = ifelse(noc_prefix_1dig %in% high_skill_noc, 1L, 0L),
high_skill_job = as.numeric(high_skill_job)
) %>%
filter(!is.na(log_income))
data_main <- data_main %>%
mutate(
weeks_numeric = as.numeric(as.character(weeks)),
full_time = ifelse(!is.na(weeks_numeric) & weeks_numeric >= 30, 1, 0)
)
#GENDER DISTRIBUTION
print(table(data_main$gender))
#IMMIGRANT STATUS DISTRIBUTION
print(table(data_main$immigrant))
#COMPREHENSIVE STATISTICS BY FIELD
table_by_field <- data_main %>%
group_by(field_name) %>%
summarise(
N = n(),
mean_income = mean(Income, na.rm = TRUE),
median_income = median(Income, na.rm = TRUE),
sd_income = sd(Income, na.rm = TRUE),
avg_income_10k = mean(Income_10k, na.rm = TRUE),
share_high_skill = mean(high_skill_job, na.rm = TRUE),
share_female = mean(as.numeric(as.character(gender)) == 2, na.rm = TRUE),
share_immigrant = mean(immigrant != "Non-immigrant", na.rm = TRUE),
avg_weeks = mean(weeks_numeric, na.rm = TRUE),
share_full_time = mean(full_time, na.rm = TRUE),
.groups = "drop"
) %>%
arrange(desc(mean_income))
print(kable(table_by_field, digits = 2, format.args = list(big.mark = ",")))
#STATISTICS BY FIELD AND GENDER
table_by_field_gender <- data_main %>%
group_by(field_name, gender) %>%
summarise(
N = n(),
mean_income = mean(Income, na.rm = TRUE),
median_income = median(Income, na.rm = TRUE),
share_high_skill = mean(high_skill_job, na.rm = TRUE),
.groups = "drop"
) %>%
arrange(field_name, gender)
print(kable(table_by_field_gender, digits = 2, format.args = list(big.mark = ",")))
#STATISTICS BY FIELD AND PROVINCE
table_by_field_province <- data_main %>%
group_by(field_name, province) %>%
summarise(
N = n(),
mean_income = mean(Income, na.rm = TRUE),
share_high_skill = mean(high_skill_job, na.rm = TRUE),
.groups = "drop"
) %>%
arrange(field_name, province)
print(kable(table_by_field_province, digits = 2, format.args = list(big.mark = ",")))
# OCCUPATION MODELS
# Model 1: Field only (baseline)
occ_model1 <- lm(high_skill_job ~ field_name, data = data_main)
# Model 2: Add demographic controls
occ_model2 <- lm(high_skill_job ~ field_name + gender + immigrant, data = data_main)
# Model 3: Add work intensity
occ_model3 <- lm(high_skill_job ~ field_name + gender + immigrant + weeks, data = data_main)
# Model 4: Add age (experience proxy)
occ_model4 <- lm(high_skill_job ~ field_name + gender + immigrant + weeks + age, data = data_main)
# Model 5: Full model with province
occ_model5 <- lm(high_skill_job ~ field_name + gender + immigrant + weeks + age + province,
data = data_main)
#Occupation Model 1: Field Only
tidy_occ1 <- tidy(occ_model1, conf.int = TRUE)
print(kable(tidy_occ1, digits = 4))
#Occupation Model 2: + Demographics
tidy_occ2 <- tidy(occ_model2, conf.int = TRUE)
print(kable(tidy_occ2, digits = 4))
#Occupation Model 3: + Work
tidy_occ3 <- tidy(occ_model3, conf.int = TRUE)
print(kable(tidy_occ3, digits = 4))
#Occupation Model 4: + Age
tidy_occ4 <- tidy(occ_model4, conf.int = TRUE)
print(kable(tidy_occ4, digits = 4))
#Occupation Model 5: Full Model
tidy_occ5 <- tidy(occ_model5, conf.int = TRUE)
print(kable(tidy_occ5, digits = 4))
#Occupation Models Fit Statistics
occ_models_fit <- data.frame(
Model = c("M1: Field", "M2: +Demographics", "M3: +Work", "M4: +Age", "M5: +Province"),
R_squared = c(summary(occ_model1)$r.squared,
summary(occ_model2)$r.squared,
summary(occ_model3)$r.squared,
summary(occ_model4)$r.squared,
summary(occ_model5)$r.squared),
Adj_R_squared = c(summary(occ_model1)$adj.r.squared,
summary(occ_model2)$adj.r.squared,
summary(occ_model3)$adj.r.squared,
summary(occ_model4)$adj.r.squared,
summary(occ_model5)$adj.r.squared),
N = c(nobs(occ_model1), nobs(occ_model2), nobs(occ_model3),
nobs(occ_model4), nobs(occ_model5))
)
print(kable(occ_models_fit, digits = 4))
# INCOME MODELS
inc_model1 <- lm(Income_10k ~ field_name, data = data_main)
inc_model2 <- lm(Income_10k ~ field_name + gender + immigrant, data = data_main)
inc_model3 <- lm(Income_10k ~ field_name + gender + immigrant + weeks, data = data_main)
inc_model4 <- lm(Income_10k ~ field_name + gender + immigrant + weeks + age, data = data_main)
inc_model5 <- lm(Income_10k ~ field_name + gender + immigrant + weeks + age + province,
data = data_main)
#Income Model 1: Field Only
tidy_inc1 <- tidy(inc_model1, conf.int = TRUE)
print(kable(tidy_inc1, digits = 4))
#Income Model 2: + Demographics
tidy_inc2 <- tidy(inc_model2, conf.int = TRUE)
print(kable(tidy_inc2, digits = 4))
#Income Model 3: + Work
tidy_inc3 <- tidy(inc_model3, conf.int = TRUE)
print(kable(tidy_inc3, digits = 4))
#Income Model 4: + Age
tidy_inc4 <- tidy(inc_model4, conf.int = TRUE)
print(kable(tidy_inc4, digits = 4))
#Income Model 5: Full Model
tidy_inc5 <- tidy(inc_model5, conf.int = TRUE)
print(kable(tidy_inc5, digits = 4))
#Income Models Fit Statistics
inc_models_fit <- data.frame(
Model = c("M1: Field", "M2: +Demographics", "M3: +Work", "M4: +Age", "M5: +Province"),
R_squared = c(summary(inc_model1)$r.squared,
summary(inc_model2)$r.squared,
summary(inc_model3)$r.squared,
summary(inc_model4)$r.squared,
summary(inc_model5)$r.squared),
Adj_R_squared = c(summary(inc_model1)$adj.r.squared,
summary(inc_model2)$adj.r.squared,
summary(inc_model3)$adj.r.squared,
summary(inc_model4)$adj.r.squared,
summary(inc_model5)$adj.r.squared),
N = c(nobs(inc_model1), nobs(inc_model2), nobs(inc_model3),
nobs(inc_model4), nobs(inc_model5))
)
print(kable(inc_models_fit, digits = 4))
# LOG INCOME MODELS
loginc_model1 <- lm(log_income ~ field_name, data = data_main)
loginc_model2 <- lm(log_income ~ field_name + gender + immigrant, data = data_main)
loginc_model3 <- lm(log_income ~ field_name + gender + immigrant + weeks, data = data_main)
loginc_model4 <- lm(log_income ~ field_name + gender + immigrant + weeks + age, data = data_main)
loginc_model5 <- lm(log_income ~ field_name + gender + immigrant + weeks + age + province,
data = data_main)
#Log Income Model 1: Field Only
tidy_loginc1 <- tidy(loginc_model1, conf.int = TRUE)
print(kable(tidy_loginc1, digits = 4))
#Log Income Model 2: + Demographics
tidy_loginc2 <- tidy(loginc_model2, conf.int = TRUE)
print(kable(tidy_loginc2, digits = 4))
#Log Income Model 3: + Work
tidy_loginc3 <- tidy(loginc_model3, conf.int = TRUE)
print(kable(tidy_loginc3, digits = 4))
#Log Income Model 4: + Age
tidy_loginc4 <- tidy(loginc_model4, conf.int = TRUE)
print(kable(tidy_loginc4, digits = 4))
#Log Income Model 5: Full Model
tidy_loginc5 <- tidy(loginc_model5, conf.int = TRUE)
print(kable(tidy_loginc5, digits = 4))
#Log Income Models Fit Statistics
loginc_models_fit <- data.frame(
Model = c("M1: Field", "M2: +Demographics", "M3: +Work", "M4: +Age", "M5: +Province"),
R_squared = c(summary(loginc_model1)$r.squared,
summary(loginc_model2)$r.squared,
summary(loginc_model3)$r.squared,
summary(loginc_model4)$r.squared,
summary(loginc_model5)$r.squared),
Adj_R_squared = c(summary(loginc_model1)$adj.r.squared,
summary(loginc_model2)$adj.r.squared,
summary(loginc_model3)$adj.r.squared,
summary(loginc_model4)$adj.r.squared,
summary(loginc_model5)$adj.r.squared),
N = c(nobs(loginc_model1), nobs(loginc_model2), nobs(loginc_model3),
nobs(loginc_model4), nobs(loginc_model5))
)
print(kable(loginc_models_fit, digits = 4))
# INTERACTION MODEL
inc_interaction <- lm(Income_10k ~ field_name * gender + immigrant + weeks + age + province,
