Peer rating has be used by open innovation and crowdsourcing platforms to evaluate submissions and select winners because it not only represents a cheaper and more scalable way but also empowers and engages users. However, the literature on scholarly peer review suggests that peer rating may suffer from some biases. One of them is caused by gender. Therefore, this paper aims to examine gender effects on peer rating in open innovation and crowdsourcing. More specifically, we examine how judge gender and gender similarity between judge and designer affect peer rating score. This question has never been examined in the OI&C literature. Using a quasi-experimental design, we collect 1,585 evaluations and find that, overall, judge gender has no significant effect on peer rating score, but gender similarity has a negative effect. Further examinations reveal that rating mode (single-blind or double-blind) may moderate such gender effects: male judges are predicted to give a higher rating score than females when the designer’s information is disclosed while in double-blind peer rating gender similarity reduces the peer rating score. This study has practical implications to the use and design of a peer rating system in OI&C platforms.