@article{McBride:309060,
      recid = {309060},
      author = {McBride, Linden and Barrett, Christopher B. and Browne,  Christopher and Hu, Leiqiu and Liu, Yanyan and Matteson,  David S. and Sun, Ying and Wen, Jiaming},
      title = {Predicting poverty and malnutrition for targeting,  mapping, monitoring, and early warning},
      address = {2021-01},
      number = {2388-2021-383},
      pages = {28},
      year = {2021},
      abstract = {More frequent and severe shocks combined with more  plentiful data and increasingly powerful predictive  algorithms heighten the promise of data science in support  of humanitarian and development programming. We advocate  for embrace of, and investment in, machine learning methods  for poverty and malnutrition targeting, mapping,  monitoring, and early warning while also cautioning that  distinct tasks require different data inputs and methods.  In particular, we highlight the differences between poverty  and malnutrition targeting and mapping, identification of  those in a state of structural versus stochastic  deprivation, and the modeling and data challenges of  developing early warning systems. Overall, we urge careful  consideration of the purpose and possible use cases of big  data and machine learning informed models.},
      url = {http://ageconsearch.umn.edu/record/309060},
      doi = {https://doi.org/10.22004/ag.econ.309060},
}