Gender Equality Index intersectionalities index__index_intersect

Time format:
Years
Unit:
Various (see name of selected indicator) (comparable)
Description:

Developed at EIGE, The Gender Equality Index is a unique measurement tool that synthesises the complexity of gender equality as a multi-dimensional concept into a user-friendly and easily interpretable measure. It is formed by combining gender indicators, according to a conceptual framework, into a single summary measure.

The Index measures gender gaps that are adjusted to levels of achievement, ensuring that gender gaps cannot be regarded positively where they point to an adverse situation for both women and men. It assigns scores for Member States, between 1, total inequality and 100, full equality.

The Gender Equality Index consists of six core domains (work, money, knowledge, time, power and health) and two additional domains (intersecting inequalities and violence). Given the different nature of the latter two domains, only the core domains can be used in the computation of the Gender Equality Index score.

The Index is computed as follows. First, achievement-adjusted gender gaps are computed for all constituent metrics. Next, gaps for closely related metrics are combined to obtain sub-domain scores. The subdomain scores are then combined to obtain domain scores. Finally, the domain scores are combined to obtain the overall Gender Equality Index score.

Note that in the Gender Statistics Database, the indicators used to calculate the Gender Equality Index, domain and sub-domain scores are reported according to the year the Gender Equality Index was published, rather than the year data was available. For example, the 2023 Gender Equality Index was mostly based on data from 2021 as this was often the latest data available at the time the index was calculated. In the Index area of EIGE's database, data for each of the indicators used is always reported under the Index year rather than the actual year that the data refer to.

A table detailing the specific data used to calculate each edition of the Gender Equality Index is available from the link below. It lists each of the indicators used under each domain/sub-domain of the index, the reference year of the data used, and details of any exceptions (e.g. when a different year is used for certain countries or when there is a break in the time-series).

List of indicators used in the Gender Equality Index

Whilst the indicators used to calculate the Index always cover the whole of the relevant population, the combination (or intersection) of gender and other characteristics can confer additional disadvantages and greater gender gaps. For each of the indicators used in the Index for which some additional breakdown is possible, this view presents data on the available intersectionalities such as family type, age group, level of education, country of birth, and disability. The link below provides access to detailed tables of metadata about the indicators and the breakdowns used for each Index edition.

