Saving Patterns and Probability of Success in Individual ...

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Saving Patterns and Probability of … 10 completion of savings. The savings goal is $1000 for the microenterprise and $1800 for the homeownership program. The “total months in the program” is not an appropriate measure for calculating the deposit ratio. This is because our definition of success is based on reaching a predefined savings limit. If participants continue to save beyond that limit, or if they stay in the program several months after they have completed savings and have stopped making more deposits it should not affect their deposit ratios. So, here we use the effective months of saving where only the number of months to complete savings is considered in the calculation of the deposit ratio. Of course, as explained before, for participants who have not completed their savings, total program months will always be equal to months to complete savings. In other words, this adjustment will only affect participants that have reached their savings limit but not the ones currently saving. The next explanatory variable in our model is N (i.e., average amount of deposit per bank visit). The inclusion of this variable is important to correct for a potential bias in favor of regular savers. To see this in a better light, imagine that a regular saver visits the bank every month and deposits $100 in his account while an irregular saver visits the bank once every quarter and deposits $300. Both have an average monthly deposit of $100. Therefore both savers have an equal chance of completing their savings within a given number of months, regardless of how many times they visit the bank. On average, 12% of all participants in the IDA program were inactive on 06/30/2005. This means that while this group of participants had open IDAs, they were not actively contributing to their accounts. Some had requested a leave of absence, while others were simply not making any 10
Saving Patterns and Probability of … 11 deposits. Inclusion of inactive accounts would have resulted in yet another bias in favor of regular savers which is not captured in N. This is because regular savers clearly cannot be inactive. Their high deposit ratio automatically indicates that the participant is active and is saving. While for savers with low deposit ratios, it is impossible to distinguish between irregular savers and those who have been inactive for a long period of time. To adjust for this bias, for every participant, we have looked at the last month they made a deposit and have compared it to the number of months to complete deposits. This ratio (L/J), also known as the activity ratio, has a direct relationship with the level of activeness in the program. Low values of this ratio suggest that the participant is inactive, while values close to one suggests activeness, regardless of frequency of bank visits. As an example, imagine John and Jane Smith both opened their accounts on 07/01/2004 and both have saved $800 in their accounts. John is inactive and made only two $400 deposits in the first two months of program while Jane is active and visits the bank once every quarter and deposits $200 in her account. By 06/30/05 both had spent 12 months in the program. John made his last deposit in the second month and Jane made her last deposit in May of 2005. The activity ratio for John is 0.16, while the activity ratio for Jane is 0.91. Inclusion of this variable among other explanatory variables conveniently separates the impact of regularity in saving from the level of activeness or inactivity in the program. Finally, due to differences in savings limits, ceteris paribus, savers in the microenterprise IDAs will be able to reach their savings goals relatively sooner than the homeownership savers. To account for this difference a dummy variable, P, was added to the model with the value (P=1) for the microenterprise and (P=0) for the homeownership IDAs. 11
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