.Rhistory

model = "random",
data = NLSY_Valid_Childcare_Part_Time_2)
pooled.part_time_all_formal <- plm(INCOME_LOG ~ FORMAL_CHILDCARE +N_CHILDREN+ MARRIED_OR_COHABITATING + BA_ABOVE +
SPOUSAL_INCOME_LOG + MOTHER_AGE_FIRST_CHILD +
as.factor(YEAR),
index = c("PUBID_1997", "YEAR"),
model = "pooling",
data = NLSY_Valid_Childcare_Part_Time_2)
stargazer(fe.part_time_all_formal,pooled.part_time_all_formal,
title="Regression Results",
align=TRUE,
dep.var.labels=c("Income"),
no.space=TRUE,
column.labels=c("Fixed Effects", "Pooled"),
dep.var.caption="",
model.numbers=FALSE,
# type = "latex",
type = "text",
omit = "Constant")
fe.all <- plm(INCOME_LOG ~ FAMILY_CARE + N_CHILDREN + MARRIED_OR_COHABITATING + BA_ABOVE + SPOUSAL_INCOME_LOG + as.factor(YEAR),
index = c("PUBID_1997", "YEAR"), # id & time variables
model = "within",
data = NLSY_Valid_Childcare_Income_One_Child)
summary(fe.all)
pooled.all <- plm(INCOME_LOG ~ FAMILY_CARE + N_CHILDREN +  MARRIED_OR_COHABITATING + BA_ABOVE + MOTHER_AGE_FIRST_CHILD + SPOUSAL_INCOME_LOG + as.factor(YEAR),
index = c("PUBID_1997", "YEAR"),
model = "pooling",
data = NLSY_Valid_Childcare_Income_One_Child) ## this is equivalent to above OLS ##
summary(pooled.all)
stargazer(fe.all,pooled.all,
title="Regression Results",
align=TRUE,
dep.var.labels=c("Income"),
no.space=TRUE,
column.labels=c("Fixed Effects"),
dep.var.caption="",
model.numbers=FALSE,
# type = "latex",
type = "text",
omit = "Constant")
stargazer(fe.all,pooled.all,
title="Regression Results",
align=TRUE,
dep.var.labels=c("Income"),
no.space=TRUE,
column.labels=c("Fixed Effects","Pooled"),
dep.var.caption="",
model.numbers=FALSE,
# type = "latex",
type = "text",
omit = "Constant")
NLSY_Valid_Childcare_Income_One_Child <-  NLSY_Valid_Childcare_Income_One_Child %>% arrange(PUBID_1997,YEAR) %>% group_by(PUBID_1997) %>%
mutate(changed_to_relative = ifelse(lag(FAMILY_CARE) == 0 & FAMILY_CARE == 1, 1, 0)) %>% mutate(ever_changed_to_relative = max(changed_to_relative)) %>% mutate(changed_to_formal = ifelse(lag(FORMAL_CHILDCARE)==0 & FORMAL_CHILDCARE==1, 1, 0)) %>% mutate(ever_changed_to_formal = max(changed_to_formal))
NLSY_Valid_Childcare_Part_Time_One_Child <- NLSY_Valid_Childcare_Part_Time_One_Child %>% arrange(PUBID_1997,YEAR) %>% group_by(PUBID_1997) %>%
mutate(changed_to_relative = ifelse(lag(FAMILY_CARE) == 0 & FAMILY_CARE == 1, 1, 0)) %>% mutate(ever_changed_to_relative = max(changed_to_relative)) %>% mutate(changed_to_formal = ifelse(lag(FORMAL_CHILDCARE)==0 & FORMAL_CHILDCARE==1, 1, 0)) %>% mutate(ever_changed_to_formal = max(changed_to_formal))
describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% select(PUBID_1997,MATERNAL_AGE,CV_HGC_EVER_EDT,CV_INCOME_FAMILY,WORK_EDU_HRS,ever_changed_to_relative,INCOME) %>% subset(ever_changed_to_relative == 1))
describe(NLSY_Valid_Childcare_Part_Time_One_Child  %>% select(PUBID_1997,MATERNAL_AGE,CV_HGC_EVER_EDT,CV_INCOME_FAMILY,WORK_EDU_HRS,ever_changed_to_relative,INCOME) %>% subset(ever_changed_to_relative == 0))
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% select(PUBID_1997,MATERNAL_AGE,CV_HGC_EVER_EDT,CV_INCOME_FAMILY,WORK_EDU_HRS,ever_changed_to_formal,INCOME) %>% subset(ever_changed_to_formal == 1))
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child  %>% select(PUBID_1997,MATERNAL_AGE,CV_HGC_EVER_EDT,CV_INCOME_FAMILY,WORK_EDU_HRS,ever_changed_to_formal,ever_changed_to_relative,INCOME) %>% subset(ever_changed_to_formal == 0 & ever_changed_to_relative == 0))
rcorr(as.matrix(NLSY_Valid_Childcare_Part_Time_2%>% select(CV_HGC_EVER_EDT,WORK_EDU_HRS,MARRIED_OR_COHABITATING, MOTHER_AGE_FIRST_CHILD,INCOME,SPOUSAL_INCOME,FAMILY_CARE,FORMAL_CHILDCARE,N_CHILDREN)),type="pearson")
# -- Changes made in childcare mode - ---------------------
## -----------------------------------------------------------------------------------------------------------------------------------
# Any income women with one child
NLSY_Valid_Childcare_Income_One_Child <-  NLSY_Valid_Childcare_Income_One_Child %>%  arrange(PUBID_1997,YEAR) %>%  group_by(PUBID_1997) %>%
mutate(changed_to_relative = ifelse(lag(FAMILY_CARE) == 0 & FAMILY_CARE == 1, 1, 0)) %>%  mutate(ever_changed_to_relative = max(changed_to_relative)) %>%  mutate(changed_to_formal = ifelse(lag(FORMAL_CHILDCARE)==0 & FORMAL_CHILDCARE==1, 1, 0)) %>%  mutate(ever_changed_to_formal = max(changed_to_formal))
# Part time women with one child
NLSY_Valid_Childcare_Part_Time_One_Child <- NLSY_Valid_Childcare_Part_Time_One_Child %>%  arrange(PUBID_1997,YEAR) %>%  group_by(PUBID_1997) %>%
mutate(changed_to_relative = ifelse(lag(FAMILY_CARE) == 0 & FAMILY_CARE == 1, 1, 0)) %>%  mutate(ever_changed_to_relative = max(changed_to_relative)) %>%  mutate(changed_to_formal = ifelse(lag(FORMAL_CHILDCARE)==0 & FORMAL_CHILDCARE==1, 1, 0)) %>%  mutate(ever_changed_to_formal = max(changed_to_formal))
