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
Predicting Poverty - World Bank
The World Bank is aiming to end extreme poverty by 2030. Crucial to this goal are techniques for determining which poverty reduction strategies work and which ones do not. But measuring poverty reduction requires measuring poverty in the first place, and it turns out that measuring poverty is pretty hard. The World Bank helps developing countries measure poverty by conducting in-depth household surveys with a subset of the country's population. To measure poverty, most of these surveys collect detailed data on household consumption – everything from food and transportation habits to healthcare access and sporting events – in order to get a clearer picture of a household's poverty status.
Can you harness the power of these data to identify the strongest predictors of poverty? Right now measuring poverty is hard, time consuming, and expensive. By building better models, we can run surveys with fewer, more targeted questions that rapidly and cheaply measure the effectiveness of new policies and interventions. The more accurate our models, the more accurately we can target interventions and iterate on policies, maximizing the impact and cost-effectiveness of these strategies.
Competition End Date:
Feb. 28, 2018, 11:59 p.m. UTC
Bonus Award: Top Contributor from Developing Country
In the spirit of thinking globally and acting locally, we're offering a bonus prize of $2,500 to the top contributor from a developing country.
Our partners at the World Bank Group have identified 84 qualifiying countries, which can be found here under the "LOW-INCOME ECONOMIES ($1,005 OR LESS)" and "LOWER-MIDDLE-INCOME ECONOMIES ($1,006 TO $3,955)" headings. If you or the majority of your team hold an official ID from one of these countries, you're eligible! Just make sure to fill out the
Your Country field on the submission page to officially be considered for the prize.
Image courtesy of the World Bank Group
Pover-T Tests: Predicting Poverty: Rules and Terms of Data Use
DrivenData Competition Rules
One account per participant
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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
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Data use and code sharing
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