Life-cycle and Dispositional Routes Into Problem Debt

Life-cycle and dispositional routes intoproblem debt

Paul WebleyUniversity of Exeter UK

Ellen K NyhusCentER for Economic Research Tilburg and Agder University College Kristiansand

The Netherlands

This paper presents analyses of the correlates of debt in a three-wave panel study ofsaving and other nancial behaviour The data used came from a representative sampleof Dutch households The results conrm the ndings of previous studies on non-representative samples and demonstrate that although economic variables alone predictdebt quite well psychological factors (especially present orientation self-control andattitudes towards debt) improve our ability to predict indebtedness The results alsosuggest that for most individualsbeing in debt is a short-termproblem chronic debtorsare a small group and are distinguished by having more limited economic and socialresources being more present-oriented and nding it more difcult to control theirexpenditure than temporary debtors Dynamic analyses suggest however that manyof the differences in psychological variables between debtors and non-debtors may bea consequence of being in debt rather than a cause of it

In recent years there has been a steady stream of research focusing on the factorsassociated with consumer debt This has partly been driven by a concern with thegrowing social problem of debt (eg Berthoud amp Kempson 1992 Davies amp Lea 1995Ford 1988 Lunde 1990 Stradling 1998 Tokunaga 1993) partly by a concern withdetermining credit risks (eg Canner amp Luckett 1991 Sexton 1977) and partly by adesire to increase our knowledge of the lsquonatural historyrsquo of debt through an economicpsychological approach to this issue (eg Lea Webley amp Levine 1993 Lea Webleyamp Walker 1995 Livingstone amp Lunt 1992 Vermeulen Dirven Kersten amp Euwals1992 Walker 1996)

In a number of western countries there was a noticeable increase in the amount ofpersonal borrowing in the 1980s and the 1990s Schor (1998) for example reports thatin the United States credit card borrowing doubled between 1990 and 1996 and similarthough not quite so dramatic increases in borrowing have been reported for theUnited Kingdom (Walker 1997) This increase in borrowing has also been associated

423British Journal of Psychology (2001) 92 423ndash446 Printed in Great Britain

copy 2001 The British Psychological Society

Requests for reprints should be addressed to Prof Paul Webley School of Psychology Washington Singer LaboratoriesUniversity of Exeter Exeter EX4 4QG UK (e-mail PWebleyexeteracuk)

with a marked increase in debt problems according to Parker (1988) approximately 2million people were experiencing such problems in the UK in the late 1980s with forexample 7 of domestic electricity users having difculty in paying their bills InNorway the growth of borrowing and debt problems in the late 1980s and early 1990sled to a change in the law to enable households to declare bankruptcy In the Netherlandsdebt problems have not been so severe Nonetheless more than 100 000 households hadproblematic debt in the late 1980s (Hulshof 1993)

The academic research that this social problem has inspired has not been atheoreticalbut it has not been theoretically driven It would be fair to characterize it as eclecticempiricism As a consequence we can now paint a reasonable though incompletepicture of the correlates of consumer debt (Lea 1999) Debt is associated with lowerincomes and larger outgoings (so the prototypic debtor is a young single parent livingin rented accommodation) and economic variables alone do a reasonable job of predict-ing who will be in debt A number of psychological factors are associated with debtand in some studies provide predictive power over and above that given by economicvariables For example attitudes towards debt and towards credit have been found to berelevant by both Livingstone and Lunt (1992) and Lea et al (1993) (although not by Leaet al 1995) and absent-mindedness with money has been found to be associated bothwith self-reported tendency to get into debt (Routh amp Burgoyne 1989) and self-reportedactual debt (Routh amp Burgoyne 1990) There is some evidence that external locus-of-control is associated with debt (Dessart amp Kuylen 1986 Livingstone amp Lunt 1992Tokunaga 1993) and rather more that reference groups particularly comparing onersquosconsumption with a group with higher income than onersquos own are implicated (Lea et al1995 Schor 1998)

Although the results seem very plausible it is not clear what the direction of causalityis between these psychological variables and debt For example poor money managementtechniques were found to be useful independent predictor variables of debt by Lea et al(1995) This could be the result of a causal link in one direction (not knowing how tomanage money leads one into debt) the reverse (chronic nancial insecurity inducinga form of learned helplessness) that money management if poor keeps people in debtor if good helps them to get out of it or be artifactual (being in debt leads one to deducethat one must be bad at managing money) Our natural history of debt is currently a verystatic one This is very important both theoretically and practically short-termindebtedness may be explained by rational expectations about future income and is lesslikely to be a serious social problem Long-term or chronic indebtedness probablyrequires alternative explanations and certainly is an important social issue Such littleevidence as there is (Vermeulen et al 1992) suggests that for most people being in debtmay be a short-term problem although these results may simply reect the generalimprovement in the economy during the period of their study

There are also major problems with most of the studies mentioned above Firstconsumer debt has been treated as a separate phenomenon and not integrated with thestudy of nancial behaviour generally nor seen in a life-cycle perspective (Lawrance 1995is a notable exception to this rule) There has been no use for example of the concept offuture expectations which plays a central role in economic models of household nancialdecision making (eg Das amp van Soest 1997) nor has there been much use of thosepsychological variables that have been demonstrated to be relevant in the linked area of

