  In this paper, we study platforms where resources and jobs are spatially
distributed, and resources have the flexibility to strategically move to
different locations for better payoffs. The price of the service at each
location depends on the number of resources present and the market size, which
is modeled as a random state. Our focus is on how the platform can utilize
information about the underlying state to influence resource repositioning
decisions and ultimately increase commission revenues. We establish that in
many practically relevant settings a simple monotone partitional information
disclosure policy is optimal. This policy reveals state realizations below a
threshold and above a second (higher) threshold, and pools all states in
between and maps them to a unique signal realization. We also provide
algorithmic approaches for obtaining (near-)optimal information structures that
are monotone partitional in general settings.
