Housing multigenerational households in Australian cities: Evidence from Sydney and Brisbane at the turn of the twenty-first century
Notice bibliographique
Résumé
The global trend towards city living, together with population ageing, has precipitated significant economic, social, political and environmental shifts, leading to changes in family configurations and living arrangements. Some changes are directly related to family forms, notably delayed childbearing, increasing divorce rates and higher incidences of re-partnering while others are less directly related and include improved employment opportunities for women, delayed retirement and more complex migration patterns both within and between countries. These changes are also happening in highly urbanised Australia. As in many developed countries, the majority of recent Australian housing and urban policies have focussed on responding to the rise in the number of small and especially single-person households in urban areas. As evidence attests, however, there is also a concurrent, yet largely unrecognised, rise in the number of multigenerational households, households where two or more generations of related adults live in the same dwelling. Between 1981 and 2006 in Australia, the number of people living in a multigenerational household increased by more than 800,000 (ABS 2011). By 2006, nearly one in four people in metropolitan Sydney (23.1%) and Melbourne (22.9%) lived in households that comprised two or more generations of related adults. The number of multigenerational households in Sydney alone totalled more than a quarter million in 2006. The share of multigenerational households as a share of all family households has also risen over this period; this is despite the concurrent increase in the number of single-person households and the overall decline in average household size. Recent Australian and international work in this area has focused on delayed home leaving amongst the younger generations (e.g. Alessie et al. 2005; Flatau et al. 2007) and the financial dis-benefits experienced by older generations as a result of this observed increase (e.g. Cobb-Clark and Ribar 2009). Some work also recognises the differences in practice in different contexts, especially the higher incidences of multigenerational households in cultures such as East Asia (Chui 2008; Izuhara 2010), Southern Europe (Billari and Rosina 2005) and the Middle East (Mehio-Sibai et al. 2009) where such household forms are more common. Evidence is also now emerging from countries where such living arrangements, while not traditional, are becoming more prominent (Gee et al. 2003) as well as the “boomerang” phenomenon, where adult offspring return to live in the parental home after periods of independent living (Kaplan 2009). The overwhelming significance of multigenerational living for Australia’s urban population raises two important questions: Who lives in these multigenerational households, and why? This chapter draws upon a detailed analysis of customised Census data and findings of a survey of members of multigenerational households in Sydney and Brisbane to answer these questions. The chapter expands upon existing research by considering a range multigenerational living arrangements, besides the common phenomena of adult children remaining at home as well as the economic and non-economic benefits and disincentives for multiple generations to cohabit.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».