Characteristics of Internationally Trafficked Stolen Vehicles along the U.S.-Mexico Border
Bibliographic record
Abstract
Abstract: Trafficking of stolen vehicles has been the subject of few studies in the United States. Little is known about patterns and characteristics of vehicles that are stolen for international export. The current research constructs a logistic regression model to identify variables associated with international vehicle trafficking in Chula Vista, California. Vehicle, spatial, and temporal independent variables are developed, including those tested in previous research and variables presented in this study for the first time. The results show that the strongest predictors differentiating vehicles recovered in Mexico from domestically recovered thefts are the type of vehicle and age. Specifically, newer sport-utility vehicles, trucks and vans are more likely to be recovered in Mexico than the U.S. None of the variables related to space and time are statistically significant predictors in the model using 95 percent confidence intervals. Policy implications emanating from this research include more focused patrol and public awareness campaigns to proactively reduce this harmful form of vehicle theft.Keywords: crime analysis, environmental criminology, motor vehicle theft, transnational crimeINTRODUCTIONThe theftof motor vehicles (MVT) for the purpose of international export harms direct victims, communities, and all insured vehicle owners. When vehicles are stolen and taken out of the country, victims may miss work, suffer emotional consequences, and often must pay for some or all of a replacement vehicle. Similarly, indirect victims are affected by the way stolen vehicles are driven and elevated insurance costs. Although international vehicle trafficking has been observed for over 30 years in the United States, changes in the national distribution of MVT indicate that the issue has become a particularly widespread problem at the U.S.-Mexico border over the past two decades. The National Insurance Crime Bureau (NICB) has estimated that approximately 200,000 vehicles are stolen from the U.S. on an annual basis for export (Clarke and Brown 2003; United States General Accounting Office 1999), yet very little has been established about the patterns and characteristics of vehicles illegally taken for this purpose.Vehicles can be exported from a country via one of three methods: air, sea, and land borders. Based on the immense costs and difficulties associated with flying vehicles out of the country, most exported stolen vehicles are assumed to be moved across borders to Canada or Mexico, or through seaports on the coasts (Brown and Clarke 2004; Clarke and Brown 2003). At the U.S.-Mexico border alone, over 30 international crossings in California, Arizona, New Mexico, and Texas serve as potential routes for vehicle exportation. In addition, the presence of seaports permits vehicles to be shipped out of the country on roll-on/roll-offshipping boats and in 40-foot containers (Clarke and Brown 2003).Previous studies of vehicle trafficking in the U.S. are mostly limited to qualitative accounts of organized crime groups (Resendiz 1998, 2001; Resendiz and Neal 1999; Richardson and Resendiz 2006), analysis of insurance company data (Field, Clarke and Harris 1991), and evaluations or discussion of prevention measures (Ethridge and Sorensen 1993; Plouffe and Sampson 2004). The current study seeks to fill gaps in the literature on stolen vehicle exporting by exploring vehicle-related, spatial, and temporal characteristics that differentiate vehicles stolen in the U.S. and recovered in Mexico from vehicles stolen in the U.S. and recovered domestically. Logistic regression models are developed using the recovery country as the dependent variable for theftincidents in Chula Vista, California, a city located only miles from the busiest road border crossing connecting the U.S. to Mexico.BACKGROUNDThe first piece of legislation aimed toward curbing vehicle trafficking was the Dyer Act of 1919. Rather than focusing on international commerce, the Dyer Act was constructed to restrict inter-state trafficking of vehicles (Richburg 1984). …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".