This dissertation examines information transmission using online experiments with jobseekers and recruiters in India, as well as survey participants in the U.S. The first chapter is based on joint work with Ronak Shah, the Head of Technology at Apna, and studies information frictions in one aspect of hiring: how amenities on job descriptions lead jobseekers to apply or not. To start, I collect novel data on seven amenities and compare this data to job posts to document a fact: many firms provide these amenities, but fewer than half are displayed on posts. I then use a survey experiment with jobseekers to measure the effect of these amenities on application decisions. Finally, I explore one reason why valued amenities are missing from posts: recruiter-side information frictions. Combining a randomized and natural experiment, I find that closing this information gap increases the share of job posts describing a valued amenity by 31 percentage points. In the secondchapter, also based on joint work with Ronak Shah, I study the role of language barriers as a source of information frictions in labor markets. Using a randomized experiment with Hindi-speaking jobseekers, I find that offering a Hindi option for job posts increases the likelihood that jobseekers apply by 26% and significantly increases match quality as well. Effects are concentrated among jobseekers with lower English proficiency. In the third chapter, I examine the effect of the political and gender identity of non-expert messengers on the acquisition of non-political information using an experiment with participants in the U.S. Participants are 8 percentage points more likely to utilize information provided by a messenger with the same political affiliation. Although I do not find significant results by gender, confidence intervals preclude the largest estimates of in-group bias in the literature. These results suggest that the effects of in-group bias by political affiliation on information acquisition are widespread.