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SSRN Electronic Journal
Article . 2014 . Peer-reviewed
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The ICTD Government Revenue Dataset

Authors: Wilson Prichard; Alex Cobham; Andrew Goodall;

The ICTD Government Revenue Dataset

Abstract

A major obstacle to cross-country research on the role of revenue and taxation in development has been the weakness of available data. This paper presents a new Government Revenue Dataset (GRD), developed through the International Centre for Tax and Development (ICTD). The dataset meticulously combines data from several major international databases, as well as drawing on data compiled from all available International Monetary Fund (IMF) Article IV reports. It achieves marked improvements in data coverage and accuracy, including a standardised approach to revenue from natural resources, and holds the promise of significant improvement in the credibility and robustness of research in this area. This paper sets out the issues with existing sources and explains the process of creating the new dataset, including a discussion of remaining limitations. It then presents data on tax and revenue trends over the past two decades, while a concluding section briefly considers potential strategies for, and barriers to, more effective data collection in future.

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    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
74
Top 10%
Top 10%
Top 10%
bronze