Artificial Intelligence and Machine Learning for Estimating Poverty


15 JULY 2018

The digital revolution continues to generate an abundance of data, which provides new opportunities to capture information about socio-economic conditions at different levels of abstraction to infer development progress. The availability of data on poverty and inequality are nevertheless limited. But such data can be used to monitor changes in prosperity level, as well as to measure the impact of government programmes.

In collaboration with Knowledge Sector Initiative (KSI), Pulse Lab Jakarta is organising an upcoming research dive for development, with a view to addressing this gap by enhancing researchers' familiarity with several related datasets, including satellite imagery, e-commerce data, social media, and socio-economic data. The seventh research dive aims to generate insights on how to leverage new and emerging datasets and Artificial Intelligence (AI) for alleviating poverty across Indonesia. The results from measuring poverty with big data are intended to complement the national socioeconomic survey (SUSENAS) data that has been collected by the National Statistics Agency (BPS).

The participants will be grouped into four research teams, focusing on:

Group 1 - Estimating poverty at the provincial level with satellite data
Group 2 - Estimating poverty at the city level with e-commerce data
Group 3 - Estimating poverty at the district level with social media data
Group 4 - Estimating poverty at the household level with social media data and household survey results

For further information, please refer to the information package.

How to Apply?

If you are interested in applying for this Research Dive, please complete and submit the online application through Research Dive EasyChair. If you do not have an EasyChair account, please sign up by following the instructions on the site.

Before you submit your application, please ensure that your supporting documents are ready to be uploaded on one PDF file, including:

  • Curriculum Vitae in English (including year of education, list of research activities and publications, without including any certificates)
  • A recommendation letter in English by anyone who is familiar with the applicant’s research skills, and
  • A one-page document in English that clearly explains the applicant’s first and second preferences among the four tasks listed, covering (a) the applicant’s relevant research experience and (b) her/his ideas for potential approaches to tackle the tasks of interest.

To download the guidelines for sending the submission, please click here.

Should you have any inquiries, please contact us by sending an email to

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