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Quantile Regression Analysis of Household Energy Demand in Iran Using Income-Expenditure National Survey (2016-2023); Heterogeneity and Key Characteristics | ||
| Iranian Journal of Economic Studies | ||
| دوره 14، شماره 1 - شماره پیاپی 27، 2025، صفحه 163-200 اصل مقاله (663.08 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22099/ijes.2025.54147.2057 | ||
| نویسندگان | ||
| Shahram Moeeni* 1؛ Maryam Moeeni2؛ Amir Mohammad Reihani1 | ||
| 1Faculty of Administration sciences and Economics, University of Isfahan, Isfahan, Iran. | ||
| 2Department of Management, Isfahan University of Medical Sciences, Isfahan, Iran. | ||
| چکیده | ||
| Iran faces pressing challenges in managing household energy consumption, This study addresses whether Iranian household energy demand is heterogeneous across different levels of consumption for the first time and explores the influence of key household characteristics on energy demand. Using micro-level data from over 126,000 households from the Iranian Household Income and Expenditure Survey, we estimate separate demand equations for electricity and gas using quantile regression. This approach reveals that both price and income elasticities, as well as the effects of household characteristics variables, vary significantly across the expenditure distribution. For example, income elasticity for electricity rises from 0.25 in the lowest decile to 0.32 in the highest, while own-price elasticity (in absolute value) is stronger for lower-consuming households (–0.60 at the 1st decile) than for the highest (–0.50 at the 9th decile). For gas, higher education of the household head is linked to reduced consumption, especially among high-use households. The quantile regression estimates indicate that, controlling for other factors, rural households tend to have higher gas expenditures, while urban households tend to have higher electricity expenditures, reflecting underlying differences in energy use patterns. Our findings confirm that Iranian household energy demand is highly heterogeneous: household characteristics variables such as age, homeownership, household size, and dwelling area exert varying influences depending on expenditure level and energy type. These results underscore the importance of targeted, data-driven policies—such as differentiated pricing or subsidy reforms—that consider the diversity of household responses across the distribution. | ||
| کلیدواژهها | ||
| Household Energy Demand؛ Quantile Regression؛ Heterogeneity؛ Income-Expenditure Survey؛ Iran | ||
| مراجع | ||
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Abrevaya, J., & Dahl, C. M. (2008). The effects of birth inputs on birthweight: Evidence from quantile estimation on panel data. Journal of Business & Economic Statistics, 26(4), 379-397. Alidadipour, A., & Khoshkalam Khosroshahi, M. (2021). Improving household electricity consumption efficiency and its rebound effect in Iran considering asymmetry in electricity price. Economic Modeling Quarterly, 2(54), 47-66. Aryanpur, V., Shafiei, E., Hosseini, S. M., & Yaghoubi, S. (2022). A scenario-based assessment of energy subsidy reform impacts on Iran’s electricity sector. Energy Policy, 165, 112934. Aslam, A., & Ahmad, E. (2023). Quantile-based analysis of electricity demand in Pakistan: A pseudo-panel data approach. Energy Policy, 175, 113463. Bache, S. H., Dahl, C. M., & Kristensen, J. T. (2013). Headlights on tobacco road to low birthweight outcomes: Evidence from a battery of quantile regression estimators and a heterogeneous panel. Empirical Economics, 44(3), 1593-1633. Banks, J., Blundell, R., & Lewbel, A. (1997). Quadratic Engel curves and consumer demand. Review of Economics and Statistics, 79(4), 527-539. Bazazan, F., Mousavi, M., & Gheshmi, F. (2015). The impact of electricity energy subsidy targeting on household demand by urban and rural areas in Iran. Iranian Economic Research Journal, 4(14), 1-32. Belaïd, F. (2016). Understanding the spectrum of domestic energy consumption: Empirical evidence from France. Energy Policy, 92, 220-233. Belaïd, F., Youssef, A. B., & Omrani, N. (2020). Investigating the factors shaping residential energy consumption patterns in France: Evidence from quantile regression. European Journal of Comparative Economics, 17(1), 127-151. Buchinsky, M. (1998). Recent advances in quantile regression models: A practical guideline for empirical research. Journal of Human Resources, 33(1), 88-126. Çebi Karaaslan, K., Acar Balaylar, N., & Gül, H. (2024). Determinants of household electricity expenditures: A quantile regression analysis using Kennedy approach. Energy, 288, 129802. Deaton, A., & Muellbauer, J. (1980). An almost ideal demand system. The American Economic Review, 70(3), 312-326. Ghoddusi, H., Rafizadeh, N., & Rahmati, M. H. (2022). Price