Abstract

ENGLISH ABSTRACT: Botswana has sustained its multiparty electoral democracy which was established since independence in 1966. However, there are concerns about one party dominance within a multiparty system. The implications are that if voting reflects long-standing social and political identities, then opportunities for minorities to become majorities are slim. Also, democratic consolidation is delayed if voting decisions are based on ascriptive social and long standing entrenched partisan identities. This study departs from the premise that Botswana has undergone significant socioeconomic transformation over the last thirty years. These events, including increases in the society’s levels of education and access to political information through a wide array of media, hold significant implications for the voting motivations of Botswana’s citizens. Using three cross-sectional Afrobarometer surveys (the 2008, 2014 and 2019 rounds), this study investigates the underlying motivations of Botswana’s voters by analyzing competing theoretical voting models, namely the sociological, partisanship and rational choice theories of voting to assess firstly, which of these theoretical families is the most powerful and persuasive, and secondly, whether Botswana’s socioeconomic developments have changed the explanatory power of voting motivations over time. The study expects to find that Short-term economic and political performance evaluations increase in importance while sociological factors and partisanship decline in their ability to structure vote choice. Moreover, increases in education levels and access to political information should produce skilled voters who rely less on long-standing sociological and partisan cues to guide their voting decisions but more on Short-term rational choice factors. Thus, the study tests whether a process of cognitive mobilization is unfolding in Botswana and is moderating the voting decisions of voters, especially among those who are cognitively mobilized voters. Bivariate and multivariate (logistic regression) techniques are used to analyze the data and address these research objectives.