A Surge on the Horizon: Improving U.S. Foresight Capacity to Anticipate Mass Migrations

– Executive Summary –

Mass migrations arriving at the southwest border of the United States are increasingly overwhelming the U.S. government’s ability to respond. When these events overwhelm U.S. immigration authorities, they can exacerbate security vulnerabilities. For example, gang members, criminals, terrorists, and foreign fugitives can all exploit the porous southwest border, especially during a mass-migration event when resources are stretched thin. This thesis advocates building the capacity for the U.S. government to conduct strategic foresight work to anticipate mass-migration flows before they occur. It argues that such foresight is not only feasible but also necessary to prevent future security threats, financial costs, and humanitarian crises along the southwest border.

The goal of strategic foresight work “is to create an evidence base to lead strategic planning.”[1] There are four main challenges in conducting strategic forecasting that this thesis attempts to overcome: data availability, issue complexity, analytic process, and institutional constraints.[2] To overcome the challenges of complexity and data availability, the United States should work to identify the key drivers of international migration utilizing Maslow’s hierarchy of needs as a framework for identifying unmet necessities in the populace. Additionally, analysts should use technological resources, such as internet search analysis and satellite imagery. Once the key drivers have been identified, the United States should establish metrics for monitoring those drivers with the aim of detecting when migration stressors reach thresholds and intervening when necessary to prevent mass-migration flows.[3]

Analyzing migration factors through the lens of Maslow’s hierarchy of needs reveals several key findings that could assist the U.S. government in evaluating which countries are at risk of mass migration and where migrants may travel. Mass migrations are more likely to come from countries with underperforming economies and those involved in conflict.[4] They are also more likely to come from countries that rate poorly on the corruption perception index, which reflects the level of crime and corruption a country is experiencing.[5] Therefore, migrants tend to be drawn to countries that are more lawful and less corrupt than their original countries. Also, migrants are drawn toward locations where they have family or cultural connections. Finally, climate change should be expected to exacerbate these migration stressors.

Next, the thesis examines emerging technologies that could be used to help improve the accuracy of migration anticipation, namely satellite technology, internet search analysis, and cell-phone tower tracking. Scientists have used satellites to detect migration stressors such as water scarcity, disease, and crop failure before they reach their breaking point.[6] Moreover, researchers have used internet search analysis to successfully forecast domestic and international migration more than one year in advance.[7] Cell-phone tower data, too, have shown population movements in real time and estimated the total number of displaced persons with a high degree of accuracy.[8] This paper recommends the complementary use of all three technologies to improve the accuracy of migration forecasting.

Having explored the underlying factors of mass migration, ways to quantify them, and the technology to increase forecasting accuracy, this thesis then discusses potential modeling methods to synthesize the data. For example, the disparate characteristics of migration flows, labor trends, and asylum applications must be modeled differently.[9] Likewise, forecasting models capture different data components, so in relying on a single model, analysts may failure to capture nuance in the data. Several types of modeling have proven useful in anticipating migration movements: agent-based modeling, probabilistic forecasting models such as Bayesian modeling, and Markov chain models.

Agent-based modeling is a useful tool for anticipating migration movements by factoring in both individual decision-making processes and the effect of relationships in social networks on those decisions.[10] Agent-based models have utility in guessing whether individuals will migrate and estimating how long it will take them and where they will go. One of the benefits of agent-based models is that many of the researchers utilizing them in the migration context have published their simulation code for public use. U.S. immigration authorities could use this code to help simulate migration destinations along the southwest border.

Another modeling method for anticipating migration trends is the Bayesian model, which allows for the combination of different data sources. It allows an analyst to coalesce different datasets, historical trends, and expert judgment coherently.[11] Bayesian models have successfully predicted migration in various scenarios with a promising degree of accuracy.

