Did urbanization or ethnicity matter more in Malaysia’s 14th general election?

This article focuses on identifying the variable which has the highest predictive power in predicting electoral behaviour. To do this, we apply a tree-based machine learning technique to data from Malaysia’s 14th General Election. We find that constituencies’ urbanization level has the most significant predictive power in determining vote share. Ethnicity, a long-touted variable of significance, plays a secondary role. Moreover, these predictors’ marginal effects on the vote share are highly complex, non-linear and difficult to pick up by conventional regression methods. Other explanatory factors do not exhibit significant predictive qualities of electoral behaviour, although the extant literature has shown them to have important causal relationships. As our analysis reflects the significant predictive power of urbanization in predicting voting behaviour, we caution against the haste to dismiss its relevance in the Malaysian context.