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This paper aims to study the determinants of financial deepening (FD) in Mexico. FD is measured as the fraction of total credit assigned to private sector as a proportion of GDP. The explanatory variables include: enforcing contracts (rule of law) and institutions; bank regulation; bank competition; formal labor; and financial literacy, among others. The data contains information of each State of the Mexican Republic. The model estimation is performed by using ordinary least squares (OLS), robust least square (RLS) with several weights functions (to deal with outliers), and a dynamic panel model (DPM) estimated with the generalized method of moments (GMM). The main findings are that: 1) the rule of law, 2) financial savings, and 3) financial education are the main determinants of financial deepening in Mexico. || Este trabajo tiene como objetivo estudiar los determinantes de la profundad financiera (PF) en México. La PF se mide como el crédito total asignado al sector privado como proporción del PIB. Las variables explicativas incluyen: cumplimiento de contratos (estado de derecho) e instituciones; regulación bancaria; competencia bancaria; trabajo formal; y educación financiera, entre otros. Los datos contienen información de cada Estado de la República Mexicana. La estimación del modelo se realiza mediante el uso de mínimos cuadrados (MCO), mínimos cuadrados robustos (MCO) con varias funciones de ponderación (para tratar con valores atípicos) y un modelo de panel dinámico (MDP) estimado con el método generalizado de momentos (MGM). Los principales hallazgos son que: 1) el estado de derecho, 2) el ahorro financiero, y 3) la educación financiera son los principales factores determinantes de la profundización financiera en México.

The study aimed to measure the level of anxiety towards mathematics in high school students and if they differs by gender and institution. For this, the scale designed by Muñoz-Cantero and Mato-Vázquez (2007) was applied to 536 Mexican students from three different institutions in the public sector. This instrument is divided into five factors: anxiety towards the evaluation, towards the temporality, towards the understanding of the problems, towards the numbers and the mathematical operations and towards real life situations. The data matrix was analyzed using the Cronbach's alpha coefficient to validate its consistency, from which a very acceptable value of 0.932 was obtained. The analysis confirmed that the data matrix is not an identity matrix and presents acceptable correlations between variables, confirming the extraction of five factors that explain 63.38 % of total assimilable variance. In addition, the ANOVA test was calculated with the F statistic and its significance to prove the hypothesis of equality of means. A significant difference of means by gender was found in four of the five factors. Only anxiety towards real-life mathematical situations had no significant difference in means. Otherwise, the academic institution variable show difference between groups only in anxiety towards real-life mathematical situation. The Levene statistic was calculated for both variables to prove the hypothesis of equality of population variances. The gender variable presented equality of population variances in the five factors. The institution variable had not equality in population variances in anxiety towards mathematical problems comprehension and anxiety towards real-life mathematical situation.

This paper identifies patterns in total and per capita CO2 emissions among 208 countries considering different emission sources, such as cement, flaring, gas, oil, and coal. This research uses linear and non-linear dimensional reduction techniques, combining K-means clustering with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), which allows the identification of distinct emission profiles among nations. This approach allows effective clustering of heterogeneous countries despite the highly dimensional nature of emissions data. The optimal number of clusters is determined using Calinski–Harabasz and Davies–Bouldin scores, of five and six clusters for total and per capita CO2 emissions, respectively. The findings reveal that for total emissions, t-SNE brings together the world's largest economies and emitters, i.e., China, USA, India, and Russia, into a single cluster, while PCA provides clusters with a single country for China, USA, and Russia. Regarding per capita emissions, PCA generates a cluster with only one country, Qatar, due to its significant flaring emissions, as byproduct of the oil industry, and its low population. This study concludes that international collaboration and coherent global policies are crucial for effectively addressing CO2 emissions and developing targeted climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

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