data = data_main)
tidy_inc_interaction <- tidy(inc_interaction, conf.int = TRUE)
print(kable(tidy_inc_interaction, digits = 4))
# SUMMARY STATISTICS
overall_summary <- data_main %>%
summarise(
Total_N = n(),
Mean_Income = mean(Income, na.rm = TRUE),
Median_Income = median(Income, na.rm = TRUE),
SD_Income = sd(Income, na.rm = TRUE),
Share_High_Skill = mean(high_skill_job, na.rm = TRUE),
Share_Female = mean(as.numeric(as.character(gender)) == 2, na.rm = TRUE),
Share_Immigrant = mean(immigrant != "Non-immigrant", na.rm = TRUE),
Mean_Weeks_Worked = mean(weeks_numeric, na.rm = TRUE)
)
print(kable(overall_summary, digits = 2, format.args = list(big.mark = ",")))
#SUMMARY BY GENDER
summary_by_gender <- data_main %>%
group_by(gender) %>%
summarise(
N = n(),
Mean_Income = mean(Income, na.rm = TRUE),
Median_Income = median(Income, na.rm = TRUE),
Share_High_Skill = mean(high_skill_job, na.rm = TRUE),
.groups = "drop"
)
print(kable(summary_by_gender, digits = 2, format.args = list(big.mark = ",")))
#SUMMARY BY IMMIGRANT STATUS
summary_by_immigrant <- data_main %>%
group_by(immigrant) %>%
summarise(
N = n(),
Mean_Income = mean(Income, na.rm = TRUE),
Median_Income = median(Income, na.rm = TRUE),
Share_High_Skill = mean(high_skill_job, na.rm = TRUE),
.groups = "drop"
)
print(kable(summary_by_immigrant, digits = 2, format.args = list(big.mark = ",")))
#SUMMARY BY PROVINCE
summary_by_province <- data_main %>%
group_by(province) %>%
summarise(
N = n(),
Mean_Income = mean(Income, na.rm = TRUE),
Median_Income = median(Income, na.rm = TRUE),
Share_High_Skill = mean(high_skill_job, na.rm = TRUE),
.groups = "drop"
)
print(kable(summary_by_province, digits = 2, format.args = list(big.mark = ",")))
# ROBUSTNESS CHECK
data_main <- data_main %>%
mutate(high_skill_narrow = ifelse(noc_prefix_1dig %in% c("0", "2", "3"), 1, 0))
occ_alt_model <- lm(high_skill_narrow ~ field_name + gender + immigrant + weeks + age + province,
data = data_main)
tidy_occ_alt <- tidy(occ_alt_model, conf.int = TRUE)
print(kable(tidy_occ_alt, digits = 4))
# FIGURES
fig1_data <- table_by_field %>%
arrange(mean_income) %>%
mutate(field_name = factor(field_name, levels = field_name))
fig1 <- ggplot(fig1_data, aes(x = field_name, y = mean_income)) +
geom_col(fill = "steelblue", alpha = 0.8) +
coord_flip() +
scale_y_continuous(labels = dollar_format(prefix = "$")) +
labs(
title = "Average Annual Income by Field of Study",
subtitle = "2021 Canadian Census (Cleaned Data)",
x = NULL,
y = "Average Income"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
axis.text.y = element_text(size = 10)
)
ggsave("/home/jovyan/Project/fig1_income_by_field_clean.png", fig1,
width = 10, height = 7, dpi = 300)
fig2_data <- table_by_field %>%
arrange(share_high_skill) %>%
mutate(field_name = factor(field_name, levels = field_name))
fig2 <- ggplot(fig2_data, aes(x = field_name, y = share_high_skill * 100)) +
geom_col(fill = "darkgreen", alpha = 0.8) +
coord_flip() +
labs(
title = "High-Skill Employment Rate by Field of Study",
subtitle = "Management, Business, Science, Health, or Education/Law",
x = NULL,
y = "High-Skill Employment Rate (%)"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
axis.text.y = element_text(size = 10)
)
ggsave("/home/jovyan/Project/fig2_highskill_by_field_clean.png", fig2,
width = 10, height = 7, dpi = 300)
fig3_data <- table_by_field_gender %>%
group_by(field_name) %>%
filter(n() == 2) %>%
ungroup() %>%
mutate(gender_label = ifelse(gender == "1", "Male", "Female"))
fig3 <- ggplot(fig3_data, aes(x = field_name, y = mean_income, fill = gender_label)) +
geom_col(position = "dodge", alpha = 0.8) +
coord_flip() +
scale_y_continuous(labels = dollar_format(prefix = "$")) +
scale_fill_manual(values = c("Male" = "#56B4E9", "Female" = "#E69F00")) +
labs(
title = "Average Income by Field and Gender",
subtitle = "Gender wage gaps across fields",
x = NULL,
y = "Average Income",
fill = "Gender"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
axis.text.y = element_text(size = 9),
legend.position = "bottom"
)
ggsave("/home/jovyan/Project/fig3_income_by_field_gender_clean.png", fig3,
width = 10, height = 8, dpi = 300)
field_coefs <- tidy_inc5 %>%
filter(str_detect(term, "^field_name")) %>%
mutate(
field_label = str_remove(term, "^field_name"),
estimate_10k = estimate,
ci_lower = conf.low,
ci_upper = conf.high
) %>%
arrange(estimate_10k)
fig4 <- ggplot(field_coefs, aes(x = reorder(field_label, estimate_10k), y = estimate_10k)) +
geom_point(size = 3, color = "darkblue") +
geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper), width = 0.2, color = "darkblue") +
geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
coord_flip() +
labs(
title = "Field of Study Effects on Income",
subtitle = "From full model (relative to reference field)",
x = NULL,
y = "Income Effect ($10,000s)"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
axis.text.y = element_text(size = 10)
)
ggsave("/home/jovyan/Project/fig4_field_coefficients_clean.png", fig4,
width = 10, height = 7, dpi = 300)
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|field_name | N| mean_income| median_income| sd_income| avg_income_10k| share_high_skill| share_female| share_immigrant| avg_weeks| share_full_time|
|:-------------------------------|-------:|-----------:|-------------:|---------:|--------------:|----------------:|------------:|---------------:|---------:|---------------:|
|Computer Science & Math | 10,022| 74,766.82| 61,000| 77,893.54| 7.48| 0.39| 0.68| 1| 5.71| 0|
|Engineering | 46,125| 71,511.39| 58,000| 71,271.35| 7.15| 0.56| 0.90| 1| 5.79| 0|
|Business | 49,123| 70,871.78| 52,000| 86,489.91| 7.09| 0.62| 0.39| 1| 5.74| 0|
|Physical Sciences | 9,219| 68,689.31| 51,000| 77,471.39| 6.87| 0.53| 0.48| 1| 5.58| 0|
|Social Sciences & Law | 26,004| 67,683.41| 50,000| 81,059.45| 6.77| 0.66| 0.33| 1| 5.58| 0|
|Health | 31,852| 64,279.56| 51,000| 65,370.96| 6.43| 0.42| 0.19| 1| 5.75| 0|
|Education | 14,668| 63,532.61| 58,000| 41,323.96| 6.35| 0.62| 0.23| 1| 6.27| 0|
|Agriculture & Natural Resources | 4,657| 59,451.59| 50,000| 51,068.31| 5.95| 0.58| 0.58| 1| 5.73| 0|
|Humanities | 11,901| 55,642.64| 44,000| 57,495.57| 5.56| 0.60| 0.38| 1| 5.80| 0|
|No Specialization | 5,707| 52,413.44| 41,000| 48,328.22| 5.24| 0.60| 0.46| 1| 5.06| 0|
|Services | 12,888| 51,709.46| 42,000| 44,225.79| 5.17| 0.69| 0.51| 1| 5.51| 0|
|Arts & Communications | 7,853| 48,339.85| 39,000| 45,716.35| 4.83| 0.68| 0.43| 1| 5.34| 0|
|Other/Interdisciplinary | 162,607| 38,109.57| 29,000| 40,792.11| 3.81| 0.51| 0.50| 1| 6.15| 0|
|field_name |gender | N| mean_income| median_income| share_high_skill|
|:-------------------------------|:------|------:|-----------:|-------------:|----------------:|
|Agriculture & Natural Resources |1 | 1,951| 51,183.65| 43,000| 0.59|
|Agriculture & Natural Resources |2 | 2,706| 65,412.70| 55,000| 0.58|
|Arts & Communications |1 | 4,505| 44,482.89| 37,000| 0.66|
|Arts & Communications |2 | 3,348| 53,529.69| 42,000| 0.71|
|Business |1 | 29,750| 58,442.76| 48,000| 0.57|
|Business |2 | 19,373| 89,958.31| 61,000| 0.70|
|Computer Science & Math |1 | 3,175| 62,710.16| 51,000| 0.42|
|Computer Science & Math |2 | 6,847| 80,357.58| 65,000| 0.37|
|Education |1 | 11,359| 60,440.81| 55,000| 0.62|
|Education |2 | 3,309| 74,146.02| 71,000| 0.63|
