New governance composite indicator: a quality and reliability-based approach

Composite indicators simplify the representation of complex realities without losing generality, attracting the attention of researchers from various fields, including operational research. Despite their advantages, many composite indicators fail with regard to quality and reliability, e.g., governance composite indicators. This study proposes an innovative quality- reliability approach for constructing composite indicators, which can be applied to represent the multidimensional governance of the 20 largest economies in the world. The composite indicator obtained showed high explanatory power (correlation of 0.85 with GDP per capita), absence of countries with discrepant scores (0% bivariate outliers), high informational power (retention of 90% of input data), and high discriminant power (well-distributed scores). Finally, the countries' uncertainty of 0.20 positions in the ranking guarantees that the composite indicator has a stable internal structure, and the results are reliable and valuable for guiding investors and public managers on making investments and/or prioritizing institutional reforms.

Authors

Institutions

Publication Details

Journal
Anais do Simpósio Brasileiro de Pesquisa Operacional
Published
2025-01-01
DOI
https://doi.org/10.59254/sbpo-2025-212680
Primary Topic
Corruption and Economic Development
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

New governance composite indicator: a quality and reliability-based approach

Iara Sibele Silva, Elisa Fusco, Alice Coimbra, Matheus Pereira Libório et al.
Anais do Simpósio Brasileiro de Pesquisa Operacional
Corruption and Economic Development
article

New governance composite indicator: a quality and reliability-based approach

Iara Sibele Silva, Elisa Fusco, Alice Coimbra, Matheus Pereira Libório, Angélica Cidália Gouveia dos Santos, Marcos Flávio Silveira Vasconcelos D’Ângelo, Petr Ekel
article en

Abstract

Composite indicators simplify the representation of complex realities without losing generality, attracting the attention of researchers from various fields, including operational research. Despite their advantages, many composite indicators fail with regard to quality and reliability, e.g., governance composite indicators. This study proposes an innovative quality- reliability approach for constructing composite indicators, which can be applied to represent the multidimensional governance of the 20 largest economies in the world. The composite indicator obtained showed high explanatory power (correlation of 0.85 with GDP per capita), absence of countries with discrepant scores (0% bivariate outliers), high informational power (retention of 90% of input data), and high discriminant power (well-distributed scores). Finally, the countries' uncertainty of 0.20 positions in the ranking guarantees that the composite indicator has a stable internal structure, and the results are reliable and valuable for guiding investors and public managers on making investments and/or prioritizing institutional reforms.

Anais do Simpósio Brasileiro de Pesquisa OperacionalVol. Vol 57, 2025
Pontifícia Universidade Católica de Minas Gerais (BR), Universidade Estadual de Montes Claros (BR), University of Florence (IT)
Openalex Percentile: Top 58%
Corruption and Economic Development
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

New governance composite indicator: a quality and reliability-based approach — Iara Sibele Silva, Elisa Fusco, et al. · Anais do Simpósio Brasileiro de Pesquisa Operacional (2025) | TGRS Research Map | TGRS