Regional assemblies: presidents and members (Romania) wmidm_pol_parl_regio__wmid_region_ro

Time format:
Years
Unit:
Percentage of total (comparable)
Unit:
Head count
Description:

The Gender Statistics Database (GSD) of the European Institute for Gender Equality (EIGE) contains data on the numbers of women and men in key decision-making positions across a number of different life domains. Data may cover international, European, national, regional and local levels and currently include 37 European countries. Data covered the United Kingdom up to 2024. The GSD aims to provide reliable statistics that can be used to monitor the current situation and trends over time.

The domains covered include politics, public administration, judiciary, business and finance, social partners and NGOs, environment, media, science and research, sports, transport, COVID-19, and EU funding. The decision-making positions covered are specific to each area.

A decision-making position is a position from which it is possible to take or influence a decision:

  • within a domain: at organisational level. This restricts coverage to organisations having a major influence in the domain at the territorial level of interest, which is usually national but can also be international, European, regional or local;
  • within an organisation: at hierarchical level. This restricts coverage to positions within the hierarchy that have a major input to decision-making within the organisation.

WMID Methodological report

The politics domain includes statistics on women and men amongst politicians at the European, national, regional and local levels. Figures include executive bodies (e.g. governments), representative bodies (parliaments and councils) as well as major political parties. Data on regional politics cover the presidents and members of the regional assemblies and executives in each country (where applicable). 

Organisations covered:

  • Regional assemblies: the representative assembly of a region (i.e. regional authority) which is composed of popularly elected representatives of constituent self-governing regions. Note that the different terms used in each country - e.g. regional parliament, regional council and regional assembly - are all treated as being equivalent.
  • Regional executives: a person or a body exercising executive functions on behalf of a region (regional authority) where these are not exercised by the representative body.

The term region refers to regional authorities that are endowed with self-government acting as the territorial authorities between the central government and local authorities. This does not necessarily imply a hierarchical relationship between the regional and local authorities. Regional self-government denotes the legal competence and the ability of regional authorities, within the limits of the constitution and the law, to regulate and manage a share of public affairs under their own responsibility, in the interests of the regional population and in accordance with the principle of subsidiarity.

Positions covered:

  • President
  • Members (count includes the president)

Mapping tables:

NUTS classification mapping and % of population covered:

Within the EU, data at regional level are normally compiled using the NUTS (Nomenclature of Territorial Units for Statistics) classification. This is a hierarchical classification with 3 levels (NUTS 1 being the highest and NUTS 3 the lowest). The regions endowed with powers of self-government covered in WMID are, however, specific to each country and do not always align with the NUTS classification. Furthermore, the restricted definition applied to the coverage means the whole population is not covered in some countries. The file below provides the closest possible mapping of the region codes used in the Gender Statistics Database and the NUTS codes. It also contains information on the percentage (%) of the total population covered by WMID data on regions for each country, calculated based on Eurostat's 2018 data on population by region (based on NUTS 2 and NUTS 3 categories).

RO_ALBA
RO_ALL
RO_ARAD
RO_ARGE
RO_BACA
RO_BIHO
RO_BIST
RO_BOTO
RO_BRAI
RO_BRAS
RO_BUCU
RO_BUZA
RO_CALA
RO_CARA
RO_CLUJ
RO_CONS
RO_COVA
RO_DAMB
RO_DOLJ
RO_GALA
RO_GIUR
RO_GORJ
RO_HARG
RO_HUNE
RO_IALO
RO_IASI
RO_ILFO
RO_MARA
RO_MEHE
RO_MURE
RO_NEAM
RO_OLT
RO_PRAH
RO_SALA
RO_SATU
RO_SIBI
RO_SUCE
RO_TELE
RO_TIMI
RO_TULC
RO_VALC
RO_VASL
RO_VRAN
T
M
W
RO_ALBA
100
81.3
18.8
RO_ALL
100
76.6
23.4
RO_ARAD
100
66.7
33.3
RO_ARGE
100
74.3
25.7
RO_BACA
100
64.9
35.1
RO_BIHO
100
82.1
17.9
RO_BIST
100
68.8
31.3
RO_BOTO
100
81.3
18.8
RO_BRAI
100
67.7
32.3
RO_BRAS
100
88.6
11.4
RO_BUCU
100
76
24
RO_BUZA
100
68.8
31.3
RO_CALA
100
87.1
12.9
RO_CARA
100
80
20
RO_CLUJ
100
80.6
19.4
RO_CONS
100
73
27
RO_COVA
100
87.1
12.9
RO_DAMB
100
60
40
RO_DOLJ
100
73
27
RO_GALA
100
62.9
37.1
RO_GIUR
100
87.1
12.9
RO_GORJ
100
67.7
32.3
RO_HARG
100
87.1
12.9
RO_HUNE
100
63.6
36.4
RO_IALO
100
77.4
22.6
RO_IASI
100
75
25
RO_ILFO
100
77.8
22.2
RO_MARA
100
84.8
15.2
RO_MEHE
100
86.2
13.8
RO_MURE
100
91.4
8.6
RO_NEAM
100
91.4
8.6
RO_OLT
100
81.3
18.8
RO_PRAH
100
68.6
31.4
RO_SALA
100
80.6
19.4
RO_SATU
100
75.8
24.2
RO_SIBI
100
75
25
RO_SUCE
100
75.7
24.3
RO_TELE
100
80.6
19.4
RO_TIMI
100
77.8
22.2
RO_TULC
100
71
29
RO_VALC
100
63.6
36.4
RO_VASL
100
80
20
RO_VRAN
100
81.8
18.2

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