# Descriptive statistics for changing childcare modes
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>%  select(PUBID_1997,MATERNAL_AGE,CV_HGC_EVER_EDT,CV_INCOME_FAMILY,WORK_EDU_HRS,ever_changed_to_relative,INCOME) %>%  subset(ever_changed_to_relative == 1))
## ----valid_childcare_pearson, eval=TRUE, echo=TRUE, message=FALSE, warning=FALSE,column.sep.width = "1pt",comment=NA,font.size=8-----------
rcorr(as.matrix(NLSY_Valid_Childcare_Part_Time_2%>%  select(CV_HGC_EVER_EDT,WORK_EDU_HRS,MARRIED_OR_COHABITATING, MOTHER_AGE_FIRST_CHILD,INCOME,SPOUSAL_INCOME,FAMILY_CARE,FORMAL_CHILDCARE,N_CHILDREN)),type="pearson")
fe.part_time_full <- plm(INCOME_LOG ~ FAMILY_CARE + MARRIED_OR_COHABITATING + BA_ABOVE + SPOUSAL_INCOME_LOG + as.factor(YEAR),
index = c("PUBID_1997", "YEAR"), # id & time variables
model = "within",
data = NLSY_Valid_Childcare_Part_Time_One_Child)
NLSY_Valid_Childcare_Part_Time_One_Child %>% select(FAMILY_CARE,RELATIVE_CARE,INCOME_LOG)
summary(fe.part_time_full)
fe.part_time_full <- plm(INCOME_LOG ~ FAMILY_CARE + MARRIED_OR_COHABITATING + BA_ABOVE + SPOUSAL_INCOME_LOG + as.factor(YEAR),
index = c("PUBID_1997", "YEAR"), # id & time variables
model = "within",
data = NLSY_Valid_Childcare_Part_Time_One_Child)
summary(fe.part_time_full)
source("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/code/results/final_results.Rmd")
source("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/code/results/final_results.Rmd")
source("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/code/results/final_results.Rmd")
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% select(INCOME, MARRIED_OR_COHABITATING, BA_ABOVE,SPOUSAL_INCOME, MOTHER_AGE_FIRST_CHILD, MATERNAL_AGE,`YCCAL-6800`,FAMILY_CARE, N_CHILDREN,FULL_TIME,Relative_within_15_minutes))
load("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_imputed_first_child.RData")
NLSY_Valid <-  NLSY_imputed %>%
filter(CV_HGC_EVER_EDT != 95 & !is.na(CV_HGC_EVER_EDT)) %>%
filter(CV_HGC_EVER_EDT >= 12)
dt <- psych::describe(NLSY_Valid %>% select(INCOME,FAMILY_CARE,`YCCAL-6800`,BA_ABOVE,N_CHILDREN,MARRIED_OR_COHABITATING,SPOUSAL_INCOME,MATERNAL_AGE, N_CHILDREN))
dt
load("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_imputed.RData")
NLSY_Valid_Childcare_2 <- Valid_Childcare(NLSY_imputed,FALSE)
p4 <- NLSY_Valid_Childcare_2 %>% dplyr::select(PUBID_1997,YEAR,INCOME,PART_TIME,`FAMILY_CARE`,`RELATIVE_CARE`,WORK_EDU_HRS,CV_BIO_CHILD_HH) %>% subset(YEAR>=2005) %>% subset(INCOME>0) %>% subset(CV_BIO_CHILD_HH>0) %>%
subset(PART_TIME ==1) %>% dplyr::mutate(FAMILY_CARE_CAT = ifelse(`WORK_EDU_HRS`==0, 'No (Mother Provides Childcare)',ifelse(`FAMILY_CARE`==1,'Yes',ifelse(FAMILY_CARE==0,'No (Other Childcare)',NaN)))) %>% dplyr::group_by(YEAR,FAMILY_CARE_CAT) %>% dplyr::mutate(FAMILY_CARE_CAT_proportion = n()) %>% ungroup() %>% dplyr::group_by(YEAR,FAMILY_CARE_CAT, FAMILY_CARE_CAT_proportion) %>% dplyr::summarize() %>%
ggplot(aes(x = YEAR, y = FAMILY_CARE_CAT_proportion)) +
geom_bar(stat="identity",aes(fill = FAMILY_CARE_CAT)) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
scale_fill_manual(values=c("#ff6f00", "#008fa0")) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = '', color = 'Response') +
labs(fill = "Primary Child Care Method is Relative-provided Care",
y="Count", x = "", title = "Intergenerational Childcare (First Child Only,\n 6 and under,\n Part Time Work )")
p4
#ggplotly(p4)
NLSY_imputed$`YCCA-450`<- as.numeric(NLSY_imputed$`YCCA-450`)
NLSY_imputed$`YCCAL-450`<- as.numeric(NLSY_imputed$`YCCAL-450`)
NLSY_imputed_FAMILY_CARE_CAT <- NLSY_imputed %>% subset(!is.na(`YCCAL-1100A~000002`))
NLSY_imputed_FAMILY_CARE_CAT$YEAR <- as.numeric(NLSY_imputed_FAMILY_CARE_CAT$YEAR)
NLSY_imputed_FAMILY_CARE_CAT$`YCCAL-6800`[is.na(NLSY_imputed_FAMILY_CARE_CAT$`YCCAL-6800`)] <- 'NA'
p5 <- NLSY_imputed_FAMILY_CARE_CAT %>% dplyr::select(PUBID_1997,YEAR,`YCCAL-6800`,CV_HH_UNDER_6,CV_BIO_CHILD_HH,INCOME,CV_ENROLLSTAT_EDT,`YCCA-450`,`YCCAL-450`,FAMILY_CARE) %>% subset(CV_HH_UNDER_6>0 & CV_BIO_CHILD_HH>0) %>%
subset(INCOME>0) %>%
dplyr::mutate(relative_15 = ifelse(`YCCAL-6800`==1, 'Yes',ifelse(`YCCAL-6800`==0,'No','NaN'))) %>% dplyr::group_by(YEAR,relative_15) %>% dplyr::mutate(relative_15_proportion = n()) %>% ungroup() %>% dplyr::group_by(YEAR,relative_15, relative_15_proportion) %>% dplyr::summarize() %>%
ggplot(aes(x = YEAR, y = relative_15_proportion)) +
geom_bar(stat="identity",aes(fill = relative_15)) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
scale_fill_futurama(alpha = 0.75) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = , color = 'Response') +
labs(fill = "Relative 15 Miles Away??", y="Count", x = "")
p5
#ggplotly(p5)
