learn$ave Project: Final Reporta greater role in predicting savings outcomes than dofinancial characteristics at baseline.Dissaving patternsDuring the saving period, participants had full access totheir own deposits in their learn$ave account <strong>and</strong> couldwithdraw their own funds at any time <strong>and</strong> for any reason,while forfeiting a proportional amount of matched creditsif it was done for non-accredited purposes. In fact, morethan a quarter of participants “dissaved,” that is, theywithdrew their own funds for a purpose other thaneligible goals in the project.The incidence of dissaving was calculated by comparingthe highest value of savings eligible for matched creditsat any time during the saving period with the value ofsavings eligible for matched credits at the end of theprogram. If participants savings declined from theirhighest or “peak” level this would reflect an un-matchedwithdrawal or dissaving. The occurrence of dissavingis not unusual in a savings instrument <strong>and</strong> even widelyavailable registered instruments such as RegisteredRetirement Savings Plans may dissuade dissaving butshow patterns suggesting it is not infrequent. Dissaving isalso not necessarily negative. Because money is fungible,learn$ave participants had to make decisions each monthabout how much to deposit into their learn$ave account<strong>and</strong> dissaving may have been a very rational attempt toreallocate money across the learn$ave <strong>and</strong> other financialcommitments.Just as baseline financial characteristics did not neatlypredict less than maximum or “early” saving, neither didthey predict dissaving. As illustrated in Figure 5.5, thosewith lower incomes at baseline, those who <strong>report</strong>eddifficulties in paying bills at baseline <strong>and</strong> those who werenot working when they entered the project were not morelikely to dissave.As noted, dissaving may have been used as a strategyfor participants to reallocate money because of a changein circumstances from one month to the next. But dissavingmay also have reflected changes in what participantsfelt they needed to save to afford (with matched credits)their planned investment in adult learning. For example,if a participant changed his or her goal from course A tocourse B costing less, then withdrawing the excess fundsfrom the learn$ave account would make sense to avoidholding up funds in the learn$ave account that would notbe used for a matched withdrawal <strong>and</strong> could be used inother ways. To differentiate between these two motivesfor dissaving, the incidence of dissaving was comparedacross participants based on their total savings. Theseresults are presented at Figure 5.6.Dissaving was most common among participants whosaved less than the maximum, <strong>and</strong> specifically thosewith savings of $120 to $999. Interestingly, participantswho were unsuccessful savers, i.e., those with less than$120 in net learn$ave savings were not as likely to drawdown their learn$ave account <strong>and</strong> dissave. This findingsuggests that not only were these participants very lowsavers, they were largely inactive or even disengagedaccountholders.Dissaving was even less common among participantswho reached the maximum savings during the savingperiod <strong>and</strong> very uncommon among the subset who werethe “early savers” who reached the maximum quickly. Thissuggests that participants made unmatched withdrawalsfrom their account for reasons other than changing theirminds about how much money they wanted to invest inadult education. Most likely then is that dissaving wasdone to manage changes in circumstance <strong>and</strong> rebalancefinancial commitments. However, this hypothesis shouldpredict that those with the lowest incomes <strong>and</strong> the lowestsavings were more likely to dissave, which is not consistentwith the findings either (figures 5.5 <strong>and</strong> 5.6).Interaction of saving <strong>and</strong> dissaving patternsWe suggest that the combined savings <strong>and</strong> dissavingresults reveal at least five different patterns in the useof the learn$ave account, offering more nuance than ouroriginal three typologies using savings data alone.As summarized in Table 5.4, once both saving <strong>and</strong>dissaving are considered, there was some diversity inthe observable patterns among participants in learn$ave.“Unsuccessful” savers, who saved little if anything, didnot qualify for even the first dollar of matched credits <strong>and</strong>saw little, if any, real financial benefit from the project.But it is also unclear if their financial circumstancesprevented them from saving more since they neitherwithdrew funds for non-project purposes nor did theyhave greater financial constraints at the time they enteredthe project. It is likely that they lost interest in the projectshortly after enrolling <strong>and</strong> then paid little attention totheir learn$ave account for either deposits or withdrawals.Their behaviour would suggest they essentiallydropped out of the project. Nevertheless, for the purposeof the analysis, they remain in the research sample as ourresearch design uses an “intent to treat” model.“Struggling” <strong>and</strong> “moderate” savers are distinguishablefrom each other only in terms of the amounts finallysaved <strong>and</strong> the likelihood that withdrawals for purposesoutside the project were made during the saving period.Both groups showed similar baseline characteristics aspredominantly Canadian-born <strong>and</strong> having low levels of52 | Chapter 5 <strong>Social</strong> <strong>Research</strong> <strong>and</strong> <strong>Demonstration</strong> <strong>Corp</strong>oration
learn$ave Project: Final ReportFigure 5.5 Proportion of Program Group Participants with Savings Lower than their Peak, by Baseline Participant Characteristics100%100%80%80%60%60%40%40%20%20%0%100%Born inCanadaImmigrant(4 years or less)(a) Immigrant Status at Baseline***Immigrant(more than 4 years)0%100%High Schoolor LessSomePostsecondaryCollege/Trade BA or More(b) Highest Education Level at Baseline***80%80%60%60%40%40%20%20%0%Below 3030 to 40(c) Age at BaselineOver 400%Below $10,000 $10,000 to $20,000 Over $20,000(d) Income at Baseline*100%80%60%40%20%0%NoYes(e) Have Difficulties in Paying Bills at BaselineSource:Note:Participant Management Information System <strong>and</strong>Baseline Survey (for participant characteristics)Sample size: 2,388 program group participants.Levels shown have been adjusted for differences inbaseline characteristics, including research group,learn$ave site, gender, age group, highest level ofeducation, marital status, presence of children,immigration status, activity limitation, labour forceparticipation, household income, monthly paymentsfor household expenses, difficulty making payments,use of household budget <strong>and</strong> future time perspective.Significant difference levels between categories areindicated as * = 10% <strong>and</strong> *** = 1%.<strong>Social</strong> <strong>Research</strong> <strong>and</strong> <strong>Demonstration</strong> <strong>Corp</strong>oration Chapter 5 | 53
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