Correa Prieto, Yovani

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    Perfil scocioeconómico de los municipios de Puerto Rico: aplicación de técnicas estadísticas multivariadas
    (2011-06) Correa Prieto, Yovani; Quintana Díaz, Julio C.; College of Arts and Sciences - Sciences; Lorenzo González, Edgardo; Santana Morant, Dámaris; Department of Mathematics; Quiñones Padovani, Carlos E.
    Classifying a particular society in similar socioeconomic groups allows to adoption of strategic conditions in those groups that are poor and at the same time to strengthen those in other groups. In the present study we applied multivariate statistical techniques (principal component, factor and cluster analyses) to fourteen socio-economic variables available in Puerto Rico census data for the year 2000 and to four indexes calculated by us. The main objective was to develop a socioeconomic profile of the municipalities of Puerto Rico. By applying the method of principal components we reduced the number of variables to four, which summarize about 80% of the initial information from the data. Subsequently, these new variables were used as classification criteria from which emerged a division into five clusters for the 78 municipalities of Puerto Rico. Then a comparison was made of the similarities of the municipalities that belong to their respective clusters and also the differences between the resulting groups. In general we found that some municipalities located at the central region of the Island belong to a particular cluster characterized by low economic development and low percentage of population obtaining at least a high school educational level. However, these municipalities show better social characteristics, such as a low proportion of homes with a woman as householder and the number of grandparents living with their grandchildrens and being responsible for them. It is also interesting that most of municipalities located at the metropolitan area of Puerto Rico show better economic conditions and simultaneously have some problems related to unequal distribution of income among the population and higher rates of mortality. The remaining clusters also have particular characteristics that are discussed in detail in the findings chapter and presented in our conclusions.