What methods does DHS use to estimate illegal border crossings versus legal entries?
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Executive summary
The Department of Homeland Security (DHS) distinguishes between measured “encounters” at ports and between ports of entry and uses a mix of administrative counts, observational systems, survey-based inputs, and statistical modeling to infer how many people enter unlawfully versus legally; CBP encounter datasets (apprehensions, inadmissibles, expulsions) are the foundation, while models and survey estimates attempt to account for “got-aways” and undetected crossings [1] [2] [3]. Independent reviewers have urged caution: because many crossings are deliberately concealed, DHS must rely on indirect estimation techniques whose assumptions strongly shape the final numbers [4] [5].
1. How DHS measures the observable: encounter and enforcement counts
The core of DHS’s measurement system is administrative encounter data produced by Customs and Border Protection (CBP): Title 8 Border Patrol apprehensions, Office of Field Operations inadmissibles at ports, and expulsions under Title 42, which are published as nationwide and regional “encounters” series [1] [6]. Those encounter datasets are compiled from operational reporting systems such as the Border Patrol Enforcement Tracking System (BPETS) for Border Patrol apprehensions and BorderStat/Unified Secondary extracts for ports data, and are distributed via the Office of Homeland Security Statistics (OHSS) and CBP statistics pages [2] [7].
2. Estimating the unseen: got‑aways, attempts and statistical models
Because some migrants evade detection, DHS supplements raw encounters with estimation methods intended to infer undetected entries — commonly termed “got‑aways” or successful illegal entries — by modeling the relationship between observed attempts and apprehensions or by running situational‑awareness models that combine sensors, agent observations, and statistical inference [4] [3]. The National Academies work cited by DHS frames the standard approach: if total attempted crossings could be estimated, undetected crossings can be calculated as total attempts minus known turn‑backs, getaways, and apprehensions, an approach that depends entirely on estimating that initial “attempts” quantity through indirect means [4].
3. Surveys, interviews and external data sources as inputs
DHS and external researchers also rely on migration surveys, interviews with migrants, and international data to inform flow estimates and to validate models; the National Research Council recommended integrating major U.S. and Mexican migration surveys to improve quarterly regional estimates because administrative data alone can’t reveal intent, repeat attempts, or population origins [5]. Non‑governmental projects such as USAFacts have replicated DHS methodology—using CBP encounter counts and estimated apprehension rates—to produce nationwide estimates, illustrating how sensitive results are to methodological choices [8].
4. Operational enhancements and situational awareness as part of measurement
DHS has invested in situational awareness—ground and aerial monitoring, sensors and intelligence sharing—not only for enforcement but as an observational input to improve estimates of movements between ports of entry; the department states these operational improvements yield "increasingly robust observational estimates" though it acknowledges residual uncertainty because people actively evade detection [3] [9]. DHS has also publicly released modeling claims (e.g., reductions in got‑aways) tied to policy actions, presenting both the operational data and modeled counterfactuals in its public materials [10] [11].
5. Limits, controversies and political framing
Multiple DHS pages and independent analysts warn that precise quantification is impossible because some flows are undetected and methodological choices matter, meaning headline claims about “reductions” or absolute counts reflect both operations and modeling assumptions [3] [4]. Political actors and agency press releases sometimes present modeled estimates as operational success metrics, which can obscure the dependence on untestable assumptions; fact‑checking outlets note gaps in updated got‑away estimates and variation between monthly and annual measures, underscoring how selective presentation can shape narratives [10] [12].
6. Bottom line: a hybrid, assumption‑sensitive system
DHS uses a hybrid approach: direct administrative encounter counts serve as the empirical backbone (apprehensions, inadmissibles, expulsions), while surveys, operational observations, situational‑awareness systems, and statistical models are used to estimate undetected crossings and to convert encounter streams into estimates of total illegal entries; every stage requires assumptions and produces uncertainty that different users and political actors can emphasize or downplay [2] [1] [4] [3]. Where public reporting is silent or dated—such as recent official estimates of “got‑aways”—the record shows DHS and outside analysts continue to update methods but that definitive counts remain elusive [12] [8].