Teacher job satisfaction is an important aspect of academic performance, student retention, and teacher retention. We propose to determine the predictive model of job satisfaction of basic education teachers using machine learning techniques. The original data set consisted of 15,087 instances and 942 attributes from the national survey of teachers from public and private educational institutions of regular basic education (ENDO-2018) carried out by the Ministry of Education of Peru. We used the ANOVA F-test filter and the Chi-Square filter as feature selection techniques. In the modeling phase, the logistic regression algorithms, Gradient Boosting, Random Forest, XGBoost and Decision Trees-CART were used. Among the algorithms evaluated, XGBoost and Random Forest stand out, obtaining similar results in 4 of the 8 metrics evaluated, these are: balanced accuracy of 74%, sensitivity of 74%, F1-Score of 0.48 and negative predictive value of 0.94. However, in terms of the area under the ROC curve, XGBoost scores 0.83, while Random Forest scores 0.82. These algorithms also obtain the highest true-positive values (479 instances) and lowest false-negative values (168 instances) in the confusion matrix. Economic income, satisfaction with life, self-esteem, teaching activity, relationship with the director, perception of living conditions, family relationships; health problems related to depression and satisfaction with the relationship with colleagues turned out to be the most important predictors of job satisfaction in basic education teachers.

Teacher life satisfaction is crucial for their well-being and the educational success of their students, both essential elements for sustainable development. This study identifies the most relevant predictors of life satisfaction among Peruvian teachers using machine learning. We analyzed data from the National Survey of Teachers of Public Basic Education Institutions (ENDO-2020) conducted by the Ministry of Education of Peru, using filtering methods (mutual information, analysis of variance, chi-square, and Spearman’s correlation coefficient) along with embedded methods (Classification and Regression Trees—CART; Random Forest; Gradient Boosting; XGBoost; LightGBM; and CatBoost). Subsequently, we generated machine learning models with Random Forest; XGBoost; Gradient Boosting; Decision Trees—CART; CatBoost; LightGBM; Support Vector Machine; and Multilayer Perceptron. The results reveal that the main predictors of life satisfaction are satisfaction with health, employment in an educational institution, the living conditions that can be provided for their family, and conditions for performing their teaching duties, as well as age, the degree of confidence in the Ministry of Education and the Local Management Unit (UGEL), participation in continuous training programs, reflection on the outcomes of their teaching practice, work–life balance, and the number of hours dedicated to lesson preparation and administrative tasks. Among the algorithms used, LightGBM and Random Forest achieved the best results in terms of accuracy (0.68), precision (0.55), F1-Score (0.55), Cohen’s kappa (0.42), and Jaccard Score (0.41) for LightGBM, and accuracy (0.67), precision (0.54), F1-Score (0.55), Cohen’s kappa (0.41), and Jaccard Score (0.41). These results have important implications for educational management and public policy implementation. By identifying dissatisfied teachers, strategies can be developed to improve their well-being and, consequently, the quality of education, contributing to the sustainability of the educational system. Algorithms such as LightGBM and Random Forest can be valuable tools for educational management, enabling the identification of areas for improvement and optimizing decision-making.

Objective: The objective of this research is to analyze the relationship between economic growth and environmental degradation through a regression model for the Peruvian case, during the period 1990-2019, based on Georgescu Roegen's Bioeconomy; who explains that, in environmental terms, economic growth seems to be more of a problem than a solution. Method: The quantitative approach was considered, of a non-experimental type, with a descriptive and correlational design; where secondary source information from the World Bank database was used. Results: According to the results obtained, it is concluded that the variable with the most relationship or that best explains economic growth is energy consumption, followed by CO2 emissions; indicators of environmental degradation explain 24% of economic growth, being a model without problems of heteroscedasticity or autocorrelation; the level of individual significance in all cases is less than 5% and according to the proposed model it is necessary to; if CO2 emissions increase, PBI per capita would increase by 19% and if energy consumption increases, PIB per capita would increase by 20%. Conclusions: Finally, it was possible to contrast the hypothesis that there is a direct and significant relationship between economic growth and environmental degradation.

