Judging foreign startups
研究发现，全球加速器的评审员会系统性地低估外国初创企业，导致每20家有潜力的公司中就有1家被错过，但评审员仍能准确判断企业质量。
Abstract Research Summary Can accelerators pick the most promising startup ideas no matter their provenance? Using unique data from a global accelerator where judges are randomly assigned to evaluate startups headquartered across the globe, we show that judges are less likely to recommend startups headquartered outside their home region by 4 percentage points. Back‐of‐the‐envelope calculations suggest this discount leads judges to pass over 1 in 20 promising startups. Despite this systematic discount, we find that—in contrast to many past studies—judges can discern startup quality and are no better at evaluating local firms. These differences emerge because the pool of startups accelerator judges evaluate is both broader and less “local,” suggesting that judging ability depends on the composition of the companies they are tasked with evaluating. Managerial Summary Accelerators often seek the most promising startup ideas. Yet, they can only do so if their judges can discern the quality of startups, both local and foreign to them, without systematic bias. We used unique data from a global accelerator where judges are assigned to evaluate startups headquartered across the globe and find that, while judges can detect the quality of both local and foreign startups, they discount startups foreign to them, hindering their ability to accept the best startup ideas. As venture capitalists increasingly source startups from accelerators, this foreign discounting can result in investors passing over promising ideas. However, simple measures like reducing the threshold for startups evaluated by foreign judges may help reduce judges' foreign discounting and enable picking the best companies.