Anti-Pandemic stringency measures and performance of tourism and hospitality firms worldwide: what makes a firm resilient to a crisis?

ABSTRACT

This study delves into the impact of COVID-19 pandemic-induced stringency measures on firm performance within the global tourism and hospitality sector, exploring the factors that shield firms from such measures. The results of a sample drawn from 41 countries reveal a negative association between stringency measures and firm performance. Notably, firms in countries with a stringency score at the 75th percentile are found to exhibit a 263% lower return on assets compared to their counterparts in countries with a stringency score at the 25th percentile. Moreover, larger and established firms, as well as those with high (low) sustainable growth (financial constraints) demonstrate a lower susceptibility to the adverse effects of stringency measures. These results underscore the critical need to customize government support schemes, ensuring they align with the vulnerability levels of businesses during abnormal operating conditions. Such customization will promote fairness and equity among businesses, recognizing their distinct challenges.

KEYWORDS:

• Stringency measures
• firm performance
• tourism and hospitality
• COVID-19
• resilience

Disclosure statement

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

Declaration of interest statement

The authors report there are no competing interests to declare.

Notes

1 For example, Sornette et al. (2020). report the death of 50 fewer people per million with an increase in the stringency index from 20 to 60 in a 20-day window.

2 https://ourworldindata.org/covid-stringency-index#learn-more-about-the-data-source-the-oxford-coronavirus-government-response-tracker

3 https://data.worldbank.org/indicator

4 The nine metrics used to calculate the Stringency Index are (i) school closures, (ii) workplace closures, (iii) cancellation of public events, (iv) restrictions on public gatherings, (v) closures of public transport, (vi) stay-at-home requirements, (vii) public information campaigns, (viii) restrictions on internal movements, and (ix) international travel controls.

5 If research and development expenditure is missing, it is set to zero.

6 The correlation between the control variables is moderate, ranging from 0.006 to 0.3560. The average variance inflation factor in our baseline model is 1.10, confirming that multicollinearity is not a concern in our multivariate regressions.

7 Based on cash flow from operating (CFO), investing (CFI) and financing (CFF) activities in the cash flow statement, Dickinson (2011). Cash flow patterns as a proxy for firm life cycle. The Accounting Review, 86(6), 1969-1994. groups firms into (i) introduction if CFO < 0, CFI < 0, and CFF > 0; (ii) growth if CFO > 0, CFI < 0, and CFF > 0; (iii) maturity if CFO > 0, CFI < 0, and CFF < 0; (iv) decline if CFO < 0, CFI > 0, and CFF ≤ 0 or ≥ 0; and (v) the remaining firm-years as shake-out stage.

8 Sustainable growth rate is calculated as the average return on equity (ROE) of year t and t-1 divided by (1 – ROE_t), following Bodnaruk and Östberg (2013).

9 Financial constraints risk is calculated following Whited and Wu (2006).