|Engineering |1 | 4,532| 60,650.37| 48,000| 0.43|
|Engineering |2 | 41,593| 72,694.82| 59,000| 0.58|
|Health |1 | 25,870| 58,240.13| 49,000| 0.42|
|Health |2 | 5,982| 90,397.93| 62,000| 0.46|
|Humanities |1 | 7,419| 50,668.21| 41,000| 0.57|
|Humanities |2 | 4,482| 63,876.76| 48,000| 0.66|
|No Specialization |1 | 3,058| 48,412.51| 39,000| 0.57|
|No Specialization |2 | 2,649| 57,032.10| 44,000| 0.63|
|Other/Interdisciplinary |1 | 80,829| 32,474.73| 26,000| 0.41|
|Other/Interdisciplinary |2 | 81,778| 43,679.03| 34,000| 0.60|
|Physical Sciences |1 | 4,760| 58,681.13| 45,000| 0.56|
|Physical Sciences |2 | 4,459| 79,373.08| 59,000| 0.51|
|Services |1 | 6,268| 38,606.82| 33,000| 0.63|
|Services |2 | 6,620| 64,115.40| 54,000| 0.74|
|Social Sciences & Law |1 | 17,409| 57,723.57| 47,000| 0.65|
|Social Sciences & Law |2 | 8,595| 87,856.86| 61,000| 0.69|
|field_name |province | N| mean_income| share_high_skill|
|:-------------------------------|:--------|------:|-----------:|----------------:|
|Agriculture & Natural Resources |10 | 33| 47,000.00| 0.52|
|Agriculture & Natural Resources |11 | 11| 53,909.09| 0.45|
|Agriculture & Natural Resources |12 | 91| 51,389.66| 0.47|
|Agriculture & Natural Resources |13 | 88| 60,286.38| 0.43|
|Agriculture & Natural Resources |24 | 1,217| 53,688.97| 0.63|
|Agriculture & Natural Resources |35 | 1,537| 61,425.65| 0.56|
|Agriculture & Natural Resources |46 | 177| 68,355.72| 0.64|
|Agriculture & Natural Resources |47 | 203| 69,936.95| 0.62|
|Agriculture & Natural Resources |48 | 614| 61,913.69| 0.63|
|Agriculture & Natural Resources |59 | 675| 58,725.22| 0.52|
|Agriculture & Natural Resources |70 | 11| 94,454.55| 0.45|
|Arts & Communications |10 | 35| 43,485.71| 0.66|
|Arts & Communications |11 | 10| 54,000.00| 0.90|
|Arts & Communications |12 | 124| 39,580.68| 0.62|
|Arts & Communications |13 | 72| 48,022.22| 0.60|
|Arts & Communications |24 | 2,145| 46,131.92| 0.70|
|Arts & Communications |35 | 3,246| 50,425.58| 0.68|
|Arts & Communications |46 | 157| 42,831.97| 0.64|
|Arts & Communications |47 | 84| 47,641.07| 0.65|
|Arts & Communications |48 | 617| 47,892.01| 0.66|
|Arts & Communications |59 | 1,360| 48,547.04| 0.67|
|Arts & Communications |70 | 3| 83,666.67| 0.33|
|Business |10 | 639| 61,102.78| 0.55|
|Business |11 | 217| 55,309.69| 0.48|
|Business |12 | 1,343| 56,091.28| 0.53|
|Business |13 | 913| 58,407.80| 0.56|
|Business |24 | 12,474| 67,135.83| 0.60|
|Business |35 | 19,331| 76,379.28| 0.64|
|Business |46 | 1,387| 63,314.00| 0.60|
|Business |47 | 1,193| 63,901.28| 0.59|
|Business |48 | 5,023| 75,266.55| 0.65|
|Business |59 | 6,519| 67,135.65| 0.62|
|Business |70 | 84| 95,459.83| 0.42|
|Computer Science & Math |10 | 97| 66,188.88| 0.45|
|Computer Science & Math |11 | 26| 57,461.54| 0.27|
|Computer Science & Math |12 | 224| 65,370.70| 0.40|
|Computer Science & Math |13 | 143| 63,251.98| 0.41|
|Computer Science & Math |24 | 2,304| 69,128.48| 0.37|
|Computer Science & Math |35 | 4,593| 79,288.82| 0.38|
|Computer Science & Math |46 | 271| 58,118.84| 0.43|
|Computer Science & Math |47 | 162| 64,591.66| 0.41|
|Computer Science & Math |48 | 860| 72,144.38| 0.43|
|Computer Science & Math |59 | 1,340| 78,999.95| 0.41|
|Computer Science & Math |70 | 2| 73,500.00| 0.50|
|Education |10 | 243| 64,705.11| 0.53|
|Education |11 | 49| 68,359.47| 0.55|
|Education |12 | 394| 62,600.74| 0.58|
|Education |13 | 342| 61,063.24| 0.58|
|Education |24 | 3,940| 58,556.68| 0.64|
|Education |35 | 4,638| 67,486.23| 0.60|
|Education |46 | 571| 62,790.55| 0.67|
|Education |47 | 512| 62,911.55| 0.64|
|Education |48 | 1,730| 68,206.02| 0.67|
|Education |59 | 2,203| 60,337.90| 0.60|
|Education |70 | 46| 99,472.72| 0.83|
|Engineering |10 | 767| 69,477.93| 0.56|
|Engineering |11 | 160| 51,661.21| 0.63|
|Engineering |12 | 1,290| 57,490.96| 0.55|
|Engineering |13 | 864| 59,746.67| 0.57|
|Engineering |24 | 11,541| 63,878.37| 0.59|
|Engineering |35 | 16,662| 72,469.48| 0.53|
|Engineering |46 | 1,307| 68,273.80| 0.60|
|Engineering |47 | 1,218| 73,995.27| 0.64|
|Engineering |48 | 6,131| 87,289.01| 0.60|
|Engineering |59 | 6,111| 72,906.13| 0.55|
|Engineering |70 | 74| 85,918.93| 0.58|
|Health |10 | 436| 62,535.08| 0.41|
|Health |11 | 114| 57,747.39| 0.33|
|Health |12 | 906| 56,390.42| 0.35|
|Health |13 | 683| 58,564.95| 0.39|
|Health |24 | 6,807| 65,802.38| 0.43|
|Health |35 | 12,379| 63,380.73| 0.42|
|Health |46 | 1,167| 63,841.26| 0.42|
|Health |47 | 959| 66,218.35| 0.43|
|Health |48 | 3,800| 66,935.02| 0.46|
|Health |59 | 4,549| 64,109.38| 0.41|
|Health |70 | 52| 115,286.88| 0.21|
|Humanities |10 | 83| 49,108.43| 0.59|
|Humanities |11 | 40| 55,465.35| 0.40|
|Humanities |12 | 268| 56,714.88| 0.58|
|Humanities |13 | 137| 52,082.59| 0.64|
|Humanities |24 | 2,991| 47,367.05| 0.62|
|Humanities |35 | 5,052| 60,939.28| 0.59|
|Humanities |46 | 332| 48,121.10| 0.57|
|Humanities |47 | 185| 51,194.61| 0.56|
|Humanities |48 | 885| 58,682.27| 0.64|
|Humanities |59 | 1,918| 55,181.15| 0.60|
|Humanities |70 | 10| 81,500.00| 0.60|
|No Specialization |10 | 150| 53,962.22| 0.56|
|No Specialization |11 | 97| 51,078.23| 0.49|
|No Specialization |12 | 270| 49,747.81| 0.57|
|No Specialization |13 | 265| 50,868.09| 0.57|
|No Specialization |24 | 714| 52,078.23| 0.63|
|No Specialization |35 | 1,995| 51,673.08| 0.60|
|No Specialization |46 | 406| 46,830.94| 0.59|
|No Specialization |47 | 336| 50,652.18| 0.60|
|No Specialization |48 | 614| 53,668.93| 0.63|
|No Specialization |59 | 701| 54,158.28| 0.62|
|No Specialization |70 | 159| 75,099.26| 0.43|
|Other/Interdisciplinary |10 | 2,619| 31,618.18| 0.44|
|Other/Interdisciplinary |11 | 652| 33,956.47| 0.55|
|Other/Interdisciplinary |12 | 4,447| 34,053.31| 0.48|
|Other/Interdisciplinary |13 | 3,995| 33,916.43| 0.48|
|Other/Interdisciplinary |24 | 34,929| 34,984.23| 0.49|
|Other/Interdisciplinary |35 | 62,233| 38,274.65| 0.49|
|Other/Interdisciplinary |46 | 6,673| 37,226.59| 0.52|
|Other/Interdisciplinary |47 | 5,366| 40,948.41| 0.55|
|Other/Interdisciplinary |48 | 18,208| 43,651.03| 0.57|
|Other/Interdisciplinary |59 | 22,939| 39,881.09| 0.53|
|Other/Interdisciplinary |70 | 546| 42,713.21| 0.39|
|Physical Sciences |10 | 72| 67,837.79| 0.49|
|Physical Sciences |11 | 26| 57,615.38| 0.15|
|Physical Sciences |12 | 222| 64,543.90| 0.47|
|Physical Sciences |13 | 118| 69,319.56| 0.50|
|Physical Sciences |24 | 2,020| 59,154.76| 0.58|
|Physical Sciences |35 | 3,959| 71,873.73| 0.52|
|Physical Sciences |46 | 233| 65,476.41| 0.50|
|Physical Sciences |47 | 158| 70,028.11| 0.48|
|Physical Sciences |48 | 1,039| 77,437.26| 0.52|
|Physical Sciences |59 | 1,364| 67,880.14| 0.53|
|Physical Sciences |70 | 8| 118,625.00| 0.62|
|Services |10 | 276| 57,885.30| 0.71|
|Services |11 | 41| 49,512.20| 0.71|
|Services |12 | 406| 46,633.01| 0.69|
|Services |13 | 334| 49,366.82| 0.66|
|Services |24 | 3,851| 49,798.29| 0.71|
|Services |35 | 4,498| 52,626.07| 0.67|
|Services |46 | 345| 49,958.85| 0.65|
|Services |47 | 351| 54,548.87| 0.69|
|Services |48 | 1,181| 57,407.64| 0.73|
|Services |59 | 1,574| 49,695.35| 0.65|
|Services |70 | 31| 68,290.32| 0.42|
|Social Sciences & Law |10 | 183| 61,149.36| 0.67|
|Social Sciences & Law |11 | 63| 63,174.95| 0.49|
|Social Sciences & Law |12 | 624| 60,367.52| 0.63|
|Social Sciences & Law |13 | 349| 56,030.06| 0.63|
|Social Sciences & Law |24 | 5,612| 62,862.82| 0.69|
