Info

Last observation: Annual: 2020 (N = 174)

First observation: Annual: 2010 (N = 162)

Last data update: 23 jul 2026, 22:39. Last compile: 24 jul 2026, 02:00

Structure

Saving Rate Gap Over Time

France, Germany, Sweden: “Very easy” vs. “Great difficulty”

Code

icw_sr_12 %>%
  filter(geo %in% c("FR", "DE", "SE"),
         lev_diff %in% c("VEASY", "GRT")) %>%
  year_to_date %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Geo, linetype = Lev_diff)) +
  geom_point(aes(x = date, y = values/100, color = Geo)) +
  theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(2005, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.title = element_blank()) +
  xlab("") + ylab("Median saving rate") +
  scale_y_continuous(labels = percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Latest Year

Code

latest_y <- icw_sr_12 %>%
  filter(!is.na(values)) %>%
  summarise(m = max(time)) %>%
  pull(m)

icw_sr_12 %>%
  filter(time == latest_y) %>%
  select(lev_diff, geo, Geo, values) %>%
  spread(lev_diff, values) %>%
  select(geo, Geo, everything()) %>%
  {if (is_html_output()) print_table(.) else .}

geo	Geo	EASY	FEASY	GRT	MOD	SOME	VEASY
AT	Austria	27.4	25.6	2.5	9.2	16.0	32.9
BE	Belgium	41.0	35.3	2.6	13.5	20.0	51.5
BG	Bulgaria	44.3	41.4	10.0	20.4	31.9	55.9
CY	Cyprus	32.1	26.7	14.7	16.8	23.0	39.5
CZ	Czechia	38.1	33.9	0.3	19.7	26.6	45.2
DE	Germany	23.6	15.8	-5.6	0.4	4.7	30.6
DK	Denmark	41.0	34.0	2.3	16.6	22.0	47.1
EA	Euro area (EA11-1999, EA12-2001, EA13-2007, EA15-2008, EA16-2009, EA17-2011, EA18-2014, EA19-2015, EA20-2023, EA21-2026)	30.5	25.5	8.4	16.4	21.5	35.3
EA21	Euro area – 21 countries (from 2026)	30.6	25.7	8.3	16.5	22.0	35.3
EE	Estonia	47.5	43.3	24.1	32.2	37.0	50.7
EL	Greece	38.8	33.5	4.3	16.1	22.1	51.2
ES	Spain	42.9	36.6	11.3	18.9	27.8	48.7
EU	European Union (EU6-1958, EU9-1973, EU10-1981, EU12-1986, EU15-1995, EU25-2004, EU27-2007, EU28-2013, EU27-2020)	31.9	28.4	6.5	16.3	23.7	36.6
EU27_2020	European Union - 27 countries (from 2020)	31.9	28.4	6.5	16.3	23.7	36.6
FI	Finland	19.5	15.1	4.8	11.2	8.0	24.2
FR	France	35.5	30.9	18.3	24.5	25.9	39.6
HR	Croatia	32.0	30.0	-1.0	13.6	23.5	37.2
HU	Hungary	30.6	25.3	8.5	17.2	20.4	36.5
IE	Ireland	42.3	32.7	-6.5	15.6	24.2	45.5
LT	Lithuania	57.9	50.7	4.0	34.1	42.6	59.3
LU	Luxembourg	31.6	27.4	8.7	14.6	20.4	36.8
LV	Latvia	38.9	34.8	14.2	22.4	30.5	46.1
MT	Malta	37.4	33.9	9.7	16.4	24.3	37.0
NL	Netherlands	23.9	14.4	-12.7	-5.2	-1.2	34.9
PL	Poland	56.3	53.7	31.2	43.4	48.3	60.0
PT	Portugal	44.0	37.5	21.4	25.2	30.8	46.3
RO	Romania	18.0	14.4	-29.3	-16.8	-1.7	30.5
SI	Slovenia	27.7	27.9	9.3	12.6	22.2	31.1
SK	Slovakia	38.1	36.1	5.2	21.9	27.6	42.3

2010

Code

icw_sr_12 %>%
  filter(time == "2010") %>%
  select(lev_diff, geo, Geo, values) %>%

  spread(lev_diff, values) %>%
  select(geo, Geo, everything()) %>%
  {if (is_html_output()) print_table(.) else .}

