Median saving rate by degree of urbanisation - experimental statistics - icw_sr_13

Data - Eurostat

Author

François Geerolf

Info

Last observation: Annual: 2020 (N = 116)

First observation: Annual: 2010 (N = 107)

Last data update: 23 jul 2026, 22:32. Last compile: 24 jul 2026, 02:01

Structure

France, Germany, Italy, Spain

Total, 2010-2020

Code 
icw_sr_13 %>%
  filter(geo %in% c("FR", "DE", "IT", "ES"),
         deg_urb == "TOTAL") %>%
  year_to_date %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  geom_point(aes(x = date, y = values, color = color)) +
  theme_minimal() + scale_color_identity() + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(2005, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("Median saving rate (% of disposable income)") +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Latest Wave by Degree of Urbanisation

Germany, France, Sweden

Code 
latest_y <- icw_sr_13 %>%
  filter(geo %in% c("SE", "FR", "DE"),
         !is.na(values)) %>%
  summarise(m = max(time)) %>%
  pull(m)

icw_sr_13 %>%
  filter(time == latest_y,
         geo %in% c("SE", "FR", "DE")) %>%
  ggplot + geom_line(aes(x = deg_urb, y = values/100, color = Geo, group = Geo, linetype = Geo)) +
  scale_color_manual(values = viridis(4)[1:3]) + theme_minimal() +
  theme(legend.position = c(0.5, 0.7),
        legend.title = element_blank()) +
  xlab("by degree of urbanisation") + ylab("Median saving rate") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1))

All Countries

Code 
icw_sr_13 %>%
  filter(time == latest_y) %>%
  select(deg_urb, geo, Geo, values) %>%
  spread(deg_urb, values) %>%
  select(geo, Geo, everything()) %>%
  {if (is_html_output()) print_table(.) else .}
geo Geo DEG1 DEG2 DEG3 TOTAL
AT Austria 18.6 24.2 27.1 23.8
BE Belgium 26.2 36.3 35.8 33.7
BG Bulgaria 31.7 27.1 20.0 26.8
CY Cyprus 25.7 24.0 24.4 25.1
CZ Czechia 33.2 29.4 31.3 31.2
DE Germany 14.5 20.1 22.6 18.3
DK Denmark 30.8 39.2 36.1 35.0
EA Euro area (EA11-1999, EA12-2001, EA13-2007, EA15-2008, EA16-2009, EA17-2011, EA18-2014, EA19-2015, EA20-2023, EA21-2026) 24.1 25.9 27.6 25.6
EA21 Euro area – 21 countries (from 2026) 24.3 25.8 27.2 25.6
EE Estonia 42.5 39.4 41.7 41.6
EL Greece 14.5 16.1 16.5 15.6
ES Spain 34.7 30.7 31.3 33.0
EU European Union (EU6-1958, EU9-1973, EU10-1981, EU12-1986, EU15-1995, EU25-2004, EU27-2007, EU28-2013, EU27-2020) 25.9 27.0 27.2 26.6
EU27_2020 European Union - 27 countries (from 2020) 25.9 27.0 27.2 26.6
FI Finland 14.5 16.3 17.2 15.8
FR France 29.9 31.3 31.6 30.9
HR Croatia 22.0 20.5 17.5 19.8
HU Hungary 21.9 20.6 20.8 21.1
IE Ireland 31.4 33.6 24.4 29.2
LT Lithuania 47.0 41.8 42.6 44.7
LU Luxembourg 29.7 27.1 30.7 28.7
LV Latvia 28.4 30.9 32.2 30.0
MT Malta 27.7 31.2 25.7 29.4
NL Netherlands 15.9 23.7 24.0 19.4
PL Poland 49.8 51.0 50.1 50.2
PT Portugal 34.7 33.2 30.6 33.3
RO Romania 2.5 3.2 -8.0 -1.0
SI Slovenia 22.3 22.8 23.1 22.8
SK Slovakia 28.1 27.4 29.0 28.2