|Social Sciences & Law |35 | 12,128| 71,500.09| 0.67|
|Social Sciences & Law |46 | 621| 60,036.74| 0.64|
|Social Sciences & Law |47 | 389| 60,595.58| 0.64|
|Social Sciences & Law |48 | 2,217| 71,122.60| 0.64|
|Social Sciences & Law |59 | 3,777| 65,210.40| 0.64|
|Social Sciences & Law |70 | 41| 70,073.17| 0.46|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 0.5817| 0.0072| 80.6463| 0.0000| 0.5676| 0.5958|
|field_nameArts & Communications | 0.0991| 0.0091| 10.8803| 0.0000| 0.0812| 0.1169|
|field_nameBusiness | 0.0372| 0.0075| 4.9304| 0.0000| 0.0224| 0.0520|
|field_nameComputer Science & Math | -0.1947| 0.0087| -22.2987| 0.0000| -0.2118| -0.1775|
|field_nameEducation | 0.0401| 0.0083| 4.8464| 0.0000| 0.0239| 0.0564|
|field_nameEngineering | -0.0185| 0.0076| -2.4381| 0.0148| -0.0333| -0.0036|
|field_nameHealth | -0.1578| 0.0077| -20.4351| 0.0000| -0.1729| -0.1427|
|field_nameHumanities | 0.0203| 0.0085| 2.3913| 0.0168| 0.0037| 0.0370|
|field_nameNo Specialization | 0.0160| 0.0097| 1.6442| 0.1001| -0.0031| 0.0350|
|field_nameOther/Interdisciplinary | -0.0764| 0.0073| -10.4435| 0.0000| -0.0907| -0.0621|
|field_namePhysical Sciences | -0.0499| 0.0088| -5.6353| 0.0000| -0.0672| -0.0325|
|field_nameServices | 0.1047| 0.0084| 12.4463| 0.0000| 0.0883| 0.1212|
|field_nameSocial Sciences & Law | 0.0813| 0.0078| 10.3860| 0.0000| 0.0660| 0.0967|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 0.5168| 0.0072| 71.5462| 0.0000| 0.5027| 0.5310|
|field_nameArts & Communications | 0.1212| 0.0090| 13.4183| 0.0000| 0.1035| 0.1389|
|field_nameBusiness | 0.0667| 0.0075| 8.9003| 0.0000| 0.0520| 0.0814|
|field_nameComputer Science & Math | -0.1977| 0.0087| -22.7945| 0.0000| -0.2147| -0.1807|
|field_nameEducation | 0.0874| 0.0082| 10.6200| 0.0000| 0.0713| 0.1035|
|field_nameEngineering | -0.0530| 0.0075| -7.0430| 0.0000| -0.0678| -0.0383|
|field_nameHealth | -0.1028| 0.0077| -13.3776| 0.0000| -0.1179| -0.0877|
|field_nameHumanities | 0.0532| 0.0084| 6.2946| 0.0000| 0.0366| 0.0697|
|field_nameNo Specialization | 0.0395| 0.0097| 4.0923| 0.0000| 0.0206| 0.0584|
|field_nameOther/Interdisciplinary | -0.0628| 0.0073| -8.6548| 0.0000| -0.0770| -0.0486|
|field_namePhysical Sciences | -0.0285| 0.0088| -3.2445| 0.0012| -0.0457| -0.0113|
|field_nameServices | 0.1143| 0.0083| 13.7047| 0.0000| 0.0980| 0.1307|
|field_nameSocial Sciences & Law | 0.1170| 0.0078| 15.0444| 0.0000| 0.1018| 0.1322|
|gender2 | 0.1249| 0.0017| 74.4440| 0.0000| 0.1216| 0.1282|
|immigrant2 | -0.0546| 0.0018| -30.4791| 0.0000| -0.0581| -0.0510|
|immigrant3 | 0.0806| 0.0051| 15.6917| 0.0000| 0.0706| 0.0907|
|immigrant88 | -0.0582| 0.0209| -2.7855| 0.0053| -0.0991| -0.0172|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 1.1652| 0.0060| 194.2425| 0e+00| 1.1534| 1.1769|
|field_nameArts & Communications | 0.0742| 0.0073| 10.1852| 0e+00| 0.0599| 0.0885|
|field_nameBusiness | 0.0639| 0.0060| 10.5730| 0e+00| 0.0521| 0.0758|
|field_nameComputer Science & Math | -0.1921| 0.0070| -27.4409| 0e+00| -0.2058| -0.1784|
|field_nameEducation | 0.1358| 0.0066| 20.4483| 0e+00| 0.1228| 0.1488|
|field_nameEngineering | -0.0363| 0.0061| -5.9755| 0e+00| -0.0482| -0.0244|
|field_nameHealth | -0.1130| 0.0062| -18.2177| 0e+00| -0.1251| -0.1008|
|field_nameHumanities | 0.0549| 0.0068| 8.0619| 0e+00| 0.0416| 0.0683|
|field_nameNo Specialization | -0.0345| 0.0078| -4.4287| 0e+00| -0.0498| -0.0192|
|field_nameOther/Interdisciplinary | -0.0195| 0.0059| -3.3349| 9e-04| -0.0310| -0.0080|
|field_namePhysical Sciences | -0.0470| 0.0071| -6.6364| 0e+00| -0.0609| -0.0331|
|field_nameServices | 0.0876| 0.0067| 13.0064| 0e+00| 0.0744| 0.1008|
|field_nameSocial Sciences & Law | 0.0929| 0.0063| 14.8043| 0e+00| 0.0806| 0.1052|
|gender2 | 0.0960| 0.0014| 70.7770| 0e+00| 0.0933| 0.0986|
|immigrant2 | -0.0452| 0.0014| -31.2611| 0e+00| -0.0480| -0.0423|
|immigrant3 | -0.0334| 0.0042| -8.0426| 0e+00| -0.0416| -0.0253|
|immigrant88 | -0.1515| 0.0169| -8.9841| 0e+00| -0.1845| -0.1184|
|weeks | -0.1101| 0.0002| -458.5274| 0e+00| -0.1106| -0.1097|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 1.0261| 0.0073| 140.5882| 0.0000| 1.0118| 1.0404|
|field_nameArts & Communications | 0.0713| 0.0070| 10.1229| 0.0000| 0.0575| 0.0851|
|field_nameBusiness | 0.0593| 0.0058| 10.1474| 0.0000| 0.0478| 0.0708|
|field_nameComputer Science & Math | -0.2145| 0.0068| -31.7057| 0.0000| -0.2278| -0.2013|
|field_nameEducation | 0.1601| 0.0064| 24.9272| 0.0000| 0.1475| 0.1727|
|field_nameEngineering | -0.0280| 0.0059| -4.7763| 0.0000| -0.0395| -0.0165|
|field_nameHealth | -0.1105| 0.0060| -18.4401| 0.0000| -0.1223| -0.0988|
|field_nameHumanities | 0.0656| 0.0066| 9.9535| 0.0000| 0.0526| 0.0785|
|field_nameNo Specialization | -0.0346| 0.0075| -4.5938| 0.0000| -0.0494| -0.0198|
|field_nameOther/Interdisciplinary | 0.0166| 0.0057| 2.9181| 0.0035| 0.0054| 0.0277|
|field_namePhysical Sciences | -0.0461| 0.0069| -6.7270| 0.0000| -0.0595| -0.0327|
|field_nameServices | 0.0872| 0.0065| 13.4013| 0.0000| 0.0744| 0.0999|
|field_nameSocial Sciences & Law | 0.0848| 0.0061| 13.9837| 0.0000| 0.0729| 0.0967|
|gender2 | 0.0937| 0.0013| 71.4708| 0.0000| 0.0911| 0.0962|
|immigrant2 | -0.0416| 0.0014| -29.5744| 0.0000| -0.0444| -0.0389|
|immigrant3 | -0.0515| 0.0041| -12.6332| 0.0000| -0.0595| -0.0435|
|immigrant88 | -0.1618| 0.0163| -9.9255| 0.0000| -0.1938| -0.1299|
|weeks | -0.0934| 0.0003| -358.1513| 0.0000| -0.0939| -0.0929|
|age7 | 0.0345| 0.0059| 5.8417| 0.0000| 0.0229| 0.0461|
|age8 | 0.0365| 0.0050| 7.3522| 0.0000| 0.0268| 0.0462|
|age9 | 0.0719| 0.0050| 14.4852| 0.0000| 0.0621| 0.0816|
|age10 | 0.0860| 0.0049| 17.3842| 0.0000| 0.0763| 0.0957|
|age11 | 0.1031| 0.0049| 20.8422| 0.0000| 0.0934| 0.1128|
|age12 | 0.1255| 0.0050| 25.2650| 0.0000| 0.1158| 0.1353|
|age13 | 0.1305| 0.0050| 26.1584| 0.0000| 0.1207| 0.1403|
|age14 | 0.1207| 0.0050| 24.2919| 0.0000| 0.1110| 0.1304|
|age15 | 0.0902| 0.0049| 18.3743| 0.0000| 0.0806| 0.0998|
|age16 | 0.0090| 0.0049| 1.8313| 0.0671| -0.0006| 0.0187|
|age17 | -0.1116| 0.0050| -22.2606| 0.0000| -0.1214| -0.1018|
|age18 | -0.1778| 0.0051| -34.5372| 0.0000| -0.1879| -0.1677|
|age19 | -0.2068| 0.0054| -38.0204| 0.0000| -0.2175| -0.1961|
|age20 | -0.2206| 0.0059| -37.1752| 0.0000| -0.2322| -0.2089|
|age21 | -0.2220| 0.0062| -35.9305| 0.0000| -0.2341| -0.2099|
|age88 | -0.0748| 0.0100| -7.4854| 0.0000| -0.0943| -0.0552|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 0.9847| 0.0089| 110.6307| 0.0000| 0.9673| 1.0022|
|field_nameArts & Communications | 0.0709| 0.0070| 10.0734| 0.0000| 0.0571| 0.0847|
|field_nameBusiness | 0.0603| 0.0058| 10.3214| 0.0000| 0.0488| 0.0718|
|field_nameComputer Science & Math | -0.2136| 0.0068| -31.5706| 0.0000| -0.2269| -0.2003|
|field_nameEducation | 0.1608| 0.0064| 25.0547| 0.0000| 0.1482| 0.1734|
|field_nameEngineering | -0.0270| 0.0059| -4.5996| 0.0000| -0.0385| -0.0155|
|field_nameHealth | -0.1095| 0.0060| -18.2826| 0.0000| -0.1213| -0.0978|
|field_nameHumanities | 0.0659| 0.0066| 10.0037| 0.0000| 0.0530| 0.0788|
|field_nameNo Specialization | -0.0273| 0.0075| -3.6148| 0.0003| -0.0420| -0.0125|
|field_nameOther/Interdisciplinary | 0.0180| 0.0057| 3.1674| 0.0015| 0.0068| 0.0291|
|field_namePhysical Sciences | -0.0454| 0.0068| -6.6229| 0.0000| -0.0588| -0.0319|
|field_nameServices | 0.0884| 0.0065| 13.5825| 0.0000| 0.0756| 0.1011|
