Down to Earth: Agriculture and Poverty Reduction in Africa

By Luc Christiaensen; Lionel Demery | Go to book overview

Appendix 2
Data Sources and Constructs Used
in Case Studies

Empirical approaches to assessing the link between agricultural productivity and poverty take three forms: econometric estimation based on country data (for example, the studies by Ravallion and Chen 2007 and Ravallion and Datt 1996, 1999, and 2002], model-based assessments with some micro underpinnings (such as Delgado, Hopkins, and Kelly 1998 and Fan, Chan-Kang, and Mukherjee 2005], and cross-country estimation (Bravo-Ortega and Lederman 2005]. Given the importance of country-specific conditions in determining this link, and the structural limitations of modeling work, the use of country micro data is the preferred approach. The four African case studies commissioned for this volume were designed to take full advantage of country micro data sets that cover both poverty and agriculture.

The Madagascar case studies have the most compelling and varied information base for assessing the role of agriculture in poverty reduction. Perhaps the best starting point is the study by Minten and Barrett (2006], which used spatially explicit data (mainly obtained through focus group discussions) from a specially conducted census of Madagascar’s 1,392 communes (1,381 were enumerated) in 2001.1 Such a community-level spatial analysis in the African setting is rare, if not unique. Minten and Barrett’s estimation specification also used Madagascar’s official 1993

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