elasticity of gasoline smuggling: Evidence from Iran. Energy Economics, 107, 105813. Harold, J., Cullinan, J., & Lyons, S. (2017). The income elasticity of household energy demand: A quantile regression analysis. Applied Economics, 49(54), 5570-5578. Huang, W. (2015). The determinants of household electricity consumption in Taiwan: Evidence from quantile regression. Energy, 87, 120-133. Izadi, H. R. (2022). Examining the consumption behavior of households caused by changing the utility function using the DSGE model. Iranian Journal of Economic Studies, 12(1), 105-121. Kaza, N. (2010). Understanding the spectrum of residential energy consumption: A quantile regression approach. Energy Policy, 38(11), 6574-6585. Khosravi-Nejad, A. A. (2021). Estimation of gasoline, electricity and domestic gas demand system for urban households in Iran. Economic Modeling Quarterly, 15(2), 21-46. Koenker, R. (2005). Quantile regression. Cambridge University Press. Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50. Kostakis, I. (2020). Socio-demographic determinants of household electricity consumption: Evidence from Greece using quantile regression analysis. Current Research in Environmental Sustainability, 1, 23-30. Lin, B., & Kuang, Y. (2020). Household heterogeneity impact of removing energy subsidies in China: Direct and indirect effect. Energy Policy, 147, 111811. Miller, M., & Alberini, A. (2016). Sensitivity of price elasticity of demand to aggregation, unobserved heterogeneity, price trends, and price endogeneity: Evidence from U.S. data. Energy Policy, 97, 235-249. Motamedi M., Moeeni S., Gharakhani S., Keyfarokhi I. (2014) The behavior of Iranian restructured electricity market in supply function equilibrium framework. International Journal of Academic Research in Business and Social Sciences., 4 (1), p. 178 Moeeni, S., & Moeeni, M. (2021). The impact of intra-household bargaining game on progression to third birth in Iran. Journal of Family and Economic Issues, 42(1), 61–72. Moshiri, S. (2015). The effects of the energy price reform on households consumption in Iran. Energy Policy, 79, 177-188. Nsangou, J. C., Kenfack, J., Nzotcha, U., & Tamo, T. T. (2022). Explaining household electricity consumption using quantile regression, decision tree and artificial neural network. Energy and Buildings, 262, 111975. Otobideh, S. A., Moeeni, S., Mohammadzadeh, Y., Rahimi, B., Shabaninejad, H., & Yusefzadeh, H. (2021). Estimation of the income and price elasticity of pharmaceutical import demand in Iran. International Journal of Pharmaceutical and Healthcare Marketing, 15(3), 466–474. Pablo-Romero, M. P., Pozo-Barajas, R., & Molleda-Jimena, G. (2021). Residential energy environmental Kuznets curve extended with non-linear temperature effects: A quantile regression for Andalusian (Spain) municipalities. Environmental Science and Pollution Research, 28(35), 48984-48999. Pajuyan, J., Foroutan, F., & Ghaffari, F. (2020). Evaluating the impact of energy market price regulation on energy demand: Quantile regression approach. Journal of Industrial Economics Research, 4(14), 1-32. Pourghorban, M., & Mamipour, S. (2020). Modeling and forecasting the electricity price in Iran using wavelet-based GARCH model. Iranian Journal of Economic Studies, 9(1), 233-260. Rafiei, F. (2022). Phasing-out natural gas subsidies based on dynamic recursive CGE approach: the case study of basic metal manufacturing in Iran. Iranian Journal of Economic Studies, 11(2), 495-517. Saboohi, Y. (2001). An evaluation of the impact of reducing energy subsidies on living expenses of households. Energy Policy, 29(3), 245-252. Schulte, I., & Heindl, P. (2017). Price and income elasticities of residential energy demand in Germany. Energy Policy, 102, 512-528. Statistical Center of Iran. (2023). Household income and expenditure survey data for urban and rural households in 2022. Tehran: Statistical Center of Iran. Sun, C., & Ouyang, X. (2016). Price and expenditure elasticities of residential energy demand during urbanization: An empirical analysis based on the household-level survey data in China. Energy Policy, 88, 56-63. Tilov, I., Farsi, M., & Volland, B. (2020). From frugal Jane to wasteful John: A quantile regression analysis of Swiss households’ electricity demand. Energy Policy, 138, 111246. Wang, Y., Chen, J., Xia, Q., Zeng, M., Xiang, Y., & Yang, H. (2022). Household heating energy demand analysis considering heterogeneity: The case of China’s hot summer and cold winter climate zone. Energy, 248, 123547. Wang, Y., Chen, J., Xia, Q., Zeng, M., Xiang, Y., Yang, H., & Chen, Y. (2023). Energy consumption efficiency and marginal abatement costs of space heating in China’s hot summer and cold winter zone. Energy and Buildings, 295, 113300. | ||
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