## 5 Results
### Robustness Checks  
Rolling regression analysis was conducted as a robustness check. This statistical method seeks to analyze the relationship between the dependent and independent variables. However, unlike other methods, a specific window size is defined and moved progressively along the sample period. Therefore, a window size of 24 observations was selected in this study generating 174 subsamples, which were used to perform the rolling regression with a step of 1 (Windows of 12 and 48 lengths were tested, yielding similar results that can be provided upon request). Henceforth, the window was two years long and moved progressively from one month to the next. The results can be delivered upon request. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**California**} & \multicolumn{4}{c}{**Texas**} & \multicolumn{4}{c}{**Other**} \\ \hline \multicolumn{2}{c}{Variables} & \multicolumn{4}{c}{2004M01 2020M02} & & \\  & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\ d(ln Real Exch. Rate) & \(-0.470\)** & \(-0.380\)** & \(-0.375\)** & \(-0.466\)** & \(-0.344\)** & \(-0.323\)** & \(-0.536\)** & \(-0.454\)** & \(-0.431\)** \\  & (0.186) & (0.172) & (0.174) & (0.214) & (0.190) & (0.194) & (0.244) & (0.0218) & (0.216) \\ d(ln IGAE) & 1.329** & 1.306** & 1.307** & 1.306** & 1.351** & 1.353** & 1.466** & **1.439** & 1.441** \\  & (0.149) & (0.148) & (0.148) & (0.185) & (0.185) & (0.184) & (0.211) & (0.203) & (0.205) \\ dummy\_uscrisris & \(-0.004\) & \(-0.005\) & \(-0.006\) & \(0.001\) & \(0.0003\) & \(-0.002\) & \(-0.023\) & \(-0.022\)** & \(-0.025\) \\  & (0.024) & (0.023) & 0.024 & (0.022) & (0.022) & (0.022) & (0.014) & (0.013) & (0.014) \\ d(lnBEMS) & \(-0.012\) & \(-0.007\) & \(-0.034\)** & & \(-0.026\)** & \(-0.034\)** & & \(-0.028\)** \\  & (0.011) & & (0.011) & (0.010) & & (0.009) & (0.013) & & (0.014) \\ d(lnmm) & & \(-0.076\)** & \(-0.072\)** & \(-0.127\)** & \(-0.108\)** & \(-0.100\)** & & \(-0.100\)** & \(-0.079\) \\  & (0.020) & (0.024) & & (0.034) & (0.036) & & (0.040) & (0.044) \\ C & \(-0.001\) & \(-0.002\) & \(-0.002\) & \(-0.003\) & (0.003) & \(-0.003\) & \(-0.001\) & \(-0.002\) & \(-0.001\) \\  & (0.004) & (0.004) & (0.004) & (0.003) & (0.003) & (0.003) & (0.005) & (0.005) & (0.005) \\ R2 adj & 0.224 & 0.247 & 0.245 & 0.235 & 0.254 & 0.278 & 0.224 & 0.217 & 0.241 \\ AIC & \(-2.174\) & \(-2.205\) & \(-2.197\) & \(-1.966\) & \(-1.990\) & \(-2.018\) & \(-1.779\) & \(-1.771\) & \(-1.796\) \\ DW & 2.484 & 2.448 & 2.442 & 2.693 & 2.693 & 2.648 & 2.563 & 2.556 & 2.539 \\ \hline \hline \end{tabular} Note: Standard errors reported are robust to heteroscedasticity. The symbols *, **, and *** refer to 10%, 5%, and 1% significance levels, respectively.  
\end{table} 
Table 7: Pedestrian crossings OLS regression results clustered by regions (period: 2004-2020M02). This analysis intended to evaluate the behavior of the coefficients of the BEMS and Imm variables using the following specifications:  
\[\Delta\ln(PV_{t})=\alpha_{i}+\beta_{1}\Delta\ln(Exch\ rate)_{t}+\beta_{2}\Delta \ln(GR)_{t}+\beta_{3}\Delta\ln(GAS)_{t}+\sum_{i=1}^{n}\theta_{i}\Delta\ln(GT_{i })_{t}+e_{i} \tag{2}\]  
\[\Delta(PD_{t})=\alpha_{i}+\beta_{1}\Delta\ln(Exch\ rate)_{t}+\beta_{2}\Delta \ln(GR)_{t}+\sum_{i=1}^{n}\theta_{i}\Delta\ln(GT_{i})_{t}+e_{i} \tag{3}\]  
where the dependent variables \(PV_{t}\) and \(PD_{t}\) denote the \(t\) month total number of personal vehicle and pedestrian crossings. In Equations (2) and (3), _Exch rate_ represents the real exchange rate and \(GR\) denotes the Mexican economy growth rate; in Equation (2) \(GAS\) is the U.S. real gas price, and \(GT_{i}\) denotes the BEMS and Imm variables. Figure 4a depicts the coefficients for the first difference of the BEMS logarithm for personal vehicle crossings. Throughout the period, the BEMS had a negative relationship with personal vehicles. However, short-run episodes with positive coefficients arose, such as the summer of 2012 and the 2013 and 2014 winter seasons. Pedestrian crossings followed a similar pattern (Figure 4c).