NLSY_imputed_FAMILY_CARE_CAT %>% dplyr::select(PUBID_1997,YEAR,`YCCAL-6800`,CV_HH_UNDER_6,CV_BIO_CHILD_HH,INCOME,CV_ENROLLSTAT_EDT,`YCCA-450`,`YCCAL-450`,FAMILY_CARE,RELATIVE_CARE,SIBLING_CARE,`YCCAL-1100A~000002`,FAMILY_DAY_CARE,WORK_EDU_HRS) %>% subset(CV_HH_UNDER_6>0 & CV_BIO_CHILD_HH>0) %>%
subset(INCOME>0) %>%
# dplyr::mutate(FAMILY_CARE_CAT = ifelse(`WORK_EDU_HRS`==0, 'No (Mother Provides Childcare)',ifelse(`FAMILY_CARE`==1,'Yes',ifelse(FAMILY_CARE==0,'No (Other Childcare)',NaN)))) %>%
dplyr::mutate(relative_15 = ifelse(`YCCAL-6800`==1, 'Yes',ifelse(`YCCAL-6800`==0,'No','NA'))) %>%
group_by(YEAR) %>% dplyr::mutate(year_count = n()) %>% ungroup() %>%
dplyr::group_by(YEAR,relative_15) %>% dplyr::mutate(relative_15_proportion = n()/year_count) %>%
ungroup() %>%
#subset(relative_15 == 'Yes') %>%
dplyr::group_by(YEAR, relative_15, relative_15_proportion) %>% dplyr::summarize() %>%
ggplot(aes(x = YEAR, y = relative_15_proportion)) +
geom_point(aes(color = relative_15))+
geom_line(stat="identity",aes(color = relative_15, linetype = relative_15)) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
scale_color_futurama(alpha = 0.75) +
scale_x_continuous(limits=c(2005,2008,2011,2015)) +
labs(title = "NLSY Panel Proportions:\nANY RELATIVES LIVE WITHIN 15 MINUTES OF CHILD'S HOME?",
y="Proportion", x = "",linetype = "Relative-Provided Childcare",color="Relative-Provided Childcare")
library(Hmisc)
NLSY_Valid_Childcare_Part_Time_One_Child = NLSY_Valid_Childcare_Part_Time_One_Child %>% filter(!is.na(`YCCAL-6800`))
rcorr(as.matrix(NLSY_Valid_Childcare_Part_Time_One_Child %>% select(`YCCAL-6800`,FAMILY_CARE)),type="pearson")
rcorr(as.matrix(NLSY_Valid_Childcare_Part_Time_One_Child %>% select(`YCCAL-6800`,FAMILY_CARE)),type="pearson")
NLSY_imputed$`YCCA-450`<- as.numeric(NLSY_imputed$`YCCA-450`)
NLSY_imputed$`YCCAL-450`<- as.numeric(NLSY_imputed$`YCCAL-450`)
NLSY_imputed_FAMILY_CARE_CAT <- NLSY_imputed %>% subset(!is.na(`YCCAL-1100A~000002`))
NLSY_imputed_FAMILY_CARE_CAT$YEAR <- as.numeric(NLSY_imputed_FAMILY_CARE_CAT$YEAR)
NLSY_imputed_FAMILY_CARE_CAT$`YCCAL-6800`[is.na(NLSY_imputed_FAMILY_CARE_CAT$`YCCAL-6800`)] <- 'NA'
p5 <- NLSY_imputed_FAMILY_CARE_CAT %>% dplyr::select(PUBID_1997,YEAR,`YCCAL-6800`,CV_HH_UNDER_6,CV_BIO_CHILD_HH,INCOME,CV_ENROLLSTAT_EDT,`YCCA-450`,`YCCAL-450`,FAMILY_CARE) %>% subset(CV_HH_UNDER_6>0 & CV_BIO_CHILD_HH>0) %>%
subset(INCOME>0) %>%
dplyr::mutate(relative_15 = ifelse(`YCCAL-6800`==1, 'Yes',ifelse(`YCCAL-6800`==0,'No','NaN'))) %>% dplyr::group_by(YEAR,relative_15) %>% dplyr::mutate(relative_15_proportion = n()) %>% ungroup() %>% dplyr::group_by(YEAR,relative_15, relative_15_proportion) %>% dplyr::summarize() %>%
ggplot(aes(x = YEAR, y = relative_15_proportion)) +
geom_bar(stat="identity",aes(fill = relative_15)) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
scale_fill_futurama(alpha = 0.75) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = , color = 'Response') +
labs(fill = "Relative 15 Miles Away??", y="Count", x = "")
p5
#ggplotly(p5)
NLSY_imputed_FAMILY_CARE_CAT %>% dplyr::select(PUBID_1997,YEAR,`YCCAL-6800`,CV_HH_UNDER_6,CV_BIO_CHILD_HH,INCOME,CV_ENROLLSTAT_EDT,`YCCA-450`,`YCCAL-450`,FAMILY_CARE,RELATIVE_CARE,SIBLING_CARE,`YCCAL-1100A~000002`,FAMILY_DAY_CARE,WORK_EDU_HRS) %>% subset(CV_HH_UNDER_6>0 & CV_BIO_CHILD_HH>0) %>%
subset(INCOME>0) %>%
# dplyr::mutate(FAMILY_CARE_CAT = ifelse(`WORK_EDU_HRS`==0, 'No (Mother Provides Childcare)',ifelse(`FAMILY_CARE`==1,'Yes',ifelse(FAMILY_CARE==0,'No (Other Childcare)',NaN)))) %>%
dplyr::mutate(relative_15 = ifelse(`YCCAL-6800`==1, 'Yes',ifelse(`YCCAL-6800`==0,'No','NA'))) %>%
group_by(YEAR) %>% dplyr::mutate(year_count = n()) %>% ungroup() %>%
dplyr::group_by(YEAR,relative_15) %>% dplyr::mutate(relative_15_proportion = n()/year_count) %>%
ungroup() %>%
#subset(relative_15 == 'Yes') %>%
dplyr::group_by(YEAR, relative_15, relative_15_proportion) %>% dplyr::summarize() %>%
ggplot(aes(x = YEAR, y = relative_15_proportion)) +
geom_point(aes(color = relative_15))+
geom_line(stat="identity",aes(color = relative_15, linetype = relative_15)) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
scale_color_futurama(alpha = 0.75) +
scale_x_continuous(limits=c(2005,2008,2011,2015)) +
labs(title = "NLSY Panel Proportions:\nANY RELATIVES LIVE WITHIN 15 MINUTES OF CHILD'S HOME?",
y="Proportion", x = "",linetype = "Relative-Provided Childcare",color="Relative-Provided Childcare")
load("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_imputed_first_child.RData")
NLSY_Valid_Childcare_1 <- Valid_Childcare(NLSY_imputed,TRUE)
NLSY_imputed <- NLSY_imputed[!is.na(NLSY_imputed$WORK_EDU_HRS),]
# This is just with everyone with any biological children in the household; it appears here that we don't have a lot of present data.