Metadata about the indicators used to measure intersecting inequalities

PL
AT
CY
NL
RO
ES
LV
PT
BG
CZ
SK
FI
EL
HR
LT
SE
EU27_2020
LU
BE
IE
IT
FR
HU
SI
MT
DE
DK
EE
work_fte
work_empl_ehs
work_flex
work_career
money_earn
money_inc
money_arop
money_incdist
know_tert
know_life
time_care
time_domestic
time_leisure
time_volunt
health_stat
health_med
health_dent
PL
72(d)
26(d)
(z)
63
1888
17695
13
28
61(d)
10(d)
83(b)
82(b)
46(b)
10(b)
84
5
2
AT
56(d)
31(d)
(z)
66
2345
26837
13
19
44(d)
18(d)
80(b)
78(b)
26(b)
16(b)
82
1
1
CY
74(d)
26(d)
(z)
52
2250
23935
12
22
69(d)
13(d)
79(b)
91(b)
6(b)
5(b)
90
0
2
NL
66(d)
46(d)
(z)
64
2907
30818
8
19
58(d)
20(d)
79(b)
76(b)
39(b)
13(b)
81
1
1
RO
67(d)
24(d)
(z)
69
1752
11643
19
21
33(d)
5(d)
79(b)
74(b)
25(b)
19(b)
95
2
5
ES
67(d)
28(d)
(z)
58
1992
19416
21
18
53(d)
19(d)
76(b)
73(b)
35(b)
11(b)
81
3
5
LV
77(d)
30(d)
(z)
67
1635
17582
12
30
61(d)
21(d)
74(b)
87(b)
17(b)
8(b)
74
7
8
PT
87(d)
28(d)
(z)
58
1750
14884
13
23
43(d)
20(d)
74(b)
87(b)
25(b)
9(b)
69
4
6
BG
83(d)
18(d)
(z)
67
1507
14130
17
12
49(d)
3(d)
73(b)
84(b)
23(b)
6(b)
91
2
3
CZ
63(d)
29(d)
(z)
64
1541
17620
8
24
40(d)
9(d)
73(b)
68(b)
23(b)
6(b)
89
2
2
SK
80(i)
24(i)
(z)
66
976(i)
9590(i)
16(i)
28(i)
49(i)
13(i)
72(b)
73(b)
29(b)
9(b)
88
5
4
FI
78(d)
44(d)
(z)
67
2856
25926
6
35
49(d)
30(d)
70(b)
76(b)
34(b)
4(b)
81
6
11
EL
63(d)
29(d)
(z)
54
1568
12124
21
18
42(d)
5(d)
69(b)
75(b)
24(b)
11(b)
95
6
19
HR
79(d)
30(d)
(z)
61
1660
14702
14
30
43(d)
5(d)
67(b)
90(b)
8(b)
4(b)
89
2
1
LT
87(d)
21(d)
(z)
68
1961
20373
11
27
67(d)
11(d)
66(b)
83(b)
15(b)
3(b)
83
3
3
SE
84(d)
41(d)
(z)
74
2501
23563
10
29
59(d)
45(d)
66(b)
72(b)
29(b)
10(b)
76
5
2
EU27_2020
66(d)
32(d)
(z)
63
2293
22323
14
24
46(d)
13(d)
65(b)
72(b)
29(b)
12(b)
83
3
4
LU
76(d)
30(d)
(z)
71
3626(b)
35530(b)
19(b)
19(b)
64(d)
25(d)
64(b)
82(b)
40(b)
6(b)
79(b)
3(b)
2(b)
BE
71(d)
40(d)
(z)
68
3117
27493
9
32
56(d)
14(d)
63(b)
76(b)
28(b)
11(b)
84
1
3
IE
67(d)
41(d)
(z)
67
2644
23453
10
22
70(d)
13(d)
62(b)
76(b)
9(b)
8(b)
88
4
2
IT
52(d)
27(d)
(z)
52
2037
20105
23
21
30(d)
9(d)
60(b)
76(b)
27(b)
13(b)
88
1
1
FR
72(d)
36(d)
(z)
65
2474(b)
25858(b)
12(b)
22(b)
53(d)
17(d)
57(b)
75(b)
28(b)
8(b)
77(b)
7(b)
9(b)
HU
80(d)
24(d)
(z)
67
1351
11786
7
19
42(d)
8(d)
54(b)
76(b)
24(b)
12(b)
86
4
2
SI
85(d)
33(d)
(z)
62
2230
20850
7
18
66(d)
28(d)
53(b)
75(b)
27(b)
12(b)
82
6
6
MT
70(d)
32(d)
(z)
69
2305
26806
14
23
41(d)
17(d)
51(b)
81(b)
14(b)
4(b)
90
0
0
DE
56(d)
36(d)
(z)
67
2708
27882
11
25
36(d)
7(d)
45(b)
47(b)
23(b)
16(b)
83
1
1
DK
77(d)
52(d)
(z)
72
3768
29322
4
31
66(d)
37(d)
37(b)
58(b)
32(b)
22(b)
76
11
12
EE
78(d)
27(d)
(z)
69
1759
23072
10
29
62(d)
30(d)
31(b)
67(b)
23(b)
5(b)
83
12
2

Available flags:

b break in time series c confidential
d definition differs, see metadata e estimated
f forecast i see metadata
m imputed n not significant
p provisional r revised
s Eurostat estimate u low reliability
x dropped due to insufficient sample size y unreliable due to small sample size
z not applicable