Quantile Regression Analysis of Household Energy Demand in Iran Using Income-Expenditure National Survey (2016-2023); Heterogeneity and Key Characteristics

نویسندگان

1 Department of Management, Isfahan University of Medical Sciences, Isfahan, Iran.

2 Faculty of Administration sciences and Economics, University of Isfahan, Isfahan, Iran.

3 Faculty of Administration sciences and Economics, University of Isfahan, Isfahan, Iran.

doi
10.22099/ijes.2025.54147.2057
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.

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