Modeling Economic Well-Being in India Through Machine Learning

Background

Economic well-being goes beyond statistical metrics, reflecting the standard of living, including necessities, comforts, and luxuries enjoyed by individuals. Traditional methods like surveys to measure economic outcomes are often outdated, prone to inaccuracies, and fail to capture temporal changes effectively. These limitations make it challenging to assess regional progress and inform policies. To address these gaps, innovative data-driven approaches are essential.

Objective

To develop AI models that accurately analyze changes in the agricultural and urban landscape of India, providing insights into economic well-being and advancing the goals of sustainable development.

Approach

The two-month project engaged a global team of 50 changemakers, hosted by the World Resources Institute. The team used publicly available satellite imagery and ground data, supported by Activeloop’s framework for building and scaling data pipelines. AI models were trained to identify features related to economic well-being, leveraging machine learning techniques to measure regional development indicators and economic productivity more effectively.

Results and Impact

The project successfully created AI-driven visualizations and models that provided deeper insights into the distribution of economic well-being across India’s districts. These tools offered a robust alternative to traditional survey methods, addressing challenges like noise, temporal variation, and data misuse. The outcomes align with the UN’s Sustainable Development Goal 8, aiming for inclusive and sustainable economic growth by 2030.

Future Implications

The findings pave the way for more comprehensive and dynamic methods to measure economic well-being, informing future research, policy-making, and planning. The approach has the potential to guide governments, international agencies, and businesses in driving sustainable development and improving the standard of living globally.