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- [Yan] It's good that we have
an army of enthusiasts

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writing Wikipedia articles,

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but sometimes when it concerns 
a disease that I might have,

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I really want the experts' input.

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♪ [music] ♪

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Wikipedia is one of the most important
references for the general public.

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It's actually one of the most top five
most visited websites in the world.

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Everyone reads Wikipedia articles,
but sometime you spot an error

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or you say, 
"Well, this is not really correct."

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But you move on

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and say, "Someone else might fix it."

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That's called the "free rider problem."

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The success of Wikipedia 
has been really surprising

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for economists because it relies 
purely on volunteer labor.

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The medical profession has found
that patients tend to bring printouts

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of Wikipedia articles 
to their doctor's office.

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Some of these articles are low quality

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because they were not written by experts.

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We're trying to figure out 
what are the some of the motivators

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to get experts to contribute 
to high quality content.

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So we decided to do a field experiment
to tease out the causalities,

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to figure out what motivates people
to contribute to Wikipedia,

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whether it's social impact 
or private benefit

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or public acknowledgement
or a combination of these factors.

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So in this study,
in this field experiment,

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we contacted about 4,000
academic economists.

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We have a generic message

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that says Wikipedia 
is a very valuable public good,

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and yet lots of the articles 
are inaccurate or not up to date.

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Would you spend 
10 to 15 minutes commenting

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on these Wikipedia articles?

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Then we vary the paragraphs 
depending on whether

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they're in the treatment 
or control group.

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In the control group, 
we don't mention that the articles

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might cite your research.

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And in the private benefit 
we say they might cite your research,

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and we have another condition
which says, "We will publicly

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acknowledge your contributions."

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Simply asking the expert,
"Would you contribute?"

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you get pretty high response rate,

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which is about 45% of the people
say, "Yes, I'm willing."

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When we send out the links,
it turns out a third of the people

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actually contributed,
and we look at what are the features

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that predict contributions,

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it turns out that if the article is 
really