<?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>0188-4611</journal-id>
<journal-title><![CDATA[Investigaciones geográficas]]></journal-title>
<abbrev-journal-title><![CDATA[Invest. Geog]]></abbrev-journal-title>
<issn>0188-4611</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Geografía]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-46112020000100108</article-id>
<article-id pub-id-type="doi">10.14350/rig.59884</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Mexico&#8217;s Credit Guarantee Programs: Shifting Regional Impacts]]></article-title>
<article-title xml:lang="es"><![CDATA[Programas de garantías de crédito en México: cambios en los impactos regionales]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Palao Mendizabal]]></surname>
<given-names><![CDATA[Agustin]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,University of Texas School of Economic, Political and Policy Sciences ]]></institution>
<addr-line><![CDATA[Richardson TX]]></addr-line>
<country>USA</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2020</year>
</pub-date>
<numero>101</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-46112020000100108&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-46112020000100108&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-46112020000100108&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Rationing of working capital in rural businesses that work in agriculture is preventing from improving living conditions in impoverished areas. Whereas government programs have their greatest impact in areas attempting to transition from subsistence production into a modern commodity production system, the attempts to achieve social goals produce varying regional impacts and do not necessarily reach the agricultural households most in need. As a response, Mexican government has established public programs that back small rural enterprises when doing financial operations with private entities. It is well documented that small rural businesses suffer from commercial credit rationing due to inherent risks of their economic activities. Lack of collaterals, credit history or formal accounting records are some of the factors that the private banks avoid credit operations with this kind of producers. Mexican government has established a set of public programs to promote rural economic development through credit guarantees. These guarantees reduce the counterpart risk in credit operations making rural businesses more at- tractive to private banks. However, the eligibility conditions of this credit guarantee schemes (CGS) can discriminate and leave out the most poor. Adjustments to such programs have been implemented to provide focalized support to the poorest regions of the country. A new set of eligibility rules were issued in 2008 through a new program called Fondo Nacional de Garantías or FONAGA. Even though there is an increase in the number guaranteed operations after the inception of FONAGA, the spatial distribution of the CGS has not been evaluated. Calculating a Gini coefficient as a measure of inequality between the income coming from guaranteed credits and the population with the most patrimony poverty shows that the distribution has been uneven across the country. This study use Geographic Hot Spot Analysis (HSA) to analyze clustering patterns of the CGS distribution before and after the incorporation of FONAGA in 2008. The analysis calculates the Getis-Ord Gi* statistic, an association measure based on spatial conceptualizations such as fixed distances among neighbors. Maximization of spatial clustering reports the areas that consistently received high or low amounts of CGS operations. A high number of CGS operations within contiguous municipalities suggests a hot spot while low number of credit guarantees within municipal neighbors poses a &#8220;cold spot&#8221; difficulty. The Mexican case offers an opportunity to study a public program that is subject to socio-economic conditions that arise from contrasting commercial relationships with developed economic partners at the north border and emergent economies at the south. HSA reveals that introduction of FONAGA resulted in a major shift of program support that favored smaller scale agriculturalists located in previously underserved and un- der developed regions of Mexico. In 2004, heavy guarantee coverage was concentrated in northern states of the country. Municipalities from Tamaulipas, Nuevo Leon, Coahuila, So- nora, Baja California, and Baja California Sur were covered by higher amounts of guarantees than the rest of Mexico. On the other end, Oaxaca and Veracruz received consistently low coverage per square km of cultivable land. From 2004 to 2007 there are consistent and statistically significant clustering patterns in the north (hot spots) and south-central (cold spots) and hot spots appeared in the northwest region in 2006. In 2008, the year when FONAGA started operations, the patterns changed significantly. Operations moved from northern to western and southeastern municipalities. In 2009 and 2010 a high number of guaranteed operations increased the allocation intensity in the municipalities of the states of Quintana Roo, Campeche and Yucatan. In general, the hot spots shifted from the north to southern regions in the 2008-2013 period. A combination of a high number of operations with high credit coverage amounts in the southeast region is associated to a downward shift of the average amount CGS per operation, meaning that small- scale farmers in greater need were included in the guarantee schemes after FONAGA took place primarily in the states of Campeche, Yucatan, Quintana Roo, and Chiapas. Until now this type of analysis has not been used to evaluate the targeting of CGS operations. Its application provides a different and important perspective regarding CGS performance. A shift in the CGS allocation after the introduction of FEGA in 2008 represents a success from the view point of the trust administrator. However, clustering patterns are evidence of a continuing uneven distribution of the credit guarantees. This points to the need of more analysis for public programs implementation to explore additional ways to provide financial access to rural businesses that do not have private funding.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El racionamiento del capital de trabajo en las empresas rurales agrícolas impide mejorar las condiciones de vida en las zonas empobrecidas. Mientras que los programas gubernamentales tienen su mayor impacto en las áreas que intentan llevar a cabo la transición de la producción de subsistencia a un sistema moderno de producción de básicos, los intentos por alcanzar objetivos sociales producen impactos regionales variables y no necesariamente llegan a los hogares agrícolas más necesitados. Como respuesta, el gobierno mexicano ha establecido programas públicos que respaldan a las pequeñas empresas rurales cuando realizan operaciones financieras con entidades privadas. Se han implementado ajustes a dichos programas para proporcionar apoyo focalizado a las regiones más pobres del país. El Análisis Geográfico de Puntos Calientes (HSA) revela que la introducción de un Esquema de Garantía de Crédito (CGS) dio como resultado un cambio importante de apoyo al programa que favoreció a los agricultores de menor escala ubicados en regiones de México previamente desatendidas y subdesarrolladas. Hasta ahora este tipo de análisis no se ha utilizado para evaluar la focalización de las operaciones de CGS. Su aplicación proporciona una perspectiva diferente e importante con respecto al rendimiento de CGS. Los patrones de agrupamiento son evidencia de una distribución continua y desigual de las garantías de crédito. Esto apunta a la necesidad de que los programas públicos exploren formas adicionales de proporcionar acceso financiero a las empresas rurales que no cuentan con fondos privados.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Hot Spot Analysis]]></kwd>
<kwd lng="en"><![CDATA[Credit Guarantees]]></kwd>
<kwd lng="en"><![CDATA[Rural Development]]></kwd>
<kwd lng="en"><![CDATA[Financial Access]]></kwd>
<kwd lng="en"><![CDATA[Program Evaluation]]></kwd>
<kwd lng="es"><![CDATA[Análisis de punto caliente]]></kwd>
<kwd lng="es"><![CDATA[garantías de crédito]]></kwd>
<kwd lng="es"><![CDATA[desarrollo rural]]></kwd>
<kwd lng="es"><![CDATA[acceso financiero]]></kwd>
<kwd lng="es"><![CDATA[evaluación de programas públicos]]></kwd>
</kwd-group>
</article-meta>
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