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Research Questions

  1. Can quasi-experimental statistical methods be used to evaluate the effects of various border-enforcement measures on outcomes that capture border security?
  2. What is the effect of deploying surveillance technology on U.S. Border Patrol apprehension levels?
  3. What are the promises and the limitations of using statistical methods to evaluate the effects or effectiveness of different border-enforcement measures (such as infrastructure, technology, personnel, and immigration-enforcement policies)?

Homeland Security Operational Analysis Center researchers sought to establish a causal connection between border-enforcement actions or policies and metrics that might be used to measure relevant outcomes at the border. Applying quasi-experimental methods, they investigated the impact of surveillance technology on levels of U.S. Border Patrol apprehensions of unlawful border-crossers between ports of entry along the southwest border. Their analysis offers insights into some of the effects of surveillance technology and serves as a demonstration of concept for the usefulness of such statistical methods. The most robust finding is that deploying integrated fixed towers (IFTs) is associated with decreased apprehension levels in the zones of deployment. Although the researchers emphasize ambiguity in the meaning of the results and the uncertainty in statistical inference with relatively small numbers of deployments, they concluded that there is strong evidence that some migrants were deterred from crossing surveilled areas of the border. The results are more inconclusive for the other surveillance assets—but there are suggestions that, unlike IFTs, tactical aerostat systems (TASs) (and, to a lesser extent, other technologies) elevate apprehension levels, which points to a boost to the U.S. Border Patrol's situational awareness. Statistical methods hold both promise and limitations for the study of the impact of border-enforcement measures beyond the analysis in this study. Although these methods cannot, on their own, yield clear answers in every case, they do have the potential to help operational commanders and policymakers understand and anticipate the impact and effectiveness of different border-enforcement measures.

Key Findings

  • Quasi-experimental statistical methods can be employed to assess the effects or effectiveness of border-enforcement measures, if the limitations of these methods are taken into account
  • Different surveillance technologies likely have different effects on outcomes at the border
  • There is strong evidence that deploying integrated fixed towers is associated with decreased apprehension levels, indicating deterrence of border crossings through surveilled zones
  • There is weaker evidence that deploying some other surveillance technology might be associated with elevated apprehension levels, indicating augmentation of the Border Patrol's situational awareness of border crossings

Recommendations

  • Extend this analysis by examining other outcomes that are potentially affected by surveillance (e.g., group size, crossings through unsurveilled routes around each surveillance asset) and contextual factors that likely contribute to the differences in effects across different surveillance technologies (e.g., personnel deployment patterns, preexisting surveillance assets, the extent to which surveillance assets are effectively networked).
  • Improve prospects for similar analysis of other border-enforcement measures (e.g., infrastructure, personnel) through increasing consistency, accuracy, and transparency of existing observable metrics (e.g., got aways, turn backs, and asset assists) and of methods to estimate unobservable metrics (e.g., total migrant flow, apprehension rate).
  • Improve prospects for useful statistical analysis through increasing availability of data to researchers and analysts (within and outside DHS) and pursuing integration of data on all border-enforcement measures in an accessible format.

Table of Contents

  • Chapter One

    Introduction: Studying the Effects of Border-Enforcement Measures

  • Chapter Two

    Methodology: Identifying the Effects of Surveillance Technology, Using Statistical Methods for Causal Inference

  • Chapter Three

    What Do Data and Statistical Analysis Tell Us?

  • Chapter Four

    The Path Forward and Conclusions: Implications for the Department of Homeland Security

  • Appendix A

    Technical Details

Research conducted by

This research was sponsored by DHS's Science and Technology Directorate and conducted within the Strategy, Policy and Operations Program of the Homeland Security Operational Analysis Center (HSOAC).

This report is part of the RAND Corporation research report series. RAND reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND reports undergo rigorous peer review to ensure high standards for research quality and objectivity.

The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.