Tunisian constituent assembly elections: how does spatial proximity matter?

Tunisian constituent assembly elections: how does spatial proximity matter? This paper presents an Exploratory Spatial Data Analysis and Spatial Econometric modeling of the 2011 National Constituent Assembly elections (NCA) in Tunisia. By using electoral data at delegation level of the six main political parties (Ennahda, Congress of the Republic, Ettakatol, the Democratic Progressive Party, the Petition and the Democratie Modernist Pole), we show that geographical proximity matters in Tunisia’s voting behavior. The results overwhelmingly support the spatial Durbin model, including spatially weighted independent variables, as the best model to explain the voting phenomenon. Employing LeSage and Pace’s approach, we find that the largest direct and indirect effects are associated with age cohort and level of educational attainment. Voters who live in poorer neighborhoods are more likely to support the Petition list. Our results also show that younger voters are more likely to vote Ennahda, while older voters with high educational attainment are more likely to support Ettakatol and the Democratie Modernist Pole parties. Men are more likely to support Congress of the Republic than women voters. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Tunisian constituent assembly elections: how does spatial proximity matter?

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Springer Netherlands
Copyright © 2014 by Springer Science+Business Media Dordrecht
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
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