Reliable measurement of income and consumption is essential for monitoring poverty and inequality in low- and middle-income countries, yet full household surveys are costly and difficult to implement regularly. This paper examines whether reduced survey instruments can preserve key distributional information. We apply Random Forest Recursive Feature Elimination (RF-RFE) to the 2018/19 Nigeria General Household Survey-Panel to identify the income sources, consumption categories and household characteristics that best classify individuals within the welfare distribution. The analysis focuses on three outcomes: poverty status, location in the quintile distribution and position relative to the Gini-based inequality line. The survey's post-planting and post-harvest periods allow us to assess performance under different seasonal contexts. Results show that RF-RFE achieves strong classification accuracy with few predictors. For consumption, poverty status and inequality-line position are accurately predicted using a small set of expenditure categories, while quintile classification reaches about 80 percent accuracy for seasonal consumption and 60--65 percent for annual consumption predicted from a single seasonal visit. For income, poverty status reaches around 90 percent accuracy with five predictors, and inequality-line position is largely captured by labour earnings. The findings suggest that machine-learning methods can help improve survey design and reduce data requirements while retaining much of the distributional information needed to measure and monitor poverty and inequality.

翻译：可靠的收入与消费测量对于监测中低收入国家的贫困与不平等至关重要，然而全面住户调查成本高昂且难以定期实施。本文探讨了缩减调查工具能否保留关键的分位数信息。我们应用随机森林递归特征消除法（RF-RFE）对2018/19年尼日利亚通用住户面板调查进行分析，旨在识别最能对福利分布中个体进行分类的收入来源、消费类别及住户特征。分析聚焦于三个目标：贫困状态、五分位数分布位置及基于基尼系数的不平等线相对位置。调查涵盖播种后与收获后两期数据，使我们能够评估不同季节背景下的模型表现。结果表明，RF-RFE在仅使用少量预测变量时即可实现较高的分类准确率。就消费而言，利用少量支出类别可准确预测贫困状态与不平等线位置，而五分位数分类准确率在单次季节性访问预测中，季节消费达约80%，年消费达60–65%。就收入而言，使用五个预测变量时贫困状态预测准确率约达90%，且不平等线位置主要由劳动收入捕捉。研究建议表明，机器学习方法有助于优化调查设计、降低数据需求，同时保留测量与监测贫困不平等所需的大部分分位数信息。