Evaluation of the Australian MoneyMinded Financial Literacy Program

Transcription

1 18 Jul 2006 Evaluaton of the Australan MoneMnded Fnancal Lterac Program Tm R.L. Fr, Sandra Mhajlo, Rosln Russell and Robert Brooks School of Economcs, Fnance and Marketng, RMIT Unverst Research Development Unt, Busness Portfolo, RMIT Unverst Department of Econometrcs and Busness Statstcs, Monash Unverst Abstract The ssue of rasng consumer fnancal lterac s a major ntatve across a range of countres. A ke dmenson of fnancal lterac programs s the sstematc evaluaton of ther effectveness. One element of such evaluaton nvolves partcpant ratng of program effectveness. An ssue n the modellng of such data s that partcpants tpcall rate multple elements of the program, and these multple ratngs result n a correlaton structure n ther responses. Ths paper eplores dfferent was of modellng the correlaton structure. We fnd strong dfferences n sgnfcance levels and margnal effects dependng on how the correlaton structure s modelled. Kewords: Fnancal lterac; Correlaton structure; Cluster samplng JEL Code: D14, C25 Acknowledgements: The research n ths project was funded b a grant from the Melbourne Centre for Fnancal Studes. We also wsh to thank the ANZ Bankng Corporaton for ther fundng of the evaluaton research that produced the data used n the analss. Further, we also wsh to thank the partcpants n the MoneMnded program for ther provson of data.

2 Evaluaton of the Australan MoneMnded Fnancal Lterac Program Abstract The ssue of rasng consumer fnancal lterac s a major ntatve across a range of countres. A ke dmenson of fnancal lterac programs s the sstematc evaluaton of ther effectveness. One element of such evaluaton nvolves partcpant ratng of program effectveness. An ssue n the modellng of such data s that partcpants tpcall rate multple elements of the program, and these multple ratngs result n a correlaton structure n ther responses. Ths paper eplores dfferent was of modellng the correlaton structure. We fnd strong dfferences n sgnfcance levels and margnal effects dependng on how the correlaton structure s modelled. Kewords: Fnancal lterac; Correlaton structure; Cluster samplng JEL Code: D14, C25 1

3 1. Introducton The ssue of ncreasng consumer fnancal lterac has become a major focus of publc polc and ntatve across a range of countres as ndvdual consumers are eposed to more sophstcated fnancal markets and greater ndvdual responsblt for ssues such as retrement savngs. In the Unted States of Amerca such programs nclude the Fnancal Lnks for Low-Income People (FLLIP program (see Anderson, Zhan and Scott, 2004, the Federal Depost Insurance Corporaton s (FDIC Mone Smart program (see Lons and Scherpf, 2004 and the Mone Talks program for teenagers (see Varcoe and Ftch, 2003; Varcoe, Martn, Devtto and Go, 2005, whle n Australa such programs nclude MoneMnded (see Russell, Brooks and Nar, A ke to the ongong mprovement of fnancal lterac s the sstematc evaluaton of programs (Fo, Bartholomae and Lee, 2005; Lons, Palmer, Jaaratne and Schrepf, Well desgned and comprehensve evaluatons wll encourage best practce, mprove the effectveness of programs and help to nform publc polc (Fo et al., In ther revew of the lterature on the evaluaton of fnancal lterac programs n the US, Lons, Palmer, Jaaratne and Schrepf (2006 pont out that the program evaluatons tpcall nvolve a wde range of elements from summar statstcs on the number of partcpants, partcpant ratngs of the program and behavour change of partcpants as a result of partcpaton n the program. Whle studes of behavour change (Lons and Schrepf, 2004; Lons, Chang and Schrepf, 2006 are mportant n dentfng the longer term mpacts as dentfed n Fo, Bartholomae and Lee (2005, there are constrants on usng such approaches mmedatel on program concluson when the are not ted to specfc savngs tpe programs. Thus, the collecton of data from partcpants on ther satsfacton wth tranng s tpcall a core component of the evaluaton of fnancal lterac educaton programs. In ther evaluaton of the FLLIP program n the US, Anderson, Scott and Zhan (2002 collect data on partcpant satsfacton and n ther evaluaton nclude the reportng of summar statstcs on partcpant satsfacton levels wth ther tranng. A challenge n the analss of data collected on partcpants n such programs s the possblt that the same partcpant wll provde ratngs at multple tes throughout the 2

