Blavatnik Index of Public Administration

The articles below provide detailed explanations of the methodology used to calculate the Blavatnik Index of Public Administration, including how we define the scope of the Index and its conceptual framework, how we have selected data sources and metrics, how we processes data and calculate the Index. The methodology is open source, and you can view the code behind the Index on Github.

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Background

Why we believe an index of public administration is needed.
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Conceptual framework

The framework the Index uses to describe the facets of public administrations and civil services.
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Data model, methodological principles and technical approach

Core principles and approaches to operationalising the index.
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Defining the Index's conceptual scope

How do we define our unit(s) of our analysis for the Blavatnik Index.
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Defining the Index's geographic scope

Adopting a more global coverage than the InCiSE Index.
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Selection of data sources

How we have selected sources for inclusion in the Blavatnik Index of Public Administration.
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Processing of data sources

How we import and process the original sources used to compile the Blavatnik Index.
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Alignment of sources to the Index's conceptual framework

How we have allocated data from sources to the domains and themes of the Index’s conceptual framework and data model.
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Data coverage and country selection

How we assess country data coverage and use this to determine the countries in the final Index.
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Data aggregation and Index calculation

How we aggregate data from source metrics into the final Index scores, including how we handle missing data and weighting.
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Sensitivity analysis

How we have tested the robustness of different aspects the Index’s methodology.
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Geographic entities reference list and country classifications

Our underlying reference documents for countries and geographic entities, regional and income classifications.
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Implied weighting structure

The weighting structure for metrics that is created by the Index’s data model.
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Pre-processing of data sources

Details of the pre-processing we apply to four of the sources used in the Index.
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Re-scaling and transformation of data

How we normalise source data into metrics with a common 0 to 1 score.