<?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>1994-3733</journal-id>
<journal-title><![CDATA[Revista Perspectivas]]></journal-title>
<abbrev-journal-title><![CDATA[Perspectivas]]></abbrev-journal-title>
<issn>1994-3733</issn>
<publisher>
<publisher-name><![CDATA[Universidad Católica Boliviana San Pablo, Unidad Académica Regional Cochabamba]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1994-37332026000100025</article-id>
<article-id pub-id-type="doi">10.35319/perspectivas.202657297</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Desigualdad en Bolivia: Descomposición contrafactual de Shapley con microdatos de la encuesta de hogares 2024]]></article-title>
<article-title xml:lang="en"><![CDATA[Inequality in Bolivia: A Shapley counterfactual decomposition using microdata from the 2024 household survey]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bernal Martínez]]></surname>
<given-names><![CDATA[Ernesto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ayaviri-Nina]]></surname>
<given-names><![CDATA[Dante]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Técnica de Oruro  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Bolivia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional de Chimborazo  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>05</month>
<year>2026</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>05</month>
<year>2026</year>
</pub-date>
<numero>57</numero>
<fpage>25</fpage>
<lpage>49</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_arttext&amp;pid=S1994-37332026000100025&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_abstract&amp;pid=S1994-37332026000100025&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_pdf&amp;pid=S1994-37332026000100025&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La investigación pretende estimar la descomposición contrafactual del coeficiente de Gini y los factores relacionados en la distribución del ingreso y en el mercado de trabajo en Bolivia. Para ello, se utilizó una metodología que incluye la estimación del coeficiente de Gini, la función de kernel y la descomposición de Shapley, basada en los datos de la Encuesta de Hogares 2024 del Instituto Nacional de Estadística. El coeficiente de Gini estimado para Bolivia es de 0,4158, lo que indica un nivel de desigualdad moderado. Al considerar el área de residencia, se observa una diferencia notable entre el ámbito urbano, donde el coeficiente es 0,38, y el rural, que alcanza 0,498. El departamento de Santa Cruz muestra el coeficiente más bajo, 0,37, lo que refleja una distribución del ingreso más equitativa. Por otro lado, Potosí registra el valor más alto, 0,483. El ancho de banda, que es 96.2385, sugiere que cada valor de ingreso influye en la estimación de la densidad de los valores próximos dentro de un rango de ±96,24 unidades. Los resultados obtenidos a través de la descomposición Shapley del coeficiente de Gini muestra que el ingreso laboral explica el 94% de la desigualdad total en Bolivia en 2024, es el determinante casi absoluto de la predicción. El modelo depende principalmente del ingreso proveniente del trabajo.  JEL: C14, D31, D33.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This research aims to estimate the counterfactual decomposition of the Gini coefficient and related factors in income distribution and the labor market in Bolivia. To this end, a methodology was used that includes estimating the Gini coefficient, the kernel function, and Shapley decomposition, based on data from the 2024 Household Survey of the National Institute of Statistics. The estimated Gini coefficient for Bolivia is 0.4158, indicating a moderate level of inequality. When considering the area of &#8203;&#8203;residence, a notable difference is observed between urban areas, where the coefficient is 0.38, and rural areas, which reach 0.498. The department of Santa Cruz shows the lowest coefficient, 0.37, reflecting a more equitable income distribution. On the other hand, Potosí registers the highest value, 0.483. The bandwidth, which is 96.2385, suggests that each income value influences the density estimate of nearby values &#8203;&#8203;within a range of ±96.24 units. The results obtained through the Shapley decomposition of the Gini coefficient show that labor income explains 94% of total inequality in Bolivia in 2024, making it the almost absolute determinant of the prediction. The model depends primarily on income from work.  JEL: C14, D31, D33.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Desigualdad]]></kwd>
<kwd lng="es"><![CDATA[Descomposición]]></kwd>
<kwd lng="es"><![CDATA[Ingreso laboral]]></kwd>
<kwd lng="en"><![CDATA[Inequality]]></kwd>
<kwd lng="en"><![CDATA[Decomposition]]></kwd>
<kwd lng="en"><![CDATA[Labor income]]></kwd>
</kwd-group>
</article-meta>
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<source><![CDATA[Instituciones laborales y desigualdad en América Latina]]></source>
<year>2021</year>
<publisher-loc><![CDATA[Santiago de Chile ]]></publisher-loc>
<publisher-name><![CDATA[CEPAL]]></publisher-name>
</nlm-citation>
</ref>
</ref-list>
</back>
</article>
