A recent explosion in startup activity, often linked to reduced experimentation costs, has made it challenging for venture capital (VC) firms to efficiently obtain information and perform due diligence. This paper explores frictions in the process of venture capital information acquisition using microdata from Product Hunt, an online platform covering a large number of technology startups' product launches. On a daily basis, launched products compete for ranking based on user upvotes - a crowdsourced measure of expected consumer demand. An exogenous downward shift in rankings leads to a 9.5% decline in seed and early-stage fundraising relative to the average probability within 6 months. Top-ranked products are disproportionately more affected by the shifts to rankings than lower-ranked products. I reconcile the findings with a theoretical framework of information acquisition, which predicts that startups with greater difficulty letting their information reach investors are more affected by these online product rankings. I provide empirical evidence that align with the theory, suggesting that the effects of product rankings are mainly driven by startup teams located away from top venture capital destinations, and more pronounced among teams with at least one female maker.
We show how data from online social networking services can help researchers better understand the effects of social interactions on economic decision making. We combine anonymized data from Facebook, the largest online social network, with housing transaction data, and explore both the structure and the effects of social networks. Individuals whose geographically distant friends experienced larger recent house price increases are more likely to transition from renting to owning. They also buy larger houses and pay more for a given house. Survey data show that these relationships are driven by the effects of social interactions on individuals' housing market expectations.