{"title":"Macroeconomic Indicators and Subjective Well-Being: Evidence from the European Union","authors":"B. Marton, Alena Mojsejová","doi":"10.54694/stat.2022.19","DOIUrl":null,"url":null,"abstract":"This paper examines the role of factors which could have influenced subjective well-being (SWB) in European countries at a national level between 2010 and 2019. Macroeconomic variables in much of the existing literature have looked at GDP, inflation, government size and expenditure and their relationship to SWB. The current analysis included corruption, property rights, poverty, life expectancy, working time and emissions to enrich the existing body of literature. The World Happiness Index (WHI) is used to measure SWB in this study. The correlation analysis in this study shows a high level of correlation between WHI and the Human Development Index (HDI) which suggests the WHI is a suitable proxy for measuring subjective well-being. Next, the fixed and random effects models were estimated since the dataset was longitudinal, and we have also compared panel regression models with OLS regression models. This analysis revealed positive relationships of GDP, income and property rights on WHI, while poverty and unemployment impact WHI negatively, thus we can conclude positive relationship between material aspects of life and subjective well-being. Corruption and working time impact SWB in a negative way while the impact of life expectancy is positive. The regression models with inflation and emissions were not found to be significant in the research. The results were compared with existing studies based on individual as well as aggregated data. Similarities in results prove that it is possible to analyze determinants of SWB from aggregated data on national level. At the end, we formulate proposals for improving quality of life in the analyzed countries.","PeriodicalId":43106,"journal":{"name":"Statistika-Statistics and Economy Journal","volume":" ","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistika-Statistics and Economy Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54694/stat.2022.19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper examines the role of factors which could have influenced subjective well-being (SWB) in European countries at a national level between 2010 and 2019. Macroeconomic variables in much of the existing literature have looked at GDP, inflation, government size and expenditure and their relationship to SWB. The current analysis included corruption, property rights, poverty, life expectancy, working time and emissions to enrich the existing body of literature. The World Happiness Index (WHI) is used to measure SWB in this study. The correlation analysis in this study shows a high level of correlation between WHI and the Human Development Index (HDI) which suggests the WHI is a suitable proxy for measuring subjective well-being. Next, the fixed and random effects models were estimated since the dataset was longitudinal, and we have also compared panel regression models with OLS regression models. This analysis revealed positive relationships of GDP, income and property rights on WHI, while poverty and unemployment impact WHI negatively, thus we can conclude positive relationship between material aspects of life and subjective well-being. Corruption and working time impact SWB in a negative way while the impact of life expectancy is positive. The regression models with inflation and emissions were not found to be significant in the research. The results were compared with existing studies based on individual as well as aggregated data. Similarities in results prove that it is possible to analyze determinants of SWB from aggregated data on national level. At the end, we formulate proposals for improving quality of life in the analyzed countries.