To prove the robustness of our findings, we conduct additional analysis of the announcement of the effectiveness of the Pfizer-BioNTech COVID-19 vaccine in the US. As highlighted by Abate et al. (2020), most governments have prioritized maintaining air transport connectivity in order to protect economic activity and jobs. State aid to airlines raises doubts about the appropriateness of the pre-COVID-19 **data** used in cross-sectional regressions in Panel 2 of Table 2 for vaccine announcement analysis. For example, due to state aid and bailouts, the ownership structure and indebtedness of airlines had already changed when vaccines were announced to be effective. According to the IATA, the global airline debt had increased by $220 billion by the end of 2020, where state aid accounted for between 1% (Latin America) and 25% (North America) of airline operating revenues in2019. 7 We evaluate Equation (4) for the seven variables, using the most recent **data** for many of the control variables to draw any relevant explanatory power from them. The control variables are calculated based on interim accounting figures for the year 2020. Panel 3 of Table 2 provides the results, which are consistent with the results of the original analysis in Panel 2 of Table 2. [-1,5]. SIZE i is the total assets (natural logarithm of total assets, millions of Euro); OWN i is the cumulated ownership of the three main shareholders (%); LIQ i is the ratio of current assets to total assets (%); TLEV i is the ratio of total debt to total assets (%); ROA i is the ratio of operating income to total average assets (%); LCD i is a dummy variable that takes the value 1 for low-cost carriers and 0 otherwise; STO i is a dummy variable that takes the value 1 if the main owner is the state or another public entity and 0 otherwise*, ** and *** denote statistical significance at the 10%, 5% and 1% level, respectively. Country-clustering robust errors are used (in parentheses).