Identifying Factors Affecting Iran's Social Welfare under Uncertainty: A Bayesian Average Approach

• Published in: Iqtiṣād-i bās̠ubāt Vol. 3; no. 1; pp. 61 - 97
• Main Authors: mohammad alizadeh, Gholamreza Nemati, Mohammad Hassan Fotros, Maryam KHodaverdi Samani, Dina Kabiri
• Format: Journal Article
• Language: Persian
• Published: University of Sistan and Baluchestan 01.04.2022
• Subjects: bayesian model averaging; social welfare; uernctainty model
• DOI: 10.22111/sedj.2022.40421.1156

Improving the quality of life and the level of social welfare in society is one of the main goals of economic policy makers. Although macroeconomic environment has an important role in the level of social welfare, but the lack of knowledge of a model that can properly explain social welfare, has created uncertainties in common models, in addition to the multiplicity of factors affecting social welfare.Uncertainty has been modeled on the accuracy of the statement and has hindered researchers' consensus on key determinants of social welfare. For this purpose, the present study has identified and determined the factors affecting the social welfare index (Amartya Sen) in the Iranian economy and using Bayesian averaging approach based on time series data from 1996 to 2018 and in conditions of model uncertainty, which is performed by calculations From an estimate of 260,000 regressions, a number of 8 variables were identified as strong and unbreakable variables; Which includes variables (exchange rate, misery index, tax revenues, oil revenues) with a negative sign and variables (urban growth rate, degree of economic openness, health indicators and information and communication technology) with a positive sign. Other variables have lost their effect in the presence of non-fragile variables and there is no strong evidence for their effectiveness on social welfare during the period under review. According to the results, it can be concluded that in order to promote and improve social welfare in the country and in formulating welfare policies and programs, more attention should be paid to the selected variables.