Abstract

As of 2019, the Migration Policy Institute estimated there were approximately 10.9 million Mexican immigrants living in the United States with 7.5 million of them participating in the U.S. labor force. With this many documented and undocumented Mexican immigrants contributing to U.S. labor, there is no doubt they play an important role and are key contributors in this country. This paper uses public data from the Integrated Public Use Microdata Series, specifically using information from the Current Population Survey (IPUMS CPS) from 1994 through 2019 to study behavior patterns of native workers, documented Mexican workers, and undocumented Mexican workers in the United States. Differentiating within the dataset who was a citizen, who was documented, and who was undocumented was a difficult task. An algorithm created by George Borjas was applied to the IPUMS dataset to achieve this. The results showed the highest employment rates and highest percentage in the labor force belonged to undocumented male Mexican immigrants followed by documented male Mexicans and then native males. The trend was the opposite for females. Results also showed those making the lowest wages for men as undocumented Mexican immigrants followed by documented Mexican immigrants and natives. This trend was the same for females with each group earning less than their male counterparts.

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