Basic Education in Peru is key factor for the development of the country. The Primary Education indicators published by the Institute of Statistics and Informatics (INEI) reveals the need for further research. Although there are various studies regarding primary education, they have not been as exhaustive as expected. For each level of basic education, the real-life situations are different, one of these levels is primary education, which the majority of Peruvians access. The objective is to establish the spatial socio-educational association of backwardness and school dropout. A data frame, called dataframe, was built with the Educational Quality Statistics (ESCALE) of the Ministry of Education (MINEDU) of Peru. In addition to spatial descriptive analysis, spatial auto-correlation at the Departmental level of backwardness and school dropout was verified, moreover, the factors chronic ch...

In the midst of the public debate regarding the comparison of official INEI data and data processed by the SBS in the construction of a new mortality table, the argument arose that the SBS needs only data from policyholders belonging to pension fund insurers as sufficient to construct a general mortality table. Due to this scenario, a set of characteristics of policyholders in the AFPs (from the ENAHO database) is contextualized in order to determine their influence on the construction of a general mortality table. To this end, the characteristics of the population affiliated with the private pension system (AFP) were examined with respect to the population affiliated with the public pension system (ONP) and the non-affiliated population. Differences were found in the characteristics that determine the mortality experience of these populations. Therefore, it is concluded that a mortality...

In this research, the Simplified Azbel model was estimated to verify that it is a good fit to mortality rates for pension purposes in Peru, using three estimation methods, maximum likelihood (MV), log-linear regression-type method and ordinary least squares (OLS) method. The mortality tables were segmented into three age groups. It was confirmed that there is a difference in the fit between men and women. With the A/E and ARL similarity metrics, the original and the estimated mortality tables, the hypothesis is corroborated since it is concluded that the best estimates resulted for the SP2005 tables, for the male gender with the Log-linear Regression-type and ordinary least squares methods, except for the range between 50 and 90 years of age. In women, there is only a good fit with the Log-linear Regression-type method from 15 years of age onwards. The fit to the SPP2017 tables is barely...