Summary:
According to data from the National Bureau of Statistics of China, by the end of 2021, the proportion of people aged 65 and above in China had reached 14.2%, which is far higher than the world average (9.54%) and exceeds the level that represents moderate aging of a society (14%). The report of the 20th National Congress of the Communist Party of China (CPC) stated that implementing the “national strategy of actively responding to population aging” is necessary to promote high-quality development. A superior financing environment and sufficient credit resources are crucial to maintain companies' operations and to realize economic transformation and upgrading. Companies' debt financing decisions are the micro embodiment of the financing environment and a frequent focus of academic research. Since MM theory was introduced, the literature on corporate financing decisions has expanded, and the trade-off and pecking order financing theories have been developed. These theories discuss the optimal capital structure for companies and emphasize the advantages of debt financing, such as a reduced tax burden and its restraining effect on overinvestment. Debt financing decisions are related to companies' asset allocations, which are affected by many internal and external factors. Different from the literature, we introduce regional population structure to study the impact of population aging on corporate debt financing decisions and the efficiency of financial resource allocation. Using 2007-2019 data on Chinese listed companies, we empirically analyze the impact of regional population aging on companies' financing decisions. The results show that deepening regional population aging significantly reduces companies' debt financing. After using previous family planning intensity as an instrumental variable to address endogeneity concerns and performing a series of robustness tests, the conclusion holds. The mechanism analysis shows that regional population aging reduces corporate debt financing through two channels: intensifying financing constraints and increasing business risk. Our heterogeneity analysis shows that the impact of population aging on corporate debt financing is more obvious among non-state-owned companies, small and medium-sized companies, traditional industries, and capital-and labor-intensive industries. Furthermore, population aging exacerbates the mismatch of financial resources. This is reflected in the fact that regional population aging significantly increases the number of companies with insufficient financing, and this effect is more obvious among companies with higher productivity. This study makes the following contributions. First, it enriches the literature on the economic impact of population aging from the micro perspective of corporate debt financing decisions. Many scholars research the effects of the increasing trend of population aging. However, their research mainly focuses on the macroeconomic level; few studies focus on the impact on micro companies. Using census data on prefecture-level cities, we are the first to explore the impact of regional population aging on the debt financing decisions of micro companies, which enriches the literature in this field. Second, this study expands the relevant research on the factors that influence corporate debt financing. Our in-depth analysis of the underlying mechanism aids understanding of how social factors affect corporate investment and financing decisions. This finding has the following implications. First, attention should be paid to the economic effects of the aging population on companies, and financial strategies should be implemented to help firms cope. As population aging reduces the supply of capital, policymakers should actively implement supply-side structural reforms to improve the efficiency of resource allocation. Second, in the face of rising labor costs brought about by population aging, companies' business risks are gradually increasing. Companies can seek positive transformation through technological innovation and by substituting robots for human labor. However, the central and local governments should introduce relevant policies to increase the labor supply and reduce labor costs, such as delayed retirement policies and flexible pension system reforms.
陈熠辉, 蔡庆丰, 王斯琪. 人口老龄化、企业债务融资与金融资源错配——基于地级市人口普查数据的实证研究[J]. 金融研究, 2023, 512(2): 40-59.
CHEN Yihui, CAI Qingfeng, WANG Siqi. Population Aging, Corporate Debt Financing, and Financial Resource Misallocation: An Empirical Study Using Prefectural Census Data. Journal of Financial Research, 2023, 512(2): 40-59.
Altman, E. I. 1968. “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy”, Journal of Finance, 23(4): 589～609.
[19]
Becker, B. 2007. “Geographical Segmentation of US Capital Markets”, Journal of Financial Economics, 85(1): 151～178.
[20]
Becker, B., Z. Ivkovic., and S. Weisbenner. 2011. “Local Dividend Clienteles”, Journal of Finance, 66(2): 655～683.
[21]
Czaja, S.J., and C.C. Lee. 2007. “The Impact of Aging on Access to Technology”, Universal Access in the Information Society, 5(4):341～ 349.
[22]
Fagereng, A., C. Gottlieb., and L. Guiso. 2017. “Asset Market Participation and Portfolio Choice over the Life Cycle”, Journal of Finance, 72(2): 705～750.
[23]
Giannetti,M., G. Liao., and X. Yu. 2015. “The Brain Gain of Corporate Boards: Evidence from China”, Journal of Finance, 70(4): 1629～1682.
[24]
Hackbarth, D. 2008. “Managerial Traits and Capital Structure Decisions”, Journal of Financial and Quantitative Analysis, 43(4): 843～882.
[25]
Hilary, G., and K. W. Hui. 2009. “Does Religion Matter in Corporate Decision Making in America？” ,Journal of Financial Economics, 93(2): 455～473.
[26]
Lee, R. and A. Mason. 2010. “Fertility, Human Capital, and Economic Growth over the Demographic Transition”, European Journal of Population, 26(2): 159～182.
[27]
Li, H. B., J. Zhang., and J. S. Zhang. 2007. “Effects of Longevity and Dependency Rates on Saving and Growth: Evidence from a Panel of Cross Countries”, Journal of Development Economics, 84(1): 138～154.
[28]
Miller, M. H. 1977. “Debt and Taxes”, Journal of Finance, 32(2):261～275.
[29]
Myers, S. C., and N. S. Majluf. 1984. “Corporate Financing Decisions When Firms Have Investment Information that Investors Do Not Have”, Journal of Financial Economics, 13(2): 187～221.
[30]
Modigliani, F., and R. Brumberg. 1980. “Utility Analysis and Aggregate Consumption Functions: An Attempt at Integration”, Cambridge: The MIT Press.
[31]
Ohlson, J. A. 1980. “Financial Ratios and the Probabilistic Prediction of Bankruptcy”, Journal of Accounting Research, 18(2): 109～131.
[32]
Petersen, M., and R. Rajan. 2002. “Does Distance Still Matter？ The Information Revolution in Small Business Lending”, Journal of Finance, 57(6): 2533～2570.
[33]
Sanz, I., and F. J. Velázquez. 2007. “The Role of Ageing in the Growth of Government and Social Welfare Spending in the OECD”, European Journal of Political Economy, 23(4): 917～931.
[34]
Serfling, M. 2016. “Firing Costs and Capital Structure Decisions”, Journal of Finance, 71(5): 2239～2286.
[35]
Tang, T. 2009. “Information Asymmetry and Firms' Credit Market Access: Evidence from Moody's Credit Rating Format Refinement”, Journal of Financial Economics, 93(2): 325～351.
[36]
Whited, T. M., and G. Wu. 2006. “Financial Constraints Risk”, Review of Financial Studies, 19(2): 531～559.
