The sharing economy and gig workers have become a major feature in modern economies. The empirical literature demonstrates that gig workers exhibit reference-dependent, loss-averse preferences. In contrast, prior theoretical sharing economy research assumes that gig workers have (classical) rational preferences. To ll the gap, we initiate a theoretical study of gig workers’ reference-dependent, loss-averse preferences in sharing economy platforms. We consider a setting where a sharing economy platform simultaneously decides its per-service price and wage to attract both customers and reference-dependent, loss-averse gig workers. Our results are as follows. First, the platform should adopt a strictly increasing per-service wage, but an N- shaped per-service price (w.r.t. reference point). Labor supply (matching realized demand rate), labor welfare and consumer surplus all exhibit an inverted N-shaped pattern (w.r.t. reference point). Second, depending on the degree of loss aversion and the weight of gain-loss utility, either the status quo (at which gig workers feel the highest sense of gain) or a higher reference point (at which gig workers feel neither gain nor loss) can bring the highest welfare for gig workers, as well as customers. Third, for su ciently low reference points, reference-dependent, loss-averse preferences can enhance the welfare of each party compared to rational preferences, creating a win-win-win situation. However, for intermediate reference points, such preferences generate higher labor welfare and consumer surplus but lower pro t, leading to a lose-win-win situation. Moreover, for extremely high reference points, such preferences reduce the welfare of each party compared to rational preferences, leading to a lose-lose-lose situation. Additionally, labor welfare (consumer surplus) can be bimodal in labor pool size (potential demand rate), contrasting the unimodal property under the rational preference assumption. This paper sheds light on the role of gig workers’ reference-dependent, loss-averse preferences in sharing economy platforms, and their implications on the platform’s price and wage decisions, labor supply, as well as the welfare of gig workers, customers and the platform.