Extend the ProFamy cohort-component method to conduct probabilistic households and living arrangement projections.

Yi Zeng, Zhenglian Wang, Qiushi Feng, Danan Gu, Junni Zhang, Wei Tang, Kenneth Land
Author Information
  1. Yi Zeng: National School of Development, Peking University, Beijing, China. ORCID
  2. Zhenglian Wang: China Population and Development Research Center, Beijing, China.
  3. Qiushi Feng: Department of Sociology and Centre for Family and Population Research, National University of Singapore, Singapore, Singapore.
  4. Danan Gu: United Nations Population Division, New York, USA.
  5. Junni Zhang: National School of Development, Peking University, Beijing, China.
  6. Wei Tang: National School of Development, Peking University, Beijing, China.
  7. Kenneth Land: Duke University Population Research Institute, Durham, USA.

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.

Keywords

References

  1. Eur J Popul. 1985 Jul;1(2-3):207-35 [PMID: 12340530]
  2. Popul Dev Rev. 2011;37(3):553-69 [PMID: 22167815]
  3. Popul Index. 1984 Summer;50(2):193-213 [PMID: 12339444]
  4. Popul Index. 1988 Summer;54(2):209-24 [PMID: 12341806]
  5. Popul Res Policy Rev. 2012 Apr 1;31(2): [PMID: 24179311]
  6. Nature. 2003 Jan 30;421(6922):489-90 [PMID: 12556874]
  7. J Aging Health. 2015 Apr;27(3):519-50 [PMID: 25213460]
  8. Int J Forecast. 1992 Nov;8(3):509-27 [PMID: 12157869]
  9. Proc Natl Acad Sci U S A. 2005 Feb 8;102(6):2248-53 [PMID: 15677714]
  10. Asian Pac Cens Forum. 1985 Aug;12(1):5-8 [PMID: 12267195]
  11. Sci Data. 2023 Feb 6;10(1):76 [PMID: 36746951]
  12. Eur J Popul. 2018 Feb 13;35(1):29-62 [PMID: 30976267]
  13. J Epidemiol. 2015;25(6):460 [PMID: 25986157]
  14. Philos Trans R Soc Lond B Biol Sci. 2010 Sep 27;365(1554):2779-91 [PMID: 20713384]
  15. J Am Stat Assoc. 1972 Jun;67(338):347-63 [PMID: 12309295]
  16. Demography. 2013 Jun;50(3):827-52 [PMID: 23208782]
  17. Int J Forecast. 1992 Nov;8(3):529-39 [PMID: 12157870]

Word Cloud

Created with Highcharts 10.0.0projectionslivingarrangementProFamyhouseholdsprobabilisticPHPshouseholdmethodscohort-componentextenddeterministicprojectionmethodsoftwarepopulationoutcomesdiscussconducthealthyagingsustainabledevelopmentstudiescommentaryfirstbrieflyreviewsignificantutilitiesmaintypesconductingsecondthirdsectionssummarizebasicideasdataneededassessmentsapplicationsextendedmethods/softwareemphasizeimportancesection 4demonstrateapproachprovidesadequatehighlyfeasiblemodellingframeworksize/structureconsistencePPPsreleasedUnitedNationsPopulationDivisionUNPDlastSectionrecommendapplyinguser-friendlyRpackageDemoRatesestimaterural/urbanrace-sex-age-specificstandardschedulesdemographicsummarymeasuresanalysessingle-parentcaregiverscareneeds/costsdisabledolderadultsage-friendlyhousinghouseholds-basedenergydemandsetcFinallyprospectsongoinginternationalcollaborativeresearchprojectsubstantiallycomparedproduceslotadditionalprobabilisticallyprojectedarrangements2021-2100uncertaintyintervalscrucialExtendDeterministicHouseholdProbabilistic

Similar Articles

Cited By