The local geographies of welfare in Tunisia: Does neighbourhood matter?

Abstract

The aim of this article is to show that spatial analysis techniques outperform non-spatial statistical counterparts for understanding the geographic determinants of welfare and poverty in Tunisia. First, an Exploratory Spatial Data Analysis, based on a Geographical Information System, was conducted to visualise the local spatial structure of welfare. Second, a spatial autoregressive (SAR) model and a geographically weighted regression (GWR) model, respectively, were used to deal with both spatial autocorrelations and unobserved spatial heterogeneity of households' behaviours. Spatial and non-spatial models were compared according to their predictive performances. Results of this case study confirm that SAR and GWR spatial models are preferable to the traditional non-spatial regression model and that they give a better approximation of the Tunisian poverty map. © 2012 Blackwell Publishing Ltd and the International Journal of Social Welfare.