Factors Contributing to Energy‐related Financial Stress in Australia

Best, Rohan; Burke, Paul J.

doi:10.1111/1475-4932.12504

Cited by 27 publications

(12 citation statements)

References 29 publications

(36 reference statements)

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“…For example, there could be a boost to the Age Pension rate or other payments such as through higher Commonwealth Rent Assistance (Coates, 2018) or through higher payments for disability pensioners. Targeting renters, including female renters, could be effective as they tend to be more at risk of dimensions of poverty and financial stress (Austen, 2016; Best & Burke, 2019). Additional targeting may be necessary as there may be constraints on general level increases for all pension recipients.…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

Quantifying Australia’s Gender Superannuation Gap

Best

Saba

2021

Economic Record

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This paper quantifies the factors contributing to Australia’s gender gap in superannuation balances. Around 60 per cent of the gap can be attributed to the prior stock of superannuation, for respondents who had superannuation balances in both 2014 and 2018 in the Household Income and Labour Dynamics in Australia Survey. This is partly due to the accumulation of the prior gap with investment returns. Financial literacy and risk aversion explain around 7 per cent of the gap in total, such as through impacts on subsequent investment returns. Work patterns and wage rates affect contributions and explain approximately 30 per cent of the superannuation gap in total.

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Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

Quantifying Australia’s Gender Superannuation Gap

Best

Saba

2021

Economic Record

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“…Potential negative rebound effects on electricity consumption driven by the desire of vulnerable households to catch up with middle-class living standards could be counterbalanced by positive health and social benefits [99,100]. Recent studies have found that the psychological stress caused by the inability to pay energy bills and the fear of debt, which triggers feelings of anxiety and depression, can be a consequence of energy vulnerability [101] and can contribute to and shape it [102].…”

Section: Discussionmentioning

confidence: 99%

Heterogeneity of Electricity Consumption Patterns in Vulnerable Households

2020

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A key aspect of the design of specific tariff structures is to identify and characterize homogeneous electricity consumption profiles. Recent research in residential electricity demand has explored load profile segmentation via cluster analysis combined with descriptive data from the dwelling and occupants, which has partly explained electricity load patterns and their underlying drivers but has failed to investigate any consumption heterogeneity among similar households. Thus, the aim of this paper is to reverse this approach and investigate the extent that households with similar characteristics have different electricity consumption patterns. This study combines population-based register data with hourly electricity consumption data for a sample of 67 Danish households. First, a homogenous household group is selected based on several indicators that signal vulnerability. The specific group under investigation is single-person, older, low-income households in detached housing. Second, K-means clustering is used to identify similarities and differences in consumption patterns. The results indicate four distinct vulnerable household profiles characterized by different start and end times of peak and off-peak times, peak intensities, and overall consumption, which vary across seasons. These profiles are discussed concerning the performance of everyday practices and the design of demand-side management strategies targeted at vulnerable households.

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“…Second, we contribute to the nascent literature on the behavioural and environmental reasons why some people exhibit a higher likelihood of being in energy poverty. Traditionally, most research on the causes of energy poverty has focused on the roles of high energy prices, low income, energy inefficient housing and energy inefficient home appliances (Best & Burke, 2019;Legendre & Ricci, 2015;Moore, 2012). While these studies have helped characterize whether people live in energy poverty, they do not tell us why one has low income and/or has high energy bills.…”

Section: Introductionmentioning

confidence: 99%