# COVID-19's Pandemic Effects on Bike Sharing Systems: A New Reality for Urban Mobility?
## 3 Results
### Number of Rides Regression Model
The main results are presented in Table 8. In the first column, we estimate a random-effects model including only our main variable of interest, namely Lockdown (\(x_{1}\)). Then, in columns (2) and (3), we estimate random-effects models including the remaining independent variables (\(x_{1},x_{2}\)). In all three columns, the estimated coefficient of lockdown is positive and statistically significant, indicating a positive effect of quarantine lockdown on the number of rides. Specifically, the estimated coefficient suggests that lockdown leads to an increase in the number of rides via bike-sharing systems, during the the lockdown period. It must be noted that for these calculations, rides with a duration of less than 5 min were excluded. Concerning the rest of the independent variables, the coefficient of the weekly average duration of rides is insignificant in all regressions, implying that the duration of rides does not affect the number of rides. Similarly, the rental stations variable exerts an insignificant coefficient, indicating that the number of rental stations is not associated with the weekly number of rides. In column (4), we estimate the baseline model using fixed effects instead of random effects in order to see whether the estimated results depend on model specification. As can be seen, estimated results for the main independent variable, i.e., Lockdown (\(x_{1}\)), hold across both random and fixed effects models. In Table 9, we re-estimated all regressions of Table 7 using an alternative dummy for lockdown which measures the effect of lockdown during and after the lockdown period. Yet again, rides with a duration of less than 5 min were excluded by these calculations. As can be seen, the results remain the same since the estimated coefficient of Lockdown is still positive and statistically significant in all regressions. Hence, our results can have an alternative interpretation: Lockdown leads to an increase in the number of rides both during the lockdown period and after the easing of restrictions. Regarding the rest independent variables, estimated results for the average duration of rides remain unchanged. On the contrary, the number of rental stations exerts a positive and statistically significant coefficient, indicating that it is positively associated with the number of rides. \begin{table}
\begin{tabular}{l c c c c} \hline  & **(1)** & **(2)** & **(3)** & **(4)** \\ \hline  & **Random Effects** & **Random Effects** & **Random Effects** & **Fixed Effects** \\ \hline Lockdown & \(101.653\)** & \(101.653\)** & \(101.62\)** & \(108.034\)* \\ (23 March–4 May 2020) & (35.095) & (35.07) & (35.035) & (33.22) \\ Average duration (min) & & \(0.031\) & \(0.03\) & \(0.027\) \\  & & (0.18) & (0.187) & (0.181) \\ Rental stations & & & \(-0.175\) & \(25.152\) \\  & & & (2.63) & (13.695) \\ Constant & 47.157** & 45.121** & 45.946* & \(-68.587\) \\  & (4.759) & (16.222) & (27.697) & (66.629) \\ Number of cities & 3 & 3 & 3 & 3 \\ Observations & 155 & 155 & 155 & \(155\) \\ R-squared (overall) & 0.146 & 0.146 & 0.146 & 0.057 \\ \hline \end{tabular} Notes: All regressions include random effects and are estimated with robust clustered standard errors. Lockdown dummy takes the value of 1 during quarantine period, i.e., the weeks between 23 March and 4 May 2020 and zero otherwise. Standard errors in parentheses. *, ** denote statistical significance at 10\% and 1\%.  
\end{table} 
Table 8: The effect of lockdown on number of rides, comparing lockdown period and non-lockdown period, excluding rides with duration less than 5 min.