## 5 Results
### Main Estimates
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Personal Vehicles**} & \multicolumn{4}{c}{**Pedestrians**} \\
**Variables** & & & **2004M01 2020M02** & \\ \hline \hline \multirow{3}{*}{d(In Real Exch. Rate)} & \(1\) & \(2\) & \(3\) & \(4\) & \(5\) & \(6\) \\  & \(-0.278\)*** & \(-0.200\)* & \(-0.193\)* & \(-0.484\)*** & \(-0.383\)*** & \(-0.368\)** \\  & (0.097) & (0.104) & (0.099) & (0.172) & (0.147) & (0.150) \\ d(In IGAE) & \(1.096\)*** & \(1.070\)*** & \(1.074\)*** & \(1.391\)*** & \(1.362\)*** & \(1.364\)*** \\  & (0.103) & (0.097) & (0.095) & (0.145) & (0.141) & (0.141) \\ d(In Real Gas Price) & \(-0.040\) & \(-0.001\) & \(-0.016\) & & & \\  & (0.031) & (0.035) & (0.032) & & & \\ Dummy\_us\_crisis & \(-0.001\) & \(-0.001\) & \(-0.002\) & \(-0.006\) & \(-0.007\) & \(-0.008\) \\  & (0.006) & (0.007) & (0.006) & (0.017) & (0.016) & (0.017) \\ d(InBES) & \(-0.019\)*** & & \(-0.014\)*** & \(-0.026\)*** & & \(-0.020\)** \\  & \(-0.006\) & & (0.005) & (0.010) & & (0.010) \\ d(InImm) & & \(-0.071\)*** & \(-0.061\)*** & & \(-0.102\)*** & \(-0.088\)*** \\  & & (0.014) & (0.015) & & (0.026) & (0.028) \\ C & \(-0.003\) & \(-0.003\) & \(-0.003\) & \(-0.002\) & \(-0.002\) & \(-0.002\) \\  & (0.002) & (0.002) & (0.002) & (0.003) & (0.003) & (0.003) \\ R2 adj & 0.413 & 0.434 & 0.455 & 0.311 & 0.332 & 0.351 \\ AIC & \(-3.365\) & \(-3.401\) & \(-3.435\) & \(-2.401\) & \(-2.433\) & \(-2.456\) \\ DW & \(2.918\) & \(2.903\) & \(2.876\) & \(2.644\) & \(2.626\) & \(2.590\) \\ \hline \hline \end{tabular} Note: The standard errors reported are robust to heteroscedasticity. *, **, and *** denote significance levels of 10%, 5%, and 1%, respectively.  
\end{table} 
Table 5: Personal vehicles and pedestrians OLS estimations (period: 2004–2020M02). there is no evidence that the price of gasoline in the U.S. is a motive for Mexican residents to cross the border. In Table 5, the BEMS index variable is added to models 1 and 4, the immigration variable is added to models 2 and 5, and both variables are included in models 3 and 6. The coefficient of both variables is negative, as predicted. An increase of 1% in the BEMS index leads to a 0.02% decrease in border crossings. Moreover, a 1% increase in the Imm leads to an approximately 0.07% decrease in northbound border crossings. By comparing the d(In Imm) coefficients calculated using models 2 and 5 in Table 5, which is significant at the 10% level, it is found that Imm is slightly larger for pedestrian crossings than for personal vehicles. These differences are statistically significant (5% significance level), using the Stata's suest-based test of equality of coefficients. After examining models 3 and 6, we find that the coefficients of Imm are larger than the coefficients of the BEMS index for both pedestrian and personal vehicle crossings. These differences are statistically significant (5% significance level), using the Stata's suest-based test of equality of coefficients. Table 6 (personal vehicle crossings) and Table 7 (pedestrian crossings) report the results for the 3 regions in which the PoEs are grouped. The BEMS index variable is included in models 1, 4, and 7 in Tables 6 and 7; the Imm variable in models 2, 5, and 8; and both variables in models 3, 6, and 9. By analyzing both tables, some interesting insights are obtained. First, in Texas, when the Imm is added, the relationship between personal vehicle crossings and the real exchange rate is not statistically significant, and for pedestrian models, the significance reduces. Moreover, for personal vehicle models in the other two regions, the significance reduces. However, the statistical significance level of the real exchange rate does not change when Imm is included in California and other region's pedestrian crossings models. If Imm captures some of the possible hostile immigration-related environment, an anti-immigrant environment weakens the economic motivation for crossing, especially in Texas PoEs.