Saving Patterns and Probability of Success in Individual ...

Saving Patterns and Probability of … 1 

Saving Patterns and Probability of Success in 

Individual Development Accounts 

Edmund Khashadourian 

United Way of Greater Los Angeles 

March, 2007 

Contact information: 

Edmund Khashadourian 

United Way of Greater Los Angeles 

523 West 6 th Street 

Los Angeles, CA 90014 

(213) 808-6519 

ekhashadourian@unitedwayla.org 

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Saving Patterns and Probability of Success in Individual Development Accounts 

Abstract: 

An ample body of literature has formed analyzing the demographic attributes of savers and their 

impact on savings outcomes in IDA programs. But in the absence of detailed savings data 

samples from different IDA programs, the impact of certain structural factors (such as conditions 

set forth in a typical IDA savings plan agreement, including the required frequency of deposits, 

length of the saving period, etc.,) has not been extensively studied. This article puts forward the 

simple hypothesis that regular savers, regardless of their demographic backgrounds, average 

monthly deposits, and type of deposits (i.e., automatic or otherwise), stand a better chance of 

reaching their savings goals. The methodology employed in this article differs from other studies 

in that in lieu of explaining Average Monthly Net Deposits (AMND), it is focusing on 

completion of the total required savings using a binary response model. In addition, a new set of 

data from the United Way of Greater Los Angeles, Saving for the American Dream IDA 

program was utilized for this purpose. Based on the findings, United Way of Greater Los 

Angeles (henceforth UWGLA) is exploring options to replace the current fixed match model 

with a different mechanism that will allow more flexibility in rewarding regularity in savings. 

One possible option is to use a points system for calculating the accrued match. 

Key words: Individual Development Accounts (IDA), Saving patterns, deposit ratios, Logit 

model, active savers, probability of success, United Way of Greater Los Angeles 

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1- Introduction: 

The importance of regularity in saving patterns and its effect on changing savers’ behaviors in 

IDA programs is a topic that has attracted much attention in recent years. 1 This issue not only 

affects the design of a successful program, but also is of relevance in the analysis of program 

costs. While there is some level of unanimity among IDA practitioners about the importance of 

regularity in changing the behavior of low-income savers, the jury is still out on the issue of what 

exactly results in this change. 2 

Many recent studies focus on the demographic attributes of savers such as age, ethnicity, sex, 

and marital status and their impact on savings outcomes. In a series of articles, researchers, 

mostly from the Center for Social Development at the University of Washington in St. Louis, 

have studied the IDA savings outcomes using data from the American Dream Demonstration 

(ADD) project in Tulsa, Oklahoma. For example, Curley and Grinstein-Weiss (2003), studied the 

rural-urban savings performance and concluded that Caucasians were among the least saving of 

most ethnic groups in both the rural and urban areas. In analyzing the behavior of the microenterprise 

IDA savers, Ssewamala and Sherraden (2004) concluded that while level of education, 

income and ownership of other assets positively influenced savings in micro-enterprise IDAs, 

number of children in the household had a negative impact on savings outcomes among this 

group. This result was partly corroborated by Grinstein-Weiss and Ssewamala (2005). They 

recognized that the cost of raising children was a factor that impacted savings outcomes in an 

adverse fashion, but partly offsetting that hypothesis, their analysis also confirmed the relevance 

of the institutional view of savings. Using a Hierarchical OLS regression model, they concluded 

that proper institutions led to increased savings levels even among households with children. 

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Studying the behavior of post-secondary IDA savers, Zhan and Schreiner (2005) concluded that 

married "education savers" saved much less than other married participants. They also 

demonstrated that single men saved more than both single women, and married men. Married 

women saved the least among the four groups. According to the authors, it follows therefore that 

women, especially married women, face more barriers to pursue postsecondary education. 

