net (Zou and Hastie, 2005). However, given the low correlation between regressors estimated in our sample (see Annex B), such alternative approaches do not provide superior estimates to Lasso.<sup>7</sup>

## 2.2 Decision Trees

Decision tree models (DTM) are a supervised learning method that can be used to perform classification tasks as well as regression analyses (Breiman, 2001). Decision tree models outperform linear regression in terms of prediction. This is because, while linearity simplifies model interpretation in linear regressions, it typically reduces predictive power. Decision trees can meanwhile easily adapt to nonlinearities in the data (Schonlau and Zou, 2020).

In a tree-based model, the given dataset is recursively divided into two groups according to a certain criterion until a preset stopping condition is satisfied. Decision trees operate by dividing the data into distinct groups based on the data’s characteristics. The decision trees will keep splitting the data into groups until there is just a limited amount of data that fits into one label (a classification). The main difference with the previous models (Logit, Lasso) is that the trees allow the different variables to interact with each other and condition the outcome variable. For example, suppose we have a dependent variable y and a set of predictors x1, x2,..,xk. The algorithm will pick a predictor and partition the space on a given point. Afterwards, another partition will be made and so forth. This procedure will be repeated recursively until a stop point has been reached. Ultimately, the algorithm generates a set of relationships between the predictors and the dependent variable similar to tree branches.

Decision trees have the advantage of being simple to interpret, make no assumptions about data distribution, and are unaffected by outliers. As such, we will use decision trees to investigate the presence of nonlinearities in the characteristics influencing citizen participation. Additionally, we will compute the variable importance metric, which assesses each variable’s relevance within the broader citizen participation model.

Given the lack of compelling evidence outside the United States—and the fact that we have no reason to believe that these relationships must necessarily follow simple, linear trends—allowing for nonlinearities relaxes restrictive assumptions made by standard econometric techniques about the relationship between political trust and citizen engagement.

# 3. Data

The data used in this study comes from the Latinobarómetro Survey for 2020 (Latinobarómetro Corporation 2021), the latest year available. The

> 7 Ridge uses the same reasoning as Lasso, but adds a penalty in the form of the sum of the squared beta coefficients; as a result, it never sends the coefficients to zero but instead shrinks them (Le Cessie and Van Houwelingen, 1992). Elastic net, in turn, incorporates both types of penalty, allowing it to benefit from both the Lasso and ridge approaches. In the context of our study, however, given the low correlation between regressors (see table B1 in Annex B) and given that the number of observations exceeds the number of regressors, the Lasso approach is more fitting to our sample than the other techniques.

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