In many markets, efficiency depends on the quality of information that participants have. However, participants may face frictions in accessing information, which could result in significant welfare losses. My dissertation studies the causes and consequences of information frictions, focusing on the security analyst market and the public cloud market. The first two chapters investigate how information frictions are generated in the security analyst market. Security analysts observe signals and compete to make forecasts on securities’ earnings, which serve as public information to investors. Here I study how analysts’ incentives affect the quality of information they provide. In Chapter 1, I consider security analysts’ incentives as a whole and estimate them using revealed preference. Security analysts are rewarded for being more accurate than their peers. This reward for relative accuracy leads analysts to distort their forecasts to differentiate themselves, but also disciplines them from being overoptimistic. I structurally estimate a contest model with incomplete information to capture both effects, disentangling the payoffs for relative accuracy, optimism and absolute accuracy. Using the model, I conduct counterfactuals to evaluate policies that reduce analysts’ payoff for relative accuracy. I simulate the effect of these policies on the quality of information in terms of forecast errors and variances. The reward for relative accuracy reduces errors by 33 - 58%, but increases variances by 4%. It is optimal to have moderate competition between the covering analysts of each security. In Chapter 2, I ask where these incentives come from. Are analysts motivated by dynamic incentives of reputation, or by short-term compensation such as bonuses? I show with reduced form evidence that low-reputation analysts may face more incentive to outperform their rivals than high reputation analysts. Building on this, I develop and estimate a dynamic model where analysts compete to build reputation and earn compensation. I find that analysts face a strong reputation-building incentive because high reputation is associated with a much higher fixed wage. Meanwhile, their forecasts have an insignificant impact on their immediate compensation. Chapter 3 studies the consequences of information frictions in the public cloud market. Firms need information about available technologies to make good adoption decisions. Inattentiveness to such information may create stickiness to outdated technology. In a joint project with Sida Peng and Peichun Wang, we study the welfare benefits of firms’ public cloud adoption and the consequence of consumer inertia in this market. We develop a novel demand model that allows for both multiple product choices and continuous quantities on each product. We estimate the model using a proprietary dataset on individual firms’ cloud usage history from a major public cloud provider. The estimated average return on investment in cloud is 2.2 times the cost of investment, which is driven by smaller firms disproportionately benefiting from access to computing resources on the cloud. On the other hand, inertia on the cloud leads to sub-optimal product choices for all firms and reduces welfare from cloud usage by almost 62%. We show that introductory discounts incentivizing firms to try new products can improve both consumer welfare and provider revenue.