Which Human Capital Characteristics Best Predict the Earnings of Economic Immigrants?

Feng Hou, Garnett Picot and Aneta Bonikowska

Analytical Studies Branch Research Paper Series from Statistics Canada, Analytical Studies Branch

Abstract:

While an extensive literature examines the association between immigrants' characteristics and their earnings in Canada, there is a lack of knowledge regarding the relative importance of various human capital factors, such as language, work experience and education when predicting the earnings of economic immigrants. The decline in immigrant earnings since the 1980s, which was concentrated among economic immigrants, promoted changes to the points system in the early 1990s and in 2002, in large part, to improve immigrant earnings. Knowledge of the relative role of various characteristics in determining immigrant earnings is important when making such changes. This paper addresses two questions. First, what is the relative importance of observable human capital factors when predicting earnings of economic immigrants (principal applicants), who are selected by the points system? Second, does the relative importance of these factors vary in the short, intermediate, and long terms? This research employs Statistics Canada's Longitudinal Immigration Database (IMDB).

Keywords: Education; training and learning; Ethnic diversity and immigration; Labour; Labour market and income; Wages; salaries and other earnings (search for similar items in EconPapers)
Date: 2015-08-26
New Economics Papers: this item is included in nep-hrm and nep-mig
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Citations: View citations in EconPapers (11)

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