Big data has introduced new ideas and technological support for precision poverty alleviation. By leveraging the speed, convenience, and efficiency of big data, we can accelerate the implementation of targeted poverty reduction strategies. The Chinese government has made significant efforts in poverty alleviation, lifting over 600 million people out of poverty. However, as of the end of 2015, more than 70 million people were still living below the poverty line. In the past, broad policies helped many escape poverty, but now, the remaining population represents the "toughest challenges," making it increasingly difficult to reduce poverty further.
By the end of the "Thirteenth Five-Year Plan," it is essential to lift 70 million people out of poverty on schedule, requiring at least 12 million people to be lifted out each year and 1 million per month. This task is extremely challenging, especially when considering the need to consolidate progress. Therefore, exploring how basic communication operators and technologies like big data, the Internet of Things, cloud computing, and mobile internet can assist in precise poverty alleviation is a crucial area of research.
Applying big data technology to poverty alleviation involves expanding information collection channels, improving data analysis capabilities, and increasing processing efficiency. This ensures that accurate, effective, and reliable data supports decision-making in poverty reduction efforts. Eliminating poverty, improving people’s livelihoods, and gradually achieving common prosperity are fundamental requirements of socialism. Over the past three decades, China's productivity has grown significantly, but the wealth gap has also widened. Precise poverty alleviation is not only a path to common prosperity but also an essential requirement of socialism.
How can big data help in precision poverty alleviation? The big data revolution has made it easier to access capital fairly and responsibly. But even with great ideas, lack of financial resources such as bank accounts or credit cards makes it hard to start a business. Karen Little from Kiva agrees, stating that many smart individuals have missed opportunities due to limited access to funding. Banks often neglect microloans because the poor lack credit histories. However, with the rise of mobile phones and the internet, this is changing. Mobile phone usage is widespread, allowing us to collect data on the location, behavior, and needs of the poor, helping to create detailed poverty maps.
Data includes 24/7 digital and network information generated by users through mobile devices and connected systems. This data, including network activity and IoT sensor inputs, forms the basis for real-time insights. In banking, analyzing mobile data helps identify the best locations for microfinance offices. Digital access and payments create credit records for those without traditional credit histories, offering alternative proof of reliability for lenders.
Companies like Kiva and Tala use big data to assess creditworthiness through algorithms and various indicators. Kiva uses data analysis to evaluate over 2 million borrowers and 1.4 million lenders, ensuring safer and more efficient lending. Big data also plays a key role in Kiva’s $7 million investment in the "Matter to a Million" project, which relies heavily on data analysis to manage risks and connect borrowers with lenders globally.
The next phase of big data application involves addressing the root causes of poverty. For example, researchers at the State University of New York used mobile phones to map poverty in Senegal more efficiently than traditional methods. Real-time updates allow local authorities to make informed decisions. Additionally, big data can predict issues like crime or natural disasters, helping to develop better strategies for poverty alleviation.
Organizations like the Data-Pop Alliance use big data to gather insights on economic conditions and poverty levels, supporting bottom-up decision-making. They believe that involving the poor in data generation and policy-making is key to sustainable change. As one expert said, "The key issue is not just how authorities use data, but how the people who generate it can participate in poverty alleviation and improve their lives."
With the power of big data, we can now reach the most vulnerable populations, understand their needs, and implement targeted solutions that truly make a difference.
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