人口老龄化与经济增长——基于GaR 模型的实证研究

doi:10.16381/j.cnki.issn1003-207x.2025.0004

Abstract

Abstract:

With the deepening of population aging in China， how to cope with the impact of aging on economic growth and tap the potential of economic growth has become one of the important issues for China’s high-quality economic development. The driving factors of economic growth are analyzed through the MRW model （the total output function is $Y = K α H β (A L) 1 - α - β$ ， where Y is total output， K and H are physical capital and human capital respectively， L is labor force， A is technical level， and αand β are output elasticities）. Then， using the GaR model combined with quantile regression， and based on China’s time-series data from 1990 to 2023， the impact of population aging on economic growth and the regulatory role of related variables is examined. The research results show that aging is significantly negatively correlated with economic growth in a large range of quantiles； physical capital investment， human capital investment， and technical level have regulatory effects on this negative inhibitory effect in a large range of quantiles. The deficiency of traditional mean regression is made up for in the research on tail risks of economic growth， a new perspective is provided for understanding the relationship between population aging and economic growth， and the conclusions and policy recommendations have certain reference value for related academic research and the formulation of economic policies to cope with aging.

Key words: growth-at-risk model, population aging, economic growth, quantile regression, risks of economic growth

CLC Number:

F061.2

Linhai Zhao,Xiaohang Gan. Population Aging and Economic Growth in China: An Empirical Study Based on GaR Model[J]. Chinese Journal of Management Science, 2025, 33(12): 57-70.

Figures/Tables 13

References 27

[1]	都阳，封永刚. 人口快速老龄化对经济增长的冲击［J］. 经济研究， 2021， 56（2）： 71-88.
	Du Y， Feng Y G. The shock of accelerating population aging on economic growth［J］. Economic Research Journal， 2021， 56（2）： 71-88.
[2]	Galasso V， Profeta P. Lessons for an ageing society： The political sustainability of social security systems［J］. Economic Policy， 2004， 19（38）： 64-115.
[3]	杜丽群. 中国的人口现状与对策分析——基于马尔萨斯人口理论的新思考［J］. 中国经济问题， 2007（3）： 29-37.
	Du L Q. Analysis on the current population situation and countermeasures in China—New thinking based on Malthus' population theory［J］. Economic Issues in China， 2007（3）： 29-37.
[4]	Teixeira A A C， Renuga Nagarajan N， Silva S T. The impact of ageing and the speed of ageing on the economic growth of least developed， emerging and developed countries， 1990–2013［J］. Review of Development Economics， 2017， 21（3）： 909-934.
[5]	Goh S K， McNown R， Wong K N. Macroeconomic implications of population aging： Evidence from Japan［J］. Journal of Asian Economics， 2020， 68： 101198.
[6]	蔡昉. 中国老龄化挑战的供给侧和需求侧视角［J］. 经济学动态， 2021（1）： 27-34.
	Cai F. Supply-and demand-side impacts of aging on China’s economic growth［J］. Economic Perspectives， 2021（1）： 27-34.
[7]	Bardazzi R， Pazienza M G. Switch off the light， please！ Energy use， aging population and consumption habits［J］. Energy Economics， 2017， 65： 161-171.
[8]	张继海，姚健，刘文玲. 家庭内部人口年龄结构对家庭消费的影响研究——基于消费支出、消费率和消费结构的实证分析［J］. 南开经济研究， 2023（12）： 237-255.
	Zhang J H， Yao J， Liu W L. Impact of population age structure on household consumption—Based on consumption expenditure， consumption rate， and consumption structure［J］. Nankai Economic Studies， 2023（12）： 237-255.
[9]	龚锋，王昭，余锦亮. 人口老龄化、代际平衡与公共福利性支出［J］. 经济研究， 2019， 54（8）： 103-119.
	Gong F， Wang Z， Yu J L. Aging population， generational balance， and public welfare expenditure［J］. Economic Research Journal， 2019， 54（8）： 103-119.
[10]	王德文，蔡昉，张学辉. 人口转变的储蓄效应和增长效应——论中国增长可持续性的人口因素［J］. 人口研究， 2004， 28（5）： 2-11.
	Wang D W， Cai F， Zhang X H. Saving and growth effects of demographic transition： The population factor in the sustainability of China’s economic growth［J］. Population Research， 2004， 28（5）： 2-11.
[11]	Bloom D E， Canning D， Sevilla J. The demographic dividend： A new perspective on the economic consequences of population change［M］. Santa Monica： RAND Corporation， 2003.
[12]	胡湛，彭希哲，吴玉韶. 积极应对人口老龄化的“中国方案”［J］. 中国社会科学， 2022（9）： 46-66+205.
	Hu Z， Peng X Z， Wu Y S. On the “Chinese approach” to actively addressing population ageing［J］. Social Sciences in China， 2022（9）： 46-66+205.
[13]	韩永辉，刘洋. 少子老龄化、工业智能化与宏观经济波动——基于内生经济增长理论的DSGE模型分析［J］. 管理世界， 2024， 40（1）： 20-37+60+38.
	Han Y H， Liu Y. Low fertility and aging， industrial intelligence and macroeconomic fluctuations： An analysis of DSGE model based on endogenous economic growth theory［J］. Journal of Management World， 2024， 40（1）： 20-37+60+38.
[14]	Adrian T， Boyarchenko N， Giannone D. Vulnerable growth［J］. American Economic Review， 2019， 109（4）： 1263-1289.
[15]	Gu X， Cheng X， Zhu Z， et al. Economic policy uncertainty and China’s growth-at-risk［J］. Economic Analysis and Policy， 2021， 70： 452-467.
[16]	Suarez J. Growth-at-risk and macroprudential policy design［J］. Journal of Financial Stability， 2022， 60： 101008.
[17]	Plagborg-Møller M， Reichlin L， Ricco G， et al. When is growth at risk？［J］. Brookings Papers on Economic Activity， 2020， 2020（1）： 167-229.
[18]	Mankiw N G， Romer D， Weil D N. A contribution to the empirics of economic growth［J］. The Quarterly Journal of Economics， 1992， 107（2）： 407-437.
[19]	李小荣，韩琳，马海涛. 内部控制与劳动力投资效率［J］. 财贸经济， 2021， 42（1）： 26-43.
	Li X R， Han L， Ma H T. The power of containment： Internal control and labor investment efficiency［J］. Finance & Trade Economics， 2021， 42（1）： 26-43.
[20]	Lee J W， Song E， Kwak D W. Aging labor， ICT capital， and productivity in Japan and Korea［J］. Journal of the Japanese and International Economies， 2020， 58： 101095.
[21]	吴俊培，赵斌. 人口老龄化、公共人力资本投资与经济增长［J］. 经济理论与经济管理， 2015， 35（10）： 5-19.
	Wu J P， Zhao B. Population aging， public human capital investment and economic growth［J］. Economic Theory and Business Management， 2015， 35（10）： 5-19.
[22]	Wang H， Chen Y. The impact of population aging on capital structure decisions and capital market efficiency： Evidence from China［J］. International Review of Financial Analysis， 2024， 95： 103408.
[23]	汪伟，姜振茂. 人口老龄化对技术创新的影响机制分析——基于DFA方法的创新评价和动态面板模型［J］. 上海财经大学学报， 2017， 19（6）： 4-17.
	Wang W， Jiang Z M. The influence of population aging on technological innovation： Based on innovation evaluation using dynamic factor analysis and dynamic GMM model［J］. Journal of Shanghai University of Finance and Economics， 2017， 19（6）： 4-17.
[24]	何凌霄，南永清，张忠根. 老龄化、健康支出与经济增长——基于中国省级面板数据的证据［J］. 人口研究， 2015， 39（4）： 87-101.
	He L X， Nan Y Q， Zhang Z G. Ageing， health expenditure and economic growth： Evidence based on the Chinese provincial panel data［J］. Population Research， 2015， 39（4）： 87-101.
[25]	Adrian T， Grinberg F， Liang N， et al. The term structure of growth-at-risk［J］. American Economic Journal： Macroeconomics， 2022， 14（3）： 283-323.
[26]	张晓晶，刘磊. 宏观分析新范式下的金融风险与经济增长——兼论新型冠状病毒肺炎疫情冲击与在险增长［J］. 经济研究， 2020， 55（6）： 4-21.
	Zhang X J， Liu L. Financial risk and economic growth under the new paradigm of macro-analysis on the impact of the covid-19 pandemic and growth at risk［J］. Economic Research Journal， 2020， 55（6）： 4-21.
[27]	Azzalini A， Capitanio A. Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t-distribution［J］. Journal of the Royal Statistical Society Series B： Statistical Methodology， 2003， 65（2）： 367-389.

