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Using repeated cross-sectional data to examine the role of immigrant birth-country networks on unemployment duration: an application of Guell and Hu (2006) approach

机译：使用重复的横截面数据来检查移民出生国网络对失业持续时间的作用：Guell和Hu（2006）的应用

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摘要

Guell and Hu (J Econom 133:307-341, 2006) propose a robust econometric estimator using repeated cross-sectional data in the context of duration/survival analysis where otherwise limited or no results are possible because reliable panel data at an individual level are missing. We present a detailed exposition of the Guell and Hu (GH) strategy and show a Monte Carlo simulation for unemployment duration where the GH method produces better estimates compared to panel data with individual matching errors. We further apply the GH model to examine the immigrant unemployment duration in the USA using the Current Population Survey data from 2001 to 2013 and focus on the role of the birth-country networks on the unemployment duration around the Great Recession. We find that birth-country networks measured at the state level significantly lower unemployment duration for all immigrants, and this effect is stronger during the pre- and post-recession periods than during the recession. We also find that networks are more effective in lowering duration for immigrants unemployed for 1-2 months than for immigrants who are unemployed for longer periods, and this effect is the strongest during the post-recession period. The findings are robust to different specifications and measures of networks including those measured at the local MSA level.
机译：Guell和Hu（J Tergure 133：307-341,2006）在持续时间/生存分析的上下文中，在持续时间/生存分析的背景下，在持续时间/生存分析的上下文中提出了一种稳健的横截面数据，因为可以在个人级别处于可靠的面板数据丢失的。我们展示了Guell和Hu（GH）战略的详细阐述，并为失业持续时间显示了Monte Carlo模拟，其中GH方法与具有单个匹配错误的面板数据相比产生更好的估计。我们进一步应用GH模型，以审查美国的移民失业持续时间，使用2001年至2013年的当前人口调查数据，并专注于出生国网络对巨大经济衰退周到失业持续时间的作用。我们发现，在州级别测量的出生国网络对所有移民的失业率明显降低，并且在经济衰退期间和衰退期间，这种效果更强。我们还发现，网络在减少失业期限为1-2个月的持续时间比失业的移民更有效，这一效果是衰退后时期最强的。该研究结果对不同规格和网络的措施具有稳健，包括在本地MSA级别测量的网络。

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