Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015
By Matti Kummu, M. Taka, Joseph H. A. Guillaume. Scientific Data, 2018
657 citations recorded by the source corpus.
An increasing amount of high-resolution global spatial data are available, and used for various assessments. However, key economic and human development indicators are still mainly provided only at national level, and downscaled by users for gridded spatial analyses. Instead, it would be beneficial to adopt data for sub-national administrative units where available, supplemented by national data where necessary. To this end, we present gap-filled multiannual datasets in gridded form for Gross Domestic Product (GDP) and Human Development Index (HDI). To provide a consistent product over time and space, the sub-national data were only used indirectly, scaling the reported national value and thus, remaining representative of the official statistics. This resulted in annual gridded datasets for GDP per capita (PPP), total GDP (PPP), and HDI, for the whole world at 5 arc-min resolution for the 25-year period of 1990–2015. Additionally, total GDP (PPP) is provided with 30 arc-sec resolution for three time steps (1990, 2000, 2015). Design Type(s) data integration objective • data refinement and optimization objective Measurement Type(s) Gross Domestic Product • Human Development Index Technology Type(s) digital curation Factor Type(s) Design Type(s) data integration objective • data refinement and optimization objective Measurement Type(s) Gross Domestic Product • Human Development Index Technology Type(s) digital curation Factor Type(s) Machine-accessible metadata file describing the reported data (ISA-Tab format)
Subfields: Economics and Econometrics, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/60aa138c171b9564a6d8d1a6682170dd65989e40
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In Economics and Econometrics
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Citations in the corpus
- The Economic Geography of Global Warming (2021, background): “An alternative approach that would give us a longer time series would be to employ data on production from Kummu et al. (2018).”