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Extend the ProFamy cohort-component method to conduct probabilistic households and living arrangement projections
Yi Zeng1,2, Zhenglian Wang3, Qiushi Feng4
1National School of Development, Peking University, Beijing, China.
Abstract:
In this commentary, we first briefly review the significant utilities of household and living arrangement projections and the main types of methods for conducting household projections. In the second and third sections, we summarize basic ideas, data needed, assessments and applications of ProFamy extended cohort-component methods/software for households and living arrangement projections; and we emphasize the importance to extend the ProFamy methods and software from deterministic to probabilistic households and living arrangement projections. In section 4, we demonstrate that the ProFamy approach provides an adequate and highly feasible modelling framework to extend probabilistic households and living arrangement projections (PHPs), in which the population size/structure projection outcomes are in consistence with those of probabilistic population projections (PPPs) released by United Nations Population Division (UNPD). In the last Section, we discuss and recommend applying the user-friendly R package DemoRates of ProFamy software to estimate rural/urban (or race)-sex-age-specific standard schedules and the demographic summary measures, to conduct analyses and projections, such as single-parent households, caregivers, and care needs/costs for disabled older adults, age-friendly housing and households-based energy demands, etc. for healthy aging and sustainable development studies. Finally, we discuss the prospects of our ongoing international collaborative research project to substantially extend ProFamy cohort-component method from deterministic into probabilistic households and living arrangement projection (PHPs). As compared with ProFamy deterministic projection method, the PHPs produces a lot of additional outcomes of probabilistically projected households and living arrangements in 2021-2100 with uncertainty intervals that are crucial for healthy aging and sustainable development studies.
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