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The relationship between unemployment and immigration with linear and nonlinear causality tests: Evidence from the United States

Author

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  • Alper Aslan
  • Buket Altinöz

Abstract

This paper investigates the relationship between the immigrant population and the unemployment rate in the United States for period from 1980 to 2013. For this purpose, firstly, coefficient of long and short run is estimated by using Autoregressive Distributed Lag (ARDL) method and then, linear and nonlinear causality test are applied. Findings/Originality: According to ARDL test results; there is a positive effect of immigration to the United States on the unemployment rate to in the long run. In other words, while there is no statistically significant relationship between two variables in the short run, an increase in the immigrant population increases the unemployment rate by 0.14 percent in the long run. The bootstrapped Toda-Yamamoto linear causality test results imply that there is no causal relationship between immigration and unemployment. Also, there is no nonlinear relationship between immigration population and unemployment rate in the United States.

Suggested Citation

  • Alper Aslan & Buket Altinöz, 2020. "The relationship between unemployment and immigration with linear and nonlinear causality tests: Evidence from the United States," Economic Journal of Emerging Markets, Universitas Islam Indonesia, vol. 12(1), pages 13-24.
    • Handle: RePEc:uii:journl:v:12:y:2020:i:1:p:13-24:id:12429
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    File URL: https://journal.uii.ac.id/JEP/article/view/12429/10060
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