|field_nameSocial Sciences & Law | 0.0855| 0.0061| 14.1036| 0.0000| 0.0737| 0.0974|
|gender2 | 0.0936| 0.0013| 71.4297| 0.0000| 0.0910| 0.0961|
|immigrant2 | -0.0432| 0.0014| -29.9531| 0.0000| -0.0460| -0.0404|
|immigrant3 | -0.0529| 0.0041| -12.9783| 0.0000| -0.0609| -0.0449|
|immigrant88 | -0.1568| 0.0163| -9.6213| 0.0000| -0.1888| -0.1249|
|weeks | -0.0934| 0.0003| -358.1020| 0.0000| -0.0939| -0.0929|
|age7 | 0.0349| 0.0059| 5.9092| 0.0000| 0.0233| 0.0465|
|age8 | 0.0372| 0.0050| 7.4874| 0.0000| 0.0274| 0.0469|
|age9 | 0.0725| 0.0050| 14.6209| 0.0000| 0.0628| 0.0822|
|age10 | 0.0868| 0.0049| 17.5492| 0.0000| 0.0771| 0.0965|
|age11 | 0.1037| 0.0049| 20.9722| 0.0000| 0.0941| 0.1134|
|age12 | 0.1262| 0.0050| 25.4113| 0.0000| 0.1165| 0.1360|
|age13 | 0.1316| 0.0050| 26.3713| 0.0000| 0.1218| 0.1413|
|age14 | 0.1218| 0.0050| 24.5186| 0.0000| 0.1121| 0.1315|
|age15 | 0.0912| 0.0049| 18.5715| 0.0000| 0.0815| 0.1008|
|age16 | 0.0099| 0.0049| 2.0178| 0.0436| 0.0003| 0.0196|
|age17 | -0.1109| 0.0050| -22.1243| 0.0000| -0.1207| -0.1011|
|age18 | -0.1771| 0.0051| -34.4032| 0.0000| -0.1872| -0.1670|
|age19 | -0.2062| 0.0054| -37.9225| 0.0000| -0.2169| -0.1956|
|age20 | -0.2200| 0.0059| -37.0901| 0.0000| -0.2316| -0.2084|
|age21 | -0.2216| 0.0062| -35.8749| 0.0000| -0.2337| -0.2095|
|age88 | -0.0668| 0.0100| -6.6826| 0.0000| -0.0864| -0.0472|
|province11 | 0.0058| 0.0110| 0.5265| 0.5986| -0.0158| 0.0274|
|province12 | 0.0197| 0.0063| 3.1480| 0.0016| 0.0075| 0.0320|
|province13 | 0.0244| 0.0066| 3.7220| 0.0002| 0.0116| 0.0373|
|province24 | 0.0447| 0.0052| 8.5450| 0.0000| 0.0344| 0.0550|
|province35 | 0.0384| 0.0052| 7.3956| 0.0000| 0.0282| 0.0485|
|province46 | 0.0420| 0.0060| 6.9659| 0.0000| 0.0302| 0.0539|
|province47 | 0.0483| 0.0062| 7.7586| 0.0000| 0.0361| 0.0605|
|province48 | 0.0405| 0.0054| 7.4936| 0.0000| 0.0299| 0.0511|
|province59 | 0.0471| 0.0053| 8.8216| 0.0000| 0.0367| 0.0576|
|province70 | -0.1296| 0.0127| -10.1843| 0.0000| -0.1546| -0.1047|
|Model | R_squared| Adj_R_squared| N|
|:-----------------|---------:|-------------:|------:|
|M1: Field | 0.0223| 0.0223| 392626|
|M2: +Demographics | 0.0394| 0.0394| 392626|
|M3: +Work | 0.3744| 0.3744| 392626|
|M4: +Age | 0.4159| 0.4158| 392626|
|M5: +Province | 0.4164| 0.4164| 392626|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 5.9452| 0.0883| 67.2983| 0e+00| 5.7720| 6.1183|
|field_nameArts & Communications | -1.1112| 0.1115| -9.9658| 0e+00| -1.3297| -0.8926|
|field_nameBusiness | 1.1420| 0.0924| 12.3551| 0e+00| 0.9609| 1.3232|
|field_nameComputer Science & Math | 1.5315| 0.1069| 14.3249| 0e+00| 1.3220| 1.7411|
|field_nameEducation | 0.4081| 0.1014| 4.0247| 1e-04| 0.2094| 0.6068|
|field_nameEngineering | 1.2060| 0.0927| 13.0105| 0e+00| 1.0243| 1.3877|
|field_nameHealth | 0.4828| 0.0946| 5.1047| 0e+00| 0.2974| 0.6682|
|field_nameHumanities | -0.3809| 0.1042| -3.6554| 3e-04| -0.5851| -0.1767|
|field_nameNo Specialization | -0.7038| 0.1190| -5.9121| 0e+00| -0.9371| -0.4705|
|field_nameOther/Interdisciplinary | -2.1342| 0.0896| -23.8201| 0e+00| -2.3098| -1.9586|
|field_namePhysical Sciences | 0.9238| 0.1084| 8.5234| 0e+00| 0.7114| 1.1362|
|field_nameServices | -0.7742| 0.1031| -7.5113| 0e+00| -0.9762| -0.5722|
|field_nameSocial Sciences & Law | 0.8232| 0.0959| 8.5815| 0e+00| 0.6352| 1.0112|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 5.0814| 0.0881| 57.6702| 0.0000| 4.9087| 5.2541|
|field_nameArts & Communications | -0.7534| 0.1101| -6.8409| 0.0000| -0.9692| -0.5375|
|field_nameBusiness | 1.6324| 0.0914| 17.8600| 0.0000| 1.4532| 1.8115|
|field_nameComputer Science & Math | 1.7040| 0.1058| 16.1040| 0.0000| 1.4966| 1.9114|
|field_nameEducation | 1.0429| 0.1004| 10.3906| 0.0000| 0.8462| 1.2396|
|field_nameEngineering | 0.7675| 0.0918| 8.3617| 0.0000| 0.5876| 0.9474|
|field_nameHealth | 1.2466| 0.0937| 13.2990| 0.0000| 1.0629| 1.4303|
|field_nameHumanities | 0.1175| 0.1030| 1.1410| 0.2539| -0.0844| 0.3194|
|field_nameNo Specialization | -0.2691| 0.1177| -2.2860| 0.0223| -0.4998| -0.0384|
|field_nameOther/Interdisciplinary | -1.9564| 0.0885| -22.1127| 0.0000| -2.1298| -1.7830|
|field_namePhysical Sciences | 1.2985| 0.1071| 12.1225| 0.0000| 1.0886| 1.5085|
|field_nameServices | -0.6579| 0.1018| -6.4655| 0.0000| -0.8574| -0.4585|
|field_nameSocial Sciences & Law | 1.3461| 0.0949| 14.1907| 0.0000| 1.1602| 1.5320|
|gender2 | 1.7577| 0.0205| 85.8743| 0.0000| 1.7176| 1.7978|
|immigrant2 | -0.6577| 0.0218| -30.1230| 0.0000| -0.7005| -0.6149|
|immigrant3 | -2.9454| 0.0627| -46.9920| 0.0000| -3.0683| -2.8226|
|immigrant88 | -0.2182| 0.2548| -0.8565| 0.3917| -0.7176| 0.2811|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 5.1307| 0.0907| 56.5890| 0.0000| 4.9530| 5.3084|
|field_nameArts & Communications | -0.7570| 0.1101| -6.8727| 0.0000| -0.9728| -0.5411|
|field_nameBusiness | 1.6322| 0.0914| 17.8578| 0.0000| 1.4530| 1.8113|
|field_nameComputer Science & Math | 1.7045| 0.1058| 16.1081| 0.0000| 1.4971| 1.9118|
|field_nameEducation | 1.0465| 0.1004| 10.4260| 0.0000| 0.8498| 1.2433|
|field_nameEngineering | 0.7688| 0.0918| 8.3755| 0.0000| 0.5889| 0.9487|
|field_nameHealth | 1.2459| 0.0937| 13.2908| 0.0000| 1.0621| 1.4296|
|field_nameHumanities | 0.1177| 0.1030| 1.1423| 0.2533| -0.0842| 0.3196|
|field_nameNo Specialization | -0.2747| 0.1177| -2.3332| 0.0196| -0.5055| -0.0440|
|field_nameOther/Interdisciplinary | -1.9531| 0.0885| -22.0728| 0.0000| -2.1266| -1.7797|
|field_namePhysical Sciences | 1.2971| 0.1071| 12.1092| 0.0000| 1.0872| 1.5071|
|field_nameServices | -0.6600| 0.1018| -6.4853| 0.0000| -0.8594| -0.4605|
|field_nameSocial Sciences & Law | 1.3442| 0.0949| 14.1710| 0.0000| 1.1583| 1.5302|
|gender2 | 1.7555| 0.0205| 85.6740| 0.0000| 1.7153| 1.7956|
|immigrant2 | -0.6570| 0.0218| -30.0874| 0.0000| -0.6998| -0.6142|
|immigrant3 | -2.9541| 0.0628| -47.0460| 0.0000| -3.0771| -2.8310|
|immigrant88 | -0.2253| 0.2548| -0.8843| 0.3765| -0.7248| 0.2741|
|weeks | -0.0084| 0.0036| -2.3059| 0.0211| -0.0155| -0.0013|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 1.2401| 0.1112| 11.1502| 0.0000| 1.0222| 1.4581|
|field_nameArts & Communications | -0.6668| 0.1073| -6.2133| 0.0000| -0.8771| -0.4565|
|field_nameBusiness | 1.5799| 0.0890| 17.7416| 0.0000| 1.4053| 1.7544|
|field_nameComputer Science & Math | 1.6033| 0.1031| 15.5490| 0.0000| 1.4012| 1.8055|
|field_nameEducation | 1.0264| 0.0979| 10.4884| 0.0000| 0.8346| 1.2182|
|field_nameEngineering | 0.7955| 0.0894| 8.8947| 0.0000| 0.6202| 0.9708|
|field_nameHealth | 1.2999| 0.0913| 14.2326| 0.0000| 1.1209| 1.4789|
|field_nameHumanities | 0.2384| 0.1004| 2.3754| 0.0175| 0.0417| 0.4351|
|field_nameNo Specialization | -0.0634| 0.1148| -0.5520| 0.5810| -0.2883| 0.1616|
|field_nameOther/Interdisciplinary | -1.2765| 0.0864| -14.7677| 0.0000| -1.4459| -1.1071|
|field_namePhysical Sciences | 1.5997| 0.1044| 15.3220| 0.0000| 1.3950| 1.8043|
|field_nameServices | -0.6216| 0.0992| -6.2695| 0.0000| -0.8160| -0.4273|
|field_nameSocial Sciences & Law | 1.3848| 0.0924| 14.9824| 0.0000| 1.2037| 1.5660|
|gender2 | 1.7526| 0.0200| 87.7675| 0.0000| 1.7135| 1.7918|
|immigrant2 | -0.9404| 0.0214| -43.8481| 0.0000| -0.9824| -0.8984|
|immigrant3 | -2.0649| 0.0621| -33.2600| 0.0000| -2.1866| -1.9432|
|immigrant88 | -0.5084| 0.2484| -2.0462| 0.0407| -0.9953| -0.0214|