Finally, when looking at all three areas of the IDA program, Grinstein-Weiss, et. al. (2006) 

found evidence in support of generally higher savings levels among married vs. unmarried IDA 

participants. Perhaps this somewhat contradictory finding suggests that married participants are 

committed more to investing in business and homeownership than education. Other studies have 

also demonstrated positive outcomes resulting from institutional factors such as the use of direct 

deposit facility and provision of financial literacy training (see for example Schreiner, [2001], 

and Zhan and Schreiner [2005]). 

An ample body of literature has formed around analyzing the demographic attributes of savers 

and their impact on savings outcomes in IDA programs. However, more research is needed to 

assess the impact of other structural factors on IDA savings outcomes such as conditions set 

forth in a typical IDA savings plan agreement, including the required frequency of deposits, 

length of the saving period, existence or lack thereof a minimum required deposit, and maximum 

lifetime matched savings limits. From a program design perspective, these can be important in 

shaping participants’ behavior in an IDA program. If a program stipulates a minimum lifetime 

savings limit, then reaching that limit becomes an important milestone in assessing success. 

Clearly, a combination of all of the above determines, at least in part, the percentage of 

successful participants who complete their savings in an IDA program. However, it is often 

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difficult to separate the impact of every single aspect of the saving plan on the probability of 

success in reaching the minimum IDA savings limit. In other words, even if regularity of 

deposits in a given sample of accounts has a meaningful correlation with success in reaching the 

savings limit, it is not clear how this relationship will hold, for example, if the plan required a 

longer saving period or if a higher maximum savings limit was stipulated. 

This article puts forward the simple hypothesis that regular savers, regardless of their 

demographic backgrounds, average monthly deposits, and type of deposits (i.e., automatic or 

otherwise), stand a better chance of reaching their savings goal. 3 The methodology used here 

differs from other studies in that in lieu of explaining Average Monthly Net Deposits (AMND), 

it is focusing on completion of minimum required matched total savings. At every given point in 

time, there are two groups of participants: those who have completed their total minimum 

required savings, and those who have not. The article also discusses inactive savers, an issue that 

is not usually addressed separately in studies on this subject. In order to test the main hypothesis 

of the article, a binary response model is constructed. And, instead of using the ADD data, a 

newer but smaller set of data from 2005 is used. This data set includes 781 IDA accounts from 

UWGLA’s micro-enterprise and homeownership IDA programs. 

This article is structured in the following way. Section 2 reviews the main features of 

UWGLA’s IDA program including enrollments and highlights of the saving plan agreement. 

Section 3 analyzes the saving behavior of IDA participants across the United Way’s partner 

agencies. The main variables of the model that will be used later in the analysis are introduced in 

this section. Section 4 explains the binary response model for saving behavior. The results of the 

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estimation are also discussed in that section. Final comments and conclusion are discussed in 

section 5. 

2. An Overview of UWGLA’s IDA Program 

Since 2001, UWGLA has received three five-year Assets for Independence (AFIA) grant awards. 

The UWGLA IDA network is composed of 24 IDA program partners throughout Los Angeles 

County that are charged with enrolling and case managing accounts. Table 1 shows the main 

features of the UWGLA’s IDA program. Maximum time limit of savings is 24 months. 4 With 

average monthly deposits of $42 to $83, participants are expected to complete their $1000 

savings within 12-18 months in the microenterprise program. Similarly, in the homeownership 

program, with average monthly deposits of $75 to $100, participants can complete their $1800 

minimum savings limit within 18-24 months. Match rate is the same (2:1) across all program 

areas. 5 

Table 2 provides information on the overall performance of the UWGLA’s IDA program in 

terms of enrollments and asset purchases. By 06/30/2005, program partners had helped 833 

participants open their IDA accounts, of which 66% were females. 6,7 Almost 69% of all accounts 

were in the homeownership program. 8 While the first ever IDA deposit by a program participant 

was made in February of 2002, a cursory review of the numbers in Table 2 reveals that the bulk 

of enrollments took place in 2004 with 391 new accounts opened in that year alone. In other 

words, 46% of all accounts were opened in 2004. For this reason, even after 4 years since the 

start of the program, by 06/30/2005, the cutoff date of our sample, a typical IDA client had only 

spent an average of 14 months in the program. 

