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.
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