Pover-T Tests: Predicting Poverty
Measuring poverty is hard. Thanks to the efforts of thousands of competitors, The World Bank can now build on open source machine learning tools to help predict poverty, optimize uses of survey data, and support work to end extreme poverty … Competition hosted by The World Bank. #development
Measuring poverty is hard. Thanks to the efforts of thousands of competitors, The World Bank can now build on open source machine learning tools to help predict poverty, optimize uses of survey data, and support work to end extreme poverty in the next decade.
No. of Entries
Ag1001st Place Team
Collecting household survey data on poverty is expensive and time-consuming, which means that policy makers are often making decisions based on old data. Machine learning could drastically change the game, making poverty measurement cheaper and much closer to real-time.
– Asli Demirguc-Kunt, Director of Research at the World Bank
The World Bank aims to end extreme poverty by 2030. To achieve this goal, they need efficient pipelines for measuring, tracking, and predicting poverty. But measuring poverty is currently hard, time consuming, and expensive. Estimates are typically collected through complex household consumption surveys with data on hundreds of different variables, each of which may be useful when assessing poverty levels.
Machine learning offers new approaches for determining which variables are most predictive and how they can be most effectively combined. In this competition, data scientists from more than 130 countries around the world built algorithms to predict household-level poverty status using surveys data from three developing countries, each with a different distribution of wealth.
The best algorithms pulled out all the stops, creating ensembles of neural networks, XGBoost, LightGBM, and even CatBoost (to leverage the mostly-categorical nature of the survey data) models. These approaches fed into a research paper publishing the winning solutions and contributing to the democratization of machine learning through resources for future application and training.
Pover-T Tests: Predicting Poverty: Rules and Terms of Data Use
DrivenData Competition Rules
One account per participant
You cannot sign up to DrivenData from multiple accounts and therefore you cannot submit from multiple accounts.
Private sharing of code
Privately sharing code or data outside of teams is not permitted.
Public dissemination of entries
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Open licensing of winners
Winning solutions need to be made available under The MIT License (an open source software license commonly described at http://opensource.org/licenses/MIT) in order to be eligible for recognition and prize money if offered.
Documentation for winning solutions
Winning solutions must be documented using the provided Winning Model Documentation Template in order to be eligible for recognition and prize money if offered.
The number of submissions per day is restricted to a fixed value on a per-competition basis. Any attempt to circumvent this limit will result in disqualification.
External data is not allowed unless otherwise noted explicitly on these competition pages. Participants agree to make no attempt to use additional data or data sources not provided on these competition pages.
Specific eligibility restrictions
All staff members from the World Bank Group, their spouse or domestic partner, and their close relatives (including Mother, Father, Son, Daughter, Sister, Brother, Half-brother, Half-sister, Aunt, Uncle, Nephew, and Niece) are not eligible to participate in the competition.
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Selection of winners
This Competition is a challenge of skill and the final results are determined by evaluating a combination of quantitative and qualitative factors, as more fully described on the Competition Website. For that portion of the Competition evaluated quantitatively, the results will be determined solely by leaderboard ranking on the private leaderboard based on an objective statistical evaluation metric published on the Competition Website prior to commencement of the Competition (subject to compliance with these Competition Rules). Participants' scores and ranks on the Competition Website at any given stage of the Competition will be based on the objective statistical evaluation metric described on the Competition Website, as determined by applying the predictions in the Submission to the ground truth of a validation dataset whose instances were a fixed set sampled from the Data. For that portion of the Competition evaluated qualitatively, the results will be determined by a panel of judges, as more fully described on the Competition Website. As more fully described on the Competition Website, the Competition may have a quantitative winner, a qualitative winner, a combined quantitative and qualitative winner, or all three.
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Data use and code sharing
DATA. "Data" means the Data or Datasets linked from the Competition Website for the purpose of use by Participants in the Competition. For the avoidance of doubt, Data is deemed for the purpose of these Competition Rules to include any prototype or executable code provided to Participants by DrivenData or via the Competition Website. Participants must use the Data only as permitted by these Competition Rules and any associated data use rules specified on the Competition Website.
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