Paul Webley and Ellen K Nyhus424

saving such as time-preferences which reect a personrsquos impatience for consumptionand self-control the willingness to resist temptations (Maital amp Maital 1977 1991Thaler amp Shefrin 1981 Warneryd 1999)

Second researchers have generally used methods which provide a snapshot of thesituation which means that we cannot make clear statements about causality and haveto rely upon recollections to derive a picture of the debtorrsquos lsquocareerrsquo The use of cross-sectional data may explain why the ndings with regard to psychological variableshave not been very robust Cross-sectional data does not allow researchers to distinguishbetween the following groups

(I) The model consumer

Their nancial arrangements may include numerous loans and other forms of credit (egmortgages hire-purchase arrangements study loans) but they repay these loans accordingto the agreed schedule and do not get behind in paying bills They may borrow or take upcredit but do not get into debt There is an assumption (on both sides) that the repaymentwill be within the borrowerrsquos means

(II) The badly organised consumer

As above but owing to disorganization may occasionally get behind with bills or havecash-ow problems The nancial integrity of the household unit is never in doubthowever Such an individual may get into debt (dened as an obligation that a borroweris unable or unwilling to discharge and where settlement has been deferred without theseller or lenderrsquos agreement Lea 1999) but it is not problematic

(III) The temporarily indebted consumer

The temporarily indebted consumer may incur debt where his or her outgoings exceedincome However the household is able through economies or through earning moreincome (eg overtime) to get back into balance

(IV) The chronically indebted consumer

Outgoings exceed income for a considerable time This is not sustainable for ever(although may be for many years)

(V) The defaulting consumer

When obligations are so huge that there is no prospect of ever settling up people maywill default Here an individual simply does not repay a loan or money owedmdashthis couldbe formal and legal (declaring personal bankruptcy) or informal and illegal (moving onwithout leaving a forwarding address) Illegal default may be used as a strategy by anyonein groups III and IV

There are also serious questions about the generalizability of most previous studiesMany of these are based on mail surveys which have had extremely low response rates

425Life-cycle and routes into debt

particularly from debtor groups Tokunaga (1993) for instance reports a response rateof only 13 and Lea et al (1993) of 11 from serious debtors As Lea et al (1995) pointout lsquowe cannot draw conclusions about debtors in general only about those 10ndash15who chose to return our questionnairesrsquo (1995 p 699) Low response rates per se are nota problem the issue is whether the minority who do respond are representative of themajority who do not

It is important to be clear at the outset about the distinction between debt defaultcredit and borrowing It is possible to have a debt without ever having borrowed money(as when one is unable to settle a utility bill) Debt is unplanned and unintended andmay be a problem both for the individual and for the unwilling extender of credit (eg autility company) Being in debt is a stage (for some) en route to default and bankruptcy

In this study the authors focus on debt and not borrowing Their approach is to assumethat indebtedness is multi-determined but that there are at least two distinct andinteresting routes First individuals develop a variety of relevant enduring dispositionsand skills in childhood and adolescence which are fostered by particular parenting stylesThese include an overall time orientation the ability to control onersquos own behaviour andconscientiousness Those individuals who are present-oriented will be less likely to defergratication and so will prefer to take out loans or sign hire-purchase agreementsrather than save up for larger purchases This makes them more vulnerable to changesin economic circumstances Their lack of self-control also means that they will be morelikely to behave in the present (eg eating smoking drinking) in ways that will havedeleterious long-term consequences (obesity lung cancer liver disease) This can becharacterized as the lsquodispositionalrsquo route Second individuals may get into debt onrational grounds If a person experiences what they see as a temporary drop in income ora temporary increase in expenditure it may well be appropriate to maintain expenditureat current levels and run up (temporary) debts rather than attempt to match expendi-ture to income Maintaining expenditure levels may not only make economic sensebut social sense as well as it ensures that one preserves onersquos reputation This can be styledthe lsquolife-cycle routersquo

There is evidence for both of these routes with regard to economic behaviour generally(though not for debt in particular) Several authors have highlighted the role ofupbringing in determining the style of adult economic behaviour Both Fisher (1930)and Strotz (1956) predicted that the poor would be more likely than the afuent to giveinto temptations and fail to delay gratication The economic psychologists Maital andMaital (1977) suggest that the children of the poor will copy this behaviour Those withless money are likely to be tempted more often (as they have more unfullled needs) andat the same time have not learned how to deal with temptations Several empiricalstudies support this view Mischel (1961) for example found in a study of AmericanIndians and African-Americans and children in Trinidad and Granada that father absenceis closely associated with childrenrsquos preference for immediate rewards He attributes thisnding to the childrenrsquos trust that the promised delayed reward indeed will be forth-coming and argues that the trust is absent or weak in households without fathers In thelaboratory children exposed to models that showed preferences for delayed rewardschanged their delay-of-gratication behaviour in favour of delayndashreward while childrenwho were exposed to a model who showed immediate-reward preferences altered theirbehaviour in favour of immediate reward (Bandura amp Mischel 1965) It also appears that