变量类型	变量名称	变量符号	说明
因变量	经济增长（%）	y_t+h	年度GDP增长率，h取1、2、3
解释变量	老龄化率（%）	Age	65岁以上人口占总人口的比重
调节变量	人力资本投资水平	Hum	人均受教育年限
	物质资本投资率（%）	Inv	社会固定资产投资占 GDP比重
	技术水平（%）	R&D	R&D投资占国内生产总值比重
控制变量	经济增长（%）	y_t	当期GDP增长率
	通胀率（%）	Inf	物价水平增长率
	开放水平（%）	Open	进出口总额占GDP比重
	政府支出力度（%）	Gov	政府财政支出占GDP比重

变量	0.1	0.25	0.5	0.75	0.9	均值
短期（h=1）
Age	-1.068^*** (-3.58)	-0.776^** (-2.99)	-0.614^** (-2.40)	-0.394 (-0.87)	-0.825^* (-1.74)	-0.688^*** (-3.9)
y_t	-0.144 (-0.34)	-0.131 (-0.30)	0.059 (0.12)	0.425 (0.79)	-0.029 (-0.06)	0.145 (0.75)
Control	Yes	Yes	Yes	Yes	Yes	Yes
R²	0.6401	0.5194	0.4182	0.3687	0.3757	0.6388
中期（h=2）
Age	-1.277^** (-3.04)	-1.047^** (-2.15)	-0.792 (-1.46)	-1.492^** (-3.55)	-0.866^** (-2.12)	-1.021^*** (-5.23)
y_t	-0.528 (-1.25)	-0.164 (-0.37)	0.333 (0.72)	-0.069 (-0.14)	-0.074 (-0.15)	-0.036 (-0.18)
Control	Yes	Yes	Yes	Yes	Yes	Yes
R²	0.6328	0.4960	0.4001	0.3668	0.4159	0.6278
长期（h=3）
Age	-1.578^** (-3.33)	-1.067^** (-2.35)	-1.401^*** (-3.80)	-1.305^*** (-3.73)	-0.922^** (-3.36)	-1.249^*** (-5.88)
y_t	-0.717 (-1.53)	-0.233 (-0.55)	-0.336 (-1.08)	-0.283 (-1.04)	-0.383 (-1.61)	-0.285 (-1.42)
Control	Yes	Yes	Yes	Yes	Yes	Yes
R²	0.6489	0.5084	0.4171	0.3923	0.4170	0.6561

Population Aging and Economic Growth in China: An Empirical Study Based on GaR Model

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