p1 <- NLSY_imputed %>% dplyr::select(PUBID_1997,YEAR,BIOCHILD_6_YR,INCOME,`YCCAL-1100A~000002`,`YCCA-1100A~000002`,`FAMILY_CARE`,`RELATIVE_CARE`,WORK_EDU_HRS,CV_BIO_CHILD_HH) %>% subset(YEAR>=2005) %>% subset(INCOME>0)  %>% subset(CV_BIO_CHILD_HH>0) %>% plyr::mutate(FAMILY_CARE_CAT = ifelse(`WORK_EDU_HRS`==0, 'No (Mother Provides Childcare)',ifelse(`FAMILY_CARE`==1,'Yes',ifelse(FAMILY_CARE==0,'No (Other Childcare)',NaN)))) %>% dplyr::group_by(YEAR,FAMILY_CARE_CAT) %>% dplyr::mutate(FAMILY_CARE_CAT_proportion = n()) %>% ungroup() %>% dplyr::group_by(YEAR,FAMILY_CARE_CAT, FAMILY_CARE_CAT_proportion) %>% dplyr::summarize() %>%
ggplot(aes(x = YEAR, y = FAMILY_CARE_CAT_proportion)) +
geom_bar(stat="identity",aes(fill = FAMILY_CARE_CAT)) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
scale_fill_futurama(alpha = 0.75) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = '', color = 'Response') +
labs(fill = "Primary Child Care Method is Relative-provided Care",
y="Count", x = "", title = "Relative-provided Childcare (Any child under 6)")
p1
ggplotly(p1)
NLSY_imputed_FAMILY_CARE_CAT <- NLSY_imputed_FAMILY_CARE_CAT %>% subset(!is.na(`YCCAL-6800`)) %>% subset(`YCCAL-6800`!='NA')
NLSY_imputed_FAMILY_CARE_CAT <- NLSY_imputed_FAMILY_CARE_CAT %>% subset(!is.na(FAMILY_CARE))
unique(NLSY_imputed_FAMILY_CARE_CAT$`YCCAL-6800`)
NLSY_imputed_FAMILY_CARE_CAT %>% dplyr::select(PUBID_1997,YEAR,`YCCAL-6800`,CV_HH_UNDER_6,CV_BIO_CHILD_HH,INCOME,CV_ENROLLSTAT_EDT,`YCCA-450`,`YCCAL-450`,FAMILY_CARE) %>% subset(CV_HH_UNDER_6>0 & CV_BIO_CHILD_HH>0) %>%
#subset(`YCCAL-450`!=0) %>%
dplyr::mutate(relative_15 = ifelse(`YCCAL-6800`==1, 'Yes',ifelse(`YCCAL-6800`==0,'No','NaN'))) %>%
dplyr::mutate(FAMILY_CARE_CAT = ifelse(`FAMILY_CARE`==0, 'No',ifelse(`FAMILY_CARE`==1,'Yes','NaN'))) %>%
dplyr::group_by(relative_15,FAMILY_CARE) %>%
dplyr::mutate(FAMILY_CARE_count = n()) %>% ungroup() %>%
mutate(perc=100*FAMILY_CARE_count/nrow(NLSY_imputed_FAMILY_CARE_CAT)) %>%
dplyr::group_by(relative_15, FAMILY_CARE_CAT,FAMILY_CARE_count,perc) %>%
dplyr::summarize() %>%
ggplot(aes(x = relative_15, y = perc)) +
geom_bar(stat="identity",aes(fill = FAMILY_CARE_CAT),position = "dodge") +
theme_ipsum(base_size = 12, axis_title_size = 14) +
scale_fill_manual(values=c("#ff6f00", "#008fa0")) +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = , color = 'Response') +
labs(fill = "Relative Care is a primary source of childcare?", y="Percent of Data", x = "Relative is within 15 Miles?")
NLSY_Valid_Childcare_1 %>% dplyr::select(PUBID_1997,YEAR,CV_BIO_CHILD_HH,CHILDCARE_TYPE) %>% subset(YEAR>2005) %>%
subset(CV_BIO_CHILD_HH==1) %>%
dplyr::group_by(YEAR) %>% mutate(Total_Year = n()) %>% ungroup() %>%
group_by(YEAR,CHILDCARE_TYPE) %>% dplyr::mutate(CHILDCARE_TYPE_COUNT = n()/Total_Year) %>% ungroup() %>%  dplyr::group_by(YEAR,CHILDCARE_TYPE, CHILDCARE_TYPE_COUNT) %>% dplyr::summarize() %>%
ggplot(aes(x = YEAR, y = CHILDCARE_TYPE_COUNT)) +
geom_point(stat="identity",aes(color = CHILDCARE_TYPE)) +
geom_line(stat="identity",aes(color = CHILDCARE_TYPE, linetype = CHILDCARE_TYPE)) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
#scale_fill_manual(values=c("#C9473B", "#75A1AD")) +
scale_color_futurama(alpha = 0.75) +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = '', color = 'Response') +
scale_x_continuous(limits=c(2010,2015)) +
labs(linetype = "Type of Childcare", color = "Type of Childcare",
y="Proportion of Mothers in Wave*", x = "", title = "Primary Source of Childcare",
caption = "*Each year's aggregated group includes mothers whose first-born child is under 6 years of age.")
