model’s complexity.<sup>15</sup> The number of variables in models 2 (participation in demonstrations) and 3 (working on a community problem) is reduced from 23 to 14. By and large, the pool of covariates that remains with Lasso estimation reassuringly coincides with covariates found significant in the preceding Logit analysis—save for the notorious dropping of trust in national governments. The resulting models are presented in Table 3, below.<sup>16</sup> The following variables remained in the Lasso estimation across all three models of citizen participation: among demographic variables, age, education, income and employment status; among perception variables, having felt discriminated against, having been a victim of a crime, having an interest in politics and feeling free to join any organization without fear.<sup>17</sup> Differences between Logit and Lasso estimations are more salient for trust variables. While trust in national government played a role in previous Logit estimates in model 1 (signing a petition), Lasso estimates do not select in such a variable. Lasso compares a covariate’s contribution to the predictive capacity of the model with the marginal increase in the model’s total variance that the covariate produces (see section 2). Given that trust in government has the highest levels of variability when compared to other trust variables, Lasso estimations are more likely to exclude this variable from the model than other types of trust. Also, Lasso estimations select trust in parliament, trust in courts and trust in electoral bodies. However, as was the case in the Logit analysis, such variables are not significant in post-Lasso Logit estimations (see Table 3). Lasso estimations in model 2 are consistent with Logit results, picking trust in police as a significant driver of participation in demonstrations. Similarly, in model 3, Lasso estimations align with Logit in selecting trust in courts and trust in electoral bodies as significant drivers.

> 15 The rigorous approach was employed in order to select the optimal lambda parameter. See Ahrens, Hansen and Schaffer (2020) for details on the rigorous approach.

> 16 For estimation purposes, we used the function _rlassoLogit_ from the LASSOPACK package in Stata 16. Table 3 shows the post-Lasso results obtained by running Logit regressions with the covariates selected by Lasso. Mullainathan and Spiess. (2017) warn that machine learning algorithms produce regression coefficients estimates that are rarely consistent. While the estimated coefficients produced by Lasso cannot be directly interpreted as one does with OLS or Logit regressions, the post-Lasso approach adopted here allows to compare post-Lasso logit results with Logit results. As Hastie, Tibshirani and Wainwright (2016) show, when Lasso correctly recovers the true support S of the real parameters, β<sup>∗</sup> , such parameters can be estimated by performing an ordinary least-squares regression restricted to this subset. Note, in addition, that this result extends to logistic regressions. For similar results see also Belloni, Chernozhukov and Hansen (2014) and <mark>Sosa-Escudero, Anauati and Brau (2022).</mark>

> 17 If participation in community affairs affects the experience of discrimination or being a victim of a crime, an endogeneity problem may arise. However, because those variables refer to the previous 12 months, they are unaffected by current participation, avoiding the problem of endogeneity.

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