Rethinking evolutionary economic geography through a gender lens
将性别视角引入演化经济地理学，通过三阶段熵分解扩展相关与无关多样性指标，发现女性就业份额增加时，相关与无关多样性反应不同，促进女性进入男性主导行业比集中在女性主导行业更能提升劳动力市场活力。
Abstract Evolutionary Economic Geography (EEG) explores how industries cluster, networks evolve, and regions grow, yet often overlooks gender. This study introduces a gender-sensitive framework, extending Related and Unrelated Variety (RV and UV) measures through a three-stage entropy decomposition. Findings reveal that RV and UV respond differently as women’s employment share increases, showing increasing returns when women are underrepresented. Thus, promoting women’s participation in male-dominated industries can more effectively enhance labor market dynamics than concentrating them in already female-dominated sectors. This underscores the importance of not only encouraging women’s labor market participation but also considering the sectors in which they are employed.