Detecting the socio-economic drivers of confidence in government with eXplainable Artificial Intelligence
Abstract The European Quality of Government Index (EQI) measures the perceived level of government quality by European Union citizens, combining surveys on corruption, impartiality and quality of provided services. It is, thus, an index based on individual subjective evaluations. Understanding the m...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-28020-5 |
| _version_ | 1797945951010160640 |
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| author | Loredana Bellantuono Flaviana Palmisano Nicola Amoroso Alfonso Monaco Vitorocco Peragine Roberto Bellotti |
| author_facet | Loredana Bellantuono Flaviana Palmisano Nicola Amoroso Alfonso Monaco Vitorocco Peragine Roberto Bellotti |
| author_sort | Loredana Bellantuono |
| collection | DOAJ |
| description | Abstract The European Quality of Government Index (EQI) measures the perceived level of government quality by European Union citizens, combining surveys on corruption, impartiality and quality of provided services. It is, thus, an index based on individual subjective evaluations. Understanding the most relevant objective factors affecting the EQI outcomes is important for both evaluators and policy makers, especially in view of the fact that perception of government integrity contributes to determine the level of civic engagement. In our research, we employ methods of Artificial Intelligence and complex systems physics to measure the impact on the perceived government quality of multifaceted variables, describing territorial development and citizen well-being, from an economic, social and environmental viewpoint. Our study, focused on a set of regions in European Union at a subnational scale, leads to identifying the territorial and demographic drivers of citizens’ confidence in government institutions. In particular, we find that the 2021 EQI values are significantly related to two indicators: the first one is the difference between female and male labour participation rates, and the second one is a proxy of wealth and welfare such as the average number of rooms per inhabitant. This result corroborates the idea of a central role played by labour gender equity and housing policies in government confidence building. In particular, the relevance of the former indicator in EQI prediction results from a combination of positive conditions such as equal job opportunities, vital labour market, welfare and availability of income sources, while the role of the latter is possibly amplified by the lockdown policies related to the COVID-19 pandemics. The analysis is based on combining regression, to predict EQI from a set of publicly available indicators, with the eXplainable Artificial Intelligence approach, that quantifies the impact of each indicator on the prediction. Such a procedure does not require any ad-hoc hypotheses on the functional dependence of EQI on the indicators used to predict it. Finally, using network science methods concerning community detection, we investigate how the impact of relevant indicators on EQI prediction changes throughout European regions. Thus, the proposed approach enables to identify the objective factors at the basis of government quality perception by citizens in different territorial contexts, providing the methodological basis for the development of a quantitative tool for policy design. |
| first_indexed | 2024-04-10T21:03:09Z |
| format | Article |
| id | doaj.art-62d5637c754f473f998d12038c2af874 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T21:03:09Z |
| publishDate | 2023-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-62d5637c754f473f998d12038c2af8742023-01-22T12:12:40ZengNature PortfolioScientific Reports2045-23222023-01-0113111910.1038/s41598-023-28020-5Detecting the socio-economic drivers of confidence in government with eXplainable Artificial IntelligenceLoredana Bellantuono0Flaviana Palmisano1Nicola Amoroso2Alfonso Monaco3Vitorocco Peragine4Roberto Bellotti5Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo MoroDepartment of Economics and Law, Sapienza University of RomeIstituto Nazionale di Fisica Nucleare, Sezione di BariIstituto Nazionale di Fisica Nucleare, Sezione di BariDipartimento di Economia e Finanza, Università degli Studi di Bari Aldo MoroIstituto Nazionale di Fisica Nucleare, Sezione di BariAbstract The European Quality of Government Index (EQI) measures the perceived level of government quality by European Union citizens, combining surveys on corruption, impartiality and quality of provided services. It is, thus, an index based on individual subjective evaluations. Understanding the most relevant objective factors affecting the EQI outcomes is important for both evaluators and policy makers, especially in view of the fact that perception of government integrity contributes to determine the level of civic engagement. In our research, we employ methods of Artificial Intelligence and complex systems physics to measure the impact on the perceived government quality of multifaceted variables, describing territorial development and citizen well-being, from an economic, social and environmental viewpoint. Our study, focused on a set of regions in European Union at a subnational scale, leads to identifying the territorial and demographic drivers of citizens’ confidence in government institutions. In particular, we find that the 2021 EQI values are significantly related to two indicators: the first one is the difference between female and male labour participation rates, and the second one is a proxy of wealth and welfare such as the average number of rooms per inhabitant. This result corroborates the idea of a central role played by labour gender equity and housing policies in government confidence building. In particular, the relevance of the former indicator in EQI prediction results from a combination of positive conditions such as equal job opportunities, vital labour market, welfare and availability of income sources, while the role of the latter is possibly amplified by the lockdown policies related to the COVID-19 pandemics. The analysis is based on combining regression, to predict EQI from a set of publicly available indicators, with the eXplainable Artificial Intelligence approach, that quantifies the impact of each indicator on the prediction. Such a procedure does not require any ad-hoc hypotheses on the functional dependence of EQI on the indicators used to predict it. Finally, using network science methods concerning community detection, we investigate how the impact of relevant indicators on EQI prediction changes throughout European regions. Thus, the proposed approach enables to identify the objective factors at the basis of government quality perception by citizens in different territorial contexts, providing the methodological basis for the development of a quantitative tool for policy design.https://doi.org/10.1038/s41598-023-28020-5 |
| spellingShingle | Loredana Bellantuono Flaviana Palmisano Nicola Amoroso Alfonso Monaco Vitorocco Peragine Roberto Bellotti Detecting the socio-economic drivers of confidence in government with eXplainable Artificial Intelligence Scientific Reports |
| title | Detecting the socio-economic drivers of confidence in government with eXplainable Artificial Intelligence |
| title_full | Detecting the socio-economic drivers of confidence in government with eXplainable Artificial Intelligence |
| title_fullStr | Detecting the socio-economic drivers of confidence in government with eXplainable Artificial Intelligence |
| title_full_unstemmed | Detecting the socio-economic drivers of confidence in government with eXplainable Artificial Intelligence |
| title_short | Detecting the socio-economic drivers of confidence in government with eXplainable Artificial Intelligence |
| title_sort | detecting the socio economic drivers of confidence in government with explainable artificial intelligence |
| url | https://doi.org/10.1038/s41598-023-28020-5 |
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