Testing Theories about the Composition of GNH Domains and Subsequently Building Causal Models by Means of Structural Equation Modeling

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Sonam Tshering
Takeo Okazaki, Satoshi Endo

Abstract

GDP, a yardstick designed to measure the progress of the economies of the world’s industrial nations after World War II has been instrumental in rebuilding the economies in the past, but now it is causing as many problems as it solves. Many questions have been raised about GDP: Increasing self-reliance means decreasing GDP; GDP doesn’t correlate with quality of life measures; GDP doesn’t account for the distribution of costs and benefits. It was against this backdrop of inadequate measure that GNH was proposed as an alternative measure of development. Therefore, the larger aim of this paper is to build a methodology to construct GNH domain-specific quantitative indicators (objective and/or subjective) and then develop a single GNH indicator from the domain-specific indicators. To achieve this, observable variables thought to belong to a specific domain according to prior theories or notions are grouped together. A latent variable has been identified and suitably renamed. Subsequently, implemented causal modeling amongst/between them—evaluated model fitness, validated the relationships and finally a decision is made about the prior notions or theories of the composition of GNH domains using the GNH survey data of the year 2010.

Keywords: Modeling, causality, confirmatory, analysis, latent, manifest, happiness.

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