Saving Patterns and Probability of Success in Individual ...

Saving Patterns and Probability of … 9
from the program.
Our goal in this section is to test the hypothesis that regular savers stand a better chance of
reaching their required savings limit, ceteris paribus. For this purpose, a regression analysis of
savings is conducted where the dependent variable can take on only two values: one, if the
accountholder has completed savings, and zero if s/he has not. In addition to a constant term,
success or failure in completion of savings is explained by the number of months in the program
(total months since opening account), the deposit ratio (i.e., the ratio of number of deposits over
months to complete deposits– see table 3 above), average deposit per bank visit, activity ratio
(explained below), and finally, a dummy variable for program type (homeownership vs.
microenterprise) in order to capture the effect of differences in savings limits in the two
programs.
In Table 4, the variable Prob denotes the dependent variable. As mentioned above, this is a
binary variable that takes on the value 1 if the participant has reached the savings goal in either
one of the programs, and 0 otherwise. 10 Though the dependent variable takes on only two values,
the binary response equation could basically fit any number between zero and one (hence the
probability of success) based on the values of estimated parameters and the given explanatory
variables. Among the explanatory variables, C denotes the constant term, and I measures months
in the program. For every participant, the first month of deposit is taken to be the first month in
the program. Participants stay in program for as long as they maintain a positive balance in their
accounts, usually until purchase of asset or termination. Variable M measures the deposit ratio.
As explained above, this variable is the ratio of the number of deposits to months until
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