incorporating a machine learning approach and expanding the analysis to the LAC region. This fills a geographical gap raised by some authors (see Rizzo, 2021), who also point out the predominance of macro-level, case-based studies that fail to account for citizens' individual perspectives.

Most importantly, our work provides an innovative approach to analyzing the relationship between political trust and citizen participation by investigating nonlinearities and complexities in the way explanatory variables interact and have an impact on the dependent variable—that is, citizen participation in the various forms considered in this study. Looking beyond linearity and simple relationships imposed by traditional econometric models can provide deeper insights into the complexity of such sociopolitical relationships. More concretely, basic Logit models—used as baseline for our analysis—need to be supplemented by decision trees and regularization methods such as Lasso. These methods are especially useful in predicting whether or not an individual in the Latin America and Caribbean region will participate politically based on her trust in government institutions and socioeconomic characteristics.<sup>4</sup> Traditional Logit models are unable to detect complex nonlinear interactions between independent variables.<sup>5</sup> In order to overcome this limitation, we advance the use of decision tree models, which are especially useful for disentangling complex, nonlinear patterns between explanatory variables that influence the outcome variable. Our key finding is that trust variables predict political participation in non-linear ways. Trust is a significant driver of participation for an individual only after certain levels of education, age, and political interest are combined with experiences of discrimination and violence.

We rely on data from the Latinobarómetro 2020 and explore the existence of a relationship between trust in government and citizen participation in the LAC region. We also investigate the extent to which additional factors (for example, socioeconomic, demographic, and attitudinal) might be influencing citizen participation. A large body of literature has identified socioeconomic status as the primary factor determining citizen participation. In this literature, the higher an individual’s socioeconomic level, the greater their ability to participate (Lee and Schachter, 2019; Dalton, Burklin and Drummond, 2001; Gastil et al., 2008; Newman, Johnson, and Lown, 2014; Verba and Nie, 1987).

The Latinobarómetro Survey offers a range of questions that allow us to address the aforementioned queries on the relationship between trust and participation, as detailed below in section 3. In particular, we make use of the following indicators contained in the survey: i) trust in governmental institutions (national government, police, the judiciary, electoral body, congress); and ii) citizen participation indicators (signing a petition, taking part in authorized demonstrations, working on an issue that affects you or

> 4 Regularized regression, such as Lasso, can also serve as a model selection technique. As Ahrens, Hansen and Schaffer (2020) point out, regularized regressions tend to select the true model as the sample size increases. In this way, Lasso provides a method for choosing the model when the sample size is large and there are many potential regressors, facilitating model interpretation. In this work, the Lasso method is used for regularization rather than prediction purposes.

> 5 Logit models can still analyze simple nonlinear relationships by, for example, allowing for quadratic specifications among independent variables. While those will also be analyzed in the empirical section of this paper, our primary interest is on complex nonlinear links.

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