Abstract:The present analysis contains a Quality of Life Index (QLI) for most medium-large Mexican cities using the equalizing-difference approach. Implicit prices were constructed using two amenity bundles which include geographical, environmental, social factors such as climate, proximity to coast or metropolitan areas, public safety, quality of education, access to health care as well as other local public goods. The ranking includes 92 medium-large cities (municipalities) from a subsample of the Household Income and Expenditure Survey. The results show that extreme temperatures and criminality are clearly bad and have negative implicit prices. Other variables such as distance to hospitals and local taxes also have negative implicit prices. The quality of education, urban metropolitan areas, access to sea coast and federal transfers have a positive impact on households' utility. Two different rankings are constructed using two slightly different amenity bundles to observe for consistency. The estimation of implicit prices shows that public safety and quality of basic education are the most valued external factors for Mexican households, followed by the access to tertiary education.

Este documento contiene una predicción financiera utilizando Redes Neuronales Artificiales. Hacemos nuestro análisis utilizando el algoritmo de Backpropagation tradicional y luego Backpropagation Resiliente para estimar los pesos en las redes. El uso del algorithm de Bacpropagation Resiliente permite resolver el problema de la determinación de la tasa de aprendizaje. Ambos algoritmos son bastante consistentes y arrojan predicciones similares. Analizamos seis índices principales de los mercados bursátiles de Europa, Asia y América del Norte para generar índices de aciertos que puedan compararse entre mercados. Usamos precios de cierre diarios para construir una variable de dependiente para dirigir el aprendizaje (aprendizaje supervisado) y una matriz de variables de características construidas utilizando indicadores de análisis técnico. El rango de datos de la serie de tiempo va desde Enero de 2000 a Junio de 2019, un periodo de grandes fluctuaciones debido a mejoras en la tecnología de la información y una alta movilidad de capital. En lugar de la predicción en sí misma, el objetivo científico es evaluar la importancia relativa de las variables independientes que permiten la predicción. Utilizamos dos medidas de contribución utilizadas en la literatura para evaluar la relevancia de cada variable para los seis mercados financieros analizados. Descubrimos que estas medidas no siempre son consistentes, por lo que construimos una medida de contribución simple que le da a cada peso una interpretación geométrica. Proporcionamos algunas pruebas de que la tasa de cambio (ROC) es la herramienta de predicción más útil para cuatro índices generales, con las excepciones siendo el índice Hang Sheng y EU50, en donde el fastK es el más destacado.

<p><strong>Objetivo:</strong> ofrecer una estimación de las medidas de distribución del ingreso para los municipios de México para el año 2015, y también un análisis de las subvenciones municipales sobre desigualdad de ingresos.</p><p><strong>Diseño metodológico:</strong> se construyeron índices de Gini y Atkinson usando microdatos de la Encuesta Intercensal Mexicana de 2015. Estos índices, junto con otras características de pobreza y marginalidad, se utilizaron para realizar un análisis de conglomerados para clasificar los municipios. Utilizando este análisis se clasificaron los municipios en cuatro grupos: desigualdad de ingresos baja, media-baja, media-alta y alta. Se realizó regresión de mínimos cuadrados ponderados para observar el efecto de las variables fiscales sobre la desigualdad.</p><p><strong>Resultados:</strong> aunque el enfoque de las subvenciones federales ha sido la pobreza en lugar de la desigualdad, se ofrece evidencia de que la desigualdad de ingresos se ve afectada inversamente por el diseño de las subvenciones federales. La regresión muestra que las subvenciones condicionales diseñadas para reducir la pobreza pueden estar aumentando la desigualdad, mientras que las subvenciones incondicionales pueden ayudar a reducir la desigualdad de ingresos, aunque este no sea el objetivo de esta política.</p><p><strong>Limitaciones de la investigación:</strong> la principal limitación es la falta de datos a nivel local para otros años para poder realizar un análisis dinámico.</p><p><strong>Hallazgos:</strong> el efecto general de la distribución de las subvenciones federales sigue siendo positivo. El efecto total es por una menor desigualdad de ingresos, especialmente en aquellos municipios con alta y muy alta desigualdad.</p>

Despite the efforts to reduce poverty in rural municipalities income inequality persists in Mexico. This study presents an analysis on rural household income distribution in the country, since it is argued that conditional federal transfers fail on improving income distribution among rural households. The hypothesis stated that, because of local public goods are also part of individual budget constraints, it is rational to think that an expansion in the provision of local public goods will increase total income and, if such public goods are financed with conditional grants that target low-income groups, it is expected that income inequality may decrease. Thus, the objective was to classify rural municipalities in order to observe which among them have benefited from federal grants and those that did not, finding the reasons why assuming grants are accepted as an instrument contributing to reduce poverty and income inequality in recent years. Each group was analysed as a cluster to observe the effect of federal transfers on rural household income distribution. Main results showed that municipalities with rural low income-inequality and better economic development indicators improve income distribution when obtaining unconditional grants. This means that, in such cases, those transfers designed to reduce poverty also reduce rural income inequality. But that was not the case for the high income-inequality groups, where conditional grants did not have any effect on inequality and, in some cases, inequality increased. For the rural high income-inequality group, unconditional grants showed not to have a positive effect on reducing inequality. The clustering and regression analyses revealed large heterogeneity in the rural areas in terms of income and economic development.

This work presents a model of credit rationing under the effects of judiciary in- efficiency and criminal extraction. Under low judiciary quality and high criminality, we argue that banks are more likely to lend to the government rather than private enterprises. We argue that credit rationing increased local public debt in Mexico before coming into effect the new law of Financial Discipline for States and Municipalities in 2016. Our scientific objective is explaining the supply of bank loans to the local public sector in Mexico under low institutional quality and credit rationing. We use Panel regression analysis and also applied an Autoregresive Distributed Lag model to obtain the long term growth rates. We also used Clustering analysis in order to classify states in Mexico in terms of their debt, defaults in the industry sector, crime and judicial inefficiency rates. Our empirical analysis shows that judiciary inefficiency and criminality induced higher amounts of bank loans to state governments during the period of 2004 to 2016. We also found that defaults in the industry sector also increased the amount of bank loans to local governments in Mexico, which may explain in part that the rationed credit is redirected to the public sector. We argue that keeping the quality of institutions low may induce higher bank lending to states, so there might be little incentive to improve the judiciary and public safety. The possible solution is to improve judicial efficiency and decrease criminality in order to reduce credit rationing and subsequently ensure local public debt stability in the long term.