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Empirical characteristics of legal and illegal immigrants in the USA

机译：美国合法和非法移民的经验特征

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We combine the New Immigrant Survey (NIS), which contains information on US legal immigrants, with the American Community Survey (ACS), which contains information on legal and illegal immigrants to the USA. Using an econometric methodology proposed by Lancaster and Imbens (J Econ 71:145-160, 1996) we compute the probability for each observation in the ACS data to refer to an illegal immigrant, conditional on observed characteristics. These results are novel, since no other work has quantified the characteristics of illegal immigrants from a random sample representative of the population. Using these conditional probability weights on the ACS data, we are able to uncover some interesting facts on illegal immigrants. We find that, while illegal immigrants suffer a large wage penalty compared to legal immigrants at all education levels, the penalty decreases with education. We also find that the total fertility rate among illegal immigrant women is significantly higher than that among legal ones, in particular for middle and higher educated women. Looking at the sector of activity, we document that the sectors attracting most illegal immigrants are constructions and agriculture. We also generate empirical distributions for state of residence, country of origin, age, sex, and number of legal and illegal immigrants. Our forecasts for the aggregate distribution of legal and illegal characteristics match imputations by the Department of Homeland Security.
机译：我们将包含有关美国合法移民信息的新移民调查（NIS）与包含有关美国合法和非法移民信息的美国社区调查（ACS）结合在一起。使用Lancaster和Imbens（J Econ 71：145-160，1996）提出的计量经济学方法，我们以观察到的特征为条件，计算ACS数据中每次观察引用非法移民的概率。这些结果是新颖的，因为没有其他工作可以从代表人口的随机样本中量化非法移民的特征。使用ACS数据上的这些条件概率权重，我们可以发现一些有关非法移民的有趣事实。我们发现，尽管在所有教育水平上，非法移民都比合法移民遭受较大的工资罚款，但随着教育程度的增加，罚款减少。我们还发现，非法移民妇女的总生育率大大高于合法移民，特别是中，高学历妇女。考察活动领域，我们记录到吸引大多数非法移民的领域是建筑和农业。我们还会针对居住州，原籍国，年龄，性别以及合法和非法移民的数量生成经验分布。我们对合法和非法特征总分布的预测与国土安全部的估算相符。

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