2010

Code 
icw_sr_13 %>%
  filter(time == "2010") %>%
  select(deg_urb, geo, Geo, values) %>%

  spread(deg_urb, values) %>%
  select(geo, Geo, everything()) %>%
  {if (is_html_output()) print_table(.) else .}
geo Geo DEG1 DEG2 DEG3 TOTAL
AT Austria 14.6 21.4 19.7 18.0
BE Belgium 7.2 11.5 6.6 8.9
BG Bulgaria 43.4 40.1 31.5 38.0
CY Cyprus 13.8 6.1 11.5 12.2
CZ Czechia 23.1 25.1 23.9 23.9
DE Germany 12.6 15.2 12.2 13.5
DK Denmark 1.5 5.3 6.7 4.3
EE Estonia 37.0 36.0 32.7 34.9
EL Greece -12.5 -9.2 -10.8 -11.2
ES Spain 19.8 13.0 11.3 16.1
FI Finland 21.6 21.7 23.1 22.2
FR France 28.9 29.8 28.0 29.1
HR Croatia 10.1 7.2 -3.7 4.0
HU Hungary 17.2 15.3 16.1 16.3
IE Ireland 21.4 16.8 16.7 18.4
IT Italy 29.0 25.5 21.7 26.4
LT Lithuania 31.2 NA 16.3 22.8
LU Luxembourg 24.7 27.6 37.8 28.4
LV Latvia 12.1 17.2 8.6 10.5
MT Malta 13.6 14.0 NA 13.7
PL Poland 26.4 25.4 22.4 24.7
PT Portugal 14.8 9.1 15.1 13.1
RO Romania 3.7 -0.9 -11.5 -5.1
SE Sweden 14.6 23.1 18.3 18.0
SI Slovenia 18.9 18.6 17.1 18.0
SK Slovakia 28.2 22.2 23.7 24.4
UK United Kingdom 22.3 23.7 23.8 22.9

2015

Code 
icw_sr_13 %>%
  filter(time == "2015") %>%
  select(deg_urb, geo, Geo, values) %>%

  spread(deg_urb, values) %>%
  select(geo, Geo, everything()) %>%
  {if (is_html_output()) print_table(.) else .}
geo Geo DEG1 DEG2 DEG3 TOTAL
AT Austria 16.8 21.6 24.5 21.1
BE Belgium 16.5 20.1 20.1 19.0
BG Bulgaria 19.7 16.1 14.2 17.4
CY Cyprus 9.9 11.0 10.8 10.5
CZ Czechia 25.0 25.2 26.2 25.5
DE Germany 11.0 16.6 19.5 14.9
DK Denmark 17.8 28.4 30.9 25.6
EA Euro area (EA11-1999, EA12-2001, EA13-2007, EA15-2008, EA16-2009, EA17-2011, EA18-2014, EA19-2015, EA20-2023, EA21-2026) 19.7 20.0 23.3 20.7
EA21 Euro area – 21 countries (from 2026) 19.7 19.7 22.6 20.5
EE Estonia 37.3 31.8 33.2 34.9
EL Greece -4.8 -6.0 -8.8 -6.5
ES Spain 21.3 18.9 15.7 19.2
EU European Union (EU6-1958, EU9-1973, EU10-1981, EU12-1986, EU15-1995, EU25-2004, EU27-2007, EU28-2013, EU27-2020) 19.0 19.8 22.0 20.1
EU27_2020 European Union - 27 countries (from 2020) 20.4 20.5 22.2 21.0
FI Finland 17.9 19.5 21.7 19.5
FR France 33.3 34.9 36.3 34.6
HR Croatia 10.6 5.5 -0.3 4.4
HU Hungary 15.1 13.4 14.8 14.6
IE Ireland 27.1 24.2 24.1 25.2
LT Lithuania 37.7 23.4 26.0 31.6
LU Luxembourg 25.3 23.7 30.4 27.0
LV Latvia 25.7 23.5 23.7 24.6
MT Malta 17.7 19.2 NA 17.8
NL Netherlands 12.9 19.2 19.5 15.8
PL Poland 30.9 30.1 27.4 29.5
PT Portugal 19.7 18.5 17.8 18.9
RO Romania 12.3 11.3 9.6 10.9
SE Sweden 23.9 25.2 24.8 24.6
SI Slovenia 23.4 23.7 22.2 22.8
SK Slovakia 18.6 17.6 16.9 17.6
UK United Kingdom 13.4 15.2 20.0 15.0

Germany, France, Sweden

Code 
icw_sr_13 %>%
  filter(time == "2015",
         geo %in% c("SE", "FR", "DE")) %>%
  
  ggplot + geom_line(aes(x = deg_urb, y = values/100, color = Geo, group = Geo, linetype = Geo)) +
  scale_color_manual(values = viridis(4)[1:3]) + theme_minimal() +
  theme(legend.position = c(0.5, 0.7),
        legend.title = element_blank()) +
  xlab("by degree of urbanisation") + ylab("Median saving rate") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1))