Journal ArticleDOI
Energy poverty and health: Panel data evidence from Australia
TLDR
This article found that a standard deviation increase in energy poverty is associated with a decline in general health between 0.099 and 0.296 standard deviations, and that energy poverty lowers health is robust to different ways of measuring health and alternative methods to addressing endogeneity of energy poverty.About:
This article is published in Energy Economics.The article was published on 2021-05-01. It has received 125 citations till now. The article focuses on the topics: Energy poverty & Fuel poverty.read more
Citations
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Journal ArticleDOI
How does low-carbon energy transition alleviate energy poverty in China? A nonparametric panel causality analysis
TL;DR: In this paper, the causal relationship between low-carbon energy transition and energy poverty was examined by using a novel nonparametric panel causality-inquantiles (PCIQ) method.
Journal ArticleDOI
Energy Poverty, Health and Education outcomes: evidence from the developing world
TL;DR: In this paper, the authors examined the effect of energy poverty on health and education outcomes for 50 developing countries in the period 1990-2017, and empirically tested if the effect on development outcomes is conditioned by thresholds determined by the degree of poverty and income per capita.
Journal ArticleDOI
Energy poverty and education: Fresh evidence from a panel of developing countries
TL;DR: In this paper, the authors empirically assess the impact of education on energy poverty through the lens of human capital theory and find that education has a negative impact on the energy poverty.
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An inquiry into the nexus between energy poverty and income inequality in the light of global evidence
TL;DR: In this article, the authors investigated the nexus between energy poverty and income inequality from multiple perspectives by drawing on a rich set of global data and applying a novel set of comprehensive empirical approaches.
Journal ArticleDOI
Evaluating energy poverty and its effects using multi-dimensional based DEA-like mathematical composite indicator approach: Findings from Asia
TL;DR: In this paper , the impacts of multidimensional energy poverty indicators using mathematical composite index and, econometric models for five Asian countries were examined, and the impact of these indicators on access to energy in South Asian region was analyzed.
References
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Journal ArticleDOI
Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.
Manuel Arellano,Stephen Bond +1 more
TL;DR: In this article, the generalized method of moments (GMM) estimator optimally exploits all the linear moment restrictions that follow from the assumption of no serial correlation in the errors, in an equation which contains individual effects, lagged dependent variables and no strictly exogenous variables.
Journal ArticleDOI
The central role of the propensity score in observational studies for causal effects
TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
Journal ArticleDOI
Another look at the instrumental variable estimation of error-components models
Manuel Arellano,Olympia Bover +1 more
TL;DR: In this paper, a framework for efficient IV estimators of random effects models with information in levels which can accommodate predetermined variables is presented. But the authors do not consider models with predetermined variables that have constant correlation with the effects.
Book
SF-36 health survey: Manual and interpretation guide
TL;DR: TheSF-36 is a generic health status measure which has gained popularity as a measure of outcome in a wide variety of patient groups and social and the contribution of baseline health, sociodemographic and work-related factors to the SF-36 Health Survey: manual and interpretation guide is tested.
Journal ArticleDOI
How to do xtabond2: An introduction to difference and system GMM in Stata
TL;DR: This paper introduced linear generalized method of moments (GMM) estimators for situations with small T, large N panels, with independent variables that are not strictly exogenous, meaning correlated with past and possibly current realizations of the error; with fixed effects; and with heteroskedasticity and autocorrelation within individuals.