One of the most important aspects in evaluating the quality of life is to take into account the effect of having multiple disadvantages since they far exceed the sum of their individual effects. The microdata of the annual Istat survey on “Everyday life” have information on the “joint distribution” of the most salient features of quality of life. We address the problem of measuring the cumulative effects of objective and subjective conjoint vulnerabilities using as a target variable the subjective well-being which is viewed as multidimensional variable influenced by objective situation of distress. We analysed the features of quality of life in the microdata of the survey on “Everyday life”, using nonlinear regression. The linear regression is restricted to reveal only relations showing a linear trend. Because of this restriction, regression methods that can deal with nonlinear relations have become more and more popular. Estimation of regression models for nonlinear relations is more computationally complex and intensive than for the linear regression model. The results show different conditions which define the subjective well-being, identifying the contributions of the situations of distress on life satisfaction.

The effects of objective and subjective conjoint vulnerabilities on life satisfaction

PISCITELLI, ALFONSO;
2014

Abstract

One of the most important aspects in evaluating the quality of life is to take into account the effect of having multiple disadvantages since they far exceed the sum of their individual effects. The microdata of the annual Istat survey on “Everyday life” have information on the “joint distribution” of the most salient features of quality of life. We address the problem of measuring the cumulative effects of objective and subjective conjoint vulnerabilities using as a target variable the subjective well-being which is viewed as multidimensional variable influenced by objective situation of distress. We analysed the features of quality of life in the microdata of the survey on “Everyday life”, using nonlinear regression. The linear regression is restricted to reveal only relations showing a linear trend. Because of this restriction, regression methods that can deal with nonlinear relations have become more and more popular. Estimation of regression models for nonlinear relations is more computationally complex and intensive than for the linear regression model. The results show different conditions which define the subjective well-being, identifying the contributions of the situations of distress on life satisfaction.
978-84-937822-4-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/605440
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