A drop out rate or a retention rate measures the percentage of clients that leave a program in a specified period of time. Drop out rates are measured using different formulas and definitions by different microfinance institutions. In order to understand an institution's drop out rate and how it compares to other institutions' rates, it is necessary to know:
We specify the formula used by each charity when reporting the drop out rate.
For more, see "The Challenges of Measuring Client Retention" (PDF), a report on this issue by the SEEP Network, and "Estimating Client Exit Rate" (PDF), a note by the microfinance rating organization M-CRIL.
From the blog:
We discuss microfinance interest rates in-depth and provide an example on our blog. In short, to fully understand the cost of a loan to a borrower, it is necessary to know:
We present interest rates in three forms: monthly rate, Annual Percentage Rate (APR), and Effective Interest Rate (EIR). The APR is equal to 12 times the monthly interest rate, which does not incorporate interest compounding. The EIR fully incorporates "compounding," whose relevance to microloans is debatable. Using information provided to us by microfinance institutions, we estimate the cash flow schedule for a loan and calculate the internal rate of return (IRR) per period (i.e. the time between payments), incorporating all fees and savings requirements. APR is calculated as the IRR multiplied by the number of periods per year. EIR is calculated as (1+IRR)^(# of periods per year) - 1.
We have found the website MFTransparency (http://www.mftransparency.org/) helpful for understanding microfinance interest rates and comparing rates across institutions. See also, a report on "Microcredit Interest Rates" (PDF) from CGAP.
From the blog:
There are a number of ways that a microfinance institution may calculate and report its repayment history. A CGAP paper, "Measuring Microcredit Delinquency: Ratios Can Be Harmful to Your Health" (PDF) identifies the following measures:
For the purpose of understanding whether clients have consistently repaid loans in the past, we prefer an on-time, current, or cumulative collection rate as these have straightforward interpretations, and, when calculated correctly, are not susceptible to an institution's decision to write-off or reschedule loans.
In cases where an organization is able to provide details on value of loans disbursed, value of loans written off (i.e. loans that have been deemed uncollectable and been removed from the loan portfolio), rescheduled (i.e. loans whose terms, especially payment size and due date, have been changed), and in arrears (i.e. loans with overdue payments) and the length of the longest loan term, we have used this data to calculate a conservative (or lower-bound) collection rate.1
From the blog:
We seek evidence that microfinance institutions (MFIs) are generally serving people who have low incomes. Information on clients' standard of living may come from surveys of entering clients or surveys of a sample of all clients. We look for evidence that surveyed clients are representative of all new or all clients. To ensure representativeness, clients generally must be selected randomly from the full population under study or all (or nearly all) clients within a population under study must be surveyed. We also look for evidence that surveys were designed to yield credible information: that there were not incentives for clients to lie (perhaps because answers were linked to loan approval) and that the questions asked were easy for clients to answer. We prefer questions such as "do you own a TV?" to ones such as "what was your income last year?"
We do not have a single standard for what qualifies as poor. We use subjective judgements in incorporating this information into our overall rating of a microfinance organization, while presenting as full a picture as possible in our reviews so that donors can come to their own conclusions.
Some MFIs use 'poverty scorecards' to estimate their clients' poverty levels. Such scorecards use clients answers to questions such as "do you have electricity," "how many meals per day do you eat," and "what is your roof made of" to estimate clients' poverty level. The Grameen Foundation's Progress Out of Poverty Index2 is an example of this type of measurement system. We find such analyses useful for rough comparisons across organizations, but prefer to see the disaggregated responses to each of the questions along with the aggregated poverty level data, as we believe that this provides a clearer (though necessarily incomplete) picture of what life is like for an MFI's clients.
In cases where MFIs provide information on client incomes and these incomes are not adjusted for purchasing power parity (PPP), we perform this adjustment. We use price level data from the World Bank's International Comparison Program.3
Lower bound collection rate during time t = (D – W – R - A) / D where
For an example of how we've done this for an MFI, see our review of the Small Enterprise Foundation.
Grameen Foundation, "What is the PPI," http://www.progressoutofpoverty.org/understanding-the-progress-out-pover... (accessed December 2, 2010). Archived by WebCite® at http://www.webcitation.org/5ugLHsdci.
For an example, see our review of the Microloan Foundation