The application of a spatial regression model to the analysis and mapping of poverty
The mapping of poverty in developing countries has become an increasingly important tool in the search for ways to improve living standards in an economically and environmentally sustainable manner. The methods used to generate poverty maps naturally come under more intense scrutiny as their policy implications become more apparent. Those most commonly used until now have involved the application of econometric models to generate local indicators of poverty.
An important issue that has arisen regarding these econometric models is whether or not they take into account the spatial dependence that may exist in human societies with regard to the distribution of income. Poor households are more likely to be close to other poor households than they are to be close to higher income households. However, classic econometric models do not take these kinds of spatial properties of poverty into account. In this report, the authors apply the techniques of spatial regression in order to model more accurately the distribution of poverty across regions in Ecuador. In the case of Ecuador, the difference between results that are adjusted for spatial patterns and the unadjusted results is statistically significant. Although the absolute differences are not dramatic, they do provide policy planners with greater confidence that the results reflect the real situation in that country.
Although the geographic focus of the paper is on Ecuador, its main purpose is methodological, mainly the comparison of results obtained from models that apply spatial regression techniques with results obtained from models that do not take spatial dependence into account.
For a copy of the publication, please contact:
GIS Manager
Environment and Natural Resources Service, FAO
GIS-Manager{at}fao.org Replace {at} with @ to send e-mail
Fax: (+39) 0657053369