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posted by martyb on Sunday August 16 2015, @12:01AM   Printer-friendly
from the {a[1]="witty";a[2]="insightful";a[3]="informative";print a[int(3*rnd(0)+1)]} dept.

A new generation of economists is trying to transform global development policy through the power of randomized controlled trials (RCTs).
...
"We have learned something about why immunization rates are low," says Esther Duflo, an economist at the Massachusetts Institute of Technology (MIT) in Cambridge, who was involved in the 2010 experiment and is working with Haryana [India] on its latest venture. The problem is not necessarily that people are opposed to immunization, she says. It is that certain obstacles, such as lack of time or money, are making it difficult for them to attend the clinics. "And you can balance that difficulty with a little incentive," she says.

This is one of a flood of insights from researchers who are revolutionizing the field of economics with experiments designed to rigorously test how well social programmes work. Their targets range from education programmes to the prevention of traffic accidents. Their preferred method is the randomized trial. And so they have come to be known as the 'randomistas'.

... the randomista movement is gaining momentum (see 'Scale the heights'). Universities are pumping out more economics graduate students with experience in RCTs every year. Organizations ranging from the UK Department for International Development to the Bill & Melinda Gates Foundation in Seattle, Washington, are throwing their financial support behind the technique. "There are hundreds and hundreds of randomized trials going on, and ten years ago that just wasn't the case," says economist Dean Karlan at Yale University in New Haven, Connecticut, who is at the forefront of the movement. "We've changed the conversation."

Can adopting more rigorous practices from the physical sciences work for economics and economic policy?


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  • (Score: 1, Insightful) by Anonymous Coward on Sunday August 16 2015, @02:29AM

    by Anonymous Coward on Sunday August 16 2015, @02:29AM (#223432)

    1) Think of all the possible things that could influence the results in addition to your treatment.
    2) Estimate the size of each of those.
    3) Determine how likely the difference in outcomes may be due to some combination of these factors
    4) If the difference seems too big to be explained, tentatively conclude the excess is due to the intervention

    There is no nil null significance test.

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