A recent article in The Economist proposes a "clever trick" to eradicate extreme poverty through a universal basic income, calculated using a machine learning algorithm. Research by Roshni Sahoo from Stanford University suggests that different amounts of money should be given to different people. The algorithm would aim to reduce, eventually to zero, the probability that any person remains below the poverty line after the intervention. Unlike previous programs, such as "Solidaridad," which was conditional (requiring children to attend school and regular medical check-ups), the new "Bienestar" program under President López Obrador is unconditional. However, the author expresses concern. They note that the increase in aggregate demand from these payments could drive up the prices of food and other goods, negating the benefits for the poorest. Furthermore, there is a risk that individuals will find ways to game the system and claim subsidies on behalf of others. In conclusion, the author emphasizes that problems of poverty, such as social constructs or mental states, require solutions that go beyond simple monetary injections.
An Algorithm Against Poverty: A New Approach in Mexico
The Economist analyzes Mexico's 'Bienestar' social program. The article examines the proposal to use an algorithm to determine the size of a universal basic income, noting potential risks such as inflation and the potential for system abuse.
