Firm Survival in Times of Crisis: Do Innovation and Financing Constraints Matter? Insights from the COVID-19 Pandemic

ABSTRACT

Utilizing survey data collected over three rounds, this paper investigates the impact of pre-COVID innovation performance, innovation type, sources of knowledge, and the moderating role of financial constraints on survival following the outbreak of COVID-19. The findings pinpoint a strong and positive link between firm survival and innovation, especially process innovation, confirming the ability of innovators to adapt to new conditions as a determinant of survival. Moreover, relying on internal knowledge is found to increase the chance of survival and adaptability to times of crisis. The results further indicate that access to finance strengthens the positive impact of innovation on firm survival/adaptation.

KEYWORDS:

• COVID-19
• credit constraints
• firm survival
• innovation
• World bank enterprise surveys

JEL Classification:

• D22
• G01
• L20
• L25
• O30
• O52

Disclosure statement

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

Notes

1. For further discussion of the studies investigating firm innovation-performance relationship see Song et al. (2007), Rosenbusch, Brinckmann, and Bausch (2011), among others.

2. For studies that explore economic consequences of COVID-19 on Central and Eastern European countries see Adžić and Al-Mansour (2021), Iancu et al. (2022), among others.

3. Oluremi, Ayoko, and Mendy (2022) and Jebran and Chen (2023) offeres detailed literature review about corporate governance practices which may affect firm survival and offers insights into the management of the pandemic crisis.

4. See Hall (2010) and Hall and Lerner Hall (2010) for a detailed survey of literature on financing innovation.

5. Iwasaki and Kočenda (2020) and Baumöhl and Kočenda (2022) are among recent studies which examine firm survival in emerging European countries.

6. Following Muzi et al. (2021), for purposes of robustness, an alternative dependent variable is used to proxy permanent exit from the market, excluding temporary closure, and the estimation is repeated. However, the results turned out to be very similar.

7. There are slight differences in terms of questions across different rounds of follow-up survey. Hence, for the first round, this indicator is coded as a dummy variable using the item “Has this establishment adjusted or converted, partially or fully, its production or the services it offers in response to the COVID-19 outbreak?.”

8. Besides firm closure, which is the most directly related indicator to the main thrust of this paper’s arguments, two other measures related with performance and expectations are considered as well. Hence, it could be stated that pandemic-related firm survival is assessed using two alternative categories of variables: (sales) performance and expectations. Along these lines, a proxy for firm performance, SALES, which is a binary variable taking the score 1 if the firm experienced a drop in sales when compared to the same month of the previous year, and 0 otherwise, is used. In addition to that, firm’s bankruptcy expectation is proxied by a dummy variable, EXPECT, that is equal to 1 if the establishment is expected to fall in arrears in any of its outstanding liabilities in the next six months and, 0 otherwise. The results are not reported for the sake of brevity.

9. In order to ensure robustness, another credit measure, which indicates whether the firm has access to a formal financing channel, is employed in empirical specifications. This credit measure identifies firms that use formal credit and gets the value 1 if the firm does not have an overdraft facility, a line of credit or a loan from a financial institution, and 0 otherwise. Estimation results using this credit measure are not reported for sake of brevity, since, in general, the results are very similar.

10. https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker

11. The threshold is in line with the one used in the study of Wellalage, Thrikawala, and Ghardallou (2022). For robustness, the results are further checked by using the 25% threshold.

Additional information

Notes on contributors

Ekin Ayşe Özşuca

Ekin Ayşe Özşuca works as an associate professor at Çankaya University. She holds a PhD in Economics from Middle East Technical University, Turkey. Her area of research includes macroeconomic theory, monetary policy and financial institutions.