|weeks | -0.0172| 0.0040| -4.3377| 0.0000| -0.0250| -0.0095|
|age7 | 0.6086| 0.0900| 6.7623| 0.0000| 0.4322| 0.7850|
|age8 | 1.1499| 0.0757| 15.1956| 0.0000| 1.0016| 1.2983|
|age9 | 2.4438| 0.0756| 32.3268| 0.0000| 2.2957| 2.5920|
|age10 | 3.6730| 0.0754| 48.7273| 0.0000| 3.5252| 3.8207|
|age11 | 4.5547| 0.0754| 60.4198| 0.0000| 4.4069| 4.7024|
|age12 | 5.1577| 0.0757| 68.1222| 0.0000| 5.0093| 5.3061|
|age13 | 5.3628| 0.0760| 70.5343| 0.0000| 5.2138| 5.5119|
|age14 | 5.3045| 0.0757| 70.0604| 0.0000| 5.1561| 5.4529|
|age15 | 4.7797| 0.0748| 63.8830| 0.0000| 4.6330| 4.9263|
|age16 | 4.0478| 0.0750| 53.9817| 0.0000| 3.9008| 4.1947|
|age17 | 3.4513| 0.0764| 45.1771| 0.0000| 3.3016| 3.6010|
|age18 | 2.9663| 0.0785| 37.8094| 0.0000| 2.8126| 3.1201|
|age19 | 2.8469| 0.0829| 34.3477| 0.0000| 2.6845| 3.0094|
|age20 | 2.7681| 0.0904| 30.6162| 0.0000| 2.5909| 2.9453|
|age21 | 3.1449| 0.0942| 33.4000| 0.0000| 2.9603| 3.3294|
|age88 | 3.5470| 0.1522| 23.3081| 0.0000| 3.2487| 3.8453|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 0.4480| 0.1353| 3.3099| 0.0009| 0.1827| 0.7133|
|field_nameArts & Communications | -0.6638| 0.1071| -6.1994| 0.0000| -0.8737| -0.4539|
|field_nameBusiness | 1.6126| 0.0888| 18.1524| 0.0000| 1.4385| 1.7867|
|field_nameComputer Science & Math | 1.6301| 0.1029| 15.8451| 0.0000| 1.4285| 1.8317|
|field_nameEducation | 1.0569| 0.0976| 10.8283| 0.0000| 0.8656| 1.2482|
|field_nameEngineering | 0.8157| 0.0892| 9.1431| 0.0000| 0.6409| 0.9906|
|field_nameHealth | 1.3023| 0.0911| 14.2946| 0.0000| 1.1238| 1.4809|
|field_nameHumanities | 0.2547| 0.1001| 2.5432| 0.0110| 0.0584| 0.4509|
|field_nameNo Specialization | -0.0122| 0.1147| -0.1060| 0.9156| -0.2369| 0.2126|
|field_nameOther/Interdisciplinary | -1.2758| 0.0862| -14.7961| 0.0000| -1.4448| -1.1068|
|field_namePhysical Sciences | 1.5975| 0.1041| 15.3385| 0.0000| 1.3933| 1.8016|
|field_nameServices | -0.5549| 0.0989| -5.6092| 0.0000| -0.7487| -0.3610|
|field_nameSocial Sciences & Law | 1.3616| 0.0922| 14.7618| 0.0000| 1.1808| 1.5424|
|gender2 | 1.7520| 0.0199| 87.9623| 0.0000| 1.7130| 1.7910|
|immigrant2 | -1.1258| 0.0219| -51.3456| 0.0000| -1.1688| -1.0829|
|immigrant3 | -2.1073| 0.0620| -33.9878| 0.0000| -2.2288| -1.9858|
|immigrant88 | -0.5034| 0.2479| -2.0311| 0.0422| -0.9892| -0.0176|
|weeks | -0.0170| 0.0040| -4.2796| 0.0000| -0.0247| -0.0092|
|age7 | 0.5600| 0.0898| 6.2377| 0.0000| 0.3840| 0.7359|
|age8 | 1.0925| 0.0755| 14.4691| 0.0000| 0.9445| 1.2405|
|age9 | 2.3928| 0.0754| 31.7235| 0.0000| 2.2449| 2.5406|
|age10 | 3.6261| 0.0752| 48.2114| 0.0000| 3.4787| 3.7736|
|age11 | 4.5184| 0.0752| 60.0683| 0.0000| 4.3710| 4.6659|
|age12 | 5.1438| 0.0755| 68.0924| 0.0000| 4.9957| 5.2918|
|age13 | 5.3465| 0.0759| 70.4791| 0.0000| 5.1978| 5.4951|
|age14 | 5.2898| 0.0755| 70.0244| 0.0000| 5.1417| 5.4378|
|age15 | 4.7734| 0.0746| 63.9524| 0.0000| 4.6271| 4.9197|
|age16 | 4.0497| 0.0748| 54.1353| 0.0000| 3.9031| 4.1963|
|age17 | 3.4600| 0.0762| 45.3973| 0.0000| 3.3106| 3.6094|
|age18 | 2.9933| 0.0783| 38.2419| 0.0000| 2.8399| 3.1467|
|age19 | 2.8745| 0.0827| 34.7646| 0.0000| 2.7125| 3.0366|
|age20 | 2.7949| 0.0902| 30.9876| 0.0000| 2.6181| 2.9716|
|age21 | 3.1515| 0.0939| 33.5522| 0.0000| 2.9674| 3.3356|
|age88 | 3.5742| 0.1520| 23.5133| 0.0000| 3.2763| 3.8721|
|province11 | -0.0855| 0.1678| -0.5097| 0.6103| -0.4145| 0.2434|
|province12 | -0.0670| 0.0954| -0.7021| 0.4826| -0.2539| 0.1200|
|province13 | -0.0902| 0.0998| -0.9039| 0.3660| -0.2859| 0.1054|
|province24 | 0.3500| 0.0795| 4.4001| 0.0000| 0.1941| 0.5059|
|province35 | 1.1279| 0.0789| 14.3003| 0.0000| 0.9733| 1.2825|
|province46 | 0.5418| 0.0918| 5.9043| 0.0000| 0.3619| 0.7216|
|province47 | 0.7142| 0.0947| 7.5449| 0.0000| 0.5287| 0.8997|
|province48 | 1.3941| 0.0822| 16.9592| 0.0000| 1.2330| 1.5553|
|province59 | 0.9656| 0.0813| 11.8813| 0.0000| 0.8063| 1.1249|
|province70 | 1.9281| 0.1935| 9.9624| 0.0000| 1.5488| 2.3075|
|Model | R_squared| Adj_R_squared| N|
|:-----------------|---------:|-------------:|------:|
|M1: Field | 0.0565| 0.0565| 392626|
|M2: +Demographics | 0.0806| 0.0805| 392626|
|M3: +Work | 0.0806| 0.0805| 392626|
|M4: +Age | 0.1273| 0.1273| 392626|
|M5: +Province | 0.1319| 0.1318| 392626|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 10.6636| 0.0186| 573.1865| 0.0000| 10.6271| 10.7001|
|field_nameArts & Communications | -0.2342| 0.0235| -9.9730| 0.0000| -0.2802| -0.1882|
|field_nameBusiness | 0.0750| 0.0195| 3.8523| 0.0001| 0.0368| 0.1131|
|field_nameComputer Science & Math | 0.1375| 0.0225| 6.1068| 0.0000| 0.0934| 0.1816|
|field_nameEducation | 0.1526| 0.0214| 7.1461| 0.0000| 0.1107| 0.1945|
|field_nameEngineering | 0.1563| 0.0195| 8.0052| 0.0000| 0.1180| 0.1945|
|field_nameHealth | 0.0660| 0.0199| 3.3132| 0.0009| 0.0270| 0.1050|
|field_nameHumanities | -0.1386| 0.0219| -6.3179| 0.0000| -0.1817| -0.0956|
|field_nameNo Specialization | -0.1654| 0.0251| -6.5988| 0.0000| -0.2146| -0.1163|
|field_nameOther/Interdisciplinary | -0.5895| 0.0189| -31.2409| 0.0000| -0.6265| -0.5525|
|field_namePhysical Sciences | 0.0076| 0.0228| 0.3336| 0.7386| -0.0371| 0.0524|
|field_nameServices | -0.1269| 0.0217| -5.8446| 0.0000| -0.1694| -0.0843|
|field_nameSocial Sciences & Law | 0.0211| 0.0202| 1.0425| 0.2972| -0.0185| 0.0607|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 10.5459| 0.0186| 568.2784| 0.0000| 10.5095| 10.5822|
|field_nameArts & Communications | -0.1686| 0.0232| -7.2669| 0.0000| -0.2140| -0.1231|
|field_nameBusiness | 0.1694| 0.0192| 8.8001| 0.0000| 0.1317| 0.2071|
|field_nameComputer Science & Math | 0.2080| 0.0223| 9.3312| 0.0000| 0.1643| 0.2516|
|field_nameEducation | 0.2495| 0.0211| 11.8041| 0.0000| 0.2081| 0.2910|
|field_nameEngineering | 0.1018| 0.0193| 5.2674| 0.0000| 0.0639| 0.1397|
|field_nameHealth | 0.1894| 0.0197| 9.5915| 0.0000| 0.1507| 0.2281|
|field_nameHumanities | -0.0477| 0.0217| -2.1998| 0.0278| -0.0902| -0.0052|
|field_nameNo Specialization | -0.0696| 0.0248| -2.8074| 0.0050| -0.1182| -0.0210|
|field_nameOther/Interdisciplinary | -0.5597| 0.0186| -30.0362| 0.0000| -0.5962| -0.5232|
|field_namePhysical Sciences | 0.0873| 0.0226| 3.8712| 0.0001| 0.0431| 0.1316|
|field_nameServices | -0.1104| 0.0214| -5.1494| 0.0000| -0.1524| -0.0684|
|field_nameSocial Sciences & Law | 0.1102| 0.0200| 5.5162| 0.0000| 0.0710| 0.1494|
|gender2 | 0.2722| 0.0043| 63.1447| 0.0000| 0.2638| 0.2807|
|immigrant2 | -0.1449| 0.0046| -31.5088| 0.0000| -0.1539| -0.1359|
|immigrant3 | -0.9911| 0.0132| -75.0778| 0.0000| -1.0170| -0.9652|
|immigrant88 | -0.1864| 0.0537| -3.4742| 0.0005| -0.2916| -0.0813|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 10.6466| 0.0191| 557.8971| 0.0000| 10.6092| 10.6840|
|field_nameArts & Communications | -0.1759| 0.0232| -7.5855| 0.0000| -0.2213| -0.1304|
|field_nameBusiness | 0.1690| 0.0192| 8.7834| 0.0000| 0.1313| 0.2067|
|field_nameComputer Science & Math | 0.2088| 0.0223| 9.3765| 0.0000| 0.1652| 0.2525|
|field_nameEducation | 0.2571| 0.0211| 12.1663| 0.0000| 0.2156| 0.2985|
|field_nameEngineering | 0.1044| 0.0193| 5.4050| 0.0000| 0.0666| 0.1423|
|field_nameHealth | 0.1878| 0.0197| 9.5174| 0.0000| 0.1491| 0.2265|
|field_nameHumanities | -0.0474| 0.0217| -2.1884| 0.0286| -0.0899| -0.0050|
|field_nameNo Specialization | -0.0811| 0.0248| -3.2726| 0.0011| -0.1297| -0.0325|
