Extreme Energy Poverty Incidence and Determinants in Nigeria: A Multidimensional Approach

Ozughalu, Uche M.; Ogwumike, Fidelis O.

doi:10.1007/s11205-018-1954-8

Cited by 49 publications

(31 citation statements)

References 28 publications

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“…This methodology has captured the attention of academia and policymakers alike from around the world, and several countries in Latin America have implemented official poverty measures based on this method. 14 The technique has also been extended to other subjects such as child poverty (Hoolda Kim, 2019;Leturcq & Panico, 2019), energy poverty (Ozughalu & Ogwumike, 2019), women's empowerment (Galiè et al, 2019;Tsiboe, 2018) and also the labour market (García-Perez et al, 2017;Sehnbruch et al 2020).…”

Section: Methodsmentioning

confidence: 99%

A Multidimensional Approach to Measuring Quality of Employment (QoE) Deprivation in Six Central American Countries

et al. 2021

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This paper proposes a methodology for measuring Quality of Employment (QoE) deprivation from a multidimensional perspective in six Central American countries (Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama) using a dataset specifically designed to measure employment conditions. Building on previous work on multidimensional poverty and employment indicators, the paper uses the Alkire/Foster (AF) method to construct a synthetic indicator of the QoE at an individual level. It selects four dimensions that must be considered as essential to QoE deprivation: income, job stability, job security and employment conditions. These dimensions then subdivide into several indicators, a threshold for each indicator and dimension is established before defining an overall cut-off line that allows for the calculation of composite levels of deprivation. The results generated by this indicator show that Central American countries can be divided into three distinct and robust performance groups in terms of their QoE deprivation. Overall, approximately 60% of the deprivation levels are attributable to non-income variables, such as occupational status and job tenure. The methodology used can allow policymakers to identify and focus on the most vulnerable workers in a labour market and highlights the fact that having a formal written contract is no guarantee of good job quality, particularly in the case of women.

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Section: Methodsmentioning

confidence: 99%

A Multidimensional Approach to Measuring Quality of Employment (QoE) Deprivation in Six Central American Countries

et al. 2021

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“…Energy poverty refers to people that do not have regular, reliable, confident, and accurate access to electricity (Nagothu, 2016;Yadoo and Cruickshank, 2017;Ozughalu and Ogwumike, 2019). Pachauri and Spreng (2003) defined energy poverty according to the access to energy services, since access to more efficient energy sources enhance the quality of human development and social prosperity (Pereira et al, 2013) 4 .…”

Section: Basic Conceptual Aspects: From Energy Poverty To Energy Accessmentioning

confidence: 99%

Review on Rural Energy Access Policies

Cabello-Vargas¹,

Escobedo-Izquierdo²,

Morales‐Acevedo

2021

IJEEP

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“…Most existing studies confirm that energy poverty harms social and economic wellbeing [ 8 , 9 , 10 , 11 ], causes education and gender inequality [ 12 , 13 , 14 , 15 ], increases residents’ health risk [ 16 , 17 , 18 , 19 , 20 ], and leads to ecological degradation [ 21 , 22 , 23 ]. Research on the influencing factors of energy poverty shows that household income, the educational level of residents, and social factors (age, ability, ethnicity) are the main causes of energy poverty [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. With the continuous promotion of energy transitions, the impact of low-carbon energy transition on energy poverty has also received extensive attention.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Interaction and Socioeconomic Determinants of Rural Energy Poverty in China

Xia

Qian

et al. 2022

IJERPH

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This study investigated the energy poverty spatiotemporal interaction characteristics and socioeconomic determinants in rural China from 2000 to 2015 using exploratory time–space data analysis and a geographical detector model. We obtained the following results. (1) The overall trend of energy poverty in China’s rural areas was “rising first and then declining”, and the evolution trend of energy poverty in the three regions formed a “central–west–east” stepwise decreasing pattern. (2) There was a dynamic local spatial dependence and unstable spatial evolution process, and the spatial agglomeration of rural energy poverty in China had a relatively higher path dependence and locked spatial characteristics. (3) The provinces with negative connections were mainly concentrated in the central and western regions. Anhui and Henan, Inner Mongolia and Jilin, Jilin and Heilongjiang, Hebei and Shanxi, and Liaoning and Jilin constituted a strong synergistic growth period. (4) From a long-term perspective, the disposable income of rural residents had the greatest determinant power on rural energy poverty, followed by per capita GDP, rural labor education level, regulatory agencies, and energy investment. In addition, our findings showed that the selected driving factors all had enhanced effects on rural energy poverty in China through interaction effects.

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Extreme Energy Poverty Incidence and Determinants in Nigeria: A Multidimensional Approach

Cited by 49 publications

References 28 publications

A Multidimensional Approach to Measuring Quality of Employment (QoE) Deprivation in Six Central American Countries

A Multidimensional Approach to Measuring Quality of Employment (QoE) Deprivation in Six Central American Countries

Review on Rural Energy Access Policies

Spatiotemporal Interaction and Socioeconomic Determinants of Rural Energy Poverty in China

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