Background: Data from social media have emerged as an auxiliary source for real-time information on migrant populations. Facebook users’ tagged ‘expat’ data – an ‘expat’ being someone who lived in country x but now lives in country y – has been used to estimate immigrants and its quality assessment has relied on household surveys and UNDESA migration estimates. Objective: Using the census as the gold standard and six definitions of migration, we examine differences between the 2020 Mexican Census and Facebook data by national origin, age, and sex. We also examine internet penetration among migrants. Methods: We estimate migration stocks by sex, age, and country of origin for nine Latin American countries, using six definitions of migration available within the census. To evaluate biases of Facebook data, we estimate a series of linear regression models on migrant stocks where our key independent variable is ‘expat,’ and we control for age, sex, and origin, as well as internet penetration rate. Results: Findings suggest that Facebook data are only associated with the definition that identifies recent immigrants according to country of residence five years prior to the census. Facebook’s ‘expat’ variable is indeed capturing recent immigrants that resided in a given country, and not migrants by other definitions using country of birth and country of prior residence, as in long-term migrants or returnees. Contribution: The contributions of this paper are fourfold. In our analysis we (1) evaluate Facebook data quality using the census as the gold standard, (2) compare Facebook data to different migrant definitions, (3) include data on migrant populations’ internet access, and (4) include differences by sex and age.

中文翻译：

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哪种移民定义更适合 Facebook 的“外籍人士”？使用墨西哥人口普查数据做出的回应（作者：Tania Varona、Claudia Masferrer、Victoria Prieto Rosas、Martín Pedemonte）


背景：社交媒体数据已成为流动人口实时信息的辅助来源。 Facebook 用户标记的“外籍人士”数据（“外籍人士”是指曾居住在 x 国但现在居住在 y 国的人）已被用来估计移民，其质量评估依赖于家庭调查和联合国经济和社会事务部的移民估计。目标：使用人口普查作为黄金标准和移民的六种定义，我们按国籍、年龄和性别研究 2020 年墨西哥人口普查和 Facebook 数据之间的差异。我们还研究了移民中的互联网普及率。方法：我们使用人口普查中提供的六种移民定义，按性别、年龄和原籍国估算了九个拉丁美洲国家的移民存量。为了评估 Facebook 数据的偏差，我们估计了一系列关于移民群体的线性回归模型，其中我们的关键自变量是“外籍人士”，我们控制了年龄、性别、出身以及互联网普及率。结果：调查结果表明，Facebook 数据仅与根据人口普查五年前居住国识别新移民的定义相关。 Facebook 的“外籍人士”变量确实捕获了居住在特定国家的新移民，而不是使用出生国和先前居住国的其他定义的移民，如长期移民或回国者。贡献：本文的贡献有四个方面。在我们的分析中，我们 (1) 使用人口普查作为黄金标准评估 Facebook 数据质量，(2) 将 Facebook 数据与不同的移民定义进行比较，(3) 包括移民人口互联网访问的数据，以及 (4) 包括性别差异和年龄。