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LAST_DOWNLOAD

source	dataset	Title	Download	Compile
eurostat	phillips	Phillips curves	NA	2023-04-10
eurostat	tipsna62	Employees, domestic concept - annual data	2023-04-01	2023-03-26
fred	phillips	Phillips curves	2023-03-23	2023-04-10
ilo	phillips	Phillips curves	NA	2023-01-10
oecd	phillips	Phillips curves	NA	2023-04-10
wdi	phillips	Phillips curves	NA	2022-11-18

LAST_COMPILE

LAST_COMPILE
2023-04-10

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) + 
  xlab("") + ylab("Hong Kong's Inflation or Unemployment") +
  scale_color_manual(values = viridis(3)[1:2]) +
  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) + 
  xlab("") + ylab("Hong Kong's Inflation or Unemployment") +
  scale_color_manual(values = viridis(3)[1:2]) +
  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")

Algeria

UNE_DEAP_SEX_AGE_RT_A %>%
  filter(ref_area %in% c("DZA"),
         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("DZ")) %>%
              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) + 
  xlab("") + ylab("Hong Kong's Inflation or Unemployment") +
  scale_color_manual(values = viridis(3)[1:2]) +
  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")

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) + 
  xlab("") + ylab("Hong Kong's Inflation or Unemployment") +
  scale_color_manual(values = viridis(3)[1:2]) +
  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) + 
  xlab("") + ylab("Inflation or Unemployment") +
  scale_color_manual(values = viridis(3)[1:2]) +
  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")

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) + 
  xlab("") + ylab("Inflation or Unemployment") +
  scale_color_manual(values = viridis(3)[1:2]) +
  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")

Argentina

All

UNE_DEAP_SEX_AGE_RT_A %>%
  filter(ref_area %in% c("ARG"),
         sex == "SEX_T",
         classif1 == "AGE_AGGREGATE_TOTAL") %>%
  mutate(date = paste0(time, "-12-31") %>% as.Date) %>%
  select(iso3c = ref_area, date, UNE_DEAP_SEX_AGE_RT_A = obs_value) %>%
  left_join(NY.GDP.DEFL.KD.ZG %>%
              mutate(date = paste0(year, "-12-31") %>% as.Date) %>%
              select(iso3c, date, NY.GDP.DEFL.KD.ZG), 
            by = c("date", "iso3c")) %>%
  gather(VARIABLE, value, - date, -iso3c) %>%
  mutate(Variable = case_when(VARIABLE == "NY.GDP.DEFL.KD.ZG" ~ "Inflation (GDP Deflator)",
                              VARIABLE == "UNE_DEAP_SEX_AGE_RT_A" ~ "Unemployment Rate")) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = value/100, color = Variable)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.title = element_blank(),
        legend.position = c(0.8, 0.15)) +
  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, 1),
                     labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "grey")

1995-

UNE_DEAP_SEX_AGE_RT_A %>%
  filter(ref_area %in% c("ARG"),
         sex == "SEX_T",
         classif1 == "AGE_AGGREGATE_TOTAL") %>%
  mutate(date = paste0(time, "-12-31") %>% as.Date) %>%
  select(iso3c = ref_area, date, UNE_DEAP_SEX_AGE_RT_A = obs_value) %>%
  left_join(NY.GDP.DEFL.KD.ZG %>%
              mutate(date = paste0(year, "-12-31") %>% as.Date) %>%
              select(iso3c, date, NY.GDP.DEFL.KD.ZG), 
            by = c("date", "iso3c")) %>%
  gather(VARIABLE, value, - date, -iso3c) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  mutate(Variable = case_when(VARIABLE == "NY.GDP.DEFL.KD.ZG" ~ "Inflation (GDP Deflator)",
                              VARIABLE == "UNE_DEAP_SEX_AGE_RT_A" ~ "Unemployment Rate")) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = value/100, color = Variable)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.title = element_blank(),
        legend.position = c(0.8, 0.15)) +
  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, 1),
                     labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "grey")

Luxembourg

UNE_DEAP_SEX_AGE_RT_A %>%
  filter(ref_area %in% c("LUX"),
         sex == "SEX_T",
         classif1 == "AGE_AGGREGATE_TOTAL") %>%
  mutate(date = paste0(time, "-12-31") %>% as.Date) %>%
  select(iso3c = ref_area, date, UNE_DEAP_SEX_AGE_RT_A = obs_value) %>%
  left_join(NY.GDP.DEFL.KD.ZG %>%
              mutate(date = paste0(year, "-12-31") %>% as.Date) %>%
              select(iso3c, date, NY.GDP.DEFL.KD.ZG), 
            by = c("date", "iso3c")) %>%
  gather(VARIABLE, value, - date, -iso3c) %>%
  mutate(Variable = case_when(VARIABLE == "NY.GDP.DEFL.KD.ZG" ~ "Inflation (GDP Deflator)",
                              VARIABLE == "UNE_DEAP_SEX_AGE_RT_A" ~ "Unemployment Rate")) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = value/100, color = Variable)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.title = element_blank(),
        legend.position = c(0.8, 0.15)) +
  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, 1),
                     labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "grey")

Phillips curves

Data - ILO

François Geerolf

Info

LAST_DOWNLOAD

LAST_COMPILE

Hong Kong

All

1976

Algeria

Benin

Togo

Mali

Argentina

All

1995-

Luxembourg