NLSY_Valid_Childcare_Income_One_Child %>% dplyr::select(PUBID_1997,YEAR,CV_BIO_CHILD_HH,SPOUSAL_CHILDCARE,FORMAL_CHILDCARE,WORK_EDU_HRS,FAMILY_CARE) %>% subset(YEAR>2005) %>%
dplyr::group_by(YEAR) %>% mutate(Total_Year = n()) %>% ungroup() %>% dplyr::mutate(FAMILY_CARE_CAT = ifelse(`WORK_EDU_HRS`==0, 'Mother Provides Childcare',ifelse(`FAMILY_CARE`==1 & FORMAL_CHILDCARE==1, 'Z Family and Formal',ifelse(`FAMILY_CARE`==1,'Family Provided Childcare',ifelse(FORMAL_CHILDCARE==1, 'Formal Childcare Options', ifelse(SPOUSAL_CHILDCARE==1, 'Spousal Childcare', 'Other Childcare Options')))))) %>%
group_by(YEAR,FAMILY_CARE_CAT) %>% dplyr::mutate(CHILDCARE_TYPE_COUNT = n()/Total_Year) %>% ungroup() %>%  dplyr::group_by(YEAR,FAMILY_CARE_CAT, CHILDCARE_TYPE_COUNT) %>% dplyr::summarize() %>%
ggplot(aes(x = YEAR, y = CHILDCARE_TYPE_COUNT)) +
geom_point(stat="identity",aes(color = FAMILY_CARE_CAT)) +
geom_line(stat="identity",aes(color = FAMILY_CARE_CAT, linetype = FAMILY_CARE_CAT)) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
#scale_fill_manual(values=c("#C9473B", "#75A1AD")) +
scale_color_futurama(alpha = 0.75) +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = '', color = 'Response') +
scale_x_continuous(limits=c(2006,2016)) +
labs(linetype = "Primary Type of Childcare", color = "Primary Type of Childcare",
y="Proportion of Mothers in Wave*", x = "Survey Year", title = "Primary Childcare Option(s)", subtitle="(all women with ONLY one biological child)",
caption = "*Each year's aggregated group includes mothers with only one child under 6 years of age.")
mean_maternal_ages <-  NLSY_Valid_Childcare_Income_One_Child %>% dplyr::select(PUBID_1997,YEAR,WORK_EDU_HRS,FAMILY_CARE,MOTHER_AGE_FIRST_CHILD,FORMAL_CHILDCARE) %>% subset(YEAR>2005) %>%
dplyr::mutate(FAMILY_CARE_CAT = ifelse(`WORK_EDU_HRS`==0, 'Mother Provides Childcare',ifelse(`FAMILY_CARE`==1,'Family Provided Childcare',ifelse(FORMAL_CHILDCARE==1, 'Formal Childcare Options', 'Other Childcare Options'))))  %>%
dplyr::group_by(YEAR,FAMILY_CARE_CAT) %>% mutate(Mean_Maternal_Age = mean(MOTHER_AGE_FIRST_CHILD)) %>% ungroup() %>%
dplyr::group_by(YEAR) %>% mutate(Overall_Mean_Maternal_Age = mean(MOTHER_AGE_FIRST_CHILD)) %>% ungroup() %>%
group_by(YEAR,Mean_Maternal_Age,Overall_Mean_Maternal_Age,FAMILY_CARE_CAT) %>% dplyr::summarize()
mean_maternal_ages
mean_maternal_ages %>%
ggplot(aes(x = YEAR)) +
geom_point(stat="identity",aes(y=Mean_Maternal_Age,color = FAMILY_CARE_CAT)) +
geom_line(stat="identity",aes(y=Mean_Maternal_Age,color = FAMILY_CARE_CAT)) +
geom_point(stat="identity",aes(y=Overall_Mean_Maternal_Age), alpha = 0.5) +
geom_line(stat="identity",aes(y=Overall_Mean_Maternal_Age), linetype = 'dashed', alpha = 0.5) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
scale_color_futurama(alpha = 0.75) +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = '', color = 'Response') +
scale_x_continuous(limits=c(2006,2016)) +
ylim(20,33) +
labs(linetype = "Primary Type of Childcare", color = "Primary Type of Childcare",
y="Age", x = "Survey Year", title = "Average Age", subtitle = "When first child is born")
NLSY_Valid_Childcare_Income_One_Child %>% dplyr::select(PUBID_1997,YEAR,WORK_EDU_HRS,FAMILY_CARE,MOTHER_AGE_FIRST_CHILD,FORMAL_CHILDCARE) %>% subset(YEAR>2005) %>%
dplyr::mutate(FAMILY_CARE_CAT = ifelse(`WORK_EDU_HRS`==0, 'Mother Provides Childcare',ifelse(`FAMILY_CARE`==1,'Family Provided Childcare',ifelse(FORMAL_CHILDCARE==1, 'Formal Childcare Options', 'Other Childcare Options'))))  %>%
ggplot(aes(x =MOTHER_AGE_FIRST_CHILD ,y=FAMILY_CARE_CAT)) +
geom_boxplot(aes(color = FAMILY_CARE_CAT))+theme_ipsum(base_size = 12, axis_title_size = 14) +
scale_color_futurama(alpha = 0.75) +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
axis.text.y=element_blank()
) +
labs(linetype = "Primary Type of Childcare", color = "Primary Type of Childcare",
y="", x = "Age", title = "Average Age", subtitle = "When first child is born")
NLSY_Valid_Childcare_Income_2 = NLSY_Valid_Childcare_Income_2[!is.na(NLSY_Valid_Childcare_Income_2$CV_INCOME_FAMILY),]
ses_care_type <-  NLSY_Valid_Childcare_Income_2 %>% dplyr::select(PUBID_1997,YEAR,MARRIED_OR_COHABITATING,INCOME,WORK_EDU_HRS,CV_INCOME_FAMILY,FAMILY_CARE,MATERNAL_AGE,FORMAL_CHILDCARE) %>% subset(YEAR>2005) %>%
dplyr::mutate(SES = ifelse(
#MARRIED_OR_COHABITATING == 1, ifelse(
CV_INCOME_FAMILY < 45000,'Lower-Class',
ifelse(CV_INCOME_FAMILY < 135000, 'Middle-Class','Upper-Class'))
#,ifelse(INCOME < 15000,'Lower-Class',
# ifelse(CV_INCOME_FAMILY < 75000, 'Middle-Class','Upper-Class'))))
) %>%
dplyr::mutate(FAMILY_CARE_CAT = ifelse(`WORK_EDU_HRS`==0, 'Mother Provides Childcare',ifelse(`FAMILY_CARE`==1,'Family Provided Childcare',ifelse(FORMAL_CHILDCARE==1, 'Formal Childcare Options', 'Other Childcare Options')))) %>%
