This project tackles three unresolved challenging problems in gender-stereotype reduction using the behavioral economics approach. First, how can we measure the impact of subconscious stereotypes on actual decisions and the resulting welfare costs? Whereas existing measurements are effective in detecting gender stereotypes, they are less effective in identifying the impact of stereotypes on actual decisions. Existing evidence of gender inequality is often confounded by innate abilities, social preferences and ambiguity attitudes. Part 1 of this project develops a new confound-free stereotype measure which directly reveals the impact of stereotypes on actual decisions and allows for the welfare costs estimation. The new measure complements existing measures, and will be effective in raising awareness of stereotypes and their economic consequences. Second, how do stereotypes evolve with new information? Despite the common view that given time and new information gender inequality will disappear, little is known about stereotype dynamics. Part 2 investigates stereotype dynamics. It identifies decision biases that prevent people from optimally processing and acting upon objective information, and thus trap people in stereotypes. For instance, the bias “ambiguity aversion” can deter women from entering male-dominant fields where career prospects are more ambiguous for women, thus reinforcing the vicious cycle of male-dominance. Eliminating decision biases will increase gender equality. Third, how can we improve policy effectiveness? Despite numerous initiatives and policy implementations, the convergence to equality has been stagnating. Part 3 provides new inequality-reducing tools. First, it estimates hidden costs for existing inequality-reducing policies. For instance, counter-intuitively, gender quota may in fact increase stereotypes as people attribute females’ success to the quota policy rather than to females’ competence. Second, it designs and evaluates two nudges that open new policy intervention channels.