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3. Saving Patterns 

Table 3 summarizes the savings characteristics of UWGLA’s IDA participants as of 06/30/2005. 

Starting from the leftmost column, the table lists partner agencies (identified by numbers only) 

with the homeownership partners on the top rows, followed by a “Total HO” row that reports the 

total number of homeownership accounts in column two and average values in the rest of the 

columns for this group of partners. The microenterprise IDA partners are listed on the bottom of 

the table followed by their total/average row, dubbed Total ME. The second column on the left 

shows the number of accounts ever opened- including the ones that have already been closed due 

to termination or asset purchase. For each agency, the number in column three lists per-person 

average savings based on account activity through 06/30/2005. This column only includes 

matched portion of participants’ deposits. In other words, for individuals who have saved beyond 

their required matched savings limit, only the matched portion has been included to show a better 

picture of average savings compared to the matched limit. 

An average of total months spent by participants in each program is shown in column four. This 

number is different than the average number of months participants needed to complete their 

savings. That information is shown in column 5. The numbers in columns four and five will be 

the same for participants who are still saving. For participants who have completed their savings 

but have not yet acquired assets and so are still in the program, the number in column four will 

be greater than the number in column five. For example if John Smith opened his IDA account in 

July of 2004 and completed his savings in May of 2005, by 06/30/05 he had spent 12 months in 

the program (column 4), while his effective months of saving were only 11 months (column 5). 

Column 6 shows the average number of times participants made deposits while in the program. 

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For example, on the row titled “Total HO”, while a typical homeownership IDA participant had 

spent 13 months in the program as of 06/30/05, s/he had made only 7 deposits during this period. 

At any given point in time a group of participants will be inactive. According to the savings plan 

agreement signed at the time of enrollment, participants are expected to deposit at least once 

during every quarter. In other words, savers can skip a maximum of two consecutive deposits in 

the saving cycle. However, practically, because most of the account statements are reported on a 

quarterly basis, usually 15 days after the end of the quarter, inactive participants can go 

unnoticed for a period of 5 to 6 months. There is no maximum limit on deposits; therefore 

participants can make up for missed deposits by making a large deposit at any time. Information 

in column 7, when compared to column 5, gives us a measure of inactivity in the program. That 

information will be used later in our binary response model discussed in the next section. Finally, 

column 8 shows the deposit ratio. This ratio measures the number of deposits to total months 

until completion of deposits (col. 6 divided by col. 5). This indicator is used to study the 

regularity of saving. 9 

4. A Binary Response Model of Saving Behavior 

As of 06/30/2005, United Way partners had opened 883 Accounts. However, of that number 102 

had not yet established a savings pattern due to their recent enrollment status. From the 

remaining 781 accounts, 236 were enrolled in the microenterprise IDAs and the rest in the 

homeownership program (see totals in Table 3). By 06/30/2005, 255 had either reached the 

savings limit or had already purchased assets in both the homeownership and microenterprise 

programs. The remaining 525 were either still saving, or were inactive, or had been terminated 

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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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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 

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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. 

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In converting this theoretical relationship into an econometric equation, consider the conditional 

probability of Prob=1 to depend on a function of the above-mentioned explanatory variables as 

shown in equation 1: 

Pr( Prob 

= 1 

I, 

M , N, 

L , P, 

βi 

) = 1− 

F( 

−( 

I, 

M , N, 

L , P), 

βi) 

J 

J 

(1) 

where β s are unknown parameters and F is the functional form of the relationship (i.e., the type 

i 

of the binary model). One of the most commonly used functional forms in the specification of a 

binary equation is the Logit model, which is based upon a cumulative distribution model for 

logistic distribution. 11 Due to the popularity of this functional form, the Logit model was used to 

estimate the parameters in equation (1). Our main task, to recap, is to measure the probability of 

success in completing savings based on values of the explanatory variables. The results from 

estimation of this model are shown in Table 4. 12 Reviewing the numbers reported in this table 

shows that all parameters have the expected signs and are all statistically significant at the 0.01% 

level. 

