Institution: International Centre for Tropical Agriculture (CIAT)
E-mail: G.leclerc@cgiar.org
Biosummary:
Grégoire Leclerc received his Ph.D. in Applied Physics from Sherbrooke University, Canada in 1991. He later specialized in Remote Sensing and GIS, through a Post-Doc in Remote Sensing with CARTEL, Canada, as well as many consulting and teaching contracts with CATIE, in Costa Rica. He joined CIAT in 1996, and is now Senior Scientist with the Land Use Dynamics and Spatial Analysis program, which is part of CIAT Natural Resources Management division. He contributed to improvement in the organization and technical capacity of the CIAT GIS lab, which is considered to be one of the best in Latin America. His main interests are in the development of novel methodologies and tools to support a wide range of applications, from digital image processing to decision support systems. He is now focusing on finding ways to bridge the social and biophysical sciences, through action research on qualitative data analysis and non-parametric modeling. He is 39 years old, has lived 9 years in Africa and 8 years in Latin America, is married to a Civil Engineer who has a Ph.D. in Remote sensing, and has a 12 year old boy and a 9 year old girl.
Title: "The use of unit-level census data for research on poverty: A multiscale approach." Co-author: A. Nelson.
Theme: 1A
Unit-level data from the 1988 population and 1993 agricultural censuses of Honduras have been obtained and integrated into a spatial database. We show how poverty indices can be computed at ‘caserio’, village, ‘municipio’, department and country level and how they compare to other "official" figures. Indicators derived from the analysis of well-being ranking by local informants in 90 communities have been extrapolated to the entire country by means of proxy indicators computed from reasonably well correlated census data. We find that the choice of the indicator as well as the scale of analysis determine different geographical distributions of poverty, which may affect significantly the relevance and impact of poverty alleviation policies. We use spatial statistical methods to process the data at a given scale, which allows the analysis of cause-effect relationships with other factors significant at this same scale. The same methods also help to find errors in the data or to determine the optimum scale of a particular indicator.