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

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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.

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Acknowledgements

This work was supported by Pulse Lab Jakarta (PLJ), which is a joint initiative of the United Nations and the Government of Indonesia.

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Authors and Affiliations

  1. School of Applied Science, Telkom University, Bandung, Indonesia

    Dedy Rahman Wijaya

  2. Statistics Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

    Ni Luh Putu Satyaning Pradnya Paramita

  3. Education and Training Center, Statistics Indonesia (BPS), Jakarta, Indonesia

    Ana Uluwiyah

  4. Pulse Lab Jakarta – United Nations Global Pulse, Jakarta, Indonesia

    Muhammad Rheza & Annisa Zahara

  5. Institute for Economic and Social Research, Universitas Indonesia, Jakarta, Indonesia

    Dwi Rani Puspita

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  1. Dedy Rahman Wijaya
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Wijaya, D.R., Paramita, N.L.P.S.P., Uluwiyah, A. et al. Estimating city-level poverty rate based on e-commerce data with machine learning. Electron Commer Res 22, 195–221 (2022). https://doi.org/10.1007/s10660-020-09424-1

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