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Population Aging and Regional Income Inequality in Taiwan: A Spatial Dimension

Abstract

In the backdrop of a spatial effect, this paper reconsiders the importance of life cycle in explaining the evolution of regional household income inequality in Taiwan. For the empirical period examined (1998–2006), a fixed effect panel data analysis reveals a high level of spatial clustering across 22 regions of Taiwan. When we control for spatial dependence we observe a positive relation between aging and income inequality. This regional inequality is explained by a decline in the multigenerational families followed by a rise in the elderly households with no additional income. Further, the level of inequality in income distribution of own province is positively and significantly determined by inequality in the neighboring province. To investigate further the process of regional development in Taiwan, we analyze the convergence–divergence dynamics employing spatial econometric methods. We observe both absolute and conditional beta divergence. The result points to the famous catching up or falling behind phenomenon. Failure to account for such spatial effect may cause biased results and incorrect policy implications.

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Notes

  1. (1) After accounting for taxes and transfers, the US has the second-highest level of inequality, after Chile. (2) Divided we stand: why inequality keeps rising, OECD report (2011). (3) http://www.gini-research.org.

  2. Jörg Lackenbauer (2004) in case of Hungary, identify the factors behind catching-up within some regions (‘winner regions’) and falling-behind in others (‘loser regions’).

  3. Kuznets (1989) gave several reasons for the use of family or household as the unit of measurement. We use Gini coefficient to capture the inequality in the distribution of household income.

  4. Result for the RE model and Hausmann test are available upon request.

  5. α’ captures the unobserved regional level heterogeneity. For example, the socio-economic status of a particular community like Taipei where ‘sophisticated urbanities’ might induce people to maintain a lower fertility rate. Hence, one would wonder aging to have an upward rising curve, specifically for a city like Taipei.

  6. For more details on this issue please see Cliff and Ord (1973).

  7. The term ‘conditional’ convergence is used by Mankiw et al. (1992), who argue that the Solow model predicts convergence among countries only after controlling for the determinants of each country’s steady state.

  8. Baumol (1986) has estimated a growth equation to test for β-convergence in the historical data (1870–1879) of per capita output for 16 industrialized countries.

  9. Cross-regional data are spatial data. Spatial data are characterized by dependence (spatial autocorrelation) and heterogeneity (spatial heterogeneity) (Anselin 1988).

  10. Fent et al. (2008) suggest that individual productivity decreases with age due to declining cognitive abilities.

  11. Economic downturn after the Asian financial crisis, trade liberalization and democratic transition were among the most influential shocks.

  12. Examples include the division: rich North-poor South (and West-East since 2004) within the EU or the core-periphery result of the New economic geography.

  13. The core periphery model was first originated by Friedmann (1966) to explain regional uneven development.

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Correspondence to Chun-Hung A. Lin.

Appendix

Appendix

See Figs. 1, 2, 3, 4.

Fig. 1
figure 1

GINI for 4 regions across Taiwan, for the time period 1998–2006

Fig. 2
figure 2

Average household income for 4 regions across Taiwan, for the time period 1998–2006

Fig. 3
figure 3

Moran’s scatter plot for GINI for the years 1999 and 2005

Fig. 4
figure 4

Moran’s scatter plot of household income growth rate

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Lin, CH.A., Lahiri, S. & Hsu, CP. Population Aging and Regional Income Inequality in Taiwan: A Spatial Dimension. Soc Indic Res 122, 757–777 (2015). https://doi.org/10.1007/s11205-014-0713-8

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Keywords

  • Population aging
  • Income inequality
  • Taiwan