University of Potsdam, 14482, Potsdam, Germany.
Hertie School, Centre for International Security, 10117, Berlin, Germany.
Sci Rep. 2023 Aug 3;13(1):11014. doi: 10.1038/s41598-023-38119-4.
State- and private-led search-and-rescue are hypothesized to foster irregular migration (and thereby migrant fatalities) by altering the decision calculus associated with the journey. We here investigate this 'pull factor' claim by focusing on the Central Mediterranean route, the most frequented and deadly irregular migration route towards Europe during the past decade. Based on three intervention periods-(1) state-led Mare Nostrum, (2) private-led search-and-rescue, and (3) coordinated pushbacks by the Libyan Coast Guard-which correspond to substantial changes in laws, policies, and practices of search-and-rescue in the Mediterranean, we are able to test the 'pull factor' claim by employing an innovative machine learning method in combination with causal inference. We employ a Bayesian structural time-series model to estimate the effects of these three intervention periods on the migration flow as measured by crossing attempts (i.e., time-series aggregate counts of arrivals, pushbacks, and deaths), adjusting for various known drivers of irregular migration. We combine multiple sources of traditional and non-traditional data to build a synthetic, predicted counterfactual flow. Results show that our predictive modeling approach accurately captures the behavior of the target time-series during the various pre-intervention periods of interest. A comparison of the observed and predicted counterfactual time-series in the post-intervention periods suggest that pushback policies did affect the migration flow, but that the search-and-rescue periods did not yield a discernible difference between the observed and the predicted counterfactual number of crossing attempts. Hence we do not find support for search-and-rescue as a driver of irregular migration. In general, this modeling approach lends itself to forecasting migration flows with the goal of answering causal queries in migration research.
国家和私人主导的搜救工作被假设会通过改变与旅程相关的决策计算，从而促进非正常移民（并因此导致移民死亡）。我们通过关注过去十年中最频繁和最致命的非正常移民路线——中地中海路线，来调查这种“拉力因素”说法。基于三个干预时期——（1）国家主导的“地中海救援行动”（Mare Nostrum），（2）私人主导的搜救，以及（3）利比亚海岸警卫队的协调驱赶——这对应了地中海地区搜救法律、政策和实践的重大变化，我们能够通过采用创新的机器学习方法与因果推理相结合，来测试“拉力因素”说法。我们采用贝叶斯结构时间序列模型来估计这三个干预时期对移民流量的影响，移民流量由穿越尝试（即到达、驱赶和死亡的时间序列总计数）来衡量，并对非正常移民的各种已知驱动因素进行了调整。我们结合了传统和非传统数据的多个来源，构建了一个合成的、预测的反事实流量。结果表明，我们的预测建模方法准确地捕捉了目标时间序列在各个感兴趣的干预前时期的行为。在干预后时期，将观察到的和预测的反事实时间序列进行比较表明，驱赶政策确实影响了移民流量，但搜救时期并没有在观察到的和预测的反事实穿越尝试数量之间产生明显的差异。因此，我们没有发现搜救是非正常移民的驱动因素。总的来说，这种建模方法适用于预测移民流量，目的是在移民研究中回答因果问题。
