Does Fairtrade Certification Meet Producers' Expectations Related to Participating in Mainstream Markets? An Analysis of Advertised Benefits and Perceived Impact

Abstract

In this paper, we identify three literature‐based hypotheses about how and whether Fairtrade labeling delivers its advertised benefits: a market mechanism that provides producers in developing countries advantaged access to consumer markets; a sustainable agriculture mechanism that improves the sustainability of Fairtrade products and a social justice mechanism that protects the rights and livelihoods of farmers and workers. We surveyed a broad cross‐section of Fairtrade‐certified producer organizations and compared their expectations with their satisfactions using an importance–performance analysis (IPA), principal component analysis (PCA) and ordered logit regression analyses. According to our results, Fairtrade producers report both high importance and high performance in terms of metrics such as empowering women, democratic decision‐making and reduction of child labor, suggesting that farmers are most satisfied with Fairtrade as a social justice mechanism. Fairtrade producers report high importance but lower performance of Fairtrade as a market mechanism and least satisfaction as a sustainable agriculture mechanism. We explore the drivers and implications of these findings for Fairtrade and provide recommendations to increase producer satisfaction.

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Cosmopolitanism and ethical consumption: An extended theory of planned behavior and modeling for fair trade coffee consumers in South Korea

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Evaluation and countermeasures of sustainable development for urban energy‐economy‐environment system: A case study of Jinan in China

“…Existing studies on the evaluation of 3E systems mainly focused on the measurement of coordination level. The evaluation methods adopted in these studies mainly include analytic hierarchy process (AHP) (Veisi, Liaghati, & Alipour, 2016), principal component analysis (Mook & Overdevest, 2018), entropy weight method (Toumi, Le Gallo, & Ben Rejeb, 2017), data envelopment analysis (DEA) (Cucchiella, D'Adamo, Gastaldi, & Miliacca, 2018), fuzzy comprehensive evaluation method (Guo, Qu, Wu, & Gui, 2018), system dynamics (Xing, Xue, & Hu, 2019), etc. For example, Xing et al (2019) employed system dynamics to develop a dynamic model of the environment–resource–economic system, and used the urban data of Wuhan, China to establish a coupling coordination model.…”