New Technologies to Measure Poverty


SEATTLE — The United Nations ranks the elimination of global poverty at the top of its list of Sustainable Development Goals to be achieved by 2030. The reality of this goal, however, is subject to whether traditional poverty measuring techniques can provide the data required to track progress and reveal the areas most affected by poverty. New technologies to measure poverty will undoubtedly be essential in reaching the number one

The World Bank sets the standards for poverty data collection and has used the same method for tracking poverty for more than 35 years: going from house to house throughout the world, collecting data. Despite this effort, there are still 77 countries without adequate poverty data collection.

It takes data collectors on average two hours to interview a household in order to accurately track its poverty levels, and if they want to get sufficient data for an entire country, they must go to a minimum of 2,000 houses in that country. In the more poverty-stricken countries like Nigeria, that number can go as high as 100,000 houses. They don’t want to just count the poor, but analyze the reasons behind poverty such as education, financial assistance and employment opportunities.

The methodology has come a long way since national statistics offices would send data collectors across the country with nothing but pencils and paper questionnaires. Human error, mistakes when transferring from paper to computer, and imbalanced diligence on the part of the data collectors all resulted in inaccurate poverty rates. These gatherers of data now come with tablets instead of paper, GPS trackers instead of outdated maps, and the technology to automatically sync their data as it is entered.

The new technologies to measure poverty haven’t stopped there. Organizations like The World Bank have put numerous technologically advanced practices into place to more accurately accumulate poverty data.

The World Bank has become one of the world’s leaders in new technologies to measure poverty. For example, The Pulse of South Sudan initiative goes a step further than simply tracking data with a video testimonial of its interviewees, putting a face to the facts. In addition to its household surveys, The South Sudan National Bureau of Statistics research team has recorded hundreds of personalized testimonials since the “first-wave” in 2015, which are all available to be viewed by the public on its website.

The World Banks’s Listening to Africa program gives its face-to-face survey participants mobile phones and solar chargers to follow up on interviews, giving data collectors a method to consistently gather information on everything from living conditions to monitoring health facilities. This could be particularly useful in conflict and war ridden areas, where it might be too dangerous for surveyors to travel.

The method has been successfully used not only in Africa, but in countries like Peru, Honduras, as well as Europe and Central Asia. The World Bank team in Tajikistan has even left behind “Smart Survey” boxes in 150 locations to collect information on energy usage and quality, monitoring things like power outages to assess the country’s energy challenges.

These solutions can be expensive and slow. Researchers at the Sustainability and Artificial Intelligence Lab at Stanford University have stated that the U.N. expectations on poverty eradication exceed the scientific world’s capabilities. They believe to have found a solution by taking advantage of our modern sky, already full of satellites. By combining existing satellite photos of under-developed countries at night (where less electric lights equal less development) with daytime photos, they can analyze things like unpaved roads, proximity to water sources and dwelling sizes.

The team has trained a computer to analyze these subtleties, using a machine learning technique (convolutional neural network) to predict and map poverty rates and distribution. Still in its initial stages, the team committed to new technologies to measure poverty is optimistic, focusing on five African countries and comparing their satellite and computer findings to the existing ground survey data.

Katherine Gallagher
Photo: Flickr


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