Markov chain models are another method that has shown relevance in migration projections. Such models are network models that anticipate the next step of an agent assuming that the future is independent of the past.[12] Notably, Markov chain models hold some advantages over agent-based models in terms of efficiency. Whereas an agent-based model may require data from dozens of files and need thousands of lines of code to operate, Markov chain models can be contained in a single file and require fewer than 500 lines of code.[13]

Following the modeling discussion, the thesis turns to the task of building the institutional capacity for strategic foresight work in the federal government. This thesis explores foreign and domestic strategic-forecasting units to identify best practices in establishing a successful migration anticipation unit. In examining the way other entities conduct strategic forecasting, this thesis highlights several relevant themes that would apply to a mass-migration anticipation unit in the United States. First, such units collaborate horizontally across the government and with external stakeholders to improve accuracy and keep foresight at the front of people’s minds. Second, such units have direct access to decision-makers who can act quickly on the units’ recommendations. Third, the units operate with relative autonomy. Finally, having freedom from responding to today’s crises allows them to focus on the future. These are all factors that could be implemented in an American mass-migration anticipation unit.

Ultimately, this thesis makes the following recommendations:

  1. Establish a strategic foresight unit within the Department of Homeland Security, tasked with anticipating mass migrations to the United States.
  2. Delegate the authority to declare a mass migration to the lowest level possible to expedite deployment of resources to the southwest border and to originating countries in anticipation of a mass-migration event.
  3. Establish an operational response unit for mass migrations along the southwest border.
  4. Establish appropriate funding mechanisms for the strategic foresight unit and mass-migration operational response unit.

[1] Iana Dreyer and Gerald Stang, “Foresight in Governments—Practices and Trends around the World,” in YES 2013: EUISS Yearbook of European Security, ed. Antonio Missiroli (Paris: European Union Institute for Security Studies, 2013), 13, https://www.iss.europa.eu/content/euiss-yearbook-european-security-2013.

[2] Thomas Juneau, Strategic Analysis in Support of International Policy Making (Lanham, MD: Rowman & Littlefield, 2017), 60.

[3] Demetrios G. Papademetriou and Kate Hooper, Building Partnerships to Respond to the Next Decade’s Migration Challenges (Washington, DC: Migration Policy Institute, 2017), 48, https://www.‌migrationpolicy.org/research/building-partnerships-respond-next-decades-migration-challenges.

[4] Reginald E. Johnson III, “Using Maslow’s Hierarchy of Needs to Identify Indicators of Potential Mass Migration Events” (master’s thesis, Joint Forces Staff College, 2016), https://apps.dtic.mil/sti/‌citations/AD1017743.

[5] Johnson.

[6] Mary D. Dysart, “Remote Sensing and Mass Migration Policy Development” (master’s thesis, Air War College, 2011).

[7] Allen Yilun Lin, Justin Cranshaw, and Scott Counts, “Forecasting U.S. Domestic Migration Using Internet Search Queries,” in Proceedings of the World Wide Web Conference (New York: Association for Computing Machinery, 2019), 1061–72, https://doi.org/10.1145/3308558.3313667; Marcus H. Böhme, André Gröger, and Tobias Stöhr, “Searching for a Better Life: Predicting International Migration with Online Search Keywords,” Journal of Development Economics 142 (January 2020): 102347, https://doi.‌org/10.1016/j.jdeveco.2019.04.002.

[8] Linus Bengtsson et al., “Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti,” PLOS Medicine 8, no. 8 (2011): e1001083, https://doi.org/10.1371/journal.pmed.1001083.

[9] Jakub Bijak et al., “Assessing Time Series Models for Forecasting International Migration: Lessons from the United Kingdom,” Journal of Forecasting 38, no. 5 (2019): 481, https://doi.org/10.1002/for.2576.

[10] Anna Klabunde and Frans Willekens, “Decision-Making in Agent-Based Models of Migration: State of the Art and Challenges,” European Journal of Population 32, no. 1 (February 2016): 74, https://doi.org/‌10.1007/s10680-015-9362-0.

[11] George Disney et al., Evaluation of Existing Migration Forecasting Methods and Models (Southhampton, UK: ESRC Centre for Population Change, University of Southampton, 2015), 21, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/467405/Migration_Forecasting_report.pdf.

[12] Joe Blitzstein, “Introducing Markov Chains,” February 28, 2020, YouTube video, 4:45, https://www.‌youtube.com/watch?v=JHwyHIz6a8A.

[13] Vincent Huang and James Unwin, “Markov Chain Models of Refugee Migration Data,” ArXiv:1903.08255 (Ithaca: Cornell University, 2019), http://arxiv.org/abs/1903.08255.

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