Immigrant entrepreneurship in the United States has grown steadily in the last four decades. In this paper, I study the occupational choices of legal permanent residents and their associated earnings in paid and self-employment. Using a unique data set with pre- and postmigration individual-level information, I analyze the role of home country work experience of immigrants in their probability of becoming entrepreneurs and their earnings after migration. To control for endogenous sector selection in the estimation of earnings distributions, I follow a novel identification strategy based on extremal quantile regressions that does not require exclusion restrictions or a large support variable. I find that foreign work experience in paid and self-employment is an important predictor of entrepreneurship after migration. In addition, it impacts earnings in the United States but differently across sectors, controlling for human capital, assimilation, time, region of origin, U.S. destination, previous migratory status, and demographic characteristics. Overall, my results highlight the role played by immigrants’ labor market history from their home countries to better understand their outcomes in the United States.

中文翻译：

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母国工作经验和移民在美国自营职业


过去四十年来，美国的移民创业稳步增长。在本文中，我研究了合法永久居民的职业选择及其在带薪和自营职业方面的相关收入。我使用包含移民前后个人信息的独特数据集，分析了移民的祖国工作经验对其成为企业家的可能性以及移民后收入的作用。为了控制收益分配估计中的内生行业选择，我遵循一种基于极值分位数回归的新颖识别策略，该策略不需要排除限制或大的支持变量。我发现国外带薪和自营职业的工作经验是移民后创业的重要预测因素。此外，它对美国的收入产生影响，但在不同部门的影响不同，在控制人力资本、同化、时间、原籍地区、美国目的地、以前的移民身份和人口特征的情况下。总体而言，我的结果强调了来自本国的移民劳动力市场历史所发挥的作用，以更好地了解他们在美国的结果。