COVID-19, Supply Chain Breakdowns, and Firm Value

ABSTRACT

By exploiting the lockdown of Wuhan on January 23, 2020, during the COVID-19 pandemic and the disclosure of public firms’ top five suppliers, we examine the impact of a supply chain disruption on stock returns. Our findings suggest that firms with major suppliers in Wuhan experience significantly worse cumulative abnormal returns than those whose suppliers are not located in Wuhan. The results are robust to alternative estimation methods, event windows, and supply chain disruption metrics. Our findings suggest that supply chain disruption contributes to negative stock returns and highlight the importance of supply chain disruption on firm value.

KEYWORDS:

• COVID-19
• supply chain breakdowns
• stock price reaction

Acknowledgments

We acknowledge the helpful comments from three anonymous reviewers and Paresh Kumar Narayan (the Editor). Xia acknowledges the financial support from the National Natural Science of China (no. 72002177). The usual caveats apply. He acknowledges the financial support from the Humanities and Social Science Foundation of the Ministryof Education of China (no. 21YJC630038).

Disclosure Statement

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

Notes

1. Please refer to http://js.people.cn/n2/2020/0408/c360303-33934967.html (accessed September 5, 2021)

2. Medical industries refer to manufacturing and selling prescription drugs. They do not include equipment, hospitals, or retail pharmacies.

3. The CAR(‒5, +5) during the same period was 25.85% for the medical firms (21 firms), which was significantly higher than the mean CAR(‒5, +5) of ‒0.45% for the full sample. Hence, including firms in the medical industry (only 6.3% of the sample) will distort our findings.

4. Firms in financial distress suffer severe financial constraints even before COVID-19. When supply chain disruption occurred due to COVID-19, we do not know whether the supply chain disruption is due to COVID-19 or due to their own financial reasons. Hence, including these firms become a noise to the analysis.

5. We perform a propensity score matching (PSM) to reexamine our baseline findings. Essentially, we use LNSIZE, LEV, ROA, DISTANCE (the distance between the provincial capital of a firm’s location to Wuhan), TOP1, SOE, and INVENTORY as the matching variables. We use a 1:5 match with replacement (treatment firms have major suppliers in Wuhan, and control firms are those without major suppliers in Wuhan). The final sample has 17 treatment and 58 control firms. After PSM, the unreported mean differences are not significant. Then, we reexamine Eq. (4)(4) $CA{R}_i={\beta }_0+{\beta }_{1}COVID\mathrm{\_}S{C}_i+\sum CONTRO{L}_i+INDUSTRY+{\in }_i,$(4) using the PSM samples. The unreported results show that the coefficients of COVID_SC are negative and significant at the 1% level. The results are available upon request.