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Significant Indicators and Determinants of Happiness: Evidence from a UK Survey and Revealed by a Data-Driven Systems Modelling Approach

Yuanlin Gu () and Hua-Liang Wei ()
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Yuanlin Gu: Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK
Hua-Liang Wei: Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK

Social Sciences, 2018, vol. 7, issue 4, 1-12

Abstract: This study aims to establish a quantitative relationship between lifestyle and happiness in the UK based on over 10,000 surveyed samples with 63 lifestyle variables from the UK Understanding Society Data. Transparent parametric models are built and a number of significant explanatory variables (lifestyle indicators) have been identified using a systems engineering modelling approach. Specifically; based on the traditional orthogonal forward regression (OFR) algorithm; the study introduces a new metrics; with which the impacts of lifestyle variables (and/or their interactions) can be quantitatively measured and identified one by one. These identified significant indicators provide a meaningful parsimonious representation of the relationship between happiness and lifestyle; revealing how happiness quantitatively depends on lifestyle; and how the lifestyle variables interactively affect happiness. For example; the quantitative results of a linear model indicate that lifestyle variables such as ‘health’; ‘income’; and ‘retirement’; impacts happiness significantly. Furthermore; the results of a bilinear model show that some interaction variables such as ‘retired’ together with ‘elder’; ‘fair health’ together with ‘low-income’ and so on; are significantly related to happiness.

Keywords: happiness; lifestyle; life satisfaction; nonlinear system; data-driven modelling; systems engineering (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2018
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