The results suggest that the probability of participants’ success in completing savings is directly 

related to the number of months since opening account and average amount of deposit during 

each visit to the bank. 13 Further, type of the program (i.e., microenterprise or homeownership) 

has a significant impact on the probability of success with the microenterprise savers standing a 

better chance of success in light of their lower savings limit. Obviously, active savers, as 

measured by the (L/J) ratio, have a much higher chance of success in terms of reaching their 

savings limit. 

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Last but not least, the results from Table 4 suggest that, ceteris paribus, people with a higher 

deposit frequency have a higher probability of completing savings. This finding is significant. 

On the one hand, if we assume that effective case management helps establish a regular saving 

pattern for IDA savers, then it could be argued that by providing the proper structure, IDA 

programs will have a net positive impact on accumulation of savings for low-income households. 

On the other hand, controlling for all other variables, if there is a positive association between 

saving regularity and the probability of completing savings, then providing proper incentives to 

reward regularity, would help reduce attrition rates across IDA programs. The implications of 

this result for program cost analysis and scale considerations could be the subject of a future 

study. 

Since in a logit model the estimated parameter values do not represent the marginal impacts of 

their respective variables, graphical techniques are utilized to show how changes in one variable 

can impact the overall probability of success while other variables in the model are held at their 

average levels, or any other constant level, for that matter. 14 In this respect, Figure 1 shows the 

relationship between probability of success in homeownership IDAs and deposits ratio for all 

active participants who stay in the program for 24 months while their amount of deposit per bank 

visit is measured at the average level for the entire sample. Simple inspection of the graph 

suggests that there is a 90% chance for participants to complete savings in 24 months if they visit 

the bank (i.e., make a deposit) on average more than 2 times per quarter (or at a deposit ratio of 

0.8). Similarly, there is an 80% chance of success for people who visit the bank only 7 times in a 

year. The implications of this relationship for a study of attrition rates are noteworthy. For 

example, in Table 3, the average deposit ratio for the UWGLA’s homeownership IDA program 

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is 0.67. Inspection of the graph in Figure 6, suggests that the probability of success at that rate 

will be less then 80%. In other words, current deposit ratios correspond to attrition rates in excess 

of 20%, which is in line with what UWGLA has experienced (24.2% in homeownership IDAs) 

in the past 4 years. 

In Figure 2, probability of success is plotted against the number of months in the program for 

two different groups of active savers in the homeownership IDAs; those with a more regular 

saving pattern (i.e., deposit ratio of 0.8- the upper curve) and savers with a less regular pattern 

(deposit ratio of 0.6). Among other things, this figure shows the importance of a longer saving 

period on the overall probability of success in the IDA program. More precisely, increasing the 

minimum saving period from 18 to 22 months could increase the chances of success by over 

30%, when people are depositing the average amounts listed in Table 3. 

5. Final Comments 

The findings from an analysis of saving patterns in UWGLA’s IDA program suggest that 

allowing for a longer saving period helps active participants reach their savings goals. A direct 

relationship between the length of the program and probability of success suggests, albeit in an 

indirect fashion, that there is no indication that longer saving periods can discourage savers and 

result in increased drop outs. Also, it can be shown that by maintaining a more regular saving 

pattern, participants could increase their chances of success in reaching the minimum IDA 

savings limit even if this does not amount to increases in average dollars deposited per month. 

This finding implicitly underlines the importance of personalized case management and financial 

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literacy training in IDA programs and lends evidence in support of the IDAs’ ability to 

encourage and promote savings. 

