Effects of Distributional Assumptions in Stochastic Frontier Models on Efficiency-Based Unit Targeting

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Key Points

This research aims to systematically assess how different distributional assumptions in stochastic frontier models affect efficiency-based intervention selection.
Conducted extensive simulations varying sample size, inefficiency signal strength, and true inefficiency distribution.
Introduced relative predictive improvement and selection accuracy as key performance metrics.
Analyzed the stability and limitations of selection lists under different distribution assumptions.
Found that changes in distribution assumptions did not significantly alter the selection lists among similar assumptions.
Identified that total estimated improvement effects were sensitive to distribution settings, especially under weak inefficiency signals.
Demonstrated the need to evaluate and report both selection list stability and uncertainty in improvement effect estimation.

Evaluates efficiency targeting based on stochastic frontier models, indicating that distribution assumptions impact decision validity.

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Kim et al. (2026) studied this question.

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