Unemployment rate by sex and age (%) - UNE_DEAP_SEX_AGE_RT_A
Data - ILO
François Geerolf
ref_area
UNE_DEAP_SEX_AGE_RT_A %>%
filter(sex == "SEX_T",
classif1 == "AGE_AGGREGATE_TOTAL") %>%
left_join(ref_area, by = "ref_area") %>%
group_by(ref_area, Ref_area) %>%
summarise(Nobs = n()) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Ref_area)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}classif1
source
sex
Number of Unemployed
UNE_DEAP_SEX_AGE_RT_A %>%
filter(sex == "SEX_T",
classif1 == "AGE_AGGREGATE_TOTAL") %>%
left_join(ref_area, by = "ref_area") %>%
mutate(obs_value = round(obs_value, 1)) %>%
group_by(ref_area, Ref_area) %>%
arrange(time) %>%
summarise(Nobs = n(),
`Year 1` = first(time),
`Inflation 1` = first(obs_value),
`Year 2` = last(time),
`Inflation 2` = last(obs_value)) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Ref_area)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Canada, Japan, United States
UNE_DEAP_SEX_AGE_RT_A %>%
filter(ref_area %in% c("USA", "JPN", "CAN"),
sex == "SEX_T",
classif1 == "AGE_AGGREGATE_TOTAL") %>%
left_join(ref_area, by = "ref_area") %>%
year_to_enddate() %>%
ggplot(.) + geom_line() + theme_minimal() +
aes(x = date, y = obs_value/100, color = Ref_area, linetype = Ref_area) +
xlab("") + ylab("Unemployment rate") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.85)) +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
labels = percent_format(a = 1)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
France, Germany, Spain
UNE_DEAP_SEX_AGE_RT_A %>%
filter(ref_area %in% c("FRA", "DEU", "ESP"),
sex == "SEX_T",
classif1 == "AGE_AGGREGATE_TOTAL") %>%
left_join(ref_area, by = "ref_area") %>%
year_to_enddate() %>%
ggplot(.) + geom_line() + theme_minimal() +
aes(x = date, y = obs_value/100, color = Ref_area, linetype = Ref_area) +
xlab("") + ylab("Unemployment rate") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.85)) +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
labels = percent_format(a = 1)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
Hong Kong, Italy, Korea
UNE_DEAP_SEX_AGE_RT_A %>%
filter(ref_area %in% c("HKG", "ITA", "KOR"),
sex == "SEX_T",
classif1 == "AGE_AGGREGATE_TOTAL") %>%
left_join(ref_area, by = "ref_area") %>%
year_to_enddate() %>%
ggplot(.) + geom_line() + theme_minimal() +
aes(x = date, y = obs_value/100, color = Ref_area, linetype = Ref_area) +
xlab("") + ylab("Unemployment rate") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.85)) +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
labels = percent_format(a = 1)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
Phillips Curves
Hong Kong
All
UNE_DEAP_SEX_AGE_RT_A %>%
filter(ref_area %in% c("HKG"),
sex == "SEX_T",
classif1 == "AGE_AGGREGATE_TOTAL") %>%
mutate(date = paste0(time, "-12-31") %>% as.Date) %>%
select(date, value = obs_value) %>%
mutate(variable = "Unemployment Rate") %>%
bind_rows(NY.GDP.DEFL.KD.ZG %>%
filter(iso2c %in% c("HK")) %>%
mutate(date = paste0(year, "-12-31") %>% as.Date) %>%
select(date, value = NY.GDP.DEFL.KD.ZG) %>%
mutate(variable = "Inflation (GDP Deflator)")) %>%
ggplot(.) + geom_line() + theme_minimal() +
aes(x = date, y = value/100, color = variable, linetype = variable) +
xlab("") + ylab("Hong Kong's Inflation or Unemployment") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.85, 0.85)) +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
labels = percent_format(a = 1)) +
geom_vline(xintercept = as.Date("1983-01-01"), linetype = "dashed", color = viridis(4)[3]) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")
1976
UNE_DEAP_SEX_AGE_RT_A %>%
filter(ref_area %in% c("HKG"),
sex == "SEX_T",
classif1 == "AGE_AGGREGATE_TOTAL") %>%
mutate(date = paste0(time, "-12-31") %>% as.Date) %>%