geo	Geo	EASY	FEASY	GRT	MOD	SOME	VEASY
AT	Austria	25.3	21.7	-7.5	4.5	10.9	30.6
BE	Belgium	18.2	11.8	-10.4	-4.9	3.9	27.5
BG	Bulgaria	54.1	51.4	26.1	36.4	44.1	NA
CY	Cyprus	31.9	19.9	-1.6	7.3	14.9	51.4
CZ	Czechia	31.3	28.6	14.8	18.9	23.3	41.6
DE	Germany	18.5	14.3	-5.2	1.1	6.1	26.4
DK	Denmark	8.1	4.4	-17.6	-14.8	-3.2	10.5
EE	Estonia	42.7	39.8	20.8	31.0	34.6	53.8
EL	Greece	9.1	0.4	-26.1	-11.7	-6.0	13.8
ES	Spain	29.6	22.8	-7.7	5.9	13.9	35.0
FI	Finland	25.6	21.4	-2.1	4.5	14.6	31.8
FR	France	39.6	33.5	15.5	20.1	26.8	49.8
HR	Croatia	22.1	15.5	-15.0	-1.9	11.5	29.3
HU	Hungary	25.2	25.4	11.0	14.4	19.5	46.5
IE	Ireland	32.1	23.0	9.0	9.5	18.0	36.4
IT	Italy	57.7	44.4	-6.3	12.8	28.4	55.6
LT	Lithuania	41.8	38.0	5.8	13.2	25.7	NA
LU	Luxembourg	34.2	27.7	3.8	14.8	17.8	37.0
LV	Latvia	21.6	22.6	-3.4	8.9	17.6	NA
MT	Malta	26.5	21.1	1.7	10.1	15.4	23.8
PL	Poland	35.2	30.8	12.5	19.6	25.5	41.2
PT	Portugal	26.7	20.7	0.6	11.0	13.0	28.7
RO	Romania	16.4	9.1	-17.4	-8.6	-1.4	NA
SE	Sweden	18.9	19.2	1.0	2.8	6.4	25.9
SI	Slovenia	26.3	23.3	1.2	12.1	19.6	25.9
SK	Slovakia	32.8	30.7	12.9	19.3	25.8	32.9
UK	United Kingdom	31.1	26.2	7.5	8.4	18.4	34.7

2015

Code

icw_sr_12 %>%
  filter(time == "2015") %>%
  select(lev_diff, geo, Geo, values) %>%

  spread(lev_diff, values) %>%
  select(geo, Geo, everything()) %>%
  {if (is_html_output()) print_table(.) else .}

geo	Geo	EASY	FEASY	GRT	MOD	SOME	VEASY
AT	Austria	26.1	23.1	7.5	8.3	17.2	30.1
BE	Belgium	26.0	21.5	2.1	11.8	13.0	33.6
BG	Bulgaria	33.3	28.5	4.9	15.7	25.1	34.0
CY	Cyprus	19.2	18.5	4.8	7.1	14.8	26.0
CZ	Czechia	32.8	29.8	11.6	19.2	25.4	41.6
DE	Germany	22.1	14.0	-8.6	-3.2	3.2	30.8
DK	Denmark	30.2	28.1	1.6	8.8	14.5	36.8
EA	Euro area (EA11-1999, EA12-2001, EA13-2007, EA15-2008, EA16-2009, EA17-2011, EA18-2014, EA19-2015, EA20-2023, EA21-2026)	26.3	23.1	0.7	11.2	21.0	33.0
EA21	Euro area – 21 countries (from 2026)	26.3	23.1	0.4	11.2	21.0	33.0
EE	Estonia	45.6	39.5	18.6	25.9	31.3	47.8
EL	Greece	15.3	10.8	-14.6	-4.2	1.2	34.1
ES	Spain	31.5	26.8	-0.9	10.9	19.0	40.4
EU	European Union (EU6-1958, EU9-1973, EU10-1981, EU12-1986, EU15-1995, EU25-2004, EU27-2007, EU28-2013, EU27-2020)	26.7	23.4	0.3	10.5	19.3	32.1
EU27_2020	European Union - 27 countries (from 2020)	27.1	24.3	2.0	12.1	21.2	33.4
FI	Finland	23.2	19.2	0.9	3.5	13.1	29.2
FR	France	40.5	37.4	27.8	26.9	32.6	50.6
HR	Croatia	24.6	20.4	-17.7	0.9	12.0	36.5
HU	Hungary	24.2	19.3	9.2	11.1	15.1	44.2
IE	Ireland	32.1	28.8	17.9	20.3	24.5	37.5
LT	Lithuania	48.1	42.5	8.0	22.5	32.8	66.3
LU	Luxembourg	33.0	27.3	0.9	15.0	19.8	37.3
LV	Latvia	33.6	32.8	11.2	20.0	28.0	34.0
MT	Malta	31.1	21.1	1.5	9.6	11.2	37.0
NL	Netherlands	21.0	16.7	-9.0	-0.5	7.1	29.7
PL	Poland	39.2	35.4	11.5	22.6	28.9	40.7
PT	Portugal	31.2	26.7	6.2	13.8	19.7	36.1
RO	Romania	26.6	21.8	-1.7	6.4	14.5	24.4
SE	Sweden	26.3	24.3	7.4	15.1	13.9	32.1
SI	Slovenia	34.0	30.4	-4.2	13.1	23.0	42.1
SK	Slovakia	24.0	23.6	6.7	14.3	17.6	31.2
UK	United Kingdom	24.5	18.9	-13.0	-4.4	6.8	28.3

Germany, France, Sweden

Code

icw_sr_12 %>%
  filter(time == "2015",
         geo %in% c("SE", "FR", "DE")) %>%
  
  ggplot + geom_line(aes(x = lev_diff, 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, 0.9),
        legend.title = element_blank()) +
  xlab("Self-declared level of difficulty to make ends meet") + ylab("Median saving rate") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1))