  Leveraging an established exercise in negotiation education, we build a novel
dataset for studying how the use of language shapes bilateral bargaining. Our
dataset extends existing work in two ways: 1) we recruit participants via
behavioral labs instead of crowdsourcing platforms and allow participants to
negotiate through audio, enabling more naturalistic interactions; 2) we add a
control setting where participants negotiate only through alternating, written
numeric offers. Despite the two contrasting forms of communication, we find
that the average agreed prices of the two treatments are identical. But when
subjects can talk, fewer offers are exchanged, negotiations finish faster, the
likelihood of reaching agreement rises, and the variance of prices at which
subjects agree drops substantially. We further propose a taxonomy of speech
acts in negotiation and enrich the dataset with annotated speech acts. Our work
also reveals linguistic signals that are predictive of negotiation outcomes.
