Data

Political corruption index

(best estimate, aggregate: average)
See all data and research on:

What you should know about this indicator

Question: How pervasive is political corruption?

Clarification: The directionality of the V-Dem corruption index runs from less corrupt to more corrupt unlike the other V-Dem variables that generally run from less democratic to more democratic situation. The corruption index includes measures of six distinct types of corruption that cover both different areas and levels of the polity realm, distinguishing between executive, legislative and judicial corruption. Within the executive realm, the measures also distinguish between corruption mostly pertaining to bribery and corruption due to embezzlement. Finally, they differentiate between corruption in the highest echelons of the executive at the level of the rulers/cabinet on the one hand, and in the public sector at large on the other. The measures thus tap into several distinguished types of corruption: both 'petty' and 'grand'; both bribery and theft; both corruption aimed and influencing law making and that affecting implementation.

Scale: Interval, from low to high (0-1).

Indicator name: v2x_corr

Political corruption index
(best estimate, aggregate: average)
Best estimate of the extent to which a country is affected by political corruption.
Source
V-Dem (2024) – processed by Our World in Data
Last updated
March 7, 2024
Next expected update
March 2025
Date range
1789–2023

Sources and processing

This data is based on the following sources

The Varieties of Democracy (V-Dem) project publishes data and research on democracy and human rights.

It acknowledges that democracy can be characterized differently and measures electoral, liberal, participatory, deliberative, and egalitarian characterizations of democracy.

The project relies on evaluations by around 3,500 country experts and supplementary work by its researchers to assess political institutions and the protection of rights.

The project is managed by the V-Dem Institute, based at the University of Gothenburg in Sweden.

This snapshot contains all 500 V-Dem indicators and 245 indices + 57 other indicators from other data sources.

Retrieved on
March 18, 2024
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.
Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Cecilia Borella, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Oskar Rydén, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson and Daniel Ziblatt. 2024. "V-Dem Country-Year Dataset v14" Varieties of Democracy (V-Dem) Project. https://doi.org/10.23696/mcwt-fr58;
Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2024. “The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data”. V-Dem Working Paper No. 21. 9th edition. University of Gothenburg: Varieties of Democracy Institute

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

The regional aggregates (including values for the World) have been estimated by averaging the country values.

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: Political corruption index”, part of the following publication: Bastian Herre, Lucas Rodés-Guirao, Esteban Ortiz-Ospina and Max Roser (2013) - “Democracy”. Data adapted from V-Dem. Retrieved from https://ourworldindata.org/grapher/political-corruption-index [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:

V-Dem (2024) – processed by Our World in Data

Full citation

V-Dem (2024) – processed by Our World in Data. “Political corruption index – (best estimate, aggregate: average)” [dataset]. V-Dem, “V-Dem Country-Year (Full + Others) v14” [original data]. Retrieved July 20, 2024 from https://ourworldindata.org/grapher/political-corruption-index