We analyze statistical discrimination  using a multi-armed bandit model where myopic firms face candidate workers arriving with heterogeneous observable characteristics. The association between the worker's skill and characteristics is unknown ex ante; thus, firms need to learn it. In such an environment, laissez-faire may result in a highly unfair and inefficient outcome -- myopic firms are reluctant to hire minority workers because the lack of data about minority workers prevents accurate estimation of their performance. Consequently, minority groups could be perpetually underestimated -- they are never hired, and therefore, data about them is never accumulated. We proved that this problem becomes more serious when the population ratio is imbalanced, as is the case in many extant discrimination problems. We consider two affirmative-action policies for solving this dilemma: One is a subsidy rule that is based on the popular upper confidence bound algorithm, and another is the Rooney Rule , which requires firms to interview at least one minority worker for each hiring opportunity . Our results indicate  temporary affirmative actions are effective for statistical discrimination caused by data insufficiency .
We analyze statistical discrimination in hiring markets using a multi-armed bandit model . Myopic firms face workers arriving with heterogeneous observable characteristics. The association between the worker's skill and characteristics is unknown ex ante; thus, firms need to learn it. Laissez-faire causes perpetual underestimation: minority workers are rarely hired, and therefore, underestimation towards them tends to persist. Even a slight population-ratio imbalance frequently produces perpetual underestimation. We propose two policy solutions: a novel subsidy rule (the hybrid mechanism) and the Rooney Rule . Our results indicate that temporary affirmative actions effectively mitigate discrimination caused by insufficient data .
A
in hiring markets
R
where myopic firms face candidate
. Myopic firms face
R
In such an environment, laissez-faire may result in a highly unfair and inefficient outcome -- myopic firms are reluctant to hire minority workers because the lack of data about minority workers prevents accurate estimation of their performance. Consequently, minority groups could be perpetually underestimated -- they are never
Laissez-faire causes perpetual underestimation: minority workers are rarely
R
data about them is never accumulated. We proved that this problem becomes more serious when the population ratio is imbalanced, as is the case in many extant discrimination problems. We consider two affirmative-action policies for solving this dilemma: One is a subsidy rule that is based on the popular upper confidence bound algorithm, and another is
underestimation towards them tends to persist. Even a slight population-ratio imbalance frequently produces perpetual underestimation. We propose two policy solutions: a novel subsidy rule (the hybrid mechanism) and
D
, which requires firms to interview at least one minority worker for each hiring opportunity
A
that
R
are effective for statistical
effectively mitigate
R
data insufficiency
insufficient data