|field_nameOther/Interdisciplinary | -0.5530| 0.0186| -29.6905| 0.0000| -0.5895| -0.5165|
|field_namePhysical Sciences | 0.0845| 0.0225| 3.7458| 0.0002| 0.0403| 0.1286|
|field_nameServices | -0.1145| 0.0214| -5.3467| 0.0000| -0.1565| -0.0725|
|field_nameSocial Sciences & Law | 0.1065| 0.0200| 5.3321| 0.0000| 0.0673| 0.1456|
|gender2 | 0.2677| 0.0043| 62.0723| 0.0000| 0.2593| 0.2762|
|immigrant2 | -0.1434| 0.0046| -31.2080| 0.0000| -0.1524| -0.1344|
|immigrant3 | -1.0088| 0.0132| -76.3320| 0.0000| -1.0347| -0.9829|
|immigrant88 | -0.2009| 0.0536| -3.7465| 0.0002| -0.3061| -0.0958|
|weeks | -0.0171| 0.0008| -22.3999| 0.0000| -0.0186| -0.0156|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 8.1211| 0.0224| 362.5335| 0.0000| 8.0772| 8.1650|
|field_nameArts & Communications | -0.1702| 0.0216| -7.8742| 0.0000| -0.2126| -0.1278|
|field_nameBusiness | 0.1654| 0.0179| 9.2220| 0.0000| 0.1302| 0.2006|
|field_nameComputer Science & Math | 0.2104| 0.0208| 10.1321| 0.0000| 0.1697| 0.2511|
|field_nameEducation | 0.2365| 0.0197| 11.9982| 0.0000| 0.1978| 0.2751|
|field_nameEngineering | 0.1120| 0.0180| 6.2193| 0.0000| 0.0767| 0.1473|
|field_nameHealth | 0.1916| 0.0184| 10.4154| 0.0000| 0.1555| 0.2276|
|field_nameHumanities | -0.0194| 0.0202| -0.9612| 0.3364| -0.0591| 0.0202|
|field_nameNo Specialization | -0.0267| 0.0231| -1.1560| 0.2477| -0.0720| 0.0186|
|field_nameOther/Interdisciplinary | -0.2952| 0.0174| -16.9586| 0.0000| -0.3294| -0.2611|
|field_namePhysical Sciences | 0.1546| 0.0210| 7.3520| 0.0000| 0.1134| 0.1958|
|field_nameServices | -0.1073| 0.0200| -5.3715| 0.0000| -0.1464| -0.0681|
|field_nameSocial Sciences & Law | 0.1110| 0.0186| 5.9618| 0.0000| 0.0745| 0.1475|
|gender2 | 0.2656| 0.0040| 66.0509| 0.0000| 0.2578| 0.2735|
|immigrant2 | -0.2434| 0.0043| -56.3473| 0.0000| -0.2519| -0.2349|
|immigrant3 | -0.8770| 0.0125| -70.1354| 0.0000| -0.9015| -0.8525|
|immigrant88 | -0.2648| 0.0500| -5.2916| 0.0000| -0.3629| -0.1667|
|weeks | -0.0444| 0.0008| -55.4612| 0.0000| -0.0460| -0.0428|
|age7 | 1.2648| 0.0181| 69.7832| 0.0000| 1.2293| 1.3004|
|age8 | 2.0183| 0.0152| 132.4171| 0.0000| 1.9884| 2.0481|
|age9 | 2.5037| 0.0152| 164.4374| 0.0000| 2.4739| 2.5336|
|age10 | 2.7308| 0.0152| 179.8750| 0.0000| 2.7010| 2.7606|
|age11 | 2.8546| 0.0152| 188.0165| 0.0000| 2.8249| 2.8844|
|age12 | 2.9194| 0.0152| 191.4502| 0.0000| 2.8895| 2.9493|
|age13 | 2.9013| 0.0153| 189.4620| 0.0000| 2.8713| 2.9313|
|age14 | 2.8473| 0.0152| 186.7187| 0.0000| 2.8174| 2.8772|
|age15 | 2.7091| 0.0151| 179.7810| 0.0000| 2.6796| 2.7387|
|age16 | 2.5875| 0.0151| 171.3310| 0.0000| 2.5579| 2.6171|
|age17 | 2.6942| 0.0154| 175.0999| 0.0000| 2.6640| 2.7243|
|age18 | 2.6917| 0.0158| 170.3483| 0.0000| 2.6608| 2.7227|
|age19 | 2.6899| 0.0167| 161.1316| 0.0000| 2.6572| 2.7226|
|age20 | 2.6975| 0.0182| 148.1341| 0.0000| 2.6618| 2.7332|
|age21 | 2.7794| 0.0190| 146.5592| 0.0000| 2.7422| 2.8165|
|age88 | 2.5127| 0.0306| 81.9809| 0.0000| 2.4526| 2.5728|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 7.9832| 0.0273| 292.3071| 0.0000| 7.9297| 8.0368|
|field_nameArts & Communications | -0.1712| 0.0216| -7.9245| 0.0000| -0.2136| -0.1289|
|field_nameBusiness | 0.1674| 0.0179| 9.3367| 0.0000| 0.1322| 0.2025|
|field_nameComputer Science & Math | 0.2114| 0.0208| 10.1824| 0.0000| 0.1707| 0.2521|
|field_nameEducation | 0.2404| 0.0197| 12.2059| 0.0000| 0.2018| 0.2790|
|field_nameEngineering | 0.1136| 0.0180| 6.3101| 0.0000| 0.0783| 0.1489|
|field_nameHealth | 0.1938| 0.0184| 10.5431| 0.0000| 0.1578| 0.2299|
|field_nameHumanities | -0.0183| 0.0202| -0.9075| 0.3641| -0.0579| 0.0213|
|field_nameNo Specialization | -0.0124| 0.0231| -0.5349| 0.5927| -0.0577| 0.0330|
|field_nameOther/Interdisciplinary | -0.2923| 0.0174| -16.8018| 0.0000| -0.3264| -0.2582|
|field_namePhysical Sciences | 0.1545| 0.0210| 7.3504| 0.0000| 0.1133| 0.1957|
|field_nameServices | -0.1029| 0.0200| -5.1544| 0.0000| -0.1420| -0.0638|
|field_nameSocial Sciences & Law | 0.1092| 0.0186| 5.8686| 0.0000| 0.0727| 0.1457|
|gender2 | 0.2656| 0.0040| 66.0770| 0.0000| 0.2577| 0.2734|
|immigrant2 | -0.2567| 0.0044| -58.0197| 0.0000| -0.2654| -0.2480|
|immigrant3 | -0.8771| 0.0125| -70.1051| 0.0000| -0.9016| -0.8526|
|immigrant88 | -0.2547| 0.0500| -5.0929| 0.0000| -0.3527| -0.1567|
|weeks | -0.0444| 0.0008| -55.4629| 0.0000| -0.0460| -0.0428|
|age7 | 1.2616| 0.0181| 69.6465| 0.0000| 1.2261| 1.2972|
|age8 | 2.0159| 0.0152| 132.3048| 0.0000| 1.9860| 2.0457|
|age9 | 2.5021| 0.0152| 164.3992| 0.0000| 2.4723| 2.5319|
|age10 | 2.7299| 0.0152| 179.8711| 0.0000| 2.7001| 2.7596|
|age11 | 2.8544| 0.0152| 188.0566| 0.0000| 2.8247| 2.8842|
|age12 | 2.9200| 0.0152| 191.5651| 0.0000| 2.8902| 2.9499|
|age13 | 2.9025| 0.0153| 189.6157| 0.0000| 2.8725| 2.9325|
|age14 | 2.8491| 0.0152| 186.9089| 0.0000| 2.8192| 2.8790|
|age15 | 2.7106| 0.0151| 179.9701| 0.0000| 2.6811| 2.7401|
|age16 | 2.5899| 0.0151| 171.5732| 0.0000| 2.5603| 2.6195|
|age17 | 2.6977| 0.0154| 175.4123| 0.0000| 2.6675| 2.7278|
|age18 | 2.6966| 0.0158| 170.7324| 0.0000| 2.6656| 2.7275|
|age19 | 2.6941| 0.0167| 161.4733| 0.0000| 2.6614| 2.7268|
|age20 | 2.7018| 0.0182| 148.4561| 0.0000| 2.6662| 2.7375|
|age21 | 2.7824| 0.0190| 146.8029| 0.0000| 2.7453| 2.8195|
|age88 | 2.5272| 0.0307| 82.3921| 0.0000| 2.4671| 2.5873|
|province11 | 0.1117| 0.0339| 3.2978| 0.0010| 0.0453| 0.1780|
|province12 | -0.0027| 0.0192| -0.1378| 0.8904| -0.0404| 0.0351|
|province13 | 0.0278| 0.0201| 1.3799| 0.1676| -0.0117| 0.0673|
|province24 | 0.1354| 0.0161| 8.4346| 0.0000| 0.1039| 0.1668|
|province35 | 0.1684| 0.0159| 10.5782| 0.0000| 0.1372| 0.1995|
|province46 | 0.0646| 0.0185| 3.4879| 0.0005| 0.0283| 0.1009|
|province47 | 0.1283| 0.0191| 6.7151| 0.0000| 0.0908| 0.1657|
|province48 | 0.1881| 0.0166| 11.3416| 0.0000| 0.1556| 0.2206|
|province59 | 0.0977| 0.0164| 5.9566| 0.0000| 0.0655| 0.1298|
|province70 | 0.1464| 0.0391| 3.7487| 0.0002| 0.0699| 0.2229|
|Model | R_squared| Adj_R_squared| N|
|:-----------------|---------:|-------------:|------:|
|M1: Field | 0.0613| 0.0613| 392626|
|M2: +Demographics | 0.0850| 0.0850| 392626|
|M3: +Work | 0.0862| 0.0862| 392626|
|M4: +Age | 0.2059| 0.2058| 392626|
|M5: +Province | 0.2071| 0.2070| 392626|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:-----------------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 0.6904| 0.1674| 4.1248| 0.0000| 0.3623| 1.0184|
|field_nameArts & Communications | -0.5047| 0.1563| -3.2291| 0.0012| -0.8111| -0.1984|
|field_nameBusiness | 0.7951| 0.1348| 5.8963| 0.0000| 0.5308| 1.0594|
|field_nameComputer Science & Math | 1.3897| 0.1661| 8.3671| 0.0000| 1.0642| 1.7153|
|field_nameEducation | 0.9452| 0.1414| 6.6822| 0.0000| 0.6680| 1.2224|
|field_nameEngineering | 1.2659| 0.1563| 8.0992| 0.0000| 0.9596| 1.5723|
|field_nameHealth | 0.8106| 0.1354| 5.9857| 0.0000| 0.5452| 1.0761|
|field_nameHumanities | 0.2101| 0.1468| 1.4310| 0.1524| -0.0777| 0.4979|
|field_nameNo Specialization | 0.1655| 0.1674| 0.9888| 0.3228| -0.1626| 0.4936|
|field_nameOther/Interdisciplinary | -1.1235| 0.1324| -8.4825| 0.0000| -1.3831| -0.8639|
|field_namePhysical Sciences | 1.2723| 0.1551| 8.2021| 0.0000| 0.9683| 1.5763|
|field_nameServices | -1.1886| 0.1495| -7.9491| 0.0000| -1.4817| -0.8955|
|field_nameSocial Sciences & Law | 0.7303| 0.1377| 5.3033| 0.0000| 0.4604| 1.0002|
|gender2 | 1.3917| 0.1713| 8.1226| 0.0000| 1.0559| 1.7276|
