Efficient Resource Allocation via Efficiency Bootstraps: An Application to R&D Project Budgeting

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Abstract
Resource allocation decisions are crucial for the success of an organization. This paper proposes an integrated approach to resource allocation problems, in which decision makers have one observation of the multiple input-output criteria of candidates. We offer important improvements over existing approaches based on the widely used data envelopment analysis (DEA), which has two major limitations in its application to resource allocation. First, traditional DEA models compute efficiency scores by optimizing firm-specific shadow prices of inputs and outputs. This could be problematic, because in practice stakeholders would usually require unanimously agreed-upon trade-offs among evaluation criteria. Second, previous allocation approaches based on DEA do not allow for controlling the risk exposure of allocation portfolios. To tackle these problems, we propose an efficiency measure based on equilibrium shadow prices of different criteria, and we use the bootstrap efficiency distributions to gather information regarding efficiency variations and correlations. Through our methodology, decision makers can obtain the risk-minimizing allocation portfolio. We illustrate the proposed approach through an empirical R&D project budgeting problem in which we allocate funding according to the projects efficiency distributions.

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