4 program. As a result, an analss of such data has to capture the correlaton structure nvolved n such responses. In other words, n cases where the data, at the ver least, ncludes multple evaluatons done b the same ndvdual, treatng the responses as ndependent can lead to erroneous results and conclusons. Indeed, n ths paper, we am to llustrate the mportance of dentfng and accountng for the correlaton structure b modellng the evaluatons of usefulness of the MoneMnded workshops. The plan of ths paper s as follows. Secton two provdes some detals on the MoneMnded fnancal lterac program. Secton three descrbes the evaluaton data that we use n our analss and outlnes the modellng framework. Secton four presents and dscusses the emprcal results. Secton fve contans some concludng remarks. 2. The MoneMnded Program The MoneMnded program s one of a range of communt bankng ntatves run b the ANZ Bankng Corporaton n Australa. The MoneMnded program has a broad objectve of assstng people to make nformed fnancal decsons and take control of ther fnances for ther future. The MoneMnded program conssts of s ke topc areas or modules separated nto 17 workshops. The s ke topcs are: Plannng and savng, Eas paments, Understandng paperwork, Lvng wth debt, Everda bankng and fnancal products and Rghts and responsbltes. The program allows ndvdual partcpants to choose whch workshops the wsh to complete. The program was developed, n part, as a response to the results from the frst-ever major surve of consumer fnancal lterac n Australa (see ANZ The results of ths surve dentfed that those of lower ncomes would beneft most from mproved fnancal lterac educaton. Further detals on the program and ts objectves are avalable from the MoneMnded webste ( The plot phase of the MoneMnded program nvolved an ntal roll-out stage wth the program beng facltated b the nvolvement of fve major communt groups. Prevous research has ndcated that delver of a matched savngs program through the nvolvement of communt groups has been successful (for detals see Russell and Fredlne, 2004; Russell and Wakeford, 2005; Russell, Brooks, Nar and Fredlne, 3

5 2005, 2006, and as such the approach of also nvolvng communt groups n the delver has also been etended to fnancal lterac educaton. 3. Partcpant Evaluatons and Modellng Framework At the concluson of each MoneMnded workshop partcpants were asked to provde a ratng on the usefulness of the partcular workshop the had just completed. Overall, a total of 112 ndvdual partcpants provded a total of 466 workshop evaluatons. The evaluatons were n the form of an ordnal ratng of the usefulness of the workshop. Ths ratng was provded on a fve-pont scale rangng from not at all useful (coded as 0 through to etremel useful (coded as 4. The structure of the MoneMnded program tself gave the partcpants the freedom to choose from 17 workshops wthn the s dfferent topcs. Thus, the number of workshops attended, and the number of evaluatons per ndvdual, vares across ndvduals and ranges from 1 to 12. A prelmnar statstcal analss of the data s reported n Brooks et al (2005. In common wth man evaluaton studes ths analss focussed upon cross tabulaton of data and treated the data as f t came from a smple random sample. The assumpton that the data came from a smple random sample s a smplfng assumpton as across the multple workshops the same partcpants have provded multple ratngs. Moreover, b focussng upon tabulaton, cross tabulaton and contngenc table testng the analss does not allow for the potental that a number of factors nfluence the evaluaton. Our analss focuses on two ke ssues relatng to the provson of multple ratngs b ndvduals: The dependence of the evaluatons on other covarates and the mpact of correlaton structure, arsng from the repeated measurements, n the evaluaton data. The modellng framework used allows us to eamne how, f at all, dfferent demographc and other characterstcs affect an ndvdual s opnon about the usefulness of a partcular workshop. To allow for the possblt that an ndvdual s evaluaton about the usefulness of a partcular workshop depends upon a set of characterstcs we wll assume that, for an partcpant, there s an underlng, contnuous varable that represents the 4

