Net Replacement Rates in unemployment - NRR

Name: Net Replacement Rates in unemployment
Creator: François Geerolf
License: https://creativecommons.org/licenses/by/4.0/

Data - OECD

Author

François Geerolf

Last observation: 2023 (N = 199680)

First observation: 2001 (N = 85413)

Last data update: 02 août 2026, 09:02

Last compile: 04 sept. 2026, 02:45

Structure

Data Structure

Code

NRR_var |>
  pluck("VAR_DESC") %>%
  {if (is_html_output()) print_table(.) else .}

id	description
LOCATION	Country
FAMILY	Family type
DURATION	Unemployment duration (months)
EARNINGS	Previous in-work earnings
HBTOPUPS	Include housing benefits
TIME	Year
OBS_VALUE	Observation Value
TIME_FORMAT	Time Format
OBS_STATUS	Observation Status

FAMILY

Code

NRR_var |>
  pluck("FAMILY") %>%
  {if (is_html_output()) print_table(.) else .}

id	label
SINGLE	Single person without children
SINGLE2C	Single person with 2 children
1EARNERC	Couple without children - partner is out of work
1EARNERC2C	Couple with 2 children - partner is out of work
2EARNERC_AW	Couple without children - partner's earnings: Average Wage (AW)
2EARNERC_67AW	Couple without children - partner's earnings: 67% of the AW
2EARNERC2C_AW	Couple with 2 children - partner's earnings: AW
2EARNERC2C_67AW	Couple with 2 children - partner's earnings: 67% of the AW

DURATION

Code

NRR_var |>
  pluck("DURATION") %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

EARNINGS

Code

NRR_var |>
  pluck("EARNINGS") %>%
  {if (is_html_output()) print_table(.) else .}

id	label
MIN	Minimum Wage
67AW	67% of the Average Wage
AW	Average Wage

Ex 1: Average Wage

Germany

Code

NRR |>
  filter(LOCATION == "DEU", 
         FAMILY == "SINGLE", 
         EARNINGS == "AW", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

France

Code

NRR |>
  filter(LOCATION == "FRA", 
         FAMILY == "SINGLE", 
         EARNINGS == "AW", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

Italy

Code

NRR |>
  filter(LOCATION == "ITA", 
         FAMILY == "SINGLE", 
         EARNINGS == "AW", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

Spain

Code

NRR |>
  filter(LOCATION == "ESP", 
         FAMILY == "SINGLE", 
         EARNINGS == "AW", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

United Kingdom

Code

NRR |>
  filter(LOCATION == "GBR", 
         FAMILY == "SINGLE", 
         EARNINGS == "AW", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

United States

Code

NRR |>
  filter(LOCATION == "USA", 
         FAMILY == "SINGLE", 
         EARNINGS == "AW", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

Switzerland

Code

NRR |>
  filter(LOCATION == "CHE", 
         FAMILY == "SINGLE", 
         EARNINGS == "AW", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

Ex 2: Minimum Wage

Germany

Code

NRR |>
  filter(LOCATION == "DEU", 
         FAMILY == "SINGLE", 
         EARNINGS == "MIN", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

France

Code

NRR |>
  filter(LOCATION == "FRA", 
         FAMILY == "SINGLE", 
         EARNINGS == "MIN", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

Italy

Code

NRR |>
  filter(LOCATION == "ITA", 
         FAMILY == "SINGLE", 
         EARNINGS == "MIN", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

Spain

Code

NRR |>
  filter(LOCATION == "ESP", 
         FAMILY == "SINGLE", 
         EARNINGS == "MIN", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

United Kingdom

Code

NRR |>
  filter(LOCATION == "GBR", 
         FAMILY == "SINGLE", 
         EARNINGS == "MIN", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

United States

Code

NRR |>
  filter(LOCATION == "USA", 
         FAMILY == "SINGLE", 
         EARNINGS == "MIN", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

Switzerland

Code

NRR |>
  filter(LOCATION == "CHE", 
         FAMILY == "SINGLE", 
         EARNINGS == "MIN", 
         HBTOPUPS == 1,
         obsTime %in% c("2001", "2005", "2018")) |>
  mutate(DURATION = DURATION |> as.numeric(),
         obsValue = obsValue / 100) |>
  arrange(obsTime, DURATION) |>
  select(obsTime, DURATION, obsValue) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = DURATION, y = obsValue, color = obsTime, linetype = obsTime)) + 
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Net Replacement Rate") +
  scale_x_continuous(breaks = seq(0, 60, 6),
                     labels = dollar_format(prefix = "", suffix = " mo")) +
  scale_y_continuous(breaks = seq(0, 1, 0.1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 1))

Related Data

Data on wages

Code

load_data("employment.RData")
employment |>
  source_dataset_file_updates()

source	dataset	Title	Updated
bls	jt	NA	NA
bls	la	NA	NA
bls	ln	NA	NA
eurostat	nama_10_a10_e	Employment by A*10 industry breakdowns - nama_10_a10_e	2026-08-31
eurostat	nama_10_a64_e	National accounts employment data by industry (up to NACE A*64) - nama_10_a64_e	2026-08-04
eurostat	namq_10_a10_e	Employment A*10 industry breakdowns - namq_10_a10_e	2026-08-31
eurostat	une_rt_m	Unemployment by sex and age – monthly data - une_rt_m	2026-08-28
oecd	ALFS_EMP	Employment by activities and status (ALFS) - ALFS_EMP	2026-08-02
oecd	EPL_T	Strictness of employment protection – temporary contracts - EPL_T	2026-08-02
oecd	LFS_SEXAGE_I_R	LFS by sex and age - indicators - LFS_SEXAGE_I_R	2026-08-02
oecd	STLABOUR	Short-Term Labour Market Statistics - STLABOUR	2026-08-02