Public integrity is a public good resulting from an equilibrium between opportunities for corruption in a society (such as power discretion and material resources) and constraints that autonomous organizations (e.g. the judiciary, media), groups (civil society) and individuals (voters, whistleblowers) can use to prevent power holders from abusing office in their own interest.

The Index of Public Integrity (IPI) aims to capture a snapshot of this balance in 114 countries for which data is available. It is a composite index consisting of six components. For the 2015, 2017 and 2019 editions, the components were: administrative burden, trade openness, budget transparency for opportunities, and judicial independence, e-citizenship and freedom of the press for constraints.

Starting from the 2021 edition, administrative burden and trade openness have been replaced by administrative transparency and online services, due to unavailable alternative data on the original components (based on the World Bank Doing Business project, which closed).. Below you will find an outline of the methodology behind the IPI, detailing recent changes.

A more extensive explanation of the methodology and the original composition of the IPI can be found in the following peer-reviewed publication:

Measuring Control of Corruption by a New Index of Public Integrity – Mungiu-Pippidi, A., Dadašov, R. Measuring Control of Corruption by a New Index of Public Integrity. European Journal on Criminal Policy Research 22, 415–438 (2016).

The six components of the IPI were selected based on years of theoretical and empirical research on the control of corruption. They are nested in the theoretical framework proposed by Alina Mungiu-Pippidi and her team at the European Research Centre for Anti-Corruption and State-Building (ERCAS). The components adumbrate the idea that corruption reflects a national-level equilibrium between resources and constraints.

Based on previous research, components were selected which reflect that theoretical framework and which are significantly associated with most common measurements of control of corruption. Tests also took into account differences in the levels of socio-economic development across countries by controlling for the Human Development Index (HDI). See the results here. In the process, particular attention was paid to choosing components which are also objective and actionable and thus can help in the design of evidence-based reform strategies.

Starting with the 2021 edition of the IPI, two original components had to be replaced due to issues related to the underlying data, namely the Doing Business Survey conducted by the World Bank. The series was fully discontinued, but direct replacements for those components could not be identified from other sources. Thus, two alternative components were devised that should in part capture what the indicators for administrative burden and trade openness were measuring: administrative transparency, based on a fraction of ERCAS’s own Transparency Index, and online services, based on the Online Services Index computed as part of the UN E-Government Development Index. The statistical validation tests indicate that the internal consistency of the index was preserved in the new composition.

In the current version, five of the components (budget transparency, administrative transparency, online services, judicial independence, freedom of the press) each rely on a single data source. Those components were standardized by constructing the so-called z-score of the variable, in order to equalize their mean values and standard deviations. For budget transparency, the mean score for the individual items considered was extracted and then standardized; administrative transparency in turn consists of the sum of individual components from the Transparency Index, which was then similarly standardized into z-scores. The final component, e-citizenship, is the only one based on different data sources. Its individual sub-components were standardized separately and then averaged.

Every final component score was then normalized to range between 1 and 10 using a min-max-transformation with higher values representing better performance in this issue area. The overall IPI was finally derived by the equally weighted average of all components.

The decision to assign equal weights resulted from a replication fo the original methodology in Mungiu-Pippidi and Dadasov 2016, when the index was first built by principal component analysis, then the impact (upload) of every component was measured. The new components, as well as the original ones fared very closely in their contribution to the component. This lead to the decision to assign equal weights and use a simpler average to build the index. The resulting aggregate correlates at 90% with the component.

The standardization procedure ensures that the IPI does not depend strongly on the component with the greatest dispersion. A country can score badly for one component, but still do well on the overall IPI. Such a situation would reflect overall a good level of control of corruption in the country, with scope for improvement in one reform area.