Volume 27, Issue 101 And 0 (inpress 2026)
Social Welfare Quarterly 2026, 27(101 And 0): 0-0 |
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Azimi S, kian poor S, BAKHSHESH M. (2026). Income, Inequality, and Happiness in Iran (2011-2024): Testing Hypotheses of Materialism and Income Distribution with Bayesian and Machine Learning Methods. Social Welfare Quarterly. 27(101), : 4
URL: http://refahj.uswr.ac.ir/article-1-4555-en.html
URL: http://refahj.uswr.ac.ir/article-1-4555-en.html
Abstract: (630 Views)
Introduction: In light of the increasing importance of happiness and mental well-being as the ultimate measure of development and the core of the "happiness economy," this research focuses on Iran during the period 2011-2024 (as a case study of an economy under shock) to examine the relationship between income and happiness and to test the moderating roles of income inequality and economic growth. Its main objective is to present and apply an innovative and localized framework for analyzing this relationship, which could potentially become an effective tool for monitoring and evaluating public policies.
Method: This study utilizes panel data from Iranian provinces (2011-2021) within an integrated Python-based analysis framework. The applied hybrid methodology includes Bayesian modeling, machine learning algorithms, and non-parametric methods. To enhance accuracy and interpretability, Ridge regression and the SHAP method were also employed.
Findings: Data analysis reveals that in Iran's economy, the decrease in real purchasing power is the main influential factor on happiness, not income inequality. The relationship between income and happiness follows an increasing and non-linear trend. The effect of inequality in the models was insignificant, and only the sustained materialism hypothesis was confirmed. The results from Bayesian and machine learning methods are definitive and reliable.
Discussion: This research demonstrated that in the context of Iran's economy, preserving real purchasing power and controlling inflation have a more direct impact on happiness compared to reducing inequality. The findings highlight the necessity of localizing happiness theories and focusing policy on economic stability. The presented framework also provides a practical tool for monitoring the impact of policies on subjective well-being.
Method: This study utilizes panel data from Iranian provinces (2011-2021) within an integrated Python-based analysis framework. The applied hybrid methodology includes Bayesian modeling, machine learning algorithms, and non-parametric methods. To enhance accuracy and interpretability, Ridge regression and the SHAP method were also employed.
Findings: Data analysis reveals that in Iran's economy, the decrease in real purchasing power is the main influential factor on happiness, not income inequality. The relationship between income and happiness follows an increasing and non-linear trend. The effect of inequality in the models was insignificant, and only the sustained materialism hypothesis was confirmed. The results from Bayesian and machine learning methods are definitive and reliable.
Discussion: This research demonstrated that in the context of Iran's economy, preserving real purchasing power and controlling inflation have a more direct impact on happiness compared to reducing inequality. The findings highlight the necessity of localizing happiness theories and focusing policy on economic stability. The presented framework also provides a practical tool for monitoring the impact of policies on subjective well-being.
Article number: 4
Keywords: Bayesian Analysis, Household Income, Income Inequality, Real Purchasing Power, Subjective Well-being, Happiness Economics
Type of Study: orginal |
Received: 2025/12/7 | Accepted: 2026/06/28 | Published: 2026/04/30
Received: 2025/12/7 | Accepted: 2026/06/28 | Published: 2026/04/30
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This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 (CC-By-NC 4.0), which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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