Polarization and the Middle Class in China: a Non-Parametric Evaluation Using CHNS and CHIP Data

Haider Khan (), Francesco Schettino and Alberto Gabriele

MPRA Paper from University Library of Munich, Germany

Abstract: The record economic growth of PRC since the 1990s has been accompanied by an increase in both inequalities and polarization. We employ Relative Distribution tools (Handcock and Morris, 1998) on different datasets in order to provide a different and more detailed point of view of the last decades’s Chinese income distribution. The main result (overall effect), consisting in a hollowing out of the lowest deciles with a corresponding movement to the highest ones, confirms the bulk of the previous studies’ outcomes. Anyway, analyzing the “pure distribution” effect (i.e., depurated by the growth one) a typical polarization profile emerged mainly in the last decade. More in detail, the typical hollowing out of the central deciles of the PRC distribution has been indeed accompanied by “fattening” of both tails of income distribution. That, substantially, can be explained with the fact that the (negative) “pure distributional” effect has been, to some extent, mitigated by the impressive GDP growth. In other words, it hided distributive changes (shape effect) that materially occurred in the 1990s and in the 21st century so far. As growth slows, unless countervailing policies are undertaken, polarization will reveal itself more sharply. Given the existing inequalities, increasing polarization would seem to imply that distributional and related conflicts are likely in PRC.

Keywords: Socialism; Polarization; Middle Class; Inequalities; Distribution (search for similar items in EconPapers)
JEL-codes: C14 F63 F68 N3 N35 (search for similar items in EconPapers)
Date: 2017-10-16
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Polarization and the middle class in China: A non-parametric evaluation using CHNS and CHIP data (2021)
Working Paper: Polarization and the Middle Class in China: a Non-Parametric Evaluation Using CHNS and CHIP Data (2017)
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