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Imputation Match Bias in Immigrant Wage Convergence.

Joni Hersch¹, Jennifer Bennett Shinall²

¹Vanderbilt Law School, 131 21st Avenue South, Nashville, TN, 37203, USA. joni.hersch@vanderbilt.edu.

|June 27, 2018

Summary

Recent immigrants may appear to earn less due to U.S. Census data imputation methods. This bias may underestimate their true wage convergence with native-born workers.

Keywords:

Immigrant assimilation Imputation match bias

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Area of Science:

Economics
Sociology
Demography

Background:

Immigrants historically show rapid earnings growth, converging to native-born levels.
Recent U.S. Census data suggests a potential slowdown in immigrant earnings assimilation.

Purpose of the Study:

To investigate potential biases in recent U.S. Census data affecting immigrant earnings assimilation estimates.
To re-evaluate the rate of immigrant wage convergence considering data imputation methods.

Main Methods:

Analysis of U.S. Census data, focusing on earnings imputation procedures.
Examination of how imputation methods, excluding immigration status, impact wage convergence calculations.
Assessment of the influence of rising immigrant workforce share and imputation rates over time.

Main Results:

The U.S. Census Bureau's method for imputing missing earnings data may underestimate immigrant wage convergence.
This imputation bias is more significant in recent periods due to increased imputation rates and immigrant workforce share.
Previous literature may have understated the pace of immigrant earnings assimilation.

Conclusions:

The observed slowdown in immigrant earnings assimilation may be an artifact of data imputation methodology.
Accurate assessment of immigrant economic integration requires accounting for imputation biases.
Further research is needed to refine methods for measuring immigrant earnings growth and convergence.