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We define as populist a party that champions short-term protection policies without regard for their long-term costs. First, we study the demand for populism: we analyze the drivers of the populist vote using individual level data from multiple waves of surveys in Europe. Individual voting preferences are in uenced directly by different measures of economic insecurity and by the decline in trust in traditional parties. However, economic shocks that undermine voters' security and trust in parties also discourage voter turnout, thus mitigating the estimated demand of populism when ignoring this turnout selection. Economic insecurity affects intentions to vote for populist parties and turnout incentives also indirectly because it causes trust in parties to fall. Second, we study the supply side: we find that populist parties are more likely to appear when the drivers of demand for populism accumulate, and more so in countries with weak checks and balances and with higher political fragmentation. The non-populist parties' policy response is to reduce the distance of their platform from that of new populist entrants, thereby magnifying the aggregate supply of populist policies.

In this article we compare two techniques that are widely used in the analysis of life course trajectories, latent class analysis (LCA) and sequence analysis (SA). In particular, we focus on the use of these techniques as devices to obtain classes of individual life course trajectories. We first compare the consistency of the classification obtained via the two techniques using an actual dataset on the life course trajectories of young adults. Then, we adopt a simulation approach to measure the ability of these two methods to correctly classify groups of life course trajectories when specific forms of "random" variability are introduced within pre-specified classes in an artificial dataset. In order to do so, we introduce simulation operators that have a life course and/or observational meaning. Our results contribute on the one hand to outline the usefulness and robustness of findings based on the classification of life course trajectories through LCA and SA, on the other hand to illuminate the potential pitfalls of actual applications of these techniques.