| Literature DB >> 26678260 |
Mathias Leidig, Richard M Teeuw.
Abstract
Year: 2015 PMID: 26678260 PMCID: PMC4682997 DOI: 10.1371/journal.pone.0145591
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Example scores of the Data Poverty Index (DPI) and relationships to the World Bank income classification.
| Top Scores | Bottom Scores | ||
|---|---|---|---|
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| 1. Iceland | 0.17 | .. 83. China | 2.05 |
| 2. Norway | 0.36 | .. 109. Indonesia | 2.75 |
| 3. Finland | 0.39 | .. 114. Nigeria | 2.88 |
| 4. Estonia | 0.51 | .. 129. India | 3.16 |
| 5. Denmark | 0.52 | .. 142. Benin | 3.49 |
| .. 8. U.S.A | 0.55 | 148. Congo, Dem. | 3.67 |
| .. 17. United Kingdom | 0.71 | 149. Malawi | 3.72 |
| .. 21. Germany | 0.76 | 150. Yemen | 3.78 |
| .. 23. Japan | 0.77 | 151. Myanmar | 3.95 |
| .. 39. Russia | 1.04 | 152. Burkina Faso | 4.04 |
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| Low-income countries | 4.04–2.62 | ||
| Lower-middle income countries | 3.78–1.41 | ||
| Upper-middle income countries | 3.32–0.97 | ||
| High-income countries | 1.53–0.17 | ||
Scores: < 1.21, low data poverty; 1.21–2.42, below average data poverty; 2.42–3.62, above average data poverty; > 3.62, high data poverty. Remark: Only countries with a complete dataset have been considered.
* China Mainland, excluding Macao and Hong Kong.