The Impact of Population Aging on Economic Development in China: Insights from Heilongjiang, Hebei, and Hunan

Yunxia Liu; Kittawit  Autchariyapanitkul; Ke  Nunthasen; Nirote  Sinnarong

doi:10.60027/iarj.2025.286770

Authors

Yunxia Liu Faculty of Economics, Maejo University, Thailand https://orcid.org/0009-0003-3584-9127
Kittawit Autchariyapanitkul Faculty of Economics, Maejo University, Thailand https://orcid.org/0009-0004-4051-4388
Ke Nunthasen Faculty of Economics, Maejo University, Thailand https://orcid.org/0009-0001-0801-1201
Nirote Sinnarong Faculty of Economics, Maejo University, Thailand https://orcid.org/0000-0002-5931-2696

DOI:

https://doi.org/10.60027/iarj.2025.286770

Keywords:

Aging Society, Economic Growth, Solow Model, Dependency Ratio, Human Capital, Labor Force Participation

Abstract

Background and Aim: China's demographic transition from a "demographic dividend" phase, characterized by a youthful population and rapid economic growth, to an aging society presents multifaceted socioeconomic challenges. This shift has been accelerated by the legacy of the one-child policy, rising life expectancy, and pronounced regional disparities. Provincial variations in aging trajectories are critical. Heilongjiang faces severe aging due to outmigration and shrinking labor pools, Hebei exhibits deep urban-rural divides in aging impacts, and Hunan demonstrates moderate aging mitigated by intergenerational family support systems. This study investigates the heterogeneous economic consequences of population aging across these provinces, with a focus on labor market dynamics, fiscal sustainability, and long-term growth trajectories. By contextualizing aging within China's unique "getting old before getting rich" paradigm, we aim to inform region-specific policy responses.

Materials and Methods: An augmented Solow growth model is employed to disentangle aging effects, incorporating province-level panel data (2004–2024) from China's National Bureau of Statistics and official provincial yearbooks. The model explicitly integrates aging-specific variables—including elderly dependency ratio, working-age population share, human capital stock, savings rate, and labor force participation rate—as both independent and interactive predictors. Econometric techniques include unit root testing for stationarity, cointegration analysis to validate long-term relationships, and fixed-effects regression to control for province-invariant characteristics. Model transparency is enhanced by specifying the functional form of aging variable incorporation and data standardization processes.

Results: The elderly dependency ratio exhibits negligible direct effects on real per capita GDP. However, working-age population proportion (β=2.29, p<0.1), human capital (β=5.08, p<0.01), savings rate (β=3.86, p<0.05), and labor participation (β=1.96, p<0.01) significantly drive growth. Regional heterogeneity emerges. Heilongjiang’s labor shortages contrast with Hunan’s resilience from human capital investments, while Hebei’s urban-rural disparities underscore uneven aging impacts.

Conclusion: Policy measures must prioritize region-specific strategies: enhancing vocational education in human-capital-deficient areas, incentivizing labor participation through flexible retirement, and expanding multi-pillar pension systems. Lessons from Japan’s automation adoption and Nordic flexible work models highlight feasible pathways. Aligning reforms with China’s “getting old before getting rich” context ensures sustainable adaptation to demographic shifts.

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