|immigrant2 | -1.1624| 0.0219| -53.0926| 0.0000| -1.2054| -1.1195|
|immigrant3 | -2.1674| 0.0619| -35.0397| 0.0000| -2.2886| -2.0462|
|immigrant88 | -0.5296| 0.2471| -2.1429| 0.0321| -1.0140| -0.0452|
|weeks | -0.0200| 0.0040| -5.0484| 0.0000| -0.0277| -0.0122|
|age7 | 0.5578| 0.0895| 6.2312| 0.0000| 0.3823| 0.7332|
|age8 | 1.1112| 0.0753| 14.7572| 0.0000| 0.9636| 1.2588|
|age9 | 2.4024| 0.0752| 31.9411| 0.0000| 2.2550| 2.5499|
|age10 | 3.6365| 0.0750| 48.4864| 0.0000| 3.4895| 3.7835|
|age11 | 4.5342| 0.0750| 60.4491| 0.0000| 4.3872| 4.6812|
|age12 | 5.1631| 0.0753| 68.5443| 0.0000| 5.0155| 5.3108|
|age13 | 5.3681| 0.0756| 70.9693| 0.0000| 5.2199| 5.5164|
|age14 | 5.3062| 0.0753| 70.4469| 0.0000| 5.1586| 5.4539|
|age15 | 4.7939| 0.0744| 64.4133| 0.0000| 4.6481| 4.9398|
|age16 | 4.0528| 0.0746| 54.3344| 0.0000| 3.9066| 4.1990|
|age17 | 3.4548| 0.0760| 45.4590| 0.0000| 3.3059| 3.6038|
|age18 | 2.9802| 0.0781| 38.1821| 0.0000| 2.8272| 3.1332|
|age19 | 2.8551| 0.0825| 34.6256| 0.0000| 2.6935| 3.0168|
|age20 | 2.7616| 0.0899| 30.7016| 0.0000| 2.5853| 2.9379|
|age21 | 3.0944| 0.0937| 33.0296| 0.0000| 2.9107| 3.2780|
|age88 | 3.5633| 0.1516| 23.5096| 0.0000| 3.2663| 3.8604|
|province11 | -0.0718| 0.1673| -0.4289| 0.6680| -0.3997| 0.2562|
|province12 | -0.0650| 0.0951| -0.6831| 0.4945| -0.2513| 0.1214|
|province13 | -0.0841| 0.0996| -0.8446| 0.3983| -0.2792| 0.1110|
|province24 | 0.3317| 0.0793| 4.1816| 0.0000| 0.1762| 0.4871|
|province35 | 1.1013| 0.0787| 14.0026| 0.0000| 0.9472| 1.2555|
|province46 | 0.5292| 0.0915| 5.7834| 0.0000| 0.3498| 0.7085|
|province47 | 0.7258| 0.0944| 7.6892| 0.0000| 0.5408| 0.9107|
|province48 | 1.3790| 0.0820| 16.8226| 0.0000| 1.2183| 1.5397|
|province59 | 0.9456| 0.0810| 11.6684| 0.0000| 0.7868| 1.1044|
|province70 | 1.9179| 0.1930| 9.9382| 0.0000| 1.5397| 2.2962|
|field_nameArts & Communications:gender2 | -0.4956| 0.2160| -2.2945| 0.0218| -0.9190| -0.0723|
|field_nameBusiness:gender2 | 1.9203| 0.1795| 10.7008| 0.0000| 1.5686| 2.2720|
|field_nameComputer Science & Math:gender2 | 0.4237| 0.2115| 2.0035| 0.0451| 0.0092| 0.8381|
|field_nameEducation:gender2 | -0.0407| 0.2057| -0.1981| 0.8430| -0.4439| 0.3624|
|field_nameEngineering:gender2 | -0.3631| 0.1936| -1.8754| 0.0607| -0.7426| 0.0164|
|field_nameHealth:gender2 | 1.8916| 0.1903| 9.9401| 0.0000| 1.5186| 2.2646|
|field_nameHumanities:gender2 | -0.0508| 0.2031| -0.2501| 0.8025| -0.4489| 0.3473|
|field_nameNo Specialization:gender2 | -0.4663| 0.2297| -2.0297| 0.0424| -0.9166| -0.0160|
|field_nameOther/Interdisciplinary:gender2 | -0.3388| 0.1738| -1.9497| 0.0512| -0.6795| 0.0018|
|field_namePhysical Sciences:gender2 | 0.6194| 0.2092| 2.9603| 0.0031| 0.2093| 1.0295|
|field_nameServices:gender2 | 1.1849| 0.1992| 5.9479| 0.0000| 0.7944| 1.5753|
|field_nameSocial Sciences & Law:gender2 | 1.6500| 0.1874| 8.8041| 0.0000| 1.2827| 2.0173|
| Total_N| Mean_Income| Median_Income| SD_Income| Share_High_Skill| Share_Female| Share_Immigrant| Mean_Weeks_Worked|
|-------:|-----------:|-------------:|---------:|----------------:|------------:|---------------:|-----------------:|
| 392,626| 54,461.36| 41,000| 62,064.15| 0.55| 0.49| 1| 5.9|
|gender | N| Mean_Income| Median_Income| Share_High_Skill|
|:------|-------:|-----------:|-------------:|----------------:|
|1 | 200,885| 46,699.31| 37,000| 0.49|
|2 | 191,741| 62,593.58| 46,000| 0.60|
|immigrant | N| Mean_Income| Median_Income| Share_High_Skill|
|:---------|-------:|-----------:|-------------:|----------------:|
|1 | 279,338| 56,118.92| 43,000| 0.56|
|2 | 103,332| 51,850.66| 38,000| 0.50|
|3 | 9,408| 33,871.66| 26,000| 0.65|
|88 | 548| 55,295.99| 38,500| 0.51|
|province | N| Mean_Income| Median_Income| Share_High_Skill|
|:--------|-------:|-----------:|-------------:|----------------:|
|10 | 5,633| 48,259.46| 37,000| 0.50|
|11 | 1,506| 46,247.45| 39,000| 0.52|
|12 | 10,609| 47,174.43| 38,000| 0.51|
|13 | 8,303| 46,245.09| 38,000| 0.52|
|24 | 90,545| 51,264.48| 41,000| 0.55|
|35 | 152,251| 56,388.10| 41,000| 0.54|
|46 | 13,647| 49,472.96| 39,000| 0.55|
|47 | 11,116| 53,143.18| 43,000| 0.57|
|48 | 42,919| 60,598.10| 45,000| 0.58|
|59 | 55,030| 54,425.07| 41,000| 0.55|
|70 | 1,067| 64,105.42| 52,000| 0.43|
|term | estimate| std.error| statistic| p.value| conf.low| conf.high|
|:---------------------------------|--------:|---------:|---------:|-------:|--------:|---------:|
|(Intercept) | 0.5940| 0.0095| 62.6987| 0.0000| 0.5754| 0.6126|
|field_nameArts & Communications | -0.1538| 0.0075| -20.5240| 0.0000| -0.1685| -0.1391|
|field_nameBusiness | -0.0083| 0.0062| -1.3361| 0.1815| -0.0205| 0.0039|
|field_nameComputer Science & Math | -0.1859| 0.0072| -25.8109| 0.0000| -0.2000| -0.1718|
|field_nameEducation | -0.1698| 0.0068| -24.8475| 0.0000| -0.1832| -0.1564|
|field_nameEngineering | 0.0497| 0.0062| 7.9639| 0.0000| 0.0375| 0.0620|
|field_nameHealth | -0.2091| 0.0064| -32.7870| 0.0000| -0.2216| -0.1966|
|field_nameHumanities | -0.0974| 0.0070| -13.8915| 0.0000| -0.1111| -0.0836|
|field_nameNo Specialization | -0.1063| 0.0080| -13.2468| 0.0000| -0.1221| -0.0906|
|field_nameOther/Interdisciplinary | 0.0298| 0.0060| 4.9335| 0.0000| 0.0179| 0.0416|
|field_namePhysical Sciences | -0.1563| 0.0073| -21.4441| 0.0000| -0.1706| -0.1420|
|field_nameServices | -0.0586| 0.0069| -8.4590| 0.0000| -0.0721| -0.0450|
|field_nameSocial Sciences & Law | -0.1223| 0.0065| -18.9396| 0.0000| -0.1349| -0.1096|
|gender2 | 0.1705| 0.0014| 122.2603| 0.0000| 0.1677| 0.1732|
|immigrant2 | -0.0197| 0.0015| -12.8112| 0.0000| -0.0227| -0.0167|
|immigrant3 | -0.0346| 0.0043| -7.9658| 0.0000| -0.0431| -0.0261|
|immigrant88 | -0.0754| 0.0174| -4.3454| 0.0000| -0.1094| -0.0414|
|weeks | -0.0538| 0.0003| -193.8000| 0.0000| -0.0543| -0.0533|
|age7 | -0.0084| 0.0063| -1.3388| 0.1806| -0.0207| 0.0039|
|age8 | -0.0415| 0.0053| -7.8546| 0.0000| -0.0519| -0.0312|
|age9 | -0.0202| 0.0053| -3.8291| 0.0001| -0.0306| -0.0099|
|age10 | -0.0034| 0.0053| -0.6396| 0.5224| -0.0137| 0.0070|
|age11 | 0.0203| 0.0053| 3.8547| 0.0001| 0.0100| 0.0306|
|age12 | 0.0317| 0.0053| 5.9914| 0.0000| 0.0213| 0.0420|
|age13 | 0.0351| 0.0053| 6.6100| 0.0000| 0.0247| 0.0455|
|age14 | 0.0404| 0.0053| 7.6387| 0.0000| 0.0300| 0.0508|
|age15 | 0.0224| 0.0052| 4.2935| 0.0000| 0.0122| 0.0327|
|age16 | -0.0259| 0.0052| -4.9454| 0.0000| -0.0362| -0.0156|
|age17 | -0.1039| 0.0053| -19.4664| 0.0000| -0.1143| -0.0934|
|age18 | -0.1465| 0.0055| -26.7334| 0.0000| -0.1572| -0.1357|
|age19 | -0.1619| 0.0058| -27.9666| 0.0000| -0.1732| -0.1505|
|age20 | -0.1746| 0.0063| -27.6608| 0.0000| -0.1870| -0.1623|
|age21 | -0.1832| 0.0066| -27.8625| 0.0000| -0.1961| -0.1703|
|age88 | -0.0812| 0.0106| -7.6300| 0.0000| -0.1020| -0.0603|
|province11 | 0.0262| 0.0117| 2.2332| 0.0255| 0.0032| 0.0493|
|province12 | -0.0031| 0.0067| -0.4593| 0.6460| -0.0162| 0.0100|
|province13 | 0.0034| 0.0070| 0.4809| 0.6306| -0.0103| 0.0171|
|province24 | 0.0070| 0.0056| 1.2610| 0.2073| -0.0039| 0.0179|
|province35 | 0.0163| 0.0055| 2.9437| 0.0032| 0.0054| 0.0271|
|province46 | 0.0136| 0.0064| 2.1187| 0.0341| 0.0010| 0.0262|
|province47 | 0.0367| 0.0066| 5.5399| 0.0000| 0.0237| 0.0497|
|province48 | 0.0255| 0.0058| 4.4339| 0.0000| 0.0142| 0.0368|
|province59 | 0.0181| 0.0057| 3.1742| 0.0015| 0.0069| 0.0292|
|province70 | -0.0743| 0.0135| -5.4860| 0.0000| -0.1009| -0.0478|