## 3 Data Analysis
After analyzing the stationarity and correlation of the time series of the SVI of each term with the border-crossings data, the more significant search terms were identified, which are \"migrantes\", \"deportaciones\", \"muro fronterizo\", \"border patrol\", and \"patrulla fronteriza\". Lastly, the SVI of these terms is grouped by adding each month's values to construct a single index named Border Economic Migrant Sentiment (BEMS). Because the BEMS variable comprises search terms related to security and \"anti-immigration\" issues, it is expected that it will capture people's concerns about crossing the border [39,42]. The SVI of terms related to border crossings is also explored, with the geographic scope limited to the U.S. A similar research strategy is employed for the other variables, with the \"immigration\" search term being the most relevant. It is assumed that when an anti-immigration sentiment occurs in the U.S., the SVI of the \"immigration\" search term will increase. Therefore, adding the \"immigration\" variable (Imm) may help determine whether a rise in the hostile environment in the U.S. toward immigration may influence northbound border crossings. Table 2 displays statistics for the main variables. The monthly average crossings for pedestrian and personal vehicles are 3.6 and 6.2 million, respectively. Figure 2 depicts the northbound border crossings for pedestrians and personal vehicles grouped by region. The annual pedestrian crossings are depicted in Figure 2a. The other region experienced a rising trend from 2004 to 2007, followed by a significant decline, which was most likely caused by the global financial crisis, reaching a minimum in 2014. Subsequently, the border crossings experienced a sluggish recovery, with nearly 7 million crossings flattening in recent years. An annual decrease in pedestrian crossing is recorded from 2004 to 2009 in the California PoEs, with a temporary recovery in 2007. Texan ports reported similar behavior, with border crossings declining from 2004 to 2011 and with a temporary increase in 2007. From 2009 to 2011, although pedestrian crossings in Californian ports increased, those in Texas decreased. However, from 2012 to 2015, there was a decrease in pedestrian crossings in California ports. The reverse is observed in Texas, where crossings in 2019 were lower than those registered 15 years earlier. This could indicate that regional factors may influence pedestrian border crossings. Figure 2b exhibits the personal vehicle border crossings for each region. From 2004 to 2011, all 3 regions experienced a similar declining trend. However, a recovery was \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Statistics**} & \multirow{2}{*}{**Pedestrians**} & \multirow{2}{*}{**Personal Vehicles**} & \multicolumn{4}{c}{**Variables**} & \multirow{2}{*}{**Real Each. Rate**} & \multirow{2}{*}{**IGAE**} & \multirow{2}{*}{**Real Gas Price**} \\ \cline{1-1} \cline{5-8}  & & & & & & & \\ \hline Mean & 3,633,678 & 6,262,869 & 35.61 & 36.05 & 81.79 & 98.19 & 1.26 \\ Std. Dev & 342,316 & 800,376 & 27.9 & 13.2 & 7.00 & 10.05 & 0.25 \\ Min. & 3,007,144 & 4,619,191 & 9 & 18 & 68.19 & 79.19 & 0.79 \\ Max. & 4,788,991 & 8,074,001 & 167 & 100 & 101.05 & 117.83 & 1.88 \\ \hline \hline \end{tabular} Note: The monthly data are up to February 2020. Source: Own estimations from sources described in the data section.  
\end{table} 
Table 2: Descriptive statistics of the main variables (2004-2M2020). registered from 2011 to 2017 in all regions, followed by a downward pattern in the last years of the sample. Following the literature [13; 14; 26; 27; 28], the real exchange rate movements are expected to explain the evolution of border crossings. For example, a depreciation of the Mexican peso makes shopping and leisure activities more costly, reducing the number of pedestrian and personnel vehicles crossings. Likewise, two variables are introduced to capture economic factors.