Finally, if there is a positive association between saving regularity and the probability of success, 

then providing additional incentives to reward regularity, would help reduce attrition rates across 

IDA programs. Based on this finding, UWGLA is exploring options to replace the current fixed 

match model with a different mechanism that will allow more flexibility in rewarding regularity 

based on a points system for match calculations. A similar pilot project will be launched in 2007 

to evaluate the savings outcomes, where the number of deposits will directly influence the 

amount of the savings bonus each participant will be entitled to receive. 

References: 

1. (2005), “Assets, An Update for Innovators”, CFED, Number 1, (http://www.cfed.org) 

2. Greene, William H. (1991), Econometric Analysis, Maxwell Macmillan. 

3. Grinstein-Weiss, M., Wagner, K., and Ssewamala, F. M., (2005), “Saving and Asset 

Accumulation among Low - Income Families with Children in IDAs”, Children and 

Youth Services Review, vol. 28, 2, pp. 193-211. 

4. Horowitz, Joel L., and Savin, N. E., (2001) “Binary Response Models: Logits, Probits 

and Semiparametrics”, Journal of Economic Perspectives, vol. 15, No. 4, pp43-56. 

5. Mills, Gregory, Rhiannon P., Larry O., and Donna D. (2004) “Evaluation of the 

American Dream Demonstration – Final Evaluation Report”, Abt Associates, Inc. 

(http://www.abtassociates.com/reports/Final_Eval_Rpt_8-19-04.pdf) 

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6. Moore, A., Beverly S., and several authors (2001) “Saving, IDA Programs, and Effects of 

IDAs: A Survey of Participants”, Center for Social Development, Washington University 

in Saint Louis. (http://gwbweb.wustl.edu/csd/Publications/2001/shortsurveyreport.pdf) 

7. Schreiner, Mark, (2004) “Program Costs for Individual Development Accounts: Final 

Figures from CAPTC in Tulsa”, Center for Social Development, Washington University 

in Saint Louis. 

(http://gwbweb.wustl.edu/csd/Publications/2004/Research_Report_IDA_Program_Costs. 

pdf ) 

8. Schreiner, M., Sherraden, M., and several other authors, (2001), “Asset Accumulation in 

Low-Resource Households: Evidence from Individual Development Accounts”, Paper for 

the Federal Reserve System’s Second Community Affairs Research Conference 

“Changing Financial Markets and Community Development”, 

(http://gwbweb.wustl.edu/users/csd/). 

9. Zhan, M. and Schreiner, M., (2005), “Saving for Post-Secondary Education in Individual 

Development Accounts”, Journal of Sociology and Social Welfare, vol. 32, 3, pp 139- 

163. 

10. Ssewamala, F. M., and Sherraden, M., (2004), “Saving for Microenterprise in Individual 

Development Accounts: Lessons from the American Dream Demonstration”, research 

report, Center for Social Development, Washington University, retrieved at 

(http://gwbweb.wustl.edu/csd). 

11. Curley, J., & Grinstein-Weiss, M., (2003) “A comparative analysis of rural and urban 

saving performance in Individual Development Accounts” [Special issue]. Social 

Development Issues, vol. 25, (1 & 2), 89-105. 

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12. Grinstein-Weiss, M., Zhan, M., and Sherraden, M., (2006), “Saving performance in 

Individual Development Accounts: Does marital status matter?” Journal of Marriage and 

Family, 68 (February 2006), 192-204. 

13. Grinstein-Weiss, M., Wagner, K., and Ssewamala, F. M., (2006) “Saving and asset 

accumulation among low-income families with children in IDAs” Children and Youth 

Services Review vol. 28, 2, pp. 193-211. 

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Table 1- UWGLA's IDA Saving and Match Information 

Education/ 

Microenterprise 

Homeownership 

(New partners - 

2005) 

Homeownership 

(Old partners - 

before 2005) 

Minimum 

Savings 

(monthly) 


Duration 

Max. 

