Industrial Disasters, Financial Constraints, and Innovation: Firm-Level Evidence from China

ABSTRACT

This paper presents empirical evidence on the impact of industrial disasters on firm-specific innovation, focusing on the role of financial constraints. Using panel data of manufacturing firms in China from 2000 to 2013, our two-way fixed effects analyses confirm a statistically negative effect of industrial disasters on innovation in both the short run and the long run, and the negative effect is enhanced with the frequency of disasters. Another prefecture-level city panel from 2000 to 2019 further confirms the long-run and frequency-enhanced negative effect. The mechanism analysis shows that industrial disasters significantly increase interest rates and reduce bank lending to firms, which in turn reduces innovation. Moreover, ownership and size discrimination lead to asymmetric innovation effects on firms in the short and long run. Our results provide empirical evidence for the government to better allocate reconstruction funds and ensure firm innovation in the post-disaster period.

KEYWORDS:

• Industrial accidents
• patent
• financial discrimination
• Chinese firms

JEL:

• G30
• O31
• Q50

Disclosure Statement

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

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/1540496X.2023.2278655

Notes

1. Firm characteristics are obtained from the Annual Survey of Industrial Enterprises (ASIE) which covers representative industrial firms in China. The most recent released data of the ASIE is for the year 2014. We do not cover 2014 in our empirical estimations because it has a lot of missing data.

2. See Online Appendix Note 1, which summarizes the relationship between disasters and economic infrastructure that fosters innovation.

3. Online Appendix Note 2 sorts out the main aspects in which industrial disasters affect regional economy.

4. The index is calculated as $\left(-0.737\times \mathit{Size}\right)+\left(-0.737\times \mathit{Siz}{e}^2\right)-\left(0.040\times \mathit{Age}\right)$, where $\mathit{Size}$ and $\mathit{Age}$ are the log of total assets and age of the firm in the year, respectively.

5. In other word, $Y_{\mathit{ict}}={\sum }_{k=0}^7{\alpha }_1^k\mathit{di}{s}_{\mathit{ck}}+{\beta }_1{X}_{\mathit{ict}-1}+{\gamma }_1{C}_{\mathit{ct}-1}+\mathit{Fir}{m}_i+\mathit{Yea}{r}_t+{\delta }_{\mathit{ict}}$ where $\mathit{di}{s}_{\mathit{ck}}$ is a dummy variable indicating the $\mathit{k}-\mathit{th}$ lag post an industrial disaster which occurs in year $\mathit{t}$ for city $\mathit{c}$.

Additional information

Funding

Ying Lin thanks financial support from the National Social Science Fund of China [20CGL030] and the Humanities and Social Science Fund of Ministry of Education of China [19YJC790075]. The work was also supported by the National Natural Science Foundation of China [72074176 and 71673216] and the Ministry of Science and Technology of the People’s Republic of China [DL2022170001L and G2022170010L].