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1.Female Labor Force(% of female above15 years of age)
The median female labor force participation rate globally is 52.3%, with a staggering 77 percentage-point gap between Solomon Islands (highest) and Afghanistan (lowest). Women, on average, participate 20.3 percentage points less than men, with a median gap of 15.75 percentage points.
The distribution is heavily left-skewed, highlighting significant barriers women face in the labor market, calling for urgent corrective measure.
library(httr)
library(jsonlite)
library(dplyr)
library(janitor)
library(rnaturalearth)
library(sf)
library(dplyr)
library(classInt)
library(ggplot2)
library(viridis)
library(tidyverse)
library(kableExtra)
library(ggthemes)
##Codes for getting Labor force participation data
# Base URL and parameters for World Bank API
base_url_flfpr <- "https://api.worldbank.org/v2/country/all/indicator/SL.TLF.CACT.FE.ZS;SL.TLF.CACT.FE.NE.ZS;SL.TLF.CACT.MA.ZS;SL.TLF.CACT.MA.NE.ZS;SL.TLF.CACT.ZS;SL.TLF.CACT.NE.ZS;SL.TLF.ACTI.1524.FE.ZS;SL.TLF.ACTI.1524.FE.NE.ZS;SL.TLF.ACTI.1524.MA.ZS;SL.TLF.ACTI.1524.MA.NE.ZS;SL.TLF.ACTI.1524.ZS;SL.TLF.ACTI.1524.NE.ZS;SL.TLF.ACTI.FE.ZS;SL.TLF.ACTI.MA.ZS;SL.TLF.ACTI.ZS?source=14"
params_flfpr <- "&format=json&per_page=10000"
# Fetch the first page to determine total pages
first_page_flfpr <- fromJSON(content(GET(paste0(base_url_flfpr, params_flfpr, "&page=1")), "text"))
total_pages_flfpr <- first_page_flfpr[[1]]$pages
# Function to fetch and clean data
fetch_data_flfpr <- function(page_flfpr ) {
url_flfpr<- paste0(base_url_flfpr, params_flfpr, "&page=", page_flfpr)
data_flfpr <- fromJSON(content(GET(url_flfpr ), "text"))[[2]]
return(data_flfpr )
}
# Fetch all data and remove NA values from 'value' column
all_data_flfpr <- bind_rows(lapply(1:total_pages_flfpr , fetch_data_flfpr )) %>%
filter(!is.na(value)) # Remove rows with NA in value column
# Clean column names by removing special characters and standardizing
cleaned_data_flfpr <- all_data_flfpr %>%
clean_names() # janitor removes spaces, special characters, and ensures lowercase
flatten_data_flfpr <- all_data_flfpr %>%
mutate(indicator_id = indicator$id,
indicator_value = indicator$value,
country_id = country$id,
country_name = country$value) %>%
select(-indicator, -country) %>%
select(country_id, country_name, countryiso3code, date, value, unit, obs_status, decimal, indicator_id, indicator_value) %>%
clean_names() # Standardize column names using janitor
map_flfpr<-flatten_data_flfpr%>%ungroup()%>%group_by(country_name)%>%
mutate(date=as.numeric(date))%>%filter(date==max(date))%>%ungroup()
map_flfpr<-map_flfpr%>%mutate(indicator_id = case_when(
indicator_id == "SL.TLF.CACT.FE.ZS" ~ "Female",
indicator_id=="SL.TLF.CACT.MA.ZS"~ "Male",
TRUE~indicator_id))%>%filter(indicator_id%in%c("Male","Female"))
map_flfpr<-map_flfpr%>%select(country_name,value,indicator_id)
map_flfpr<-map_flfpr%>%pivot_wider(names_from =indicator_id,values_from = value)%>%
mutate(across(where(is.numeric), ~ round(.x, 2)))
time_series<-flatten_data_flfpr%>%filter(country_name%in%c("United Kingdom",
"United States","China","India"))%>%filter(date%in%c(1990:2023))
map_flfpr<-map_flfpr%>%mutate(Rank=dense_rank(desc(Female)))
top_20 <- map_flfpr%>%
arrange(desc(Female)) %>%
slice_head(n = 20)%>%mutate(Cat="Top 20")
bottom_20 <- map_flfpr %>%
arrange(Female) %>%
slice_head(n = 20)%>%mutate(Cat="Bottom 20")
# Merge the top 20 and bottom 20
merged_data <- bind_rows(top_20, bottom_20)