Objective: The objective of the scientific investigation was to determine the relationship between the preventive tax audit in the refunds linked to the general sales tax of exporting companies in the province of Tambopata, Peru. Method: The quantitative approach was considered, of a non-experimental type, with a descriptive and correlational design; considering a sample of 66 exporters from a universe of 80 to whom we applied two instruments, the first with 2 indicators and the second with 11, valid, with an alpha of 0.911 and 0.920 respectively. The statistical analysis was considered through the Pearson Chi square test and through a multiple linear regression. Results and conclusion: It were determined that the relationship between the preventive tax audit with the balance in favor of benefit, the perceptions not applied of the IGV and the excess or undue payments, is a high positive correlation. In view of the fact that, in the event of a 1% increase in the balance in favor of benefit matters, then the preventive tax audit will increase by 61.22%; if the non-applied IGV perceptions by the taxpayer are high, then the preventive tax audit will increase by 3.91%; and if the excess or improper payments increase by 1%, then preventive tax audit will increase by 15.47%. Finally, there is a high positive relationship between the preventive tax audit and the refunds linked to the IGV of the exporting companies of the province of Tambopata, since a Pearson correlation of 0.6957 was found. Implications of the investigation: The investigation shows an application of the theory to reality in the subject of the preventive tax audit in the returns linked to the general sales tax, the same that was carried out to the exporting companies of the province of Tambopata; all this in view of the fact that taxpayers and companies, when seeking to obtain greater economic profitability, avoid developing contribution actions with the State, since it is an obligation; That is why the supervisory and tax administration entities exercise the role of inspection, inspecting, investigating and controlling compliance with tax obligations. Originality/value: there are few investigations that analyze the relationship between the preventive tax audit with the refunds related to the general tax of the companies that are dedicated to the export of goods and services; which deserves to promote further investigations by types of exportable goods from Peru to the international market.

The objective of the research was to determine the effect of public services in the household on poverty in Peru, in the period 2021, for which a quantitative, non-experimental research approach was considered with a descriptive and correlational design. The information from the National Household Survey of the National Institute of Statistics and Informatics (INEI) database was used, considering the modules “Dwelling and Household Characteristics”, “Household Members’ Characteristics”, “Education”, “Employment and Income”, “Household Equipment”, “Summaries (Calculated Variables)” and “Citizen Participation”. It was possible to determine that the following variables had negative effects on household poverty in Peru: access to potable water, sanitation, electric power, cell phone services; achieving higher, secondary, and primary education levels; having a washing machine, motorcycle, tricycle, motorcycle taxi, computer, kitchen, refrigerator in the household; having a property title; being part of an association or organization; living in a rural residence area; and having remittances. However, the number of household members had a positive effect on poverty. Therefore, it was concluded that access to public services in the household contributed to reducing the probability of being poor in Peru.

La región de Puno cuenta con diversos recursos potenciales mineros, agrícolas y pecuarios; donde la extracción de los minerales se realiza de manera poco sostenible, afectando al medio ambiente, toda vez que la producción de papa, quinua y pecuaria de carne de alpaca y ovino al ser parte de las actividades productivas potenciales, en muchos de los casos es afectado por los pasivos ambientales de la actividad minera. El objetivo del estudio es determinar la relación existente entre la actividad minera y la producción de la papa, quinua y producción pecuaria de la carne de alpaca y ovino en la región Puno-Perú, durante el periodo 2006-2020; considerando el enfoque cuantitativo, no experimental, con diseño descriptivo- correlación y longitudinal, aplicando el modelo econométrico de tipo Lin-Lin. La producción de papa, está relacionado de manera negativa por la producción del oro y del estaño en 0.3527 y 14.33 toneladas por año; la producción de quinua se relaciona con la producción minera del estaño de manera negativa en 0.711 toneladas al año; la producción de la carne de ovino, está relacionado de manera inversa con la producción minera del Oro en 0.00040 toneladas al año; y la producción de la carne de alpaca, se relaciona de manera inversa con la producción minera del estaño en 0.03676 toneladas al año. Por lo cual, existe una relación inversa entre la producción de la actividad minera con la producción agrícola y pecuaria.