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DAT102x: Predicting Poverty Around the World
Hosted By Microsoft

 

Combatting Global Poverty

Poverty is a multi-dimensional concept encompassing the various deprivations experienced by poor people in their daily lives—including inadequate living standards, poor health, lack of education, and disempowerment. Global poverty has been declining thanks to enormous efforts by a range of organizations to advance human wellbeing. Still, there is much work to be done. The 1st Sustainable Development Goal (SDG) set by the UN is to eradicate poverty completely by 2030.

According to The World Bank:

The world attained the first Millennium Development Goal target—to cut the 1990 poverty rate in half by 2015—five years ahead of schedule, in 2010. Despite the progress made in reducing poverty, the number of people living in extreme poverty globally remains unacceptably high. And given global growth forecasts, poverty reduction may not be fast enough to reach the target of ending extreme poverty by 2030.

Unfortunately, the progress in reducing poverty has not been spread evenly over the globe. The World Bank reports:

More than half of the extreme poor live in Sub-Saharan Africa. In fact, the number of poor in the region increased by 9 million, with 413 million people living on less than US$1.90 a day in 2015, more than all the other regions combined. If the trend continues, by 2030, nearly 9 out of 10 extreme poor will be in Sub-Saharan Africa.

Paired with data collection and poverty measurement efforts, data science tools can help evaluate how poverty is changing, for which populations, and in what contexts. There are many factors that can increase or decrease a person's likelihood of being in poverty, ranging from socioeconomic and demographic factors to political circumstances. And the strength of the relationship between these variables and poverty can vary significantly across countries.

In this challenge, your goal is to predict the probability that individuals across seven different countries live below the poverty line at the $2.50/day threshold, given more than 50 demographic, economic, education, employment features. The probability of being in poverty was calculated using the Poverty Probability Index (PPI), which estimates an individual's poverty status using 10 questions about a household’s characteristics and asset ownership. The remaining data comes from the Financial Inclusion Insights household surveys conducted by InterMedia.

Competition title photo from dany13