kiva: Kiva crowdfunding data
In m-clark/noiris: Data sets

Description Usage Format Details Note Source Examples

From the Kaggle website description: Kiva.org is an online crowdfunding platform to extend financial services to poor and financially excluded people around the world. Kiva lenders have provided over $1 billion dollars in loans to over 2 million people. In order to set investment priorities, help inform lenders, and understand their target communities, knowing the level of poverty of each borrower is critical. However, this requires inference based on a limited set of information for each borrower...

kiva

Several data frames combined by id: kiva_loans, kiva_mpi_region_locations, loan_theme_ids, loan_themes_by_region. Original names and descriptions are provided.

id: Unique ID for loan
funded_amount: The amount disbursed by Kiva to the field agent(USD)
activity: More granular category
sector: High level category
use: Exact usage of loan amount
country_code: ISO country code of country in which loan was disbursed
country: Full country name of country in which loan was disbursed
region: Full region name within the country
currency: The currency in which the loan was disbursed
partner_id: ID of partner organization
posted_time: The time at which the loan is posted on Kiva by the field agent
disbursed_time: The time at which the loan is disbursed by the field agent to the borrower
funded_time: The time at which the loan posted to Kiva gets funded by lenders completely
term_in_months: The duration for which the loan was disbursed in months
lender_count: The total number of lenders that contributed to this loan
tags: No description provided
borrower_genders: Comma separated Male, Female, where each instance represents a single male/female in the group. This was broken out into separate counts for male and female.
repayment_interval
date

LocationName: region, country
ISO: some sort of unique abbreviation for country
country: country
region: region with in country
world_region: parts of the world
MPI: multidimensional poverty index
geo: (latitude, longitude)
lat: latitude
lon: longitude

id: Unique ID for loan (Loan ID)
Loan Theme ID: ID for Loan Theme
Loan Theme Type: Category name of type of loan
Partner ID: Unique ID for field partners

Field Partner Name: No description provided.
sector: No description provided.
Loan Theme ID: No description provided.
Loan Theme Type: No description provided.
country: No description provided.
forkiva: No description provided.
region: No description provided.
geocode_old: No description provided.
ISO: No description provided.
number: No description provided.
amount: No description provided.
LocationName: No description provided.
geocode: No description provided.
names: No description provided.
geo: No description provided.
lat: No description provided.
lon: No description provided.
mpi_region: No description provided.

Kiva has provided a dataset of loans issued over the last two years, and participants are invited to use this data as well as source external public datasets to help Kiva build models for assessing borrower welfare levels... With a stronger understanding of their borrowers and their poverty levels, Kiva will be able to better assess and maximize the impact of their work.

This is a merged version of the four data sets provided at Kaggle. I began with the largest dataset, loan_theme_ids, and joined the others by id to that one. While most did ids had a match across datasets, many did not match.

In terms of cleanup, the 'geo' variables were removed as they were just text strings of latitude and longitude coordinates. The borrower_genders column was broken out into male and female counts. Where possible, strings were converted to integer or date formats. Column names were made more appropriate, and rearranged to reflect conceptual information.

More context on the variables can be found at the links below, but specific information is sparse.

Known issues: Some character strings may have formatting issues.

License is CC0

Data link.