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Impacts of Demographic Trends on US Household Size and Structure

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  • Leiwen Jiang
  • Brian C. O'Neill

Abstract

We use a household projection model to construct future scenarios for the United States designed to reflect a wide but plausible range of outcomes, including a new set of scenarios for union formation and dissolution rates based on past trends, experience in other countries, and current theory. The period covered is from 2000 to 2100. We find that the percentage of people living in households headed by the elderly may climb from 11 percent in 2000 to 20–31 percent in 2050 and 20–39 percent in 2100, while the average size of households could plausibly be as low as 2.0 or as high as 3.1 by the second half of the century. We assess the sensitivity of household size and structure to various demographic events, and show that outcomes are most sensitive to changes in fertility rates and rates of union formation and dissolution. They are less sensitive to the timing of marriage and childbearing and to changes in life expectancy.

Suggested Citation

  • Leiwen Jiang & Brian C. O'Neill, 2007. "Impacts of Demographic Trends on US Household Size and Structure," Population and Development Review, The Population Council, Inc., vol. 33(3), pages 567-591, September.
    • Handle: RePEc:bla:popdev:v:33:y:2007:i:3:p:567-591
    DOI: 10.1111/j.1728-4457.2007.00186.x
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    1. World Bank, 2004. "World Development Indicators 2004," World Bank Publications - Books, The World Bank Group, number 13890.
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    Cited by:

    1. Qiushi Feng & Zhenglian Wang & Simon Choi & Yi Zeng, 2020. "Forecast Households at the County Level: An Application of the ProFamy Extended Cohort-Component Method in Six Counties of Southern California, 2010 to 2040," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 39(2), pages 253-281, April.
    2. Overman, Henry G. & Puga, Diego & Turner, Matthew A., 2008. "Decomposing the growth in residential land in the United States," Regional Science and Urban Economics, Elsevier, vol. 38(5), pages 487-497, September.
    3. Jeofrey Bautista Abalos & Wei-Jun Jean Yeung, 2023. "Demographic, socioeconomic, and cultural factors for the rise in one-person households in developing countries: the case of the Philippines," Journal of Population Research, Springer, vol. 40(4), pages 1-32, December.
    4. Christos Koulovatianos & Polina Minkovski & Carsten Schröder, 2009. "Per-capita income versus household-need adjusted income: a cross-country comparison," Journal of Income Distribution, Ad libros publications inc., vol. 18(3-4), pages 11-23, September.
    5. Leiwen Jiang & Karen Hardee, 2011. "How do Recent Population Trends Matter to Climate Change?," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 30(2), pages 287-312, April.
    6. Tom Wilson, 2013. "The sequential propensity household projection model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(24), pages 681-712.
    7. Solveig Christiansen & Nico Keilman, 2013. "Probabilistic household forecasts based on register data- the case of Denmark and Finland," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(43), pages 1263-1302.
    8. Ting Li & Wenting Fan & Jian Song, 2020. "The Household Structure Transition in China: 1982–2015," Demography, Springer;Population Association of America (PAA), vol. 57(4), pages 1369-1391, August.
    9. Cecchini, Michele, 2018. "The hidden economics of informal elder-care in the United States," The Journal of the Economics of Ageing, Elsevier, vol. 12(C), pages 218-224.

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