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Anqi Fu  
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 More options May 23 2012, 8:34 pm
From: Anqi Fu <anqi.fu.1...@gmail.com>
Date: Wed, 23 May 2012 17:34:00 -0700 (PDT)
Subject: Kiva Data Collection
Hi, my name is Anqi Fu, and I am an economist at Stanford GSB. I am
conducting a research study on social lending behavior on Kiva. I was
wondering if it was possible to access data on the specific amount
each lender contributed to a particular loan. For example, if a
borrower asks for total funds of $100 for a project, I would like to
know if lender X contributed $50 on a specific date to this project.
Right now, using the Kiva API, I have only been able to find the
aggregate loan amount over all lenders.

Thanks for your time!


 
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Paul Ericksen  
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 More options May 23 2012, 11:01 pm
From: Paul Ericksen <liquidmon...@gmail.com>
Date: Wed, 23 May 2012 20:01:22 -0700
Local: Wed, May 23 2012 11:01 pm
Subject: Re: Kiva Data Collection

Unfortunately, the amount a given lender has loaned to a borrower is not
public information. For currently fundraising loans, it is possible to *
infer* this information from the API
http://api.kivaws.org/v1/lending_actions/recent.xml -- if you keep track of
what the loaned amount was prior to the current lending action, and you see
a new lending action and the new loaned amount of that loan is up by $50
but there's only one new lender, you can infer that new lender gave $50,
rather than the default $25. But, for past loans, this information is not
public.

--
Sent from my computer. No excuses for misspellings or missing words.

 
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Pim Schaaf  
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 More options May 28 2012, 4:09 am
From: Pim Schaaf <pimsch...@gmail.com>
Date: Mon, 28 May 2012 01:09:40 -0700 (PDT)
Local: Mon, May 28 2012 4:09 am
Subject: Re: Kiva Data Collection

Hi Anqi,

Like Paul says, there is no direct way of getting that data. I must warn
you that once in a while the lending_actions/recent.xml<http://api.kivaws.org/v1/lending_actions/recent.xml> method
won't contain overlapping data (even when checked with only 1 min.
intervals), so that might not be as reliable as you want it to be.

Liu et al. (2012) have, for similar reasons, used the loan amount divided
by the amount of lenders as a proxy for the specific amount each lender
contributed to that loan. Bigger loans are found when less people lend to
projects with higher total funds required. See the following quote:

In addition to lending frequency, we are also interested in the effects

> of motivation categories and team affiliation on the amount
> lent. However, to protect lender privacy, individual loan amount is
> not available through Kiva data API. Therefore, for this analysis,
> we employ a proxy variable for the amount lent. We know the list
> of projects that each lender lends to, as well as the total amount
> lent to each project. We therefore assume that each lender to a
> project lends an equal amount. Once we apply this assumption to
> all projects, we have a proxy for the total amount lent by each user.

and the results they find:

Table 9 presents four OLS regressions using the proxy lending

> amount as the dependent variable. Independent variables in each
> regression are the same as those in Table 8. While the significance
> and direction of motivation categories and team effects remain the
> same as those in Table 8, it is informative to highlight the size of
> some of these effects. Specifically, a lender motivated by general or
> group-specific altruism lends $6 less per month than others, while
> those motivated by external reasons lend approximately $7 less. By
> contrast, a lender who sees Kiva as an effective development tool
> lends $5 more per month, while one motivated by religious duty
> lends $9 more. Again, when controlling for team affiliation (column
> 2), we find that a lender belonging to any team(s) lends $31
> more per month than those without any team affiliation, while each
> additional team joined is associated with $16 more lent per month.
> Overall, the effects of motivation categories and team affiliation on
> amount lent is consistent with those on lending frequency.

Check out the article through http://adf.ly/8YlDA (skip the ad).

Good luck!


 
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