  Street-level bureaucrats interact directly with people on behalf of
government agencies to perform a wide range of functions, including, for
example, administering social services and policing. A key feature of
street-level bureaucracy is that the civil servants, while tasked with
implementing agency policy, are also granted significant discretion in how they
choose to apply that policy in individual cases. Using that discretion could be
beneficial, as it allows for exceptions to policies based on human interactions
and evaluations, but it could also allow biases and inequities to seep into
important domains of societal resource allocation. In this paper, we use
machine learning techniques to understand street-level bureaucrats' behavior.
We leverage a rich dataset that combines demographic and other information on
households with information on which homelessness interventions they were
assigned during a period when assignments were not formulaic. We find that
caseworker decisions in this time are highly predictable overall, and some, but
not all of this predictivity can be captured by simple decision rules. We
theorize that the decisions not captured by the simple decision rules can be
considered applications of caseworker discretion. These discretionary decisions
are far from random in both the characteristics of such households and in terms
of the outcomes of the decisions. Caseworkers typically only apply discretion
to households that would be considered less vulnerable. When they do apply
discretion to assign households to more intensive interventions, the marginal
benefits to those households are significantly higher than would be expected if
the households were chosen at random; there is no similar reduction in marginal
benefit to households that are discretionarily allocated less intensive
interventions, suggesting that caseworkers are improving outcomes using their
knowledge.