dplyr::group_by(SES) %>% mutate(SES_TOT = n()) %>% ungroup() %>%
dplyr::group_by(SES,FAMILY_CARE_CAT) %>% mutate(CARE_TOT = n()) %>% ungroup() %>%
mutate(CARE_PROP = CARE_TOT/SES_TOT) %>% ungroup() %>%
group_by(SES,FAMILY_CARE_CAT,CARE_PROP) %>% dplyr::summarize()
ses_care_type %>%
ggplot(aes(x = SES, y= CARE_PROP)) +
geom_bar(stat="identity",aes(color = FAMILY_CARE_CAT,fill = FAMILY_CARE_CAT)) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
scale_color_futurama(alpha = 0.75) +
scale_fill_futurama(alpha = 0.75) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = '', color = 'Response') +
labs(linetype = "Socioeconomic Status", color = "Child Care Category", fill = "Child Care Category",
y="Proportion", x = "Socioeconomic Status (household income)", title = "Social Class and Childcare Options")
load("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_imputed.RData")
# No filtering for only individuals with valid data around inter-generational childcare
NLSY_Valid <-  NLSY_imputed %>%
filter(CV_HGC_EVER_EDT != 95 & !is.na(CV_HGC_EVER_EDT)) %>%
filter(BA_ABOVE== 1)
NSLY_Valid_Summarized <- NLSY_Valid %>% dplyr::mutate(ANY_CHILDREN = ifelse(N_CHILDREN == 0,0,1)) %>% group_by(YEAR,ANY_CHILDREN) %>% dplyr::summarize(mean_income = mean(INCOME,na.rm=TRUE)) %>% mutate(ANY_CHILDREN = ifelse(ANY_CHILDREN==0,'No Children', 'One or More Children'))
NSLY_Valid_Summarized
NSLY_Valid_Summarized$YEAR <- as.numeric(NSLY_Valid_Summarized$YEAR)
NSLY_Valid_Summarized %>%
ggplot(.,aes(x=YEAR, y=mean_income/1000)) +
geom_point(color = "#273237") +
geom_line(color = "#273237",aes(linetype = ANY_CHILDREN)) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
) +
labs(title = "",
y="Mean Annual Income\n (USD, thousands)", x = "",linetype = "")
write.csv(NLSY_Valid_Childcare_Part_Time_One_Child,"~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_Valid_Childcare_Part_Time_One_Child.csv")
# Childcare Variables here are averaged across children (YCCAL), looking at respondents while their first child is under 6 years old
load("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_imputed_first_child.RData")
NLSY_Valid_Childcare_Part_Time_1 <- Valid_Childcare_Income(NLSY_imputed,TRUE)
NLSY_Valid_Childcare_Part_Time_1 <- NLSY_Valid_Childcare_Part_Time_1[!is.na(NLSY_Valid_Childcare_Part_Time_1$FAMILY_CARE), ]
# Childcare Variables here are averaged across children (YCCAL), looking at respondents while their first child is under 6 years old
load("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_imputed_first_child.RData")
NLSY_Valid_Childcare_Part_Time_1 <- Valid_Childcare_Income(NLSY_imputed,TRUE)
# Select relevant columns and filter for individuals with valid data about their education, filter for women with some income generating work; filter for High School +
# Make df that includes working women with valid data about childcare
Valid_Data <- function(df) {
NLSY_Valid <-  df %>%
filter(CV_HGC_EVER_EDT != 95 & !is.na(CV_HGC_EVER_EDT)) %>%
filter(CV_HGC_EVER_EDT >= 12) %>% filter(YEAR>=2005)
}
Valid_Childcare <- function(df,first_child) {
NLSY_Valid <- Valid_Data(df)
NLSY_Valid_Childcare <- NLSY_Valid %>% subset(INCOME>0) %>% subset(WORK_EDU_HRS>=0) %>%
subset(WORK_EDU_HRS != 999) %>%
subset(SELF_CARE==0) %>% subset(YEAR <= BIOCHILD_6_YR)
if(first_child == TRUE) {
NLSY_Valid_Childcare <- NLSY_Valid_Childcare %>% subset(YEAR <= BIOCHILD_6_YR)
}
else{
NLSY_Valid_Childcare <- NLSY_Valid_Childcare %>% subset(CV_HH_UNDER_6>0)
}
}
# Filter for data that includes working women (Part Time and up) with valid data about childcare
Valid_Childcare_Income <- function(df,first_child) {
NLSY_Valid_Childcare <- Valid_Childcare(df,first_child)
NLSY_Valid_Childcare_Income <- NLSY_Valid_Childcare %>% subset(INCOME>0) %>% subset(PART_TIME==1)
}
# Childcare Variables here are averaged across children (YCCAL), looking at ANY child of respondent under 6
load("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_imputed.RData")
NLSY_Valid_Childcare_Part_Time_2 <- Valid_Childcare_Income(NLSY_imputed,FALSE) # Part Time and Above
NLSY_Valid_Childcare_Income_2 <- Valid_Childcare(NLSY_imputed,FALSE) %>% subset(INCOME > 0) # Any Income Generated
NLSY_Valid_Childcare_Part_Time_2 <- NLSY_Valid_Childcare_Part_Time_2[!is.na(NLSY_Valid_Childcare_Part_Time_2$FAMILY_CARE), ]
# Childcare Variables here are averaged across children (YCCAL), looking at respondents while their first child is under 6 years old
load("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_imputed_first_child.RData")
NLSY_Valid_Childcare_Part_Time_1 <- Valid_Childcare_Income(NLSY_imputed,TRUE)
NLSY_Valid_Childcare_Part_Time_1 <- NLSY_Valid_Childcare_Part_Time_1[!is.na(NLSY_Valid_Childcare_Part_Time_1$FAMILY_CARE), ]
