Randomization bias occurs when the random assignment used to estimate program effects influences the types of individuals that participate in a program. This on a form of randomization bias called “applicant inclusion bias,” which can occur in evaluations of discretionary programs that normally choose which of the eligible applicants to serve. If this nonrandom selection process is replaced by a process that randomly assigns eligible applicants to receive the intervention or not, the types of individuals served by the program—and thus its average impact on program participants—could be affected.

To estimate the impact of discretionary programs for the individuals that they normally serve, we propose an experimental design called Preferred Applicant Random Assignment (PARA). Prior to random assignment, program staff would identify their “preferred applicants,” those that they would have chosen to serve. All eligible applicants are randomly assigned, but the probability of assignment to the program is set higher for preferred applicants than for the remaining applicants.

This paper demonstrates the feasibility of the method, the cost in terms of increased sample size requirements, and the benefit in terms of improved generalizability to the population normally served by the program.

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