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The labor market effects of immigration on natives: Evidence from Hong Kong

Yuk-Shing Cheng and Hongliang Zhang

China Economic Review, 2018, vol. 51, issue C, 257-270

Abstract: The local labor market in Hong Kong is characterized with two distinct features: the limited geographical mobility of native workers and their ethnic homogeneity to immigrants. Both of these features may exert a greater labor market pressure on native workers to immigrant inflows in Hong Kong compared to other local markets. Moreover, over the past three decades, new immigrants in Hong Kong are disproportionately less educated youths from Mainland China who are admitted through the one-way permit scheme for family reunion. In this paper, we introduce an immigrant exposure measure capturing the extent of exposure of natives in a given skill group to immigrants in terms of occupational competition, and identify the effects of immigration on natives' employment and earnings by relating the changes in natives' employment and earnings across skill groups to their changes in the immigrant exposure measure. To address the potentially endogenous responses of workers to occupational demand shocks, we further construct the projected immigrant exposure measure applying the lagged skill-group specific occupational employment structure to the contemporary skill distribution and employ it as a Bartik-style instrument. We find that competition from immigrants reduces the employment prospect of native females but not that of native males. However, for native males competition from immigrants yields significant adverse earnings effects, whereas for native females such earnings effects – though still negative – are smaller in magnitude and less often statistically significant.

Keywords: Immigration; Employment; Earnings; Occupational competition; Immigrant exposure (search for similar items in EconPapers)
JEL-codes: F22 J24 J31 J61 (search for similar items in EconPapers)
Date: 2018
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China Economic Review is currently edited by B.M. Fleisher, K. X. D. Huang, M.E. Lovely, Y. Wen, X. Zhang and X. Zhu

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