## 2 Literature Review
### Decision-Maker Sentiment  
Traditional economic theories leave no room for irrational behavior; they assume that decisions are made through rational decision-making processes [29], and economic agents consider all publicly available information when making decisions [30]. However, empirical evidence reveals that agents are constrained in the amount of information they can process [31], questioning the rationality assumptions in classical theory. Furthermore, behavioral economics has highlighted the relevance of economic agents' sentiments in financial markets [7], and their behavioral biases, which lead to overly optimistic/pessimistic beliefs and drive an irrational behavior [32]. The literature provides several definitions for the term \"sentiment\". In the financial literature, investor sentiment is commonly characterized as either the proclivity to take risks and speculate or the overall sense of optimism or pessimism toward risky assets [33]. For example, [PERSON] et al. [34] related sentiment to noise trading. In contrast, [PERSON] and [PERSON] [35] attribute it to feelings of optimism or pessimism about risky assets. Scholars have devised various proxies for measuring the sentiments of economic agents, especially investors. Based on the data source from which the proxy is extracted, [PERSON] et al. [7] grouped these proxies into three categories. Indirect proxies are based on stock market data and survey results. [PERSON] and [PERSON] [35] used six proxies for sentiment retrieved from the stock market, including the closed-end fund discount and NYSE share turnover. Based on noise-trader sentiment models, [PERSON] and [PERSON] [36] employed the University of Michigan survey of consumer sentiment and the Conference Board survey of consumer confidence as proxies for investor sentiment. [PERSON] et al. [37] suggested that earlier indirect sentiment measures do not capture all decision-makers' sentiments. They claimed that market-based indicators can capture more than investors' sentiments. Moreover, survey-based proxies are infrequent, and respondents cannot be incentivized to submit truthful answers. Because of this, they stated that the volume of internet search inquiries should be used to create direct sentiment indicators. Emerging literature has applied internet search volume data as a source to construct proxies of economic and noneconomic variables [38]. For example, [PERSON] et al. [39] analyzed the relationship between the search volume of employment-related terms with monthly U.S. unemployment data. [PERSON] et al. [40] used Google search volume to predict the incidence of influenza diseases. [PERSON] and [PERSON] [41] used search volume from Google to estimate macroeconomic variables, including automobile sales, initial claims for unemployment benefits, travel destination planning, and consumer confidence. [PERSON] [42] employed search query data to examine inflation expectations. [PERSON] and [PERSON] [43] constructed a private consumption measure based on search queries. Finally, [PERSON] et al. [37] hold that sentiment at the market level can be measured using search volume data. Thus, using online searches relevant to households in the U.S., the authors developed a Financial and Economic Attitudes Revealed by Search (FEARS) index. In this study, it is important to emphasize that the primary goal was to explore the relationship between people's sentiment to cross the border at the aggregate level and its effect on border crossings. This study proposes that online search query volume data should be used to develop a direct measure, based on the claim of [PERSON] et al. [39] that people's search behavior reveals information about their needs, desires, preferences, and concerns. As [PERSON] [42] proposed, search behavior can be interpreted as a measure of disclosed expectations. According to [PERSON] et al. [37], aggregating search volume data reveals market-level sentiment connected to specific topics.