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Estimating poverty using cell phone data : evidence from Guatemala

Author

Listed:
  • Hernandez,Marco
  • Hong,Lingzi
  • Frias-Martinez,Vanessa
  • Frias-Martinez,Enrique

Abstract

The dramatic expansion of mobile phone use in developing countries has given rise to a rich and largely untapped source of information about the characteristics of communities and regions. Call Detail Records (CDRs) obtained from cellular phones provide highly granular real-time data that can be used to assess socio-economic behavior including consumption, mobility, and social patterns. This paper examines the results of a CDR analysis focused on five administrative departments in the south west region of Guatemala, which used mobile phone data to predict observed poverty rates. Its findings indicate that CDR-based research methods have the potential to replicate the poverty estimates obtained from traditional forms of data collection, like household surveys or censuses, at a fraction of the cost. In particular, CDRs were more helpful in predicting urban and total poverty in Guatemala more accurately than rural poverty. Moreover, although the poverty estimates produced by CDR analysis do not perfectly match those generated by surveys and censuses, the results show that more comprehensive data could greatly enhance their predictive power. CDR analysis has especially promising applications in Guatemala and other developing countries, which suffer from high rates of poverty and inequality, and where limited fiscal and budgetary resources complicate the task of data collection and underscore the importance of precisely targeting public expenditures to achieve their maximum antipoverty impact.

Suggested Citation

  • Hernandez,Marco & Hong,Lingzi & Frias-Martinez,Vanessa & Frias-Martinez,Enrique, 2017. "Estimating poverty using cell phone data : evidence from Guatemala," Policy Research Working Paper Series 7969, The World Bank.
  • Handle: RePEc:wbk:wbrwps:7969
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    Citations

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    Cited by:

    1. Andreas Erlström & Markus Grillitsch & Ola Hall, 2022. "The geography of connectivity: a review of mobile positioning data for economic geography," Journal of Geographical Systems, Springer, vol. 24(4), pages 679-707, October.
    2. Emily Aiken & Guadalupe Bedoya & Joshua Blumenstock & Aidan Coville, 2022. "Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan," Papers 2206.11400, arXiv.org.
    3. Lee, Kamwoo & Braithwaite, Jeanine, 2022. "High-resolution poverty maps in Sub-Saharan Africa," World Development, Elsevier, vol. 159(C).
    4. Erlström, Andreas & Grillitsch, Markus & Hall, Ola, 2020. "The Geography of Connectivity: Trails of Mobile Phone Data," Papers in Innovation Studies 2020/6, Lund University, CIRCLE - Centre for Innovation Research.
    5. Aiken, Emily L. & Bedoya, Guadalupe & Blumenstock, Joshua E. & Coville, Aidan, 2023. "Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan," Journal of Development Economics, Elsevier, vol. 161(C).
    6. Kamwoo Lee & Jeanine Braithwaite, 2020. "High-Resolution Poverty Maps in Sub-Saharan Africa," Papers 2009.00544, arXiv.org, revised May 2021.

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