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Imputation Match Bias in Immigrant Wage Convergence
Author(s) -
Joni Hersch,
Jennifer Bennett Shinall
Publication year - 2018
Publication title -
demography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.099
H-Index - 129
eISSN - 1533-7790
pISSN - 0070-3370
DOI - 10.1007/s13524-018-0686-3
Subject(s) - imputation (statistics) , immigration , earnings , demographic economics , economics , census , wage , convergence (economics) , workforce , labour economics , econometrics , missing data , geography , population , demography , statistics , economic growth , sociology , mathematics , accounting , archaeology
Although immigrants to the United States earn less at entry than their native-born counterparts, an extensive literature has found that immigrants have faster earnings growth that results in rapid convergence to native-born earnings. However, recent evidence based on U.S. Census data indicates a slowdown in the rate of earnings assimilation. We find that the pace of immigrant wage convergence based on recent data may be understated in the literature as a result of the method used by the census to impute missing information on earnings, which does not use immigration status as a match characteristic. Because both the share of immigrants in the workforce and earnings imputation rates have risen over time, imputation match bias for recent immigrants is more consequential than in earlier periods and may lead to an underestimate of the rate of immigrant wage convergence.

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