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What you should know about this indicator

  • This data comes from the LGBTI National Policy Dataset, compiled by Kristopher Velasco at Princeton University. It tracks the adoption, scope, and enforcement of 27 LGBTI-related policies across 197 countries, with annual data starting in 1991.
  • The indicator is coded as follows: Self-declaration is enough (legal change of the gender marker is possible based on self-declaration), Diagnosis required (legal change requires a medical or psychological diagnosis), Surgery required (legal change requires gender-affirming surgery), Surgery and sterilization required (legal change requires both gender-affirming surgery and sterilization), Legally possible, requirement unknown (legal change is in effect but the procedural requirement is not recorded in the source), Varies by region (rules differ between subnational jurisdictions, or legal change is only partially in effect) and Not legally possible (no legal procedure exists to change the gender marker on official identity documents).
Gender marker can be legally changed
Velasco
Whether a legal process exists to change the gender marker on official identity documents, and what the process requires — self-declaration, medical or psychological diagnosis, surgery, or surgery plus sterilization.
Source
Velasco (2026) – with major processing by Our World in Data
Last updated
May 11, 2026
Next expected update
May 2027
Date range
1991–2025

Sources and processing

This data is based on the following sources

Velasco – LGBTI National Policy Dataset

The LGBTI National Policy Dataset (Version 2.0) is a country-year-policy panel dataset tracking the adoption, scope, and enforcement of LGBTI-related policies across 197 countries from 1991 to 2025. The dataset is distributed in long format, where each row represents a single country in a single year for one specific policy-status combination. It is designed for use in comparative political research, policy diffusion analysis, and quantitative cross-national studies of LGBTI rights.

The dataset covers 27 distinct policy areas organized into 54 policy-status combinations (each policy paired with two mutually exclusive status directions). Of these 54 combinations, 36 contain substantive data; the remaining 18 are structural placeholders with all-zero values, included to maintain a fully balanced panel suitable for econometric methods.

Retrieved on
May 11, 2026
Retrieved from
https://osf.io/wta9p/overview
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Velasco, Kristopher. 2026. "LGBTI National Policy Dataset, Version 2.0." [Dataset].

The LGBTI National Policy Dataset (Version 2.0) is a country-year-policy panel dataset tracking the adoption, scope, and enforcement of LGBTI-related policies across 197 countries from 1991 to 2025. The dataset is distributed in long format, where each row represents a single country in a single year for one specific policy-status combination. It is designed for use in comparative political research, policy diffusion analysis, and quantitative cross-national studies of LGBTI rights.

The dataset covers 27 distinct policy areas organized into 54 policy-status combinations (each policy paired with two mutually exclusive status directions). Of these 54 combinations, 36 contain substantive data; the remaining 18 are structural placeholders with all-zero values, included to maintain a fully balanced panel suitable for econometric methods.

Retrieved on
May 11, 2026
Retrieved from
https://osf.io/wta9p/overview
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Velasco, Kristopher. 2026. "LGBTI National Policy Dataset, Version 2.0." [Dataset].

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline
Notes on our processing step for this indicator

From the original dataset, we combined the gender-marker-change proportion (the legal direction) with the recorded legal-pathway requirement to produce a single categorical indicator. When legal change of the gender marker is fully in effect, we classify countries by their procedural requirement ("Self-declaration is enough", "Diagnosis required", "Surgery required", "Surgery and sterilization required", or "Legally possible, requirement unknown" when the requirement is not recorded). Otherwise the country is classified as "Varies by region" (legal change is only partially in effect across subnational jurisdictions) or "Not legally possible" (no legal procedure exists).

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Gender marker can be legally changed”, part of the following publication: Bastian Herre and Pablo Arriagada (2023) - “LGBT+ Rights”. Data adapted from Velasco. Retrieved from https://claude-cms-base.owid.pages.dev/grapher/gender-marker-can-be-legally-changed [online resource]

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Velasco (2026) – with major processing by Our World in Data

Full citation

Velasco (2026) – with major processing by Our World in Data. “Gender marker can be legally changed – Velasco” [dataset]. Velasco, “LGBTI National Policy Dataset 2.0” [original data]. Retrieved June 12, 2026 from https://claude-cms-base.owid.pages.dev/grapher/gender-marker-can-be-legally-changed

Download

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Download the data shown in this chart as a ZIP file containing a CSV file, metadata in JSON format, and a README. The CSV file can be opened in Excel, Google Sheets, and other data analysis tools.

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Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://claude-cms-base.owid.pages.dev/grapher/gender-marker-can-be-legally-changed.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://claude-cms-base.owid.pages.dev/grapher/gender-marker-can-be-legally-changed.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://claude-cms-base.owid.pages.dev/grapher/gender-marker-can-be-legally-changed.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://claude-cms-base.owid.pages.dev/grapher/gender-marker-can-be-legally-changed.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://claude-cms-base.owid.pages.dev/grapher/gender-marker-can-be-legally-changed.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://claude-cms-base.owid.pages.dev/grapher/gender-marker-can-be-legally-changed.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://claude-cms-base.owid.pages.dev/grapher/gender-marker-can-be-legally-changed.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://claude-cms-base.owid.pages.dev/grapher/gender-marker-can-be-legally-changed.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear