Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty

Abhijit V. Banerjee, Esther Duflo

"Billions of government dollars, and thousands of charitable organizations and NGOs, are dedicated to helping the world's poor. But much of the work they do is based on assumptions that are untested generalizations at best, flat out harmful misperceptions at worst. Banerjee and Duflo have pioneered the use of randomized control trials in development economics. Work based on these principles, supervised by the Poverty Action Lab at MIT, is being carried out in dozens of countries. Their work transforms certain presumptions: that microfinance is a cure-all, that schooling equals learning, that poverty at the level of 99 cents a day is just a more extreme version of the experience any of us have when our income falls uncomfortably low.

Business & EconomicsBusiness & Economics · medium confidenceBusiness & Economics81 signalsLlm Classifier: Business & Economics -> Business & EconomicsOpen Library: Economics -> Business & EconomicsOpen Library: Poverty -> Society & CultureBusiness, Capitalism & CorporationsClass, Poverty & InequalityLabor, Work & Organizing
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies

This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.

  1. 01 Central figures

    None extracted with sufficient confidence.

  2. 02 Central places

    None extracted with sufficient confidence.

  3. 03 Suggested argument

    No argument inferred with sufficient confidence.

    This suggestion did not meet the normal display threshold and is more likely to be wrong.

    Randomized evaluations of anti-poverty programs show that several widely held assumptions—such as microfinance being a cure-all and schooling automatically producing learning—are often harmful misperceptions or untested generalizations.

    Model confidence 52%

  4. 04 Reading orientation

    55 / 100 academic

    Trade / academic crossover

    An estimate of intended readership and scholarly apparatus—not quality or importance.

Confidence percentages are the model's own estimates. Profile confidence: 63%.