Introduction
Modern poverty analysis is moving away from dry statistics toward examining the micrologic of survival. Nobel laureates Abhijit Banerjee and Esther Duflo demonstrate that living in scarcity is not just a lack of resources, but a state of permanent pressure that radically alters decision-making. Understanding this world requires analyzing return curves and the cognitive barriers that often cause even the most affordable prevention to lose out to the toll of daily struggles. This article examines the mechanisms governing life in scarcity and the role of new technologies in breaking down the barriers of exclusion.
The Micrologic of Survival: Rationality Under Duress
The lives of the poor are shaped by two structures: the S-curve (where small efforts yield no results until a threshold is crossed) and the inverted L-curve (quick initial gains that rapidly taper off). Under these conditions, rationality under duress emerges. The decision to use cheap prevention, such as water chlorination, loses out to immediate costs and exhaustion. A lack of time becomes an epistemic burden—poverty leaves no room to think about anything beyond immediate survival.
For prevention to be effective, it must be built into the architecture of default options. For example, a chlorine dispenser at a water source or a small reward for vaccination relieves the individual of the burden of constant choice. Without such solutions, psychological costs and procrastination block access to high-return technologies, perpetuating scarcity despite the availability of solutions.
Microcredit and AI: Between Emancipation and the Algorithm
Microcredit appears as an ambivalent tool. While it stabilizes consumption and protects against usury, it rarely serves as a springboard for significant growth, often merely sustaining a survival economy. Artificial intelligence offers new hope. By analyzing alternative data, AI is transforming credit scoring, opening the market to those without a banking history. These models can serve as a poverty cartographer, precisely identifying needs where traditional systems see only noise.
However, algorithmic risk assessment carries the risk of "digital redlining"—reproducing structural biases hidden in historical data. If AI turns a human being into a "black box," it may deepen exclusion rather than counteracting it. The key question becomes whether technology will increase the predictability of rules or merely automate the arbitrariness of credit decisions.
RCTs and the Geography of Prevention: Foundations for New Policies
Modern poverty economics relies on RCTs (randomized controlled trials). These allow us to distinguish ideology from facts by testing specific interventions in the field. Through them, we understand the geography of prevention: from the institutional model in the EU to the market-based model in the US and the cultural need for "visible" treatment in Arab countries. Each region requires a different incentive architecture to break through local infrastructural barriers.
This approach is sometimes criticized for being too modest in the face of global power structures. However, looking toward the 2030 horizon, it is clear that without patient micro-analysis of data, the fight against poverty will remain a futile debate. Success depends on our ability to combine rigorous methodological skepticism with a deep respect for the micrologic of everyday life, which cannot be summarized by a single indicator.
Summary
In a world of algorithmically managed finance, where poverty is defined by a lack of control over access to services, are we destined to replicate inequality? Or will artificial intelligence, paradoxically, become a catalyst for fairer redistribution, unlocking potential that has remained hidden within the labyrinth of S-curves and inverted L-curves? Can we harness this powerful technology to build bridges toward more sustainable development instead of reinforcing divisions?
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