NLSY_Valid_Childcare_Income_1 <- Valid_Childcare(NLSY_imputed,TRUE) %>% subset(INCOME > 0)
# Only looking at parent having one child (first child) - while their first child is under 6 years old
NLSY_Valid_Childcare_Part_Time_One_Child <- NLSY_Valid_Childcare_Part_Time_1 %>% subset(N_CHILDREN==1)
#write.csv(NLSY_Valid_Childcare_Part_Time_One_Child,"~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_Valid_Childcare_Part_Time_One_Child.csv")
NLSY_Valid_Childcare_Income_One_Child <- NLSY_Valid_Childcare_Income_1 %>% subset(N_CHILDREN==1)
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% select(INCOME, MARRIED_OR_COHABITATING, BA_ABOVE,SPOUSAL_INCOME, MOTHER_AGE_FIRST_CHILD, MATERNAL_AGE,`YCCAL-6800`,FAMILY_CARE, N_CHILDREN,FULL_TIME,Relative_within_15_minutes,YEAR))
write.csv(NLSY_Valid_Childcare_Part_Time_One_Child,"~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_Valid_Childcare_Part_Time_One_Child.csv")
load("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_imputed.RData")
# No filtering for only individuals with valid data around inter-generational childcare
NLSY_Valid <-  NLSY_imputed %>%
filter(CV_HGC_EVER_EDT != 95 & !is.na(CV_HGC_EVER_EDT)) %>%
filter(BA_ABOVE== 1)
NSLY_Valid_Summarized <- NLSY_Valid %>% dplyr::mutate(ANY_CHILDREN = ifelse(N_CHILDREN == 0,0,1)) %>% group_by(YEAR,ANY_CHILDREN) %>% dplyr::summarize(mean_income = mean(INCOME,na.rm=TRUE)) %>% mutate(ANY_CHILDREN = ifelse(ANY_CHILDREN==0,'No Children', 'One or More Children'))
NSLY_Valid_Summarized
NSLY_Valid_Summarized$YEAR <- as.numeric(NSLY_Valid_Summarized$YEAR)
NSLY_Valid_Summarized %>%
ggplot(.,aes(x=YEAR, y=mean_income/1000)) +
geom_point(color = "#273237") +
geom_line(color = "#273237",aes(linetype = ANY_CHILDREN)) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
) +
labs(title = "",
y="Mean Annual Income\n (USD, thousands)", x = "",linetype = "")
NLSY_imputed_FAMILY_CARE_CAT <- NLSY_imputed_FAMILY_CARE_CAT %>% subset(!is.na(`YCCAL-6800`)) %>% subset(`YCCAL-6800`!='NA')
# Load Data - YCCA / YCCAL - any child under 6 years old
load("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_imputed.RData")
NLSY_Valid_Childcare_Part_Time_2 <- Valid_Childcare_Income(NLSY_imputed,FALSE) # Part Time and Above
library(plm)
library(dplyr)
library(plyr)
library(QMSS)
library(tidyverse)
library(stargazer)
library(psych)
library(ggplot2)
library(ggthemes)
library(ggsci)
library(viridis)
library(hrbrthemes)
library(imputeTS)
library(kableExtra)
library(naniar)
library(plotly)
library(DT)
library(tidyr)
# Select relevant columns and filter for individuals with valid data about their education, filter for women with some income generating work; filter for High School +
# Make df that includes working women with valid data about childcare
Valid_Data <- function(df) {
NLSY_Valid <-  df %>%
filter(CV_HGC_EVER_EDT != 95 & !is.na(CV_HGC_EVER_EDT)) %>%
filter(CV_HGC_EVER_EDT >= 12) %>% filter(YEAR>=2005)
}
Valid_Childcare <- function(df,first_child) {
NLSY_Valid <- Valid_Data(df)
NLSY_Valid_Childcare <- NLSY_Valid %>% subset(INCOME>0) %>% subset(WORK_EDU_HRS>=0) %>%
subset(WORK_EDU_HRS != 999) %>%
subset(SELF_CARE==0) %>% subset(YEAR <= BIOCHILD_6_YR)
if(first_child == TRUE) {
NLSY_Valid_Childcare <- NLSY_Valid_Childcare %>% subset(YEAR <= BIOCHILD_6_YR)
}
else{
NLSY_Valid_Childcare <- NLSY_Valid_Childcare %>% subset(CV_HH_UNDER_6>0)
}
}
# Filter for data that includes working women (Part Time and up) with valid data about childcare
Valid_Childcare_Income <- function(df,first_child) {
NLSY_Valid_Childcare <- Valid_Childcare(df,first_child)
NLSY_Valid_Childcare_Income <- NLSY_Valid_Childcare %>% subset(INCOME>0) %>% subset(PART_TIME==1)
}
# Load Data - YCCA / YCCAL - any child under 6 years old
load("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_imputed.RData")
NLSY_Valid_Childcare_Part_Time_2 <- Valid_Childcare_Income(NLSY_imputed,FALSE) # Part Time and Above
NLSY_Valid_Childcare_Income_2 <- Valid_Childcare(NLSY_imputed,FALSE) %>% subset(INCOME > 0) # Any Income Generated
NLSY_Valid_Childcare_Part_Time_2 <- NLSY_Valid_Childcare_Part_Time_2[!is.na(NLSY_Valid_Childcare_Part_Time_2$FAMILY_CARE), ]
# Childcare Variables here are averaged across children (YCCAL), looking at respondents while their first child is under 6 years old
load("~/QMSS/Intergenerational_Childcare_Maternal_Wage_Gap/data/NLSY_imputed_first_child.RData")
NLSY_Valid_Childcare_Part_Time_1 <- Valid_Childcare_Income(NLSY_imputed,TRUE)
NLSY_Valid_Childcare_Part_Time_1 <- NLSY_Valid_Childcare_Part_Time_1[!is.na(NLSY_Valid_Childcare_Part_Time_1$FAMILY_CARE), ]
NLSY_Valid_Childcare_Income_1 <- Valid_Childcare(NLSY_imputed,TRUE) %>% subset(INCOME > 0)
