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Tunisian constituent assembly elections: how does spatial proximity matter?

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Abstract

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.

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Notes

  1. National Democratic Institute: Final report on the National Constituent Assembly Elections.

  2. Instance Supérieure Indépendante pour les Elections.

  3. Decree-law 27 (18 April 2011) establishes the ISIE, including its membership and responsibilities (see the electoral law for more details).

  4. AfDB African Development Bank (AfDB) Group, 2012 ‘Tunisia: Economic and Social Challenges Beyond the Revolution’.

  5. INS, “Mesure de la pauvreté et des inégalités en Tunisie 2000–2010 ” (http://www.ins.tn).

  6. Data available on www.isie.tn.

  7. The 2004 census and the 2005 national survey of household consumption are available on www.ins.nat.tn.

  8. By imposing certain restrictions to the SDM model, we can get the SEM and SAR specifications. In particular, if \(\theta = 0\) the SDM reduces to the SAR model. If the restriction \(\theta =-\rho \delta \) is imposed, the SDM is a simplified representation of SEM model.

  9. We use the first-order neighbors matrix for the Moran scatterplot and local Moran’s significance map. Complete results with higher order neighbor weights are available upon request from the authors.

  10. Ofcom Broadcast Bulletin, Issue 204, 23 April 2012.

    Fig. 4
    figure 4

    Local Moran’s significance map of vote-share

    Full size image

  11. Great Tunis is composed of the following four governorates: Tunis (the capital), Ariana, Ben Arous and Manouba.

  12. Although the OLS results are not included in this paper, they are available from the authors upon request.

    Table 4 Model specification tests: spatial error or spatial lag
    Full size table

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Authors and Affiliations

  1. High Institute of Management of Sousse, 763, Street of Abdelaziz El Behi, 4000, Sousse, Tunisia

    Mohamed Amara & AbdelRahmen El Lahga

  2. High Institute of Management of Tunis, 41, Street of freedom, City Bouchoucha, Bardo, 2000, Tunis, Tunisia

    AbdelRahmen El Lahga

Authors
  1. Mohamed Amara
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  2. AbdelRahmen El Lahga
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Correspondence to Mohamed Amara.

Additional information

This paper is dedicated to the people of Tunisia, and to those who gave their lives for dignity and freedom.

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Fig. 6
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Map of Tunisia

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Amara, M., El Lahga, A. Tunisian constituent assembly elections: how does spatial proximity matter?. Qual Quant 50, 65–88 (2016). https://doi.org/10.1007/s11135-014-0137-1

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