cpdrc dcHealth ProFamy (A User-Friendly Software for Projections
of Households and Living Arrangements)
  A Brief Summary of ProFamy Extended Cohort-Component Approach for Household Projections and Comparison to the Headship-Rate Method  
Time: 2018/9/6 [724]

      Based on Bongaarts’ and Zeng’s one-sex life table models, Zeng, Vaupel, and Wang (1997, 1998) developed a two-sex multistate dynamic extended cohort-component model, known as “ProFamy,” which includes marital status, number of co-residing children and parents, and parity. Zeng, Land, Wang, and Gu (2006) extended the ProFamy model by adding cohabitation and race dimensions to all computation and estimation procedures. The basic mechanism of the ProFamy model is that projections of changes in demographic components (marriage/union formation and dissolution, fertility, leaving parental home, mortality, and migration) are made for each of the cohorts that produce household distributions in future years. This is analogous to, and a substantive extension of, the conventional cohort-component population projection model in the sense that the ProFamy model simultaneously projects households, living arrangements and population age/sex distributions.
     In contrast to the headship-rate method, the “ProFamy” model does not suffer the vague, ill-defined and arbitrary chosen designation of the household heads; it uses as inputs conventional data that are available from ordinary surveys, vital statistics and censuses; it projects all individuals grouped by cohorts and specified attributes (race or rural/urban residence, sex, age, marital/union status, parity, and co-residence with parents and children). The calculations of the ProFamy model proceed iteratively, group-by-group, cohort-by-cohort, and period-by-period, using changing (or constant, as users define) demographic rates as input and projecting much more detailed household types, sizes, and living arrangements for all members of the population (Zeng, Land et al. 2006; 2013; 2014). Note that detailed projections of household sizes by various types are particularly useful in academic, socioeconomic, and market analyses.


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