<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-1079</journal-id>
<journal-title><![CDATA[Gestión y política pública]]></journal-title>
<abbrev-journal-title><![CDATA[Gest. polít. pública]]></abbrev-journal-title>
<issn>1405-1079</issn>
<publisher>
<publisher-name><![CDATA[Centro de Investigación y Docencia Económicas A.C., División de Administración Pública]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-10792024000100141</article-id>
<article-id pub-id-type="doi">10.60583/gypp.v33i1.8191</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Incumplimiento en el pago de impuestos: Intervención con el empleo de nudges y normas sociales]]></article-title>
<article-title xml:lang="en"><![CDATA[Default in Paying Taxes: Intervention with Nudges and Social Standards]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bevilacqua]]></surname>
<given-names><![CDATA[Solon]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Miranda]]></surname>
<given-names><![CDATA[Edivan do Socorro Fonseca de]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidade Federal de Goiás  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Conselho Administrativo de Defesa Econômica  ]]></institution>
<addr-line><![CDATA[ Brasília]]></addr-line>
<country>Brazil</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2024</year>
</pub-date>
<volume>33</volume>
<numero>1</numero>
<fpage>141</fpage>
<lpage>162</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-10792024000100141&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-10792024000100141&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-10792024000100141&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: En esta investigación se analizó un conjunto de mensajes de recaudación para el pago de impuestos. En total, se evaluaron doce variaciones de los mensajes de cobro (normas sociales, simplificación, divulgación, compromiso previo, recordatorios y decisiones pasadas) y se determinó su efectividad. Los mensajes fueron transmitidos a microempresarios morosos en cuatro estados brasileños. A partir de una base de datos que contiene información sobre microempresarios morosos, se enviaron 250 000 mensajes de texto para hacer el cobro. Los datos se obtuvieron de la Secretaria Especial da Micro e Pequea Empresa (Sempe). Se utilizaron pruebas para analizar la diferencia entre medias y se utilizó una secuencia de regresión logística. Se utilizaron los widgets Random Forest, regresión logística y Naïve Bayes para definir la solidez del modelo predictivo de Machine Learning. Los resultados de la investigación indicaron que los formatos &#8220;simplificación&#8221;, &#8220;decisiones pasadas&#8221; y &#8220;alerta&#8221; no tuvieron ningún efecto en la lucha contra el incumplimiento. Sin embargo, al combinarlos con mensajes que aludan a normas sociales, los mensajes con el estilo de &#8220;decisiones pasadas&#8221; y &#8220;recordatorios&#8221; aumentaron el pago de la deuda. Los widgets empleados mostraron un ajuste excelente al modelo de machine learning. La herramienta Random Forest determinó que el modelo es robusto y adecuado para la función predictiva. Los resultados de la encuesta brindan un aporte a las políticas públicas, pues presentan una acción efectiva para reducir la morosidad tributaria. El uso de mensajes que apelan a las normas sociales puede adaptarse a otras situaciones, y se sugiere aplicarlo en futuras investigaciones.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: In this research, a group of charging messages for the payment of taxes was investigated. Altogether 12 variations of billing messages (social norms, simplification, disclosure, previous engagement, reminders and previous choices) were evaluated, and their effectiveness was tested. The messages were transmitted to defaulting microentrepreneurs in four Brazilian states. From a database containing information about defaulting micro entrepreneurs, 250 thousand text messages were sent making charges. The data were obtained from the Secretaria Especial da Micro e Pequena Empresa (Sempe). Tests were used to analyse the difference between means and Logistic Regression was used in sequence. The Random Forest, Logistic Regression and Naïve Bayes widgets were used to indicate the robustness of the Machine Learning predictive model. The research findings indicated that the formats &#8220;simplification&#8221;, &#8220;previous choices&#8221; and &#8220;alert&#8221;, employees, did not have an effect in combating default. However, when aligned with social norms, messages in the form of &#8220;past options&#8221; and &#8220;reminders&#8221; increase the payment of debts. The widgets used indicated an excellent fit to the machine learning model. The Random Forest tool attested with superiority that the model is robust and suitable for the predictive function. The results of the research provide a contribution to public policies when they present an effective action to reduce defaults in the payment of taxes. The use of messages in the social norm format can be adapted to other situations, constituting a suggestion for future research.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[morosidad]]></kwd>
<kwd lng="es"><![CDATA[nudges]]></kwd>
<kwd lng="es"><![CDATA[normas sociales]]></kwd>
<kwd lng="es"><![CDATA[emprendedores]]></kwd>
<kwd lng="es"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[default]]></kwd>
<kwd lng="en"><![CDATA[nudges]]></kwd>
<kwd lng="en"><![CDATA[social norms]]></kwd>
<kwd lng="en"><![CDATA[entrepreneur]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
</kwd-group>
</article-meta>
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