DOI: 10.1016/j.enpol.2020.111734
论文题名: Power infrastructure and income inequality: Evidence from Brazilian state-level data using dynamic panel data models
作者: Medeiros V. ; Ribeiro R.S.M.
刊名: Energy Policy
ISSN: 03014215
出版年: 2020
卷: 146 语种: 英语
中文关键词: Brazil
; Econometrics
; Income inequality
; Infrastructure heterogeneities
; Power infrastructure
英文关键词: Method of moments
; Dynamic panel data
; Generalized method of moments
; Income inequality
; Power infrastructures
; Power supply
; Electric power systems
; electricity generation
; energy efficiency
; energy policy
; income distribution
; panel data
; power generation
; Brazil
英文摘要: A broad literature has indicated the essential role of power infrastructure in reducing income inequality. However, it is uncertain whether this relationship remains in scenarios with heterogeneities in terms of provision, quality, and access to electricity. This article intends to contribute to the literature by evaluating, in light of the Brazilian reality, how provision, quality, and the interaction between these two characteristics affects income inequality. To account for possible reverse causality problems, we apply the Generalized Method of Moments (GMM) estimators with different specifications to verify the robustness of our estimates. In a scenario where the vast majority of the population has access to electricity, our findings indicate that an expansion in power provision reduces income inequality. Nonetheless, the higher the power infrastructure quality, the smaller the returns of a growing power supply to the reduction of inequality, thus suggesting that richer populations tend to benefit the most from improvements in power quality. © 2020 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/167353
Appears in Collections: 气候变化与战略
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作者单位: Faculty of Economics, Federal University of Minas Gerais, Brazil
Recommended Citation:
Medeiros V.,Ribeiro R.S.M.. Power infrastructure and income inequality: Evidence from Brazilian state-level data using dynamic panel data models[J]. Energy Policy,2020-01-01,146