Yale School of Management, New Haven, CT, USA.
Yale School of Management, Yale School of Public Health, Yale School of Engineering and Applied Science, Yale University, New Haven, CT, USA.
Risk Anal. 2021 Sep;41(9):1643-1661. doi: 10.1111/risa.13658. Epub 2020 Dec 29.
Accurately estimating the size of the undocumented immigrant population is a critical component of assessing the health and security risks of undocumented immigration to the United States. To provide one such estimate, we use data from the Mexican Migration Project (MMP), a study that includes samples of undocumented Mexican immigrants to the United States after their return to Mexico. Of particular interest are the departure and return dates of a sampled migrant's most recent sojourn in the United States, and the total number of such journeys undertaken by that migrant household, for these data enable the construction of data-driven undocumented immigration models. However, such data are subject to an extreme physical bias, for to be included in such a sample, a migrant must have returned to Mexico by the time of the survey, excluding those undocumented immigrants still in the United States. In our analysis, we account for this bias by jointly modeling trip timing and duration to produce the likelihood of observing the data in such a "snapshot" sample. Our analysis characterizes undocumented migration flows including single-visit migrants, repeat visitors, and "retirement" from circular migration. Starting with 1987, we apply our models to 30 annual random snapshot surveys of returned undocumented Mexican migrants accounting for undocumented Mexican migration from 1980 to 2016. Scaling to population quantities and supplementing our analysis of southern border crossings with estimates of visa overstays, we produce lower bounds on the total number of undocumented immigrants that are much larger than conventional estimates based on U.S.-based census-linked surveys, and broadly consistent with the more recent estimates reported by Fazel-Zarandi, Feinstein, and Kaplan.
准确估计无证移民人口规模是评估无证移民对美国健康和安全风险的关键组成部分。为了提供这样的估计，我们使用来自墨西哥移民项目（MMP）的数据，该研究包括返回墨西哥后的无证墨西哥移民样本。特别感兴趣的是抽样移民最近一次在美国逗留的离开和返回日期，以及该移民家庭进行的此类旅行总数，因为这些数据使构建数据驱动的无证移民模型成为可能。然而，这些数据受到极端身体偏见的影响，因为要被包括在这样的样本中，移民必须在调查时已经返回墨西哥，排除仍留在美国的无证移民。在我们的分析中，我们通过联合建模旅行时间和持续时间来考虑这种偏见，以产生在这种“快照”样本中观察数据的可能性。我们的分析描述了无证移民流动，包括单次访问移民、重复访问者和循环移民的“退休”。从 1987 年开始，我们将我们的模型应用于 30 次年度随机返回无证墨西哥移民的快照调查，这些调查涵盖了 1980 年至 2016 年期间的无证墨西哥移民。我们将无证移民数量规模化，并补充我们对南部边境过境点的分析，包括签证逾期逗留的估计，得出的无证移民总数下限远大于基于美国人口普查相关调查的传统估计，并且与 Fazel-Zarandi、Feinstein 和 Kaplan 报告的最近估计大致一致。
