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Estimating city-level poverty rate based on e-commerce data with machine learning

Author

Listed:
  • Dedy Rahman Wijaya

    (Telkom University)

  • Ni Luh Putu Satyaning Pradnya Paramita

    (Institut Teknologi Sepuluh Nopember)

  • Ana Uluwiyah

    (Statistics Indonesia (BPS))

  • Muhammad Rheza

    (Pulse Lab Jakarta – United Nations Global Pulse)

  • Annisa Zahara

    (Pulse Lab Jakarta – United Nations Global Pulse)

  • Dwi Rani Puspita

    (Universitas Indonesia)

Abstract

There are many big data sources in Indonesia, for example, data from social media, financial transactions, transportation, call detail records, and e-commerce. These types of data have been considered as potential resources to complement periodic surveys and censuses to monitor development indicators such as poverty levels. Data from e-commerce in particular could potentially represent the real expenditure of households, better complying with the formal calculation of the poverty line than other datasets. The contribution of this research is to propose a framework for poverty rate estimation based on e-commerce data using machine learning algorithms. The influence of items and aspects in e-commerce data was investigated in conjunction with poverty rate estimation. The experimental result showed that e-commerce data could potentially be used as a proxy for calculating city-level poverty rates. It was also found that cars and motorbikes are the two most significant items for poverty prediction in Indonesia.

Suggested Citation

  • Dedy Rahman Wijaya & Ni Luh Putu Satyaning Pradnya Paramita & Ana Uluwiyah & Muhammad Rheza & Annisa Zahara & Dwi Rani Puspita, 2022. "Estimating city-level poverty rate based on e-commerce data with machine learning," Electronic Commerce Research, Springer, vol. 22(1), pages 195-221, March.
    • Handle: RePEc:spr:elcore:v:22:y:2022:i:1:d:10.1007_s10660-020-09424-1
    DOI: 10.1007/s10660-020-09424-1
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    References listed on IDEAS

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    1. United Nations UN, 2015. "The Millennium Development Goals Report 2015," Working Papers id:7097, eSocialSciences.
    2. Njuguna, Christopher & McSharry, Patrick, 2017. "Constructing spatiotemporal poverty indices from big data," Journal of Business Research, Elsevier, vol. 70(C), pages 318-327.
    3. Jitendra Kumar Rout & Kim-Kwang Raymond Choo & Amiya Kumar Dash & Sambit Bakshi & Sanjay Kumar Jena & Karen L. Williams, 2018. "A model for sentiment and emotion analysis of unstructured social media text," Electronic Commerce Research, Springer, vol. 18(1), pages 181-199, March.
    4. Charlotta Mellander & José Lobo & Kevin Stolarick & Zara Matheson, 2015. "Night-Time Light Data: A Good Proxy Measure for Economic Activity?," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-18, October.
    5. United Nations UN, 2015. "The Millennium Development Goals Report 2015," Working Papers id:7222, eSocialSciences.
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    Cited by:

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    2. Brychka, Bohdan & Vyslobodska, Halyna & Voitovych, Nadiia, 2023. "Poverty in Ukraine: evolution of interpreting and analysis of impact factors," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 9(2), June.

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