This paper examines the energy-related behaviour of occupants and owners of multifamily dwellings in Canada, some of whom do not pay directly for electricity or heat, but instead have these costs included in their rent or condo fees. Using data from the 2003 Survey of Household Energy Use, we look at the extent to which split incentives that result from bill-paying arrangements effect a variety of activities including the setting of temperatures at various times of the day and the use of eco-friendly options in basic household tasks. Findings suggest that these split incentives do indeed impact some aspects of occupant behaviour, with households who do not pay directly for their heat opting for increased thermal comfort and being less sensitive to whether or not somebody is at home and the severity of the climate when deciding on temperature settings. Regardless of who pays for utilities, Canadian households who live in multifamily dwellings are generally unresponsive to fuel prices. Our empirical results suggest that the possibility of environmental benefits from policies aimed at improving energy-efficiency in this sector, especially if targeted at reducing the impacts of the behaviour of those who do not pay directly for energy use.

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PurposeDespite rising incomes and reduction of extreme poverty, the feeling of being poor remains widespread. Support programs can improve well-being, but they first require identifying who are the households that judge their income is insufficient to meet their basic needs, and what factors are associated with subjective poverty.Design/methodology/approachHouseholds report the income level they judge is sufficient to make ends meet. Then, they are classified as being subjectively poor if their own monetary income is inferior to the level they indicated. Second, the study compares the performance of three machine learning algorithms, the random forest, support vector machines and least absolute shrinkage and selection operator (LASSO) regression, applied to a set of socioeconomic variables to predict subjective poverty status.FindingsThe random forest generates 85.29% of correct predictions using a range of income and non-income predictors, closely followed by the other two techniques. For the middle-income group, the LASSO regression outperforms random forest. Subjective poverty is mostly associated with monetary income for low-income households. However, a combination of low income, low endowment (land, consumption assets) and unusual large expenditure (medical, gifts) constitutes the key predictors of feeling poor for the middle-income households.Practical implicationsTo reduce the feeling of poverty, policy intervention should continue to focus on increasing incomes. However, improvements in nonincome domains such as health expenditure, education and family demographics can also relieve the feeling of income inadequacy. Methodologically, better performance of either algorithm depends on the data at hand.Originality/valueFor the first time, the authors show that prediction techniques are reliable to identify subjective poverty prevalence, with example from rural China. The analysis offers specific attention to the modest-income households, who may feel poor but not be identified as such by objective poverty lines, and is relevant when policy-makers seek to address the “next step” after ending extreme poverty. Prediction performance and mechanisms for three machine learning algorithms are compared.

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