  We identify and explore differential access to population-level signaling
(also known as information design) as a source of unequal access to
opportunity. A population-level signaler has potentially noisy observations of
a binary type for each member of a population and, based on this, produces a
signal about each member. A decision-maker infers types from signals and
accepts those individuals whose type is high in expectation. We assume the
signaler of the disadvantaged population reveals her observations to the
decision-maker, whereas the signaler of the advantaged population forms signals
strategically. We study the expected utility of the populations as measured by
the fraction of accepted members, as well as the false positive rates (FPR) and
false negative rates (FNR).
  We first show the intuitive results that for a fixed environment, the
advantaged population has higher expected utility, higher FPR, and lower FNR,
than the disadvantaged one (despite having identical population quality), and
that more accurate observations improve the expected utility of the advantaged
population while harming that of the disadvantaged one. We next explore the
introduction of a publicly-observable signal, such as a test score, as a
potential intervention. Our main finding is that this natural intervention,
intended to reduce the inequality between the populations' utilities, may
actually exacerbate it in settings where observations and test scores are
noisy.