merged_data<-merged_data%>%select(country_name,Female,Cat,Rank)
difference<-map_flfpr%>%mutate(Difference=Female-Male)
difference<-difference%>%select(country_name,Difference)%>%
mutate(Rank=dense_rank(desc(Difference)))
mean_value <- mean(difference$Difference)
median_value <- median(difference$Difference)
merged_data %>%
kable("html", caption = "Female Labor Force Participation Rate",
col.names = c("Country", "Female (%)", "Category", "Rank")) %>%
kable_styling("striped", full_width = FALSE) %>%
add_header_above(c(" " = 1, "Huge difference between top 1 and bottom most country" = 3)) %>%
footnote(general = "Data source: World Bank",
general_title = "Twenty countries with highest and lowest female Labor Force Participation Rate (%)")| Country | Female (%) | Category | Rank |
|---|---|---|---|
| Solomon Islands | 82.73 | Top 20 | 1 |
| Madagascar | 82.58 | Top 20 | 2 |
| Burundi | 78.78 | Top 20 | 3 |
| Mozambique | 78.42 | Top 20 | 4 |
| Tanzania | 77.14 | Top 20 | 5 |
| Ethiopia | 74.75 | Top 20 | 6 |
| Angola | 72.76 | Top 20 | 7 |
| Liberia | 72.42 | Top 20 | 8 |
| Kenya | 72.20 | Top 20 | 9 |
| Moldova | 72.17 | Top 20 | 10 |
| Eritrea | 71.37 | Top 20 | 11 |
| South Sudan | 70.33 | Top 20 | 12 |
| Iceland | 70.07 | Top 20 | 13 |
| Korea, Dem. People’s Rep. | 69.65 | Top 20 | 14 |
| Cambodia | 69.46 | Top 20 | 15 |
| Viet Nam | 68.48 | Top 20 | 16 |
| Uganda | 67.59 | Top 20 | 17 |
| New Zealand | 67.57 | Top 20 | 18 |
| Azerbaijan | 67.40 | Top 20 | 19 |
| Cameroon | 67.29 | Top 20 | 20 |
| Afghanistan | 4.83 | Bottom 20 | 224 |
| Yemen, Rep. | 5.10 | Bottom 20 | 223 |
| Iraq | 11.38 | Bottom 20 | 222 |
| Jordan | 14.05 | Bottom 20 | 221 |
| Syrian Arab Republic | 14.14 | Bottom 20 | 220 |
| Iran, Islamic Rep. | 14.38 | Bottom 20 | 219 |
| Middle East & North Africa (excluding high income) | 15.60 | Bottom 20 | 218 |
| Middle East & North Africa (IDA & IBRD) | 15.60 | Bottom 20 | 218 |
| Egypt, Arab Rep. | 16.45 | Bottom 20 | 217 |
| Algeria | 16.81 | Bottom 20 | 216 |
| Djibouti | 18.53 | Bottom 20 | 215 |
| West Bank and Gaza | 18.89 | Bottom 20 | 214 |
| Middle East & North Africa | 18.94 | Bottom 20 | 213 |
| Morocco | 19.81 | Bottom 20 | 212 |
| Arab World | 19.98 | Bottom 20 | 211 |
| Somalia | 21.15 | Bottom 20 | 210 |
| Pakistan | 24.46 | Bottom 20 | 209 |
| Mauritania | 26.41 | Bottom 20 | 208 |
| Tunisia | 26.85 | Bottom 20 | 207 |
| Lebanon | 27.48 | Bottom 20 | 206 |
| Twenty countries with highest and lowest female Labor Force Participation Rate (%) | |||
| Data source: World Bank |
ggplot(difference, aes(x = Difference)) +
geom_density(fill = "lightblue", alpha = 0.5) + # Density plot with fill
geom_vline(aes(xintercept = mean_value), color = "red", linetype = "dashed", size = 1) + # Mean line
geom_vline(aes(xintercept = median_value), color = "blue", linetype = "dotted", size = 1) + # Median line
annotate("text", x = mean_value, y = 0.02, label = paste("Mean =", round(mean_value, 2)), color = "red", angle = 90, vjust = -0.5) + # Mean annotation
annotate("text", x = median_value, y = 0.02, label = paste("Median =", round(median_value, 2)), color = "blue", angle = 90, vjust = 1.5) + # Median annotation
labs(title = "Density distribution of the difference of Female and Male LFPR",
subtitle = "Difference is in percentage points",
x = "Value",
y = "Density",
caption = "Data Source:World Bank") +
theme_stata()
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