This summer I worked with Professor Argudo and four other interns to continue research on the wage and wealth gaps between people born in the United States (henceforth called natives), authorized immigrants, and unauthorized immigrants. Previous research on this topic has documented wage disparities between all three nativity groups and wealth disparities between natives and immigrants. Our research is novel in its distinction between types of immigrants when surveying wealth gaps, as well as the inclusion of asset holdings. 

 Our work this summer built on Professor Argudo’s 2021 Ford Scholar project. We continued to use Survey of Income and Program Participation (SIPP) data, which changed considerably during the time period we examined. Since we did not finish matching the variables we needed last summer, the team and I began homogenizing the remaining relevant variables such as race, occupation, and industry from 1996 to 2020. This left us with a nationwide dataset of individuals that could be tracked over multiple months and years.

Sample of homogenization code for industries

 The initial stages of our analysis focused on using the homogenized data to replicate figures from previous papers that compared immigrants and natives to confirm our data was valid. Once confirmed, we distinguished between authorized and unauthorized immigrants using  George J. Borjas’s methodology, and split the data into workers of different education levels. The final days of the project were spent starting a paper to summarize our findings and introduce our research, along with creating graphs on wealth holdings by nativity group. 

Comparison between different datasets of percent of unauthorized immigrants at each education level in the population.

This project has not only sharpened my ability to consider economic scenarios in real life, but it has given me skills in Python and data analysis that are indispensable in research and data science.