Matchable 


$42 - $83 12- 24 months $1000 

$75 - $100 18 - 24 

months 

$75 - $100 18 - 24 

months 

$1800 

$1800 

Match Cap 

(2:1 ME) 

(2:1 and 3:1 HO) 

$2000 per individual 

/ 

$4000 per household 


/ $10800 per 

household 


/ $7200 per 

household 

Total IDA 


(savings + 

match) 

$3000 individual 

/ $6000 per 

household 

$7200 per 

individual / 

$14400 per 

household 

$5400 per 

individual / 

$10800 per 

household 

Table 2- IDA Annual Results (Note that D=D1+D2 and C=D+G) 

HO: Homeownership 2002 2003 2004 2005* All Years 

ME: Microenterprise Total Total Total Total Total 

HO ME HO ME HO ME HO ME HO ME 

A- Number of Partners 3 2 12 5 12 6 12 6 

B- Number of New Accounts Opened 37 26 182 60 257 134 98 39 574 259 

C- Number of Open Accounts (end of year) 36 22 194 72 351 182 405 171 

D- Number of Active Savers 36 17 173 44 285 153 353 139 

D1- Active Savers Reaching Savings Goal 4 1 19 9 43 41 108 61 

D2- Active Current Savers 32 16 155 35 241 112 242 78 

E- Purchased Assets 0 0 2 3 9 9 5 29 16 41 

F- Number of Terminations 1 4 22 6 77 16 39 10 139 36 

G- Inactive Accounts 0 5 20 28 66 29 52 32 

*- Data as of 06/30/2005. The numbers do not reflect activity of the new partners under the third AFIA grant 

received in 2004. 

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Table 3- IDA participants’ saving patterns 

1 2 3 4 5 6 7 8 9 10 

No. of 

Accounts 

Average 

Total 


Months in 

Program 

Months to 

Complete 


Number of 

Deposits 

Last 

Deposit 

Month 

Deposit 

Ratio 

Average 

Deposit 

per Bank 

Visit 

Average 

Deposit per 

Month 

Agency 1 35 $991 19 17 11 14 0.58 $107 $54 

Agency 2 36 $890 15 11 6 8 0.53 $158 $88 

Agency 3 33 $1,127 13 13 11 11 0.79 $105 $83 

Agency 4 59 $1,180 15 13 10 12 0.77 $133 $100 

Agency 5 101 $384 8 8 4 6 0.59 $81 $55 

Agency 6 41 $1,324 20 13 9 12 0.74 $161 $126 

Agency 7 36 $1,046 17 13 8 11 0.64 $134 $99 

Agency 8 29 $1,068 13 12 10 10 0.82 $126 $105 

Agency 9 48 $832 10 9 7 8 0.82 $115 $94 

Agency 10 65 $882 13 12 7 9 0.62 $120 $77 

Agency 11 47 $570 12 12 6 8 0.54 $104 $56 

Agency 12 15 $1,525 7 7 5 6 0.77 $293 $227 

Total HO 545 $885 13 11 7 9 0.67 $122 $85 

Agency 1 67 $886 17 13 10 12 0.77 $100 $71 

Agency 2 38 $518 16 13 6 9 0.49 $108 $48 

Agency 3 18 $684 15 12 9 10 0.70 $78 $58 

Agency 4 38 $459 8 7 4 6 0.69 $112 $83 

Agency 5 46 $696 15 12 7 9 0.65 $98 $63 

Agency 6 29 $319 15 13 5 8 0.47 $70 $30 

Total ME 236 $636 15 12 7 9 0.65 $97 $62 

Table 4- Estimation Results for Participants Savings Patterns 

Variable Coefficient Std. Error z-Statistic Prob. 

C – (Constant term) -35.44 4.39 -8.07 0.00 

I - (Months in the program) 0.47 0.05 10.09 0.00 

M - (Deposit Ratio) 8.87 1.29 6.85 0.00 

N - (Average deposit per visit) 0.04 0.00 8.58 0.00 

L/J - (activity ratio) 14.75 3.92 3.76 0.00 

P - (Me / Ho - Dummy Var.) 3.55 0.43 8.26 0.00 

McFadden R-squared = 0.72 

LR statistic (5 df) = 707.00 

Probability (LR stat) = 0.00 

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Figure 1- Probability of Completion of Savings in 24 Months 

for Active Participants with Different Deposit Ratios. 