6 5 perceved level of usefulness of a partcular workshop. In turn, s determned b a latent regresson model 1 where: u + β ' The observed categorcal responses are then a result of the followng mappng between ths latent varable and the cut-off ponts, s: < < < f f f f f Usng ths mappng and assumng ~ N(0,1 u, we obtan an Ordered Probt model wth the probabltes gven b: Φ Φ Φ Φ Φ Φ Φ Φ ( 1 4 Pr( ( ( 3 Pr( ( ( 2 Pr( ( ( 1 Pr( ( 0 Pr( Pr( 4 3 ' 4 ' 2 ' 3 ' 1 ' 2 ' 1 β β β β β β β β j j P Ths Ordered Probt model can be estmated under a range of assumptons concernng the samplng scheme (or correlaton structure of the data. A naïve approach would be to gnore an structure n the data and treat all the evaluaton responses as separate and ndependent observatons. Ths s the approach taken to the samplng scheme b Russell et al (2005. That s, we could gnore the fact that those 466 evaluatons were provded b 112 ndvduals, dsregardng the realt that some ndvduals attended and evaluated multple workshops. Another characterstc of our data that needs to be addressed s that there ests an nherent program structure whereb the workshops themselves are grouped nto several modules. Ideall therefore, we would lke to at least account for the 1 There s no constant n the regresson model

7 correlaton structure at these multple levels, f not model t eplctl. At ths pont t s mportant to note that n cases such as ours, where the data s grouped at several levels, a mult-level modellng approach mght be the most sensble method. However, gven the small sample sze, whch s commonl a characterstc of the data obtaned from plot programs, ths approach s unable to be mplemented. Ths, on the other hand, does not mean that one should gnore the structure completel n such nstances. Recall that the log-lkelhood for the ordered probt s gven b: j ln L ( θ j ln( P j where: j 1 f j j 0 otherwse If we gnore the correlaton structure, then we estmate the parameters and the varance-covarance matr va the conventonal means. On the other hand, assumng that there are M groups (ndvduals, modules etc: G 1, G 2 G M that are ndependent of each other but wth wthn-group correlaton, then we could correct the varancecovarance matr for ths ntra-group correlaton. The followng etenson to the usual Huber/Whte/Sandwch robust estmator of varance, as frst proposed b Froot (1989, allows us to adjust the standard errors for ths tpe of cluster correlaton n the observatons: ( G' ( G ˆ ν Vˆ( u u Vˆ where Vˆ s the conventonal estmate of varance-covarance matr and ln L( θ contrbuton of the kth group to (Rogers, θ j j (G u k s the In order to llustrate the mportance of accountng for the correlaton structure n data, we estmate several models. Frstl, we take the naïve approach and assume that evaluatons are ndependent of each other, regardless of whether the are from the 6

8 same ndvdual or assocated wth workshops from the same module. Ths gves rse to Model 1. For Model 2 we eplot the potental correlaton at multple levels. In partcular, we assume that correlaton ests between evaluatons of those workshops that belong to the same module and that were gven b the same ndvdual. For eample, sa that an ndvdual attended three workshops each for Plannng and Savng and Lvng wth Debt. We assume that correlaton ests between hs/her evaluatons of the three Plannng and Savng workshops but not between these evaluatons and those of the three Lvng wth Debt workshops. As a result, ths ndvdual s evaluatons are treated as two separate clusters. We should note that the estmaton of Model 1 and of Model 2 wll eld dentcal estmates of the parameters of the ordered probablt model but wll eld dfferent standard errors of those estmates. Ths could have mportant mplcatons as we mght conclude that certan varables were or were not sgnfcant n determnng the evaluatons. Our Model 3 dffers from Model 2 b postulatng that perfect correlaton ests between evaluatons of workshops that belong to the same module and the same ndvdual. In other words, f an ndvdual attended all three Plannng and Savng workshops, we assume that he/she rated them all the same. Lookng at our MoneMnded data shows that ths s a reasonable assumpton as n 96% of cases where more than one workshop wthn a partcular module was attended, the workshops receved the same ratng n terms of usefulness. The mplcaton of the assumpton of perfect correlaton s that we onl requre one evaluaton from an ndvdual for an module that the attend. Our fnal model, Model 4 s an etenson to Model 3, wth the addtonal assumpton of correlaton wthn ndvdual evaluatons. In other words, ths essentall equates to assumng that partcpants rated the modules rather than ndvdual workshops and then correctng for correlaton between evaluatons done b the same ndvdual. As wth Models 1 and 2, the estmaton of Model 3 and of Model 4 wll eld dentcal estmates of the parameters of the ordered probablt model but wll eld dfferent standard errors of those estmates. 7

