Probing the limits of mobile phone metadata for poverty prediction and impact evaluation
在海地紧急现金转移项目中，结合调查和手机通话记录，发现基于通话记录的支出预测远不如财富预测准确，且无法有效评估项目对家庭支出的影响。
A series of recent papers demonstrate that mobile phone metadata can, together with machine learning, estimate the wealth of individual subscribers and accurately target cash transfer programs. In the context of an emergency cash transfer program in Haiti, we combine surveys and mobile phone call detail records (CDR) to test whether such methods can be used to estimate the program’s impact on household expenditures . We find that CDR-based predictions of total and food expenditures are much less accurate than predictions of wealth—particularly when estimated on a relatively homogeneous sample of rural communities eligible for the program. While impact estimates based on conventional survey data are positive and statistically significant, estimates based on CDR predictions are not statistically significant. In a postmortem discussion, we assess reasons for this failure and discuss the implications for using big data in poverty measurement and impact evaluation.