Danan Gu

Household and Living Arrangement Projections in China at the National Level

Our projection study demonstrated that, while the population in China will be aging at a rapid speed and to a huge scale, particularly the oldest-old aged 80+, Chinese family households will continue to contract to a substantially smaller average size in the next a few decades. The proportion of elderly households with at least one person aged 65+ will increase dramatically in China in the next few decades. By the years 2030 and 2050, the proportion of the elderly aged 65+ living in empty-nest households without children among the total population will be 2.5 and 3.7 times that in 2000. The increase in percentages of the oldest-old living in empty-nest households will be even more dramatic: 4 and 11.5 times as high as in 2000 for the years 2030 and 2050. These aging population structure problems – with respect to proportion of elderly and elderly households as well as proportion of elderly living in empty-nest households – will be much more serious in rural areas than in urban areas. This strongly suggests that, to avoid serious social problems in the future China needs to change its household registration policy which restricts free movement from rural to urban areas and to adopt policies encouraging rural-to-urban family migration or family reunion after young migrants settle down in urban areas.

Dynamics of Households and Living Arrangements in the Eastern, Middle, and Western Regions of China

In this chapter presented the dynamics of household and living arrangements in the Eastern, Middle, and Western regions of China. The results showed that, if the current age distribution of rural-to-urban migrants with a high concentration of young people remains unchanged, the Middle region will have the most serious problems of population and household aging, followed by the Eastern region; population and household aging will be twice as severe in rural areas compared to urban areas in Middle and Eastern regions. Our multi-regional projections and analysis clearly showed that population and household aging problems under the current fertility policies unchanged would be much more serious than that under the two-child policy. Our study suggests that the inclusion of elderly parents in regional and rural to urban family migration, which implies co- or proximate-residence between old parents and adult children, would help to avoid the over-aging problems in rural areas and the Middle region; this strategy may result in a win-win outcome for both old and young generations.

Household and Living Arrangement Projections at the Small Area Level

In this chapter presented and discussed the basic concepts and methodology for applying the ProFamy approach in combination with ratio methods (including the constant-share and shift-share ratio methods) to project household and living arrangement projections at the small area level. To assess the accuracy of the combined ratio method and ProFamy approach and present illustrative applications, we calculated projections from 1990 to 2000 and compared projected estimates with census-observed counts in 2000 for sets of randomly selected 25 small counties and 25 small cities which were more or less evenly distributed across the United States. The comparisons show that, in general, most forecast errors are reasonably small – mostly less than or slightly more than 5 %. These results evidently illustrated the utility of the ProFamy approach in combination with ratio methods to project households and living arrangements at the small area level.

Epilogue: Summary and Future Perspectives

To provide a summarized profile of our work, we briefly review the major findings of the ProFamy approach’s methodological issues and empirical applications to the United States and China in Sects. 18.1, 18.2, 18.3, 18.4, and 18.5. We then outline the limitations and future research perspectives in the last section.

Setting Up the Projection Model

This chapter presents a tutorial with detailed explanations to help users set up the projection model. You may simply use the example input data files that accompany the software to quickly go through the main steps.

Data Needs and Estimation Procedures

This chapter discusses the data needed for household and living arrangements projections at the national or sub-national level employing the ProFamy extended cohort-component model.

Application of Household and Living Arrangement Projections to Policy Analysis in China

The tremendous reduction in fertility rates combined with baby boomers entering the labor force has resulted in a demographic “dividend” in China since the 1980s, which features a low child dependency ratio, still not-yet high elderly dependency ratio, and a rich labor force supply. Although high social and political costs were paid, the demographic dividend contributed significantly to China’s economic boom in the past 20 years. However, this dividend will disappear in a couple of decades (Cai 2006; Wang and Mason 2006), producing many questions about China’s future. What will the social and economic consequences be if China continues to implement its current strict fertility control policy? Is it necessary to change the current fertility policy? If so, what are the optimistic and feasible options? This chapter addresses these important questions with an application of the ProFamy extended cohort-component method. The unique features of this study include a comparative analysis of possible options for fertility policy transition based on demographic projections of population aging, households and elderly living arrangements, dependency ratios, pension deficits, labor force supply, the marriage squeeze, and economic costs under different fertility policy scenarios.

Empirical Assessments and a Comparison with the Headship Rate Method

One useful way to validate a projection model and computer program is to project between two past dates for which the observations are known, and then compare the observed data with the projected data. We assessed the accuracy of the ProFamy method and program by projecting: (1) U.S. households by race from 1990 to 2000 (Zeng et al. 2006), (2) Chinese households by rural and urban areas from 1990 to 2000 (Zeng et al. 2008), and (3) Chinese households by rural and urban areas and Eastern, Middle, and Western regions from 2000 to 2010 (Zeng et al. 2013b).

Effects of Changes in Household Structure and Living Arrangements on Future Home-Based Care Costs for Disabled Elders in the United States

In this chapter, we projected numbers of activities-of-daily-living disabled elderly and yearly payments and workdays of home-based care for them by age, gender, race, and living arrangements from 2010 to 2050 for the United States (with low, medium, and high scenarios). The chapter focused on how changes in household structure and living arrangements may affect future home-based care costs for disabled elders based on census micro datasets, the National Long Term Care Survey data and the ProFamy extended cohort-component method. The results showed a remarkable acceleration in numbers of disabled elderly aged 65+ after 2020 with a much faster increase in disabled oldest-old aged 80+, such that after 2030 they outnumber the disabled young-old aged 65–79. Increases in yearly workdays and payments of home-based care for disabled elders will dramatically accelerate after 2020, especially for the disabled oldest-old. We also discussed similarities and differentials across racial groups and genders and the policy implications of future trends in home-based care needs and costs for disabled elderly.

U.S. Family Household Momentum and Dynamics: Projections at the National Level

In this chapter, we apply the ProFamy extended cohort-component model to project U.S. households by race from 2000 to 2050. We address important questions such as: How may demographic changes alter the number and proportion of different types and sizes of households in future years? How may demographic changes affect the living arrangements of elderly persons? We also provide evidence of “family household momentum,” which is similar to the well-known phenomenon of population momentum.