## 3 Data Analysis  
The Bureau of Transportation Statistics provides monthly statistics for inbound crossings between the U.S. and Mexico at the PoE level. The data are classified by port, state, and means of transportation. In this study, the dependent variables were the monthly number of pedestrians and the personal vehicle crossings from Mexico to the U.S.  
All PoEs were grouped into three regions: California, Texas, and other (Arizona and New Mexico). Table 1 presents the descriptive statistics of pedestrians and personal vehicles aggregated by region. In 2019, California accounted for 42.6% of the pedestrian crossings (42.9% for personal vehicle); Texas, 41.8% (44.1%); and Arizona and New Mexico, 15.6% (13%). Google is the largest and most popular search engine on the internet, and since 2004, it has provided the Google Trends services, in which the historical Search Volume Index (SVI) of search terms can be downloaded [37]. This tool provides the search volume of each term in hourly, daily, weekly, or monthly frequencies. In addition, the SVI data for each keyword within a particular geographical region is scaled from 0 to 100 by the period's maximum. To test the effect that an \"anti-immigrant environment\" has on economic agents' sentiments (e.g., shopping-trip travelers) and, hence, on their attitudes and decisions toward northward pedestrian and personal vehicle crossings along the US-Mexico border, it is proposed that two variables should be constructed as proxies to capture the phenomenon from both sides of the border. According to [PERSON] et al. [39] and [PERSON] [42], people's search behavior can be expected to reveal matters relevant to them. These variables are derived from the Google Trends database. If hedonic shopping drives economic agents to cross the border to pursue fun and enjoyment [6], it is reasonable to expect that an \"anti-immigrant environment\" in the U.S. border cities will affect economic agents' attitudes and sentiments toward crossing the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Regions**} & \multirow{2}{*}{**Statistics**} & \multicolumn{2}{c}{**Variables**} \\  & & **Pedestrians** & **Personal Vehicles** \\ \hline California & Mean & 1,416,099 & 2,478,202 \\  & Std. Dev. & 173,819 & 303,700 \\  & Min. & 896,650 & 1,882,214 \\  & Max. & 1,825,929 & 3,137,415 \\ Texas & Mean & 1,504,445 & 3,016,686 \\  & Std. Dev. & 174,942 & 485,218 \\  & Min. & 1,168,377 & 2,195,680 \\  & Max. & 2,105,010 & 4,069,654 \\ Other & Mean & 713,134 & 767,981 \\  & Std. Dev. & 155,993 & 90,761 \\  & Min. & 473,677 & 541,297 \\  & Max. & 1,224,899 & 937,418 \\ Total & Mean & 3,633,678 & 6,262,869 \\  & Std. Dev. & 342,316 & 800,376 \\  & Min. & 3,007,144 & 4,619,191 \\  & Max. & 4,788,991 & 8,074,001 \\ \hline \hline \end{tabular} Note: The data are up to February 2020. Source: Own estimations using data from the Bureau of Transportation Statistics.  
\end{table} 
Table 1: Descriptive statistics of pedestrians and personal vehicles crossing the border from Mexico to the U.S. (2004-2M2020). border for shopping trips, thereby influencing border crossings. Therefore, it is proposed that a variable that captures the sentiments of these economic agents at the aggregate level should be developed using the online search volume data as a proxy for their concerns about and interests in the Mexican side of the border. Based on the online activity of economic agents in the U.S., a second variable was introduced to reflect the \"anti-immigrant environment\" in the U.S.  
For the first variable, different monthly SVI data of border-crossing-related terms, including terms associated with border crossings and migration (e.g., \"tiempo de cruce,\" \"puente internacional,\" and \"cruce fronterizo\"), were individually retrieved using the Google Trends tool; the search was restricted to Mexico and the period analyzed. It was followed by a \"snowball technique\", which was implemented by including the related terms that Google Trends suggests in the analysis.