Multiple Scenarios of Quality of Life Index Using Fuzzy Linguistic Quantifiers: The Case of 85 Countries in Numbeo

Abstract

In economic development, in addition to comparing the gross domestic product (GDP) between nations, it is critical to assess the quality of life to gain a holistic perspective of their different aspects. However, the quality of life index (QOLI) is a subjective term that can be difficult to quantify. Although this composite index is typically calculated using universal weights proposed by experts to aggregate indicators, such as safety indexes, healthcare indexes, pollution indexes, and housing indicators, it is complicated to balance multiple dimensions whose weights are adjusted to account for different countries’ circumstances. Therefore, this paper aims to construct various scenarios of the QOLI, using linguistic quantifiers of the ordered weighted averaging (OWA) operator, and the 2-tuple linguistic model. Numbeo, one of the largest quality of life information databases, was used in this paper to estimate the QOLI in 85 countries. Uncertainty and sensitivity analyses were employed to assess the robustness of the QOLI. The results of the proposed model are compared with those obtained using the Numbeo formulation. The results show that the proposed model increases the linguistic interpretability of the QOLI, and obtains different QOLIs, based on diverse country contexts.

Comparative Analysis between Quality of Life and Human Labor in Countries Belonging to G7 and BRICS Blocks: Proposition of Discriminant Analysis Model

“…The prediction model introduced in this study allowed for a classification of all the countries on NUMBEO's platform. Similar research was carried out by Shu et al (2022) based on linguistic quantifiers, aimed at comparing the NUMBEO's QoL index to the data analyzed in their research. Our model enabled slight adjustments to new QoL index scenarios so managers can recognize their own QoL level based on the conditions in their countries.…”

“…According to Shu et al (2022), not all countries presenting a high GDP have a high QoL, and the Statistictimes (2023) ranking of global GDP sets the classification of countries based on GDP from the first to the tenth position, namely: US, China, Japan, Germany, United Kingdom, France, India, Italy, and Brazil. Although Brazil is classified as having a medium QoL, it emerges as one of the countries with the highest world GDP, because other variables influence this index's calculation, such as purchasing power index, pollution index, price/income ratio, cost of living index, safety index, health care index, traffic commute time index, and climate index (NUMBEO).…”

“…The selection criteria applied to the variables met their relevance and association with the QoL and human labor topics. These two platforms were selected for the study because they provide consolidated data on all countries and because they are recurrent research-data sources (Carlsen and Bruggemann 2020;Guo et al 2022;Lee and Chou 2018;Peiró-Palomino and Picazo-Tadeo 2018;Polzin et al 2015;Saturno-Hernández et al 2019;Shu et al 2022).…”

“…Other variables are taken into consideration to calculate development indices, among them, Purchasing Power index, Safety index, Health Care index, Cost of Living index, Property Price to Income Ratio index, Traffic Commute Time index, Pollution index, and Climate index. These variables help to estimate the general quality of life of a country's inhabitants; they are disclosed by the NUMBEO platform, as shown herein (Shu et al 2022).…”

Multi-Criteria Decision under Uncertainty as Applied to Resource Allocation and Its Computing Implementation

“…Continuing analysis, it is possible to apply the min operator [47] or, generally, the ordered weighted averaging operator [48][49][50][51] for X k , K = 1, 2, . .…”