9 3. Results Descrptve statstcs for varables n our dataset are reported n Table 1. As shown the majort of our varables are bnar n nature. In addton to the usual demographcs such as age, gender and educatonal attanment, we also nclude measures of household sze n terms of number of adults and chldren under 18 n the household. The ncome varable s categorcal n nature, rangng from 1 ( less than $120 per week to 12 ( $1500 or more per week. The varable Uses Cheques s equal to 1 f a respondent uses cheques as a pament method and 0 otherwse. Schrener and Sherraden (2005 argue that balancng a chequebook and avodng bounced cheques requres math sklls and perseverance and sgnals fnancal sophstcaton. Thus, we nclude ths varable as a measure of respondents ntal fnancal sophstcaton/lterac. The last set of varables was created from a set of questons n the pre-tranng surve where the partcpants were asked f the felt the needed more fnancal knowledge n each of the areas. Incluson of these varables nto our models wll allow us to evaluate how useful the workshops were n terms of addressng each of those topcs. TABLE 1 ABOUT HERE The results of the ntal estmaton of our four models are reported n Tables 2A and 2B. Note there was no not useful at all evaluatons and therefore we were onl able to estmate 2, 3 and 4. As shown n Table 2A 2, for Model 1 we fnd a wde range of soco-demographc varables to be sgnfcant. All of the age and household structure varables are found to be sgnfcant, along wth ncome and the varables relatng to unverst educaton and the use of cheques. The results also ndcate that the partcpants ntal fnancal knowledge requrements regardng plannng and savng, tpes of paperwork and everda bankng have a sgnfcant mpact on how the workshops were rated n terms of usefulness. In contrast, when we eplot the correlaton structure n ndvdual responses to workshops n the same module (Model 2 although the coeffcents sta the same, we fnd that the statstcal sgnfcance of all of the varables falls. In other words, when we penalse the standard errors b 2 In Table 2A and all subsequent tables there s no estmate of 1 for the reason stated prevousl. 8

10 assumng that correlaton ests between evaluatons of workshops that belong to the same module and that were gven b the same ndvdual, man of the varables that were sgnfcant n the naïve model no longer have a sgnfcant mpact. However, the ncome and unverst educaton effects reman ver strong, along wth the ntal knowledge requrements regardng everda bankng. TABLE 2A ABOUT HERE In Model 3, we proceed b etendng the correlaton structure assumpton to that of correlaton beng perfect between an ndvdual s evaluatons of the workshops n the same module. The results of estmaton are shown n Tables 3A and 3B. Gven that we onl used a sngle evaluaton per module from each ndvdual, our sample sze s smaller and the coeffcents estmated are dfferent from those n models 1 and 2. As can be seen, we stll fnd a ver strong ncome effect, but most of the remanng varables are now nsgnfcant. Ths ncludes the unverst educaton varable whch was prevousl found to have a hghl sgnfcant mpact on the perceved usefulness of workshops. If we then allow for the correlaton of an ndvdual s responses across modules, we agan fnd less statstcal sgnfcance across all varables, although the statstcall sgnfcant ncome effect stll remans. As shown n Table 3B, not much else s sgnfcant at 5% apart from the ncome and the age group varables. TABLE 2B ABOUT HERE In order to better llustrate the dfferences between the results and the potental conclusons that one would draw from each of the models, we proceed b estmatng parsmonous (restrcted versons of each model. We do so b restrctng the set of varables to those cases where the z-scores on the varables eceed unt n absolute terms. The results are reported n Table 3, and are broadl consstent wth the results from the ntal estmaton. TABLES 3A AND 3B ABOUT HERE Comparson of the results n Tables 2 and 3 clearl llustrate the dfferences n terms of whch varables are found to be statstcall sgnfcant dependng on how the 9

