Multidimensional Energy Poverty and Mental Health: Micro-Level Evidence from Ghana

Lin, Boqiang; Okyere, Michael Adu

doi:10.3390/ijerph17186726

Cited by 63 publications

(40 citation statements)

References 61 publications

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“…The cause of depression is complicated, and existing studies attribute depression to a complex interaction of social [ 3 , 4 ] and biological factors [ 5 , 6 ]. Among these factors, the literature has provided evidence that energy poverty is correlated with mental health [ 7 , 8 , 9 , 10 ], and few studies have suggested that energy poverty may affect depression [ 11 , 12 ]. However, the evidence of how energy poverty affects depression is still limited, especially in developing countries [ 10 ], and no research has focused on rural individuals in developing countries.…”

Section: Introductionmentioning

confidence: 99%

Energy Poverty and Depression in Rural China: Evidence from the Quantile Regression Approach

Zhang

2022

IJERPH

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Despite the growing awareness and interest in the impact of energy poverty on depression, studies in developing economies are relative limited, and there is a gap of knowledge of such impact among rural individuals in China. In this study, we investigate the impact of energy poverty on depression among rural Chinese individuals aged 16 and above, and our sample includes 13,784 individuals from 6103 households. With data from the 2018 China Family Panel Studies, we apply the instrumental variable (IV) quantile regression approach to address the potential endogeneity of energy poverty and allow for heterogeneous effects of energy poverty on depression across individuals with different levels of depression. Our estimates from the IV quantile regression suggest a strong positive impact of energy poverty on depression at the upper quantile of depression scores, but no impact at the middle and lower quantiles. The primary results are robust and consistent with alternative energy poverty measures, and we find that energy poverty does not affect depression of low-risk individuals (with low depression scores), but it does affect that of high-risk individuals. We also find individual socio-demographic factors of age, gender, household size, religious belief, education, marriage and employment status play roles in affecting depression. The findings of this study generate policy implications for energy poverty alleviation and mental health promotion.

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Section: Introductionmentioning

confidence: 99%

Energy Poverty and Depression in Rural China: Evidence from the Quantile Regression Approach

Zhang

2022

IJERPH

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“…This has led to a change in rhetoric from whether poverty is associated with depression to identifying what dimensions of poverty are associated with depression. For example, a recent study conducted in Ghana explored the relationship of energy poverty and depression in which energy poverty was defined based on the extent of deprivations in four dimensions and six indicators: electricity access, modern cooking fuel access, indoor air pollution, household appliance ownership, ownership of a radio or television, and means of telecommunications (mobile phone) [ 32 ]. They found that a deprivation in household appliance ownership had the highest impact on the depression levels of household heads.…”

Section: Discussionmentioning

confidence: 99%

Characterizing multidimensional poverty in Migori County, Kenya and its association with depression

et al. 2021

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Introduction Narrow, unidimensional measures of poverty often fail to measure true poverty and inadequately capture its drivers. Multidimensional indices of poverty more accurately capture the diversity of poverty. There is little research regarding the association between multidimensional poverty and depression. Methods A cross-sectional survey was administered in five sub-locations in Migori County, Kenya. A total of 4,765 heads of household were surveyed. Multidimensional poverty indices were used to determine the association of poverty with depression using the Patient Health Questionnaire (PHQ-8) depression screening tool. Results Across the geographic areas surveyed, the overall prevalence of household poverty (deprivation headcount) was 19.4%, ranging from a low of 13.6% in Central Kamagambo to a high of 24.6% in North Kamagambo. Overall multidimensional poverty index varied from 0.053 in Central Kamagambo to 0.098 in North Kamagambo. Of the 3,939 participants with depression data available, 481 (12.2%) met the criteria for depression based on a PHQ-8 depression score ≥10. Poverty showed a dose-response association with depression. Conclusions Multidimensional poverty indices can be used to accurately capture poverty in rural Kenya and to characterize differences in poverty across areas. There is a clear association between multidimensional poverty and depressive symptoms, including a dose effect with increasing poverty intensity. This supports the importance of multifaceted poverty policies and interventions to improve wellbeing and reduce depression.

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“…Perhaps more importantly, it can cause long-term psychological problems. Studies have found that energy-poor people think they are inferior to others [37,38]. Additionally, energy poverty can lead to environmental damage and resource stress, inhibiting sustainable development of the economy and society [3,38].…”

Section: Literature Reviewmentioning

confidence: 99%

Factors of Energy Poverty: Evidence from Tibet, China

Zang

Chandio

2021

Sustainability

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Energy poverty due to rising energy demand is a matter of global concern. Therefore, examination of the causes of energy poverty and development of effective solutions are urgent concerns. Using survey data on livelihood development in Tibetan farming and pastoral areas in 2019, this study applied logistic and ordinary least squares models to empirically investigate the factors that influence energy poverty in Tibet. We found that energy poverty is (1) unrelated to age, gender, education, marital status, political and village cadre experience, planting, or expenditures related to religious activities; (2) negatively correlated with migrant work, village status, household income, and traffic conditions; (3) positively correlated with employment, area, and breeding; and (4) both positively and negatively affected by family residence altitude. The results offer new insights and empirical evidence for the identification and elimination of energy poverty, improving livelihoods, and promoting rural revitalisation in Tibet.

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Multidimensional Energy Poverty and Mental Health: Micro-Level Evidence from Ghana

Cited by 63 publications

References 61 publications

Energy Poverty and Depression in Rural China: Evidence from the Quantile Regression Approach

Energy Poverty and Depression in Rural China: Evidence from the Quantile Regression Approach

Characterizing multidimensional poverty in Migori County, Kenya and its association with depression

Factors of Energy Poverty: Evidence from Tibet, China

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