Population aging and corporate innovation: evidence from China

ABSTRACT

Using a sample of Chinese listed firms from 2007 to 2017, this study documents that corporate innovation is negatively associated with population aging, indicating that the aging workforce impedes corporate innovation. We further find evidence suggesting that education and stock-based incentive plans are two ways to alleviate the negative impact of population aging on corporate innovation. Our study complements existing research investigating the determinants of corporate innovation and extends the literature examining the economic consequences of population aging. This study also sheds light on the critical role of non-executive employees on corporate innovation.

KEYWORDS:

• Population aging
• corporate innovation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. The data is available online at http://data.stats.gov.cn/easyquery.htm?cn=C01.

2. The data is available at https://population.un.org/wpp/Download/Standard/Population/.

3. In 2000, the proportion of the population over the age of 65 reached 7.0%, indicating that China began to enter an aging society. As of 2018, China’s population over the age of 65 has reached over 167million, far more than other countries. The United Nations predicts that, by 2030, China’s aging population will account for a quarter of the world’s elderly population.

4. The marketization index by Fan, Wang, and Yu (2016) is a comprehensive index considering many important aspects of marketization, including regulation, private property, product market, financial and labor market and law environment. Therefore, we do not control for the law environment in model (1). Our results remain robust to separately controlling for the local law environment.

5. Our results remain robust when the standard errors are clustered at province level.

6. There may be some systematic difference between ESOP firms and non-ESOP firms, such as size and ownership dispersion. Therefore, we use PSM method to construct a PSM sample and then conduct this cross-sectional test. Unreported results show that our results remain robust for using this research design.

7. Our results remain robust when we use 0.05 and 0.01 as the matching caliper.

8. The most recent National Population Census was in 2020, but currently the data is not publicly available.

9. There is a sample reduction due to that the data of some small cities is not available on the web site of NBS.