select(date, value = obs_value) %>%
mutate(variable = "Unemployment Rate") %>%
bind_rows(NY.GDP.DEFL.KD.ZG %>%
filter(iso2c %in% c("HK")) %>%
mutate(date = paste0(year, "-12-31") %>% as.Date) %>%
select(date, value = NY.GDP.DEFL.KD.ZG) %>%
mutate(variable = "Inflation (GDP Deflator)")) %>%
filter(date >= as.Date("1976-01-01")) %>%
ggplot(.) + geom_line() + theme_minimal() +
aes(x = date, y = value/100, color = variable, linetype = variable) +
xlab("") + ylab("Hong Kong's Inflation or Unemployment") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.85, 0.85)) +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
labels = percent_format(a = 1)) +
geom_vline(xintercept = as.Date("1983-01-01"), linetype = "dashed", color = viridis(4)[3]) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")
Benin
UNE_DEAP_SEX_AGE_RT_A %>%
filter(ref_area %in% c("BEN"),
sex == "SEX_T",
classif1 == "AGE_AGGREGATE_TOTAL") %>%
mutate(date = paste0(time, "-12-31") %>% as.Date) %>%
select(date, value = obs_value) %>%
mutate(variable = "Unemployment Rate") %>%
bind_rows(NY.GDP.DEFL.KD.ZG %>%
filter(iso2c %in% c("BJ")) %>%
mutate(date = paste0(year, "-12-31") %>% as.Date) %>%
select(date, value = NY.GDP.DEFL.KD.ZG) %>%
mutate(variable = "Inflation (GDP Deflator)")) %>%
filter(date >= as.Date("1976-01-01")) %>%
ggplot(.) + geom_line() + theme_minimal() +
aes(x = date, y = value/100, color = variable, linetype = variable) +
xlab("") + ylab("Hong Kong's Inflation or Unemployment") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.85, 0.85)) +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
labels = percent_format(a = 1)) +
geom_vline(xintercept = as.Date("1983-01-01"), linetype = "dashed", color = viridis(4)[3]) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
Togo
UNE_DEAP_SEX_AGE_RT_A %>%
filter(ref_area %in% c("TGO"),
sex == "SEX_T",
classif1 == "AGE_AGGREGATE_TOTAL") %>%
mutate(date = paste0(time, "-12-31") %>% as.Date) %>%
select(date, value = obs_value) %>%
mutate(variable = "Unemployment Rate") %>%
bind_rows(NY.GDP.DEFL.KD.ZG %>%
filter(iso2c %in% c("TG")) %>%
mutate(date = paste0(year, "-12-31") %>% as.Date) %>%
select(date, value = NY.GDP.DEFL.KD.ZG) %>%
mutate(variable = "Inflation (GDP Deflator)")) %>%
filter(date >= as.Date("1976-01-01")) %>%
ggplot(.) + geom_line() + theme_minimal() +
aes(x = date, y = value/100, color = variable, linetype = variable) +
xlab("") + ylab("Inflation or Unemployment") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.85, 0.85)) +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
labels = percent_format(a = 1)) +
geom_vline(xintercept = as.Date("1983-01-01"), linetype = "dashed", color = viridis(4)[3]) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
Mali
UNE_DEAP_SEX_AGE_RT_A %>%
filter(ref_area %in% c("MLI"),
sex == "SEX_T",
classif1 == "AGE_AGGREGATE_TOTAL") %>%
mutate(date = paste0(time, "-12-31") %>% as.Date) %>%
select(date, value = obs_value) %>%
mutate(variable = "Unemployment Rate") %>%
bind_rows(NY.GDP.DEFL.KD.ZG %>%
filter(iso2c %in% c("ML")) %>%
mutate(date = paste0(year, "-12-31") %>% as.Date) %>%
select(date, value = NY.GDP.DEFL.KD.ZG) %>%
mutate(variable = "Inflation (GDP Deflator)")) %>%
filter(date >= as.Date("1976-01-01")) %>%
ggplot(.) + geom_line() + theme_minimal() +
aes(x = date, y = value/100, color = variable, linetype = variable) +
xlab("") + ylab("Inflation or Unemployment") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.85, 0.85)) +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
labels = percent_format(a = 1)) +
geom_vline(xintercept = as.Date("1983-01-01"), linetype = "dashed", color = viridis(4)[3]) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")