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How “Dependent” Are We? A Spatiotemporal Analysis of the Young and the Older Adult Populations in the US

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Abstract

The shifting of a country’s age structure has far-reaching socioeconomic and policy implications. In the US, the changing age structure at the sub-national level has received little research attention. To address this gap, we examine age dependencies across states in the US between 1990 and 2010 using decennial census data. We find that dependency changes have been gradual with a distinct graying of states during this period. Within this overarching trend, the sources of states’ dependencies follow complicated trajectories without clear spatiotemporal patterns. Nevertheless, changes in states’ old-age dependency contributions to respective total dependencies are geographically clustered and the inverse link between old-age dependency and economic productivity across states may be waning. Additional research is justified to further unravel these trends in old-age dependencies. The analytic framework that we apply can be adopted to conduct sub-national age dependency studies for other countries, including some European nations with relatively large proportions of older adults and many developing nations with an increasing share of older adults.

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Notes

  1. See for example, Bloom et al. (2011), and Ingham et al. (2009).

  2. For example, the US Bureau of Labor Statistics (BLS) uses the three age groups of 0–15, 16–64, and 65 and older (BLS, 2017). The US Bureau of Census employs 0–17, 18–64, and 65 and older as the three age groups (Vespa et al. 2018). The US Social Security Administration (SSA) uses 0–20, 20–64, and 65 and older (SSA n.d.).

  3. See for example the discussion in Loichinger et al. (2017).

  4. See for example discussions in Skirbekk et al. (2012), Sanderson and Shervob (2010), and Harwood et al. (2004).

  5. These three age categories closely match the age boundaries adopted by the US Bureau of Labor Statistics (BLS) as we noted in an earlier footnote. These categories are also compatible with the conventional definition of dependency ratio applied in the field of demography and population research. Despite recent criticism (Morgan 2019; Sanderson and Shervob 2015), demographic or age-based dependency ratios are still used as standardized indices (Lau and Tsui 2017; Sanderson and Shervob 2015; Jahan et al. 2014). Currently, organizations as the United Nations, United States Bureau of Census, and the World Bank utilize this measure at the global, national, and sub-national levels.

  6. Demographic dividend is a one-time economic benefit that often result after a period of low dependency in countries undergoing demographic transition.

  7. For studies examining demographic dividend, see for example, Bloom et al. (2007), Kelley and Schmidt (2005), Bloom et al. (2000) and Bloom and Williamson (1998). For studies evaluating macroeconomic impacts of a changing population age structure, see for example, Lee and Mason (2010), Ingham et al. (2009), Verdugo (2007) and Paes de Barros et al. (2001).

  8. See for example, IMF (2019), UN (2017), Zaiceva (2014) and Muszyńska and Rau (2012).

  9. See for example, Conway et al. (2015), Sandberg et al. (2012) and Cournane et al. (2015).

  10. See for example, Li et al. (2016) and Pezzulo et al. (2017).

  11. The NHGIS dataset, housed under the Integrated Public Use Microdata Series (IPUMS) website, provides U.S. Census data and GIS boundary files that can be downloaded from: https://nhgis.org/.

  12. In the period between 1976 and 2010, the (general) fertility rates in the US continued to fluctuate; it nevertheless exhibited a trend of slight decline—from 65 in 1976 to 64.1 per 1000 women aged 15–44 in 2010 (CDC 2019). More importantly, we found that at the state-level, the correlations between total fertility rate (TFR) and YDR were 0.60, 0.87, and 0.80 in 1990, 2000, and 2010, respectively, and all were significant at the 1% significance level.

  13. A reference map of the US states with Census regions and divisions is provided in the “Appendix” and can be found in https://www2.census.gov/geo/maps/general_ref/pgsz_ref/CensusRegDiv.pdf.

  14. This nine-class categorization is more detailed than the above- vs. below-average used by File and Kominski (2012).

  15. Over the two decades, none of the states had a low–low combination of the two dependency ratios.

  16. These state-level birth and fertility trends are discussed in CDC (2003, 2012).

  17. These micromaps were created using MicromapST: Version 1.98.5, which can be downloaded from https://cran.r-project.org/web/packages/micromapST/index.html.

  18. We do not include these micromaps due to space limitations and also as TDR exhibited no clear spatial pattern over the decades.

  19. In the GWR4 software, the local model fitting process allows the use of an adaptive kernel function for geographical weighting using data from k-nearest neighbors. The number of k nearest neighbors needs to be specified with an upper (maximum) and lower (minimum) limit. In our GWR models, we defined this range as 5–6 nearest neighbors.

  20. The GWR4 is a Windows-based statistical software housed at the Arizona State University Spatial Analysis Research Center at https://sgsup.asu.edu/sparc/gwr4.

  21. AICc provides the small-sample bias corrected Akaike Information Criterion.

  22. See for example, Bloom et al. (2011).

  23. See Footnote 12.

  24. The correlations between YDR and percent Hispanic/Latino at the state-level were 0.07, 0.32 (p < 0.05), and 0.18 for year 1990, 2000, and 2010, respectively.

  25. We computed correlations between YDR and state income tax rate (lowest rate) for the year 2000 and 2010 which were 0.35 (p < 0.05) and 0.23, respectively.

  26. We used average annual temperature as an indicator of individual’s perception of climate. The correlation of this indicator was low (< 0.30) and not significant (p > 0.05).

  27. Approaches to tax personal income have varied over time and across the US with some states levying no income tax to others adopting either a regressive or a progressive or a flat rate in taxing individual income, and/or wage and salary income or dividend and interest incomes.

  28. Wealth may include personal wealth and social security benefits (Gustman and Steinmeier 2001; Gustman et al. 1999).

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  1. Das Gupta and Wong should be considered joint first authors.

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  1. Department of Kinesiology and Health Science (KHS), Utah State University, 7000 Old Mail Hill, Logan, UT, 84322, USA

    Debasree Das Gupta

  2. Department of Geography and Geoinformation Science, George Mason University, 4400 University Dr., Fairfax, VA, 22030, USA

    David W. S. Wong

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Appendix

Appendix

See Figs. 10 and 11.

Fig. 10
figure 10

Source US Bureau of Census, Geography Division, Department of Commerce, Economics, and Statistics Administration. Retrieved from https://www2.census.gov/geo/maps/general_ref/pgsz_ref/CensusRegDiv.pdf

Reference map of states of the US.

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Fig. 11
figure 11

Source US Bureau of Census, Geography Division, Department of Commerce, Economics, and Statistics Administration. Retrieved from https://www2.census.gov/geo/maps/general_ref/pgsz_ref/CensusRegDiv.pdf

Reference map of Census regions and divisions in the US.

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Das Gupta, D., Wong, D.W.S. How “Dependent” Are We? A Spatiotemporal Analysis of the Young and the Older Adult Populations in the US. Popul Res Policy Rev 40, 1221–1252 (2021). https://doi.org/10.1007/s11113-020-09590-y

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