11 correlaton structure n the data s modelled. When we gnore the correlaton structure and treat all of the evaluatons as ndependent (Model 1 onl two varables have a z- score of less than 1, s varables are statstcall sgnfcant at the 1% level, and a further fve varables are also statstcall sgnfcant at the 5% level. In contrast, when we allow for the most comple set of correlatons n our modellng (Model 4 across both ndvduals and modules, seven varables have a z-score of less than 1, no varables are statstcall sgnfcant at the 1% level, and onl two varables are statstcall sgnfcant at the 5% level. Thus, f we treat the usefulness ratngs as ndependent, we would conclude that n addton to the age, ncome and household composton varables, both unverst educaton and the ntal level of fnancal lterac also have a strong and sgnfcant mpact. However, n model 4 where we allow for the most comple correlaton structure there s no longer a sgnfcant effect of these two varables and thus the do not appear n the parsmonous verson. TABLES 4A AND 4B ABOUT HERE A further llustraton of the dfferent nferences drawn n the dfferent models s eplored n the contet of the margnal effects reported n Tables 4A and 4B, from models 1 and 4, respectvel. As can be seen, accordng to the naïve model (Model 1, the partcpants educaton as well as the ntal fnancal lterac levels and knowledge requrements do have a sgnfcant mpact on how the workshops are evaluated. We fnd that those ndvduals who felt that the needed more knowledge regardng plannng and savng, tpes of paperwork and everda bankng, were sgnfcantl more lkel to fnd the workshops etremel useful. In case of everda bankng, these ndvduals were also sgnfcantl less lkel to rate the workshops as not ver useful. Smlarl, those wth a unverst degree or a hgher level of fnancal sophstcaton, as measured b the use of cheques varable, are found to be sgnfcantl less lkel to rate the workshops as etremel useful. However, when we account for the correlaton structure at the module and ndvdual levels, we fnd that these ntal knowledge levels and requrements do not sgnfcantl affect the usefulness ratng. The factor that consstentl comes through all of our specfcatons s that regardng the ndvduals ncome, ndcatng that those wth lower ncome levels are more lkel to report that the found the program to be etremel useful. 10

12 The dfferences n conclusons drawn are clear. The naïve model would lead us to beleve that the program worked better for lower ncome groups as well as those wth lower levels of educatonal attanment and fnancal lterac. However, when we account for the repeated measurement feature and the nherent structure of our data, we fnd that much of what was prevousl found to be sgnfcant s ether not sgnfcant or has a reduced sgnfcance level. Ths s partcularl relevant gven that tpcall the evaluatons of such programs do not even go as far as to estmate more comple econometrc models but rather focus on descrptve statstcs and contngenc tables. 4. Concluson Ths paper has eplored the mportance of modellng the correlaton structure n a dataset nvolvng the evaluaton of a fnancal lterac program. We fnd that a cluster samplng modellng tpe approach works well n the contet of evaluaton of fnancal lterac programs and our results demonstrate that the modellng of the correlaton structure can have mportant mplcatons. In other words, our analss llustrates that treatng the evaluatons as ndependent and dsregardng the nherent correlaton structure can lead to erroneous conclusons n terms of varable sgnfcance and the assocated sgnfcance of margnal effects. Across all of the specfcatons that were eplored, the varable that comes through as havng the strongest nfluence s ncome. More specfcall, we fnd that lower ncome levels are assocated wth hgher satsfacton ratngs of the fnancal lterac program. Ths s an mportant fndng n a publc polc contet gven the results n the natonal fnancal lterac stud conducted b ANZ (2003 show a relatonshp between ncome and fnancal lterac levels. It also shows that the targetng of man of these programs nternatonall at low ncome groups as detaled n Anderson, Zhan and Scott (2004 and Lons, Chang and Schrepf (2006 s relevant and should be an effectve means of rasng natonal fnancal lterac levels. 11

