Generating Gridded Agricultural Gross Domestic Product for Brazil : A Comparison of Methodologies
This paper examines two new methods to generate gridded agricultural Gross Domestic Product (GDP) and compares the results with a traditional method. In the case of Brazil, these two new methods of spatial disaggregation and cross-entropy outperfor...
| Main Authors: | , , , , , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2019
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/677071566217273585/Generating-Gridded-Agricultural-Gross-Domestic-Product-for-Brazil-A-Comparison-of-Methodologies http://hdl.handle.net/10986/32310 |
| Summary: | This paper examines two new methods to
generate gridded agricultural Gross Domestic Product (GDP)
and compares the results with a traditional method. In the
case of Brazil, these two new methods of spatial
disaggregation and cross-entropy outperform the prediction
of agricultural GDP from the traditional method that
distributes agricultural GDP using rural population. The
paper finds that the best prediction method is spatial
disaggregation using a regression approach for all the key
crops and contributors to agricultural GDP. However, the
issue of degrees of freedom is an important limiting factor,
as the approach requires sufficient subnational data. The
cross-entropy method with readily available spatially
distributed crop, livestock, forest, and fish allocation far
outperforms the traditional method, at least in the case of
Brazil, and can operate with national- and/or
subnational-level data. |
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