Phu Vo ECON 120: Economics of Crime April 30, 2026 Paper 6: Independent Project
For the Final Project, I looked at U.S. Customs and Border Protection (CBP) encounter data from 2022-2025. Immigration enforcement is a heated topic in our political climate, but the public debate about how we should approach it has blurred the lines between actual unauthorized immigration, border enforcement activity, and the statistics recorded by government agencies. Rather than measuring every illegal alien in the United States, which would be virtually impossible, the goal of the project is to examine where and when CBP encounters occur, and which demographics of people appear mostly in the data.
The data comes from the U.S. Department of Homeland Security, within the CBP’s Nationwide Encounters dataset, which uses government surveillance such as encounters processed by U.S. Border Patrol and the Office of Field Operations, and biometrics, in addition to bulk purchases of data from commercial brokers to track the movements of undocumented people across the United States (U.S. Department of Homeland Security, 2026). Variables for analysis include encounter counts, state, year, month, citizenship, land border region (at which border the alien was encountered), and title of authority (Title 8 apprehensions and in-admissables, and Title 42 expulsions). State is used to map the geographic distribution of encounters, while fiscal year and month are used to examine change over time. Citizenship is used to show which nationalities account for the largest number of encounters, and land border region separates encounters into broader geographic categories such as the Southwest Border, Northern Border, and other border regions. I also created calculated fields to make the data easier to use in Tableau, including a full month-name field for tooltips, a month-year date field for trend analysis, and a citizenship-region grouping that organizes countries into broader regions such as Mexico, Central America, South America, the Caribbean, Asia, and Europe.
The initial data-viz shows a treemap of the top 10 citizenship groups by encounter count– each rectangle represents a nationality, with its size determined by the total number of encounters. While Mexico remains the largest citizenship group migrating to the U.S. illegally, Central American countries in addition to countries such as India, China, or even Ukraine appear in the broader list of citizenships, suggesting that U.S. border encounters are a result of global migration pressures. AP News reports that the number of illegal immigrants in the U.S. surged to an “all-time high of 14 million in 2023,” driven by relaxed Biden-era policies to grant temporary legal asylum and to exercise rights to seek asylum (Schneider, 2025).
The second visualization uses a line chart to show monthly CBP encounters by land border region. The chart helps to identify whether or not encounters are concentrated in particular seasons, and if seasonal patterns change over time. In each fiscal year, illegal immigration sharply increases during the final financial quarter along the southern border of the United States. In addition to seasonal weather patterns that assist with ease of migration, the number of non-farm job openings in averaged above “10.4 million per month during the Biden administration[‘s Septembers]” (Nowrasteh, 2023). As legal immigration to the states is heavily restricted, and there are not enough temporary visas available in the U.S. economy to meet the work demand at the end of the year, many migrants come to the U.S. illegally to work. This can be explained through the rational choice theory of crime– undocumented aliens can make more than a “four-fold [to] ten-fold increase” compared to labor in Latin American and Caribbean countries, even after accounting for the higher costs of living in the United States (Clemens et al., 2019).
The final visualization is a hex map of state-level encounter counts. While traditional maps suffer from the “Alaska Effect,” which creates distortions due to its disproportionately large size and location, the hex-map eliminates discrepancies in U.S. state sizes, alleviating the issue (Taylor, 2017). In the hex map, each state receives the same visual space, making it easier to compare state-level intensity. I also used a scaffold table so that states with no encounters in a particular year remain visible as zero instead of disappearing from the map. The hex map confirms that encounters are highly concentrated in border states, especially states along the Southwest Border. The strong concentration of encounters along the Southwest Border suggests that enforcement resources are likely to be most heavily demanded in certain states and regions. As encounters are more geographically concentrated than evenly distributed, concentrated enforcement can be effective if it targets high-activity states such as Texas, but it could also create displacement effects as efforts become more intense in one region. This connects to deterrence theory because migrants’ decisions may respond to the expected costs of apprehension, removal, or delay, while enforcement agencies allocate resources toward high-encounter areas. Migration attempts may shift to different routes or ports of entry, or other strategies, such as through water. The line chart and hex map help visualize this possibility by separating encounters by region and state over time.
However, this projected is limited in the scope of official crime data– border encounter data does not equate to total illegal immigration. People can evade enforcement agencies like ICE, or can be encountered more than once. In general, the CBP encounters between 2022-2025 show that encounters are concentrated geographically along the southern border, vary by season, and involve much more citizenship groups than common political narratives often suggest. U.S. border encounters are a product of legislation, enforcement, migration incentives, and policy changes.
References
Clemens, M. A., Montenegro, C. E., & Pritchett, L. (2019). The place premium: Bounding the price equivalent of migration barriers. The Review of Economics and Statistics, 101(2), 201–213. https://doi.org/10.1162/rest_a_00776 Nowrasteh, A. (2023, November 16). The US Labor Market Explains Most of the Increase in Illegal Immigration. Cato Institute. https://www.cato.org/blog/us-labor-market-explains-most-increase-illegal-immigration Schneider, M. (2025, August 21). Illegal immigration hit a record-high of 14 million in the US in 2023, Pew report finds. AP News. https://apnews.com/article/immigration-pew-homeland-security-a2cf8f2f908c98ca17d3 83b57fd65daa Taylor, K. (2017, January 13). How to use hex-tile maps to eliminate the Alaska effect. Salesforce. Tableau. https://www.tableau.com/blog/viz-whiz-hex-tile-maps-64713 U.S. Department of Homeland Security. (2026).U. S. Customs and border protection. https://www.cbp.gov/
Author’s Statement
During the write-up of the Final Project, and the creation of the dashboard, no generative AI tools were used to assist with the production of this assignment. All research, writing, and revision were completely and independently done by me to develop foundational writing and research skills.