Share of women in parliament, annual

Commercial use OKFreshLicense: CC-BY-SA-4.0

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Country-year panel of the percentage of women in the lower (or unicameral) chamber of the national legislature — V-Dem v2lgfemleg, Country-Year Dataset v16 (2026 release), 1900-2025, ~200 political systems. With within-year share rank, 10-year change in percentage points, and a parity band. Raw data: V-Dem Institute, v16 (CC BY-SA 4.0), via Our World in Data.

Source: V-Dem women in parliament
Rows: 17,148
Columns: 10
Source cadence: Yearly
Last refreshed: Oct 7, 2026
Theme: government

Column	Type	Description
entity	string	V-Dem political system / country name as published (includes separately coded Palestine/West Bank and OWID population-weighted regional aggregates such as 'World (population-weighted)').
country_code	string	ISO 3166-1 alpha-3 code where the entity maps to one (plus OWID pseudo-codes such as OWID_SML for Somaliland); null for regional aggregates and separately-coded territories.
year	integer	Observation year (V-Dem v16 country-year, 1900-2025; OWID backfills pre-sovereignty years from predecessor polities — see module docstring). (unit: year)
world_region	string	OWID world-region assignment for the entity ('World region according to OWID'); null for regional aggregates.
women_parliament_share_pct	float	Share of women in the lower (or unicameral) chamber of the national legislature, percent (V-Dem v2lgfemleg; SDG indicator 5.5.1 territory). (unit: percent)
share_rank	integer	Within-year rank of the share among coded entities (1 = highest share, ties share the best rank); null for regional aggregates and separately-coded territories. (unit: rank)
change_10y_pp	float	Absolute 10-year change in the share, in percentage points (share[year] minus share[year-10] for the same entity); null where the base year is missing. (unit: percentage_points)
parity_band	string	Coarse representation band: very_low (<10%), low (10-30%), high (30-50%), parity_plus (>=50% — at or above gender parity).
fetch_date	string	Date this snapshot was fetched (ISO date). (unit: date)
row_hash	string	Deterministic 12-hex row identity hash (entity\|year).

First 10 sample rows — a preview, not the complete dataset.

entity	country_code	year	world_region	share_rank	change_10y_pp	parity_band	fetch_date	row_hash
Africa	OWID_AFR	1,900	—	1	—	very_low	2026-10-07	eb76a4986e32
Algeria	DZA	1,900	Africa	1	—	very_low	2026-10-07	af96c410b233
Argentina	ARG	1,900	South America	1	—	very_low	2026-10-07	ac431001647c
Asia	OWID_ASI	1,900	—	1	—	very_low	2026-10-07	ca096d607e9f
Bangladesh	BGD	1,900	Asia	1	—	very_low	2026-10-07	1e59b3d80d54
Barbados	BRB	1,900	North America	1	—	very_low	2026-10-07	9c661e8fa818
Belgium	BEL	1,900	Europe	1	—	very_low	2026-10-07	41a742cf0154
Bolivia	BOL	1,900	South America	1	—	very_low	2026-10-07	6289ab067617
Brazil	BRA	1,900	South America	1	—	very_low	2026-10-07	da35f8567990
Bulgaria	BGR	1,900	Europe	1	—	very_low	2026-10-07	eca6f648706f

Profiled Oct 7, 2026 from snapshot 20261007T003642Z-177c626cafa1

Measured

Completeness: 96.4%
Rows: 17,148
Columns: 10
Columns with gaps: 4

Rows per yearyear · 1900–2025 · 126 years

19002025

Column	Missing	Distinct	Range	Distribution
entityvarchar	0%	186	—	Africa126 Asia126 Canada126
country_codevarchar	4.2%	242	—	CAN126 CHE126 FIN126
yearbigint	0%	138	1,900 → 2,025median 1,977	131 outside 1st–99th percentile
world_regionvarchar	9.6%	5	—	Europe4,224 Asia3,961 Africa3,790
women_parliament_share_pctdouble	0%	2,735	0 → 63.8median 5.12	170 outside 1st–99th percentile
share_rankbigint	4.2%	205	1 → 180median 55	156 outside 1st–99th percentile
change_10y_ppdouble	18.4%	3,844	-32.55 → 44.45median 1.14	279 outside 1st–99th percentile
parity_bandvarchar	0%	4	—	very_low10,833 low4,916 high1,347
fetch_datevarchar	0%	1	—	2026-10-0717,148
row_hashvarchar	0%	16,431	—	0003e79f9dc51 0005eabcb2411 000725dcf22b1

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/vdem_women_parliament_intel/women_parliament_share_annual" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/vdem_women_parliament_intel/women_parliament_share_annual").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/vdem_women_parliament_intel/women_parliament_share_annual

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20261007T003642Z-177c626cafa1 and its content hash, so readers get exactly the data you used.

APA

V-Dem women in parliament. (2026). Share of women in parliament, annual [Data set, snapshot 20261007T003642Z-177c626cafa1, sha256 177c626cafa1]. Datazimuts. Retrieved 2026-10-07, from https://datazimuts.com/en/datasets/vdem_women_parliament_intel/women_parliament_share_annual?snapshot=20261007T003642Z-177c626cafa1

BibTeX

@misc{dz_vdem_women_parliament_intel_women_parlia_177c626c,
  title        = {{Share of women in parliament, annual}},
  author       = {{V-Dem women in parliament}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/vdem_women_parliament_intel/women_parliament_share_annual?snapshot=20261007T003642Z-177c626cafa1}},
  note         = {Snapshot 20261007T003642Z-177c626cafa1, sha256 177c626cafa130213aa56bb0a9707ed754f01fa7b4e60db3934faca0be1758ad; accessed 2026-10-07}
}

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