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Does the quality grading system matter? Chinese consumer response to pork quality grading labels and the impact of information

Qinyuan Shen, Zhifeng Gao and Zhanguo Zhu

China Agricultural Economic Review, 2024, vol. 16, issue 4, 828-849

Abstract: Purpose - A meat quality grading system is essential to meet consumers' increasingly diversified demand for food quality in the global market. This study aims to determine the effectiveness of the upcoming Chinese quality grading labels and examine the information effect of labeling standards on pork consumption choices. Design/methodology/approach - Using an online survey with choice experiments, this study estimates consumer valuation for the fat thickness of different pork primal cuts by simulating three scenarios. Generalized mixed logit models in WTP space are used to analyze the choice experiment data. Findings - Chinese consumers prefer lean pork to fatty pork and this preference does not vary significantly between primal cuts. Consumer valuation for ungraded high-quality (lean) pork increases after the implementation of the quality grading. Meanwhile, they are willing to pay high premiums for labeled pork (including level 1, 2, 3), and there are higher premiums for pork with higher levels. Besides, incomplete information on labeling standards could achieve more premiums for pork than relatively complete information. Originality/value - This study pays attention to essential but few-noticed pork quality grading. The findings provide references for pork industry practices and policy-making of the meat quality grading system in China and globally by examining incomplete and relatively complete information effects on consumer choices.

Keywords: Quality grading; Fat thickness; Primal cuts; Incomplete information; Consumer preference (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eme:caerpp:caer-12-2023-0364

DOI: 10.1108/CAER-12-2023-0364

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China Agricultural Economic Review is currently edited by Dr Fu Qin, Dr Jikun Huang, Dr Kevin Z Chen, Dr Weiming Tian, Prof Daniel Sumner, Prof Xian Xin and Prof Holly Wang

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