Political ideology mismatch as a determinant of the intention to migrate: evidence from Latin America

Saravia, Antonio; Marroquín, Andrés

doi:10.1108/ijse-01-2022-0028

Cited by 2 publications

(1 citation statement)

References 26 publications

(28 reference statements)

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“…Marek (2021) applied statistical techniques to Latinobarómetro data from 2002 to 2010 to explore the correlation between the population's perception of police and national government corruption. Overall, there is a wide diversity in the use of this research data in academic literature (Power & Jamison, 2005;Ateca-Amestoy et al, 2014;Barredo Ibáñez, 2018;Saravia & Marroquín, 2023), but there are few studies that focus on the application of machine learning techniques and studies exclusively data from Brazil.…”

Section: Introductionmentioning

confidence: 99%

Predicting satisfaction with democracy in Brazil considering data form an opinion survey

Rosa,

Dos Santos,

Lima

2023

GEPROS

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Purpose – This article compared machine learning algorithms in the context of satisfaction with democracy in Brazil. The models were trained with data from the Latinobarómetro survey, a private non-profit institution. Theoretical foundation – The Support Vector Classifier (SVC), Random Forest (RF), and Artificial Neural Networks (ANN) classification techniques were described, followed by evaluation metrics, such as accuracy, precision, recall, f1-score and the area under a receiver operating characteristic (auc-roc). Methodology – The data set was cleaned and the questionnaire was reduced to variables related to the local democracy index (IDL). Then, attribute transformations were performed, hyperparameters were analyzed and subsets with different class balances were created to evaluate the performance of the classifiers in different scenarios. Also, the attributes that most contributed to satisfaction with democracy were analyzed. Results – The best classifier was RF for the class of those dissatisfied with democracy, however, the ANN and SVC techniques obtained better results in the class of satisfied individuals. Evaluating the most important attributes for satisfaction with democracy, it was identified that they are related to the country's economic situation and political and governmental issues. Research implications – The models created were mainly able to identify people dissatisfied with democracy. The most important variables in this context were economy performance, government, political positioning, and democracy. This indicates directions for future studies and enables the development of strategies to change the perception of dissatisfied individuals. Originality/Value – Exploring data on democracy from the Latinobarómetro using machine learning techniques in the context of Brazil. Keywords: Machine learning; Democracy; Classification; Satisfaction.

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Section: Introductionmentioning

confidence: 99%