As a child, Esther Duflo often had to give up her mother during the holidays. She was a doctor and used the vacation time to help in war zones. The current economist was never angry or sad about it. Her mother had told her that renunciation was something she could do for the poor. To date, the Paris-born economist has made this fight against poverty a life's work. Together with her husband, the Indian economist Abhijit Banerjee, and the economist Michael Kremer, she is now awarded the Nobel Prize for her achievements in poverty research.
All three economists teach at US universities. The 55-year-old Kremer conducts research at the renowned Harvard University, where he also studied. He specializes in education and health in developing countries. 58-year-old Banerjee works with his wife at the Massachusetts Institute of Technology (MIT). The Mumbai-born researcher took over the Ford Foundation International Chair in Economics in 2003 and co-founded the Abdul Latif Jameel Poverty Action Lab with Duflo and Sendhil Mullainathan: a research network that develops new methods of poverty reduction, and economic and development policies examined for their effect. Kremer, who worked for a while in Kenya as a teacher and was managing director of the NGO WorldTeach, is part of this network of poverty researchers. More than 100 scientists work at the Abdul Latif Jameel Poverty Action Lab alone, and there are countless people all over the world who are networked through the facility. "We're hundreds, we work with NGOs, we're almost a movement," Dulfo said at the Nobel Prize announcement.
In this respect, the award is also a tribute to poverty research in general. Duflo, Banerjee and Kremer assume that poverty can not be tackled by opening up markets or money alone. But that poverty is determined by a variety of factors that are often not so easily identified by politics, let alone that they can be changed quickly and easily. Therefore, the three have developed a research approach that is strictly scientific: With so-called randomized controlled field experiments, they are looking for a causality and try to obtain clear statements. So one experiment leads to the next. At the same time they look at the effect of a measure. Actually, this approach comes from the field of medicine, where studies with control groups are used to obtain an evidence-based outcome. Because, as in medicine, where a drug is supposed to fight a certain disease or a symptom, a political measure should also alleviate poverty in the fight against poverty.
The children suddenly learned better
This means examining a complex finding for a variety of causes and analyzing why a particular measure may not work so far. For example, in the educational situation of children in developing countries: In many poor countries children go to school rarely or only occasionally, many drop out of school prematurely, many do not come along, learn little. But the reasons are highly complex. It may be that schools and teachers are missing, that the security situation is unstable, that hungry children can not go to school or work at home on the field or in the market.
What is important in the approach of Duflo, Banerjee and Kremer is: It examines a - for example regional - limited case and not the big picture. Only in this way can answers be found for the larger relationships on the basis of definable, secured insights. Duflo and her colleagues found out in such experiments in India and Kenya that there were quite different reasons for the poor education: in both countries, the school education was not adapted to the child's level, the curricula provided a sometimes much too high level which the children could not cope with in terms of developmental psychology. Especially not if they came from extreme poverty. Some schools also taught English, but for many children teaching in the official language was too difficult. Poverty researchers therefore suggested that pupils should not be graded according to their age but according to their abilities - the effect was great. The children were more motivated and learned better.
Duflo has also developed an estimation method for causal relationships. She found that an assumed effect is often overestimated. For example, the economist has shown that many microcredits, for example, do not have the expected positive effect because the poorest do not receive them or there are other inhibiting factors.