the ability to delay gratication is stable over time (Mischel Shoda amp Rodriguez 1992)and the evidence presented by Maital and Maital (1977) suggests that time preferencepatterns are rmly established (for life) by the time a child reaches adolescence A morerecent study by Bernheim Garrett and Maki (1997) suggests that the teaching of self-controlling techniques is important They studied the effect of consumer educationpolicies (in particular the effect of household nancial decision-making courses in highschool) on subsequent asset accumulation in adulthood This study had a quasi-experimental design Some states never adopted the educational programmes whileothers adopted them at different times making it possible to compare subsequent savingacross states and over time Analysing those young enough to have been exposed to theeducation they found that asset accumulation was higher in the states that had adopted theeducational programme Moreover Bernheim et al (1997) found that those whose parentshad encouraged them to save in a bank account when they were children saved more thanothers in their adult life Similarly those who characterized their parents as having savedmore than average saved more than others The effect of the educational programme waslargest for those who characterized their parents saving less than average indicating asubstitution effect between teaching by parents and teaching in school

Evidence for the lsquorationalrsquo life-cycle approach is widely available (for economicpsychological evidence on saving see Warneryd 1999 Webley Burgoyne Lea ampYoung 2001) There is broadly speaking a lsquohump-shapedrsquo prole for asset holdingsover the life-cycle although young people save rather more than would be expectedon strictly rational grounds and retired people dissave rather less Nonetheless in the USand elsewhere average saving increases with age until the 50s or 60s and then drops(Browning amp Lusardi 1996)

In addition to these rather individualistic routes into debt it is important to bearin mind the social and normative aspects of debt In the UK and the USA in the 19thcentury (and the early part of the 20th century) indebtedness was strongly disapproved ofthroughout society Living on next weekrsquos pay and being unable to pay in cash implied alack of respectability (Johnson 1985 Tucker 1991) This shame of debt is still prevalentin some social groups as is apparent from the work of Lunt and Livingstone (1992) whoreport that some of their sample saw credit as a form of debt and something that wasshameful to be avoided and a source of problems

This study has four main aims First to see whether the ndings of recent economicpsychological studies can be replicated on a representative sample of the Dutchpopulation Second to explore the relative contribution of economic and psychologicalfactors to the explanation of consumer debt Third to explore the impact of importantvariables not measured in previous studies especially income expectations incomeuncertainty time preferences and personality (conscientiousness) Fourth to study thedynamic aspects of consumer debt to see how far debt is a short-term problem to explorethe direction of causality of psychological variables (eg is a less negative attitude to debtsimply a consequence of getting into debt as Lea (1999) claims) and to examine whatdifferentiates those who stay in debt (chronically indebted consumers) from those whoget out (temporarily indebted consumers) In order to accomplish these aims the authorsuse the data from three waves of the CentERdata panel (which has data on a wide range ofnancial and other behaviour) to try to overcome some of the problems outlined aboveand to extend the understanding of the economic psychology of debt


Method

ParticipantsData were collected from a representativesample of the Dutch population(part of the CentER savings surveyformerly known as the VSB panel) The initial sampling for this panel was carried out using telephonedirectories as the sampling frame About 97 of the Dutch population have a telephone An EqualProbability Selection Method (EPSEM) was used for the selection of telephone numbers This approach oftenleads to self-weighting samples A procedure was used that gave new and unlisted numbers the same chanceof being selected as listed numbers In order to obtain a sample which was representative with respect toregion and urbanizationa four-step stratied sampling procedure was used This involved selecting primarysample areas (communities) secondary sampling units (banks of 100 telephone numbers within the selectedcommunities) telephone numbers from those banks and nally the households themselves The specicsampling procedure meant that all the largest communities in the Netherlands were represented and abouthalf of the smaller ones Potential participants were telephoned asked for background information andwhether they would be willing to take part in the panel Those expressing willingness were then interviewedand introduced to the computer-aidedinterviewingtechniqueused Those individualsultimately agreeing toparticipate agreed to complete questionnaires administered by computer in return for the use of a PC (andmodem) This group constituted around 185 of those households that were originally telephonedHouseholds with older members one-person households and families without children were less willing toparticipate than others and in order to correct for this quotas were assigned for some demographiccharacteristics Note that this sample was not especially recruited for research into nancial behaviour butparticipantshad to agree to answer questions on a variety of topics on a regular basis A full descriptionof thesampling method used in the CentER data-panel is given in Nyhus (1996)1

Panel members were categorized as head of household spouse unmarried partner parent (in law) childliving at home housemate and other In the analyses reported here only data from individuals who were thehead of the households spouse or partner were used Of these 4147 individuals 1133 were in the panel forall three waves (referred to subsequently as lsquostayersrsquo) 1086 were in the panel for two of the years and 1928were in the panel for just one of the years (1994 1995 and 1996)

Participants consisted of 2156 men (of whom 2040 were heads of households) and 1991 women (of whom506 were heads of households) Ages ranged from 18 to 91 (mean 46) years Of the participants 50