1.0 

0.8 

Probability 

0.6 

0.4 

0.2 

0.0 

0.4 0.5 0.6 0.7 0.8 0.9 1.0 

Deposit Ratio 

Figure 1- Probability of Completion of Savings for Active Participants 

in Homeownership IDAs under two assumptions about saving regularity. 

1.0 

0.8 

Deposit Ratio=0.8 

0.6 

0.4 

0.2 

Deposit Ratio=0.6 

0.0 

10 12 14 16 18 20 22 24 

Months in the Program 

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1 IDAs or Individual Development Accounts are special savings accounts offered to low-income households. These 

accounts allow low income people to save money, in most cases up to 5 years, and earn a match in order to invest in 

a first-time home, business, education, or other assets. The match rates vary depending on the design of the program. 

In addition, in many cases there are minimum and maximum contribution limits imposed on these accounts. IDA 

programs are generally partly supported by federal and state dollars and partly by contributions from foundations 

and non-profit organization. 

2 While differences of opinion exist among IDA practitioners with regards to what actually changes behavior, two 

different aspects of this change need to be pointed out. One can look at “saving” as a process, or at “savings” as the 

stock of accumulated financial wealth. Both are important in impacting behavior but through different mechanisms. 

While committing to a regular saving plan clearly helps individuals to become fiscally responsible and to manage 

money in a more effective way, higher savings usually equip the saver with a higher degree of risk-taking capacity 

in a capitalist economy. In other words, if the proportion of the amount saved is considerable relative to one’s 

monthly flow of income, this would most likely impact the individual’s decision making pattern. 

3 A similar point was first broached by Schreiner et. al. (2001), pp 12, in the following comment: “Although 

causality is difficult to determine, some evidence suggests that frequent depositors accumulate more than infrequent 

depositors.” This result was also corroborated by Curley and Grinstein-Weiss (2003). 

4 In practice, this has become a soft restriction. 

5 Before 2005, UWGLA offered a maximum of 2:1 match on all IDA savings. However, in the newest round of 

funding due to rapid increases in area home prices, it has raised the match rate in the homeownership IDAs to 3:1 

for all program enrollees after June of 2005. This last group of participants has not been included in the data pool 

used for the present study, as by 06/30/2005 they had still not established a saving pattern long enough to include in 

the study. 

6 The number of enrollments in UWGLA’s IDA program has increased rapidly since 2005. As of 12/31/2006, over 

1300 accounts had been enrolled in the program. 

7 The gender distribution of UWGLA’s IDA participants is in line with the national average reported by CFED. (See 

“Assets” Nov.05, pp7). 

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8 Enrollment in large numbers in the post-secondary IDAs is not expected to take place before January of 2006. 

Only a handful of people have enrolled in the post-secondary IDAs as of the fourth quarter of 2005. 

9 A deposit ratio close to one shows a regular saving pattern, while a deposit ratio of less than 0.6 shows an irregular 

saving pattern. 

10 It is not important if the participant has made a purchase, or even has decided to withdraw from the program after 

reaching the savings limit. The focus here is just to see what types of saving patterns result in a higher probability of 

success in completion of savings. 

11 For a more in dept analysis of the Logit and other binary response models see Greene (1991) or Horowitz and 

Savin (2001). 

12 The Eviews v.4 software package was used for the estimation. This software is using an iterative maximum 

likelihood process for the estimation of the parameters and their asymptotic standard errors. 

13 As explained before, this is different than average monthly deposit per participant. 

14 To clarify this point let us consider the variable N with its estimated parameter of 0.04 in table 8. If all variables 

remain at their current levels, a one-dollar increase in the average amount saved per deposit will not increase the 

probability of success by 0.04. The binary response models are different than regular regression analyses in that 

estimated parameters do not represent the marginal effects of the regression equations. 

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