to assess all households, census-like, and to apply an extensive questionnaire that asks all potentially relevant questions. In practice, administrative budgets of social protection programmes are limited, so that neither all households will be assessed nor all questions asked. When eligible households are missed from the assessment process, exclusion errors will arise no matter how accurate the PMT model. For Indonesia, World Bank [2012] identifies insufficiently broad assessment as a key area for improving targeting outcomes, and its simulation shows that a full survey would lower exclusion error significantly. Since budget constraints normally preclude universal assessment, a practical option may be to streamline the data collection process by asking fewer questions. If this could be done without significantly compromising model accuracy, more households could be assessed for the same budget, which would improve targeting outcomes at no additional cost.

The aim of this paper is to develop such a method, achieving a similar level of targeting accuracy as a standard PMT while asking fewer questions. We propose an alternative to the PMT algorithm that applies a measure of predictive uncertainty to household consumption estimation. We use prediction intervals of a quantile regression-style estimator to identify those households for which consumption is most likely above or below the socioeconomic eligibility threshold. By adjusting the intervals as data is collected, enumeration can be stopped early (in the sense that not all questions need to be asked) for a substantial share of households. In addition, questionnaire length is limited to the most predictive subset of questions. Our approach combines these mechanisms to expose a trade-off between predictive accuracy and questionnaire length that policymakers can set according to their preferences or constraints. In contrast to more computationally demanding methods proposed for an adaptive poverty classification, its limited computational requirements make deployment feasible on the modest mobile devices commonly used for digital data collection in low- and middle income countries. The financial resources freed up by this approach could thus be directed to more regular or more extensive targeting surveys.

In our empirical application for Indonesia, we find that the reduction in questionnaire length compared with the PMT is rather substantial. For a 40% coverage programme, the number of questions can be reduced by 61% (from an average of 22.8 to 8.9) when maintaining the exclusion error rate at the PMT baseline of 26.44%. Tentative estimates for an urban setting, namely Jakarta, in which we simulate a health insurance programme with 50% population coverage, suggest an increase in the proportion of eligible recipients from 65.6% under PMT-based assignment to 78.3% under the proposed method, assuming enumerators can survey additional households by deploying shorter, adaptive questionnaires.

The paper is structured as follows. Section 2 reviews the relevant literature, outlines the PMT process, and provides context for the data used in subsequent simulations. Section 3 describes the components and function3