# Only looking at parent having one child (first child) - while their first child is under 6 years old
NLSY_Valid_Childcare_Part_Time_One_Child <- NLSY_Valid_Childcare_Part_Time_1 %>% subset(N_CHILDREN==1)
NLSY_Valid_Childcare_Income_One_Child <- NLSY_Valid_Childcare_Income_1 %>% subset(N_CHILDREN==1)
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% select(INCOME, MARRIED_OR_COHABITATING, BA_ABOVE,SPOUSAL_INCOME, MOTHER_AGE_FIRST_CHILD, MATERNAL_AGE,`YCCAL-6800`,FAMILY_CARE, N_CHILDREN,FULL_TIME,Relative_within_15_minutes))
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% select(INCOME, MARRIED_OR_COHABITATING, BA_ABOVE,SPOUSAL_INCOME, MOTHER_AGE_FIRST_CHILD, MATERNAL_AGE,`YCCAL-6800`,FAMILY_CARE, FORMAL_CHILDCARE, N_CHILDREN,FULL_TIME,Relative_within_15_minutes) %>% subset(FORMAL_CHILDCARE ==1))
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% subset(FORMAL_CHILDCARE ==1) %>% select(INCOME, MARRIED_OR_COHABITATING, BA_ABOVE,SPOUSAL_INCOME, MOTHER_AGE_FIRST_CHILD, MATERNAL_AGE,`YCCAL-6800`,WORK_EDU_HRS, N_CHILDREN,Relative_within_15_minutes) %>% )
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% subset(FORMAL_CHILDCARE ==1) %>% select(INCOME, MARRIED_OR_COHABITATING, BA_ABOVE,SPOUSAL_INCOME, MOTHER_AGE_FIRST_CHILD, MATERNAL_AGE,`YCCAL-6800`,WORK_EDU_HRS, N_CHILDREN,Relative_within_15_minutes))
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% subset(FAMILY_CARE ==1) %>% select(INCOME, MARRIED_OR_COHABITATING, BA_ABOVE,SPOUSAL_INCOME, MOTHER_AGE_FIRST_CHILD, MATERNAL_AGE,`YCCAL-6800`,WORK_EDU_HRS, N_CHILDREN,Relative_within_15_minutes))
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% subset(WORK_EDU_HRS ==0) %>% select(INCOME, MARRIED_OR_COHABITATING, BA_ABOVE,SPOUSAL_INCOME, MOTHER_AGE_FIRST_CHILD, MATERNAL_AGE,`YCCAL-6800`,WORK_EDU_HRS, N_CHILDREN,Relative_within_15_minutes))
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% select(INCOME, MARRIED_OR_COHABITATING, BA_ABOVE,SPOUSAL_INCOME, MOTHER_AGE_FIRST_CHILD, MATERNAL_AGE,`YCCAL-6800`,WORK_EDU_HRS, N_CHILDREN,Relative_within_15_minutes))
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% select(INCOME, MARRIED_OR_COHABITATING, CV_HGC_EVER_EDT,SPOUSAL_INCOME, MOTHER_AGE_FIRST_CHILD, MATERNAL_AGE,`YCCAL-6800`,WORK_EDU_HRS, N_CHILDREN,Relative_within_15_minutes))
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% subset(FAMILY_DAY_CARE ==1) %>%  select(INCOME, MARRIED_OR_COHABITATING, CV_HGC_EVER_EDT,SPOUSAL_INCOME, MOTHER_AGE_FIRST_CHILD, MATERNAL_AGE,`YCCAL-6800`,WORK_EDU_HRS, N_CHILDREN,Relative_within_15_minutes))
psych::describe(NLSY_Valid_Childcare_Part_Time_One_Child %>% subset(FAMILY_CARE ==1) %>%  select(INCOME, MARRIED_OR_COHABITATING, CV_HGC_EVER_EDT,SPOUSAL_INCOME, MOTHER_AGE_FIRST_CHILD, MATERNAL_AGE,`YCCAL-6800`,WORK_EDU_HRS, N_CHILDREN,Relative_within_15_minutes))
NSLY_Valid_Summarized <- NLSY_Valid %>% dplyr::mutate(ANY_CHILDREN = ifelse(N_CHILDREN == 0,0,1)) %>% group_by(YEAR,ANY_CHILDREN) %>% dplyr::summarize(mean_income = mean(INCOME,na.rm=TRUE)) %>% mutate(ANY_CHILDREN = ifelse(ANY_CHILDREN==0,'No Children', 'One or More Children'))
NSLY_Valid_Summarized
NSLY_Valid_Summarized$YEAR <- as.numeric(NSLY_Valid_Summarized$YEAR)
NSLY_Valid_Summarized %>%
ggplot(.,aes(x=YEAR, y=mean_income/1000)) +
geom_point(color = "#273237") +
geom_line(color = "#273237",aes(linetype = ANY_CHILDREN)) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
) +
labs(title = "",
y="Mean Annual Income\n (USD, thousands)", x = "",linetype = "")
NSLY_Valid_Summarized %>%
ggplot(.,aes(x=YEAR, y=mean_income/1000)) +
geom_point(aes(color = ANY_CHILDREN)) +
geom_line(aes(color = ANY_CHILDREN)) +
scale_color_manual(values = c("#ffb000","#002728")) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
) +
labs(title = "",
y="Mean Annual Income\n (USD, thousands)", x = "",linetype = "")
# No filtering for only individuals with valid data around inter-generational childcare
NLSY_Valid <-  NLSY_imputed %>%
filter(CV_HGC_EVER_EDT != 95 & !is.na(CV_HGC_EVER_EDT)) %>% group_by(PUBID_1997) %>% mutate(max_education = max(CV_HGC_EVER_EDT)) %>% ungroup() %>% subset(max_education == CV_HGC_EVER_EDT)
NSLY_Valid_Summarized <- NLSY_Valid %>% dplyr::mutate(ANY_CHILDREN = ifelse(N_CHILDREN == 0,0,1)) %>% group_by(YEAR,ANY_CHILDREN) %>% dplyr::summarize(mean_income = mean(INCOME,na.rm=TRUE)) %>% mutate(ANY_CHILDREN = ifelse(ANY_CHILDREN==0,'No Children', 'One or More Children'))
NSLY_Valid_Summarized
NSLY_Valid_Summarized$YEAR <- as.numeric(NSLY_Valid_Summarized$YEAR)
NSLY_Valid_Summarized %>%
ggplot(.,aes(x=YEAR, y=mean_income/1000)) +
geom_point(aes(color = ANY_CHILDREN)) +
geom_line(aes(color = ANY_CHILDREN)) +
scale_color_manual(values = c("#ffb000","#002728")) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
) +
labs(title = "",
y="Mean Annual Income\n (USD, thousands)", x = "",linetype = "")