13 References Anderson, S., Scott, J. and Zhan, M. (2002, Fnancal lnks for low-ncome people (FLLIP: an evaluaton of mplementaton and ntal tranng actvt, School of Socal Work, Unverst of Illnos at Urbana-Champagn. Anderson, S., Zhan, M. and Scott, J. (2004, Targetng fnancal management tranng at low ncome audences, Journal of Consumer Affars 38, ANZ Bankng Group (2003, ANZ surve of adult fnancal lterac n Australa, Ro Morgan Research. Fo, J., Bartholomae, S. and Lee, J. (2005, Buldng the case for fnancal educaton, Journal of Consumer Affars 39, Lons, A., Chang, Y. and Schrepf, E. (2006, Translatng fnancal educaton nto behavour change for low-ncome populatons, Fnancal Counselng and Plannng Journal, forthcomng. Lons, A., Palmer, L., Jaaratne, K. and Schrepf, E. (2006, Are we makng the grade? A natonal overvew of fnancal educaton and program evaluaton, Journal of Consumer Affars, forthcomng. Lons, A. and Schrepf, E. (2004, Movng from unbanked to banked: evdence from the Mone Smart program, Fnancal Servces Revew 13, Russell, R., Brooks, R. and Nar, A. (2005, Evaluaton of MoneMnded: An Adult Fnancal Educaton Program, RMIT Unverst. Russell, R., Brooks, R., Nar, A. and Fredlne, L. (2005 Saver Plus Improvng Fnancal Lterac Through Encouragng Savngs: Evaluaton of the Savers Plus Plot Phase 1 - Fnal Report (Melbourne: RMIT Unverst. Russell, R., Brooks, R., Nar, A. and Fredlne, L. (2006, The ntal mpacts of a matched savngs program: the Saver Plus program, Economc Papers 25, Russell, R. and Fredlne, L. (2004 Saver Plus Progress and Perspectves: Evaluaton of the Savers Plus Plot Project Interm Report (Melbourne: RMIT Unverst. Russell, R. and Wakeford, M. (2005, Saver Plus: Savng for the Future, Proceedngs of the Transton and Rsk: New Drectons for Socal Polc Conference, Centre for Publc Polc, Unverst of Melbourne. Schrener, M. and Sherraden, M. (2005 Drop out from ndvdual development accounts: predcton and preventon, Fnancal Servces Revew 14, Varcoe, K. and Ftch, P. (2003, Mone talks a program to mprove the fnancal lterac of teens, Internatonal Journal of Consumer Studes 27, Varcoe, K., Martn, A., Devtto, Z. and Go, C. (2005, Usng a fnancal educaton currculum for teens, Fnancal Counselng and Plannng 16,

14 Table 1: Descrptve Statstcs Varable Mean Medan Mode Mn Ma Age Groups Female (Y/N Income No. of Adults n H/hold No. of Chldren under 18 n H/Hold Hgh School or less (Y/N TAFE/Tech. College Educaton (Y/N On Job Tranng (Y/N Unverst Educaton (Y/N Uses Cheques (Y/N More Fnancal Knowledge Needed: Plannng and Savng (Y/N Pament Methods (Y/N Tpes of Paperwork (Y/N Debt (Y/N Everda Bankng (Y/N Rghts and Responsbltes (Y/N

