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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.
Demography
|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.
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.
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