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    The data was conducted by three Organizations: EDRI, IFPRI and University of Sussex to see the Impact of biomassweb on the economies of developing countries using the 2005 Household Income Consumption Expenditure Survey. It covers 65 production accounts, 100 consumption accounts, 16 households, 4 factors of production and, government, I-S and ROW accounts.

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    Using the methodology developed by Graw and Husmann (2014), the map overlays three indicators (high agricultural potential, high poverty mass and high yield gaps) to identify areas with high potential for agricultural development and poverty reduction in Kenya. Data sources and thresholds: Agricultural potential: Suitability of currently available land area for rainfed crops, using maximising crop and technology mix, FGGD map 6.61 (2005), High: top 3 suitability classes (medium high, high and very high) Poverty mass: Number of poor people in Kenya (by district), KIHBS (2005/06), High: >300,000 per district Yield gap: Yield gap for a combination of major crops, FAO/IIASA - GAEZ (2000/05), High: < 0.25 (on a scale from 0-1, with the highest value in Kenya ca. 0.44) District boundaries: Kenya Central Bureau of Statistics (2003)

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    Overlay of medium, high and very high agricultural potential and yield gaps for Bangladesh. Indicators: Suitability of land area for rainfed crops: FGGD map 6.60, classes: medium to very high Yield Gaps: FAO/IIASA - GAEZ < 0.5 The here presented simple analysis should give insights in potential suitable areas for expecting high yields. Yield gap information for Bangladesh further showed were gaps for production occur. By resampling the statistical data into a raster dataset an overlap with suitability information based on sateliite imagery was possible. The overlay was processed in ArcGIS classifying those areas where yield gaps occur with suitability areas as defined by the dataset cited above. Yield gaps can be found in the southern part of Bangladesh. More intersting are the dark green areas where according to satellite information high potential areas are located but not as many yields are produced. Here, more detailed analysis might be useful.

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    Overlapping Marginality Dimensions in Bangladesh. Methodology see also: Graw, V. and C. Ladenburger. 2012. Mapping Marginality Hotspots - Geographical Targeting for Poverty Reduction. (ZEF Working Papers 88). Indicators: Per capita income (HIES 2010, cut-off-point: least 3rd quantile) Under-five child mortality: DHS Survey 2008 (cut-off-point: least 3rd quantile) Accessibility: Nelson 2008; cut-off-point: more than 3 hours distance) Gender: Difference of men and women in eduction; secondary school complete and higher: DHS Survey 2008 (cut-off-point: least 3rd quantile) An overlay was created with the above mentioned indicators based on the respective thresholds. Those areas where most indicators - with low performance - overlap were ranked as those areas experiencing highest marginality.

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    Overlaying the number of marginality dimensions with percentage of people living below 1.25$/day. This map is included in a global study on mapping marginality focusing on Sub-Saharan Africa and South Asia. The Dimensions of Marginality are based on different data sources representing different spheres of life. The poverty dataset used in this study is based on calculations by Harvest Choice. The underlying Marginality map is based on the approach on Marginality Mapping (http://www.zef.de/fileadmin/webfiles/downloads/zef_wp/wp88.pdf). The respective map can be found here: https://daten.zef.de/#/metadata/ae4ae68c-cea3-44e7-8199-1c2ae04abb88