15 Table 2A: Estmaton Results General Specfcatons of Models 1 and 2 Age Groups Coeff. Estmate Model 1 Model 2 Standard Error P-value Standard Error P-value Female (Y/N Income No. of Adults n H/hold No. of Chldren n H/Hold TAFE/Tech. College (Y/N On Job Tranng (Y/N Unverst Educaton (Y/N Use Cheques (Y/N More Fn. Knowledge Needed: Plannng and Savng (Y/N Pament Methods (Y/N Tpes of Paperwork (Y/N Debt (Y/N Everda Bankng (Y/N Rghts and Responsbltes (Y/N Model 1 Model 2 Log (PseudoLkelhood Number of Observatons Number of Clusters N/A 282 Ch Squared ( P-value

16 Table 2B: Estmaton Results General Specfcatons of Models 3 and 4 Age Groups Coeff. Estmate Model 3 Model 4 Standard Error P-value Standard Error P-value Female (Y/N Income No. of Adults n H/hold No. of Chldren n H/Hold TAFE/Tech. College (Y/N On Job Tranng (Y/N Unverst Educaton (Y/N Use Cheques (Y/N More Fn. Knowledge Needed: Plannng and Savng (Y/N Pament Methods (Y/N Tpes of Paperwork (Y/N Debt (Y/N Everda Bankng (Y/N Rghts and Responsbltes (Y/N Model 3 Model 4 Log (PseudoLkelhood Number of Observatons Number of Clusters N/A 112 Ch Squared ( P-value

17 Table 3A: Estmaton Results Parsmonous Specfcatons of Models 1 and 2 Age Groups Coeff. Estmate Model 1 Model 2 Standard Error P-value Standard Error P-value Female (Y/N Income No. of Adults n H/hold No. of Chldren n H/Hold TAFE/Tech. College (Y/N On Job Tranng (Y/N Unverst Educaton (Y/N Use Cheques (Y/N More Fn. Knowledge Needed: Plannng and Savng (Y/N Pament Methods (Y/N Tpes of Paperwork (Y/N Debt (Y/N Everda Bankng (Y/N Rghts and Responsbltes (Y/N Model 1 Model 2 Log (PseudoLkelhood Number of Observatons Number of Clusters N/A 282 Ch Squared ( P-value

18 Table 3B: Estmaton Results Parsmonous Specfcatons of Models 3 and 4 Age Groups Model 3 Model 4 Coeff. Estmate Std.Error P-value Coeff. Estmate Std. Error P-value Female (Y/N Income No. of Adults n H/hold No. of Chldren n H/Hold TAFE/Tech. College (Y/N On Job Tranng (Y/N Unverst Educaton (Y/N Use Cheques (Y/N More Fn. Knowledge Needed: Plannng and Savng (Y/N Pament Methods (Y/N Tpes of Paperwork (Y/N Debt (Y/N Everda Bankng (Y/N Rghts and Responsbltes (Y/N Model 3 Model 4 Log (PseudoLkelhood Number of Observatons Number of Clusters N/A 112 Ch Squared ( P-value

19 Table 4A: Margnal Effects from Parsmonous Specfcatons of Model 1 Model 1 Not Ver Useful Somewhat Useful Useful Etremel Useful M.E. z M.E. z M.E. z M.E. z Age Groups Female (Y/N Income No. of Adults n H/hold No. of Chldren n H/Hold TAFE/Tech. College (Y/N Unverst Educaton (Y/N Use Cheques (Y/N More Fn. Knowledge Needed: Plannng and Savng (Y/N Pament Methods (Y/N Tpes of Paperwork (Y/N Everda Bankng (Y/N Rghts and Resp. (Y/N Table 4B: Margnal Effects from Parsmonous Specfcatons of Model 4 Model 4 Not Ver Useful Somewhat Useful Useful Etremel Useful M.E. z M.E. z M.E. z M.E. z Age Groups Female (Y/N Income No. of Chldren n H/Hold More Fn. Knowledge Needed: Plannng and Savng (Y/N Tpes of Paperwork (Y/N Everda Bankng (Y/N Rghts and Resp. (Y/N