Finland’s dependency ratio and pension contributions - http://ilari.scheinin.fi/finlands-dependency-ratio-and-pension-contributions/

finland-dependency-ratio.R

library(pxweb)
library(dplyr)
library(stringr)
library(tidyr)
library(ggplot2)
library(animation)

api <- "http://pxnet2.stat.fi/PXWeb/api/v1/"

past_original <- get_pxweb_data(
  url=paste0(api, "en/StatFin/vrm/vaerak/125_vaerak_tau_106.px"),
  dims=list(Vuosi="*", Sukupuoli="*", Ikä="*"),
  clean=TRUE)

future_original <- get_pxweb_data(
  url=paste0(api, "en/StatFin/vrm/vaenn/010_vaenn_tau_101.px"),
  dims=list(Vuosi="*", Sukupuoli="*", Ikä="*"),
  clean=TRUE)

past <- past_original %>%
  filter(!str_detect(Age, "total"), Sex != "Both sexes") %>%
  transmute(
    year=as.integer(as.character(Year)),
    age=as.integer(gsub("[^0-9]", "", Age)),
    sex=tolower(as.character(Sex)),
    population=values
  ) %>%
  tbl_df()

future <- future_original %>%
  filter(!Year %in% unique(past$year)) %>%
  filter(!str_detect(Age, "total"), Sex != "Both sexes") %>%
  transmute(
    year=as.integer(as.character(Year)),
    age=as.integer(gsub("[^0-9]", "", Age)),
    sex=tolower(as.character(Sex)),
    population=values
  ) %>%
  tbl_df()

both <- rbind(past, future)

pension_fees <- get_pxweb_data(
  url=paste0(api, "en/StatFin/jul/vermak/102_vermak_tau_120.px"),
  dims=list(Verolaji = c("D61111_TYO", "D61121_TYO"), Sektori = c("S13141"),
    Tieto = c("Suhde1"), Vuosi = c("*")),
  clean=TRUE) %>%
  tbl_df() %>%
  select(-Sector, -Data) %>%
  mutate(year=as.integer(as.character(Year))) %>%
  group_by(year) %>%
  summarise(contribution=sum(values))

dependency <- both %>%
  group_by(year) %>%
  summarise(
    child=sum(population[age < 15]),
    labor=sum(population[age >= 15 & age < 65]),
    aged=sum(population[age >= 65]),
    child_ratio=child / labor * 100,
    aged_ratio=aged / labor * 100,
    total_ratio=(child + aged) / labor * 100
  ) %>%
  select(year, aged_ratio)

combined <- dependency %>%
  left_join(pension_fees)

toplot <- combined %>%
  gather(key, value, -year)
toplot$lty <- "actual"
toplot$lty[toplot$key == "aged_ratio" & toplot$year %in% future$year] <-
  "forecast"

labs <- c(
  expression("aged dependency ratio" ==
    frac(
      "number of people aged 65 and over",
      "number of people aged between 15 and 64"
    ) %*% 100
  ),
  "statutory pension contributions as percent of GDP")

pnga4("finland-dependency-ratio.png")
toplot %>%
  ggplot(aes(year, value, color=key, linetype=lty)) +
  geom_line(size=3) +
  scale_color_manual(labels=labs, values=c("#ffffff", "#18447e")) +
  scale_linetype(guide=FALSE) + 
  theme(text=element_text(size=16),
    legend.background=element_rect(fill="#9ecae1"),
    legend.key=element_rect(fill=NA, color=NA),
    legend.key.width=unit(4, "cm"),
    legend.text.align=0,
    legend.title=element_blank(),
    legend.position="bottom",
    legend.direction="vertical",
    legend.box="horizontal",
    panel.background=element_rect(fill="#9ecae1"),
    plot.background=element_rect(fill="#9ecae1")) +
  labs(title="Finland's dependency ratio and pension contributions",
    x="year", y="") +
  annotate("text", x=1975.625, y=49.5, hjust=0, vjust=1,
    label="By: Ilari Scheinin\nSource: Statistics Finland\nLicense: CC BY")
dev.off()

# 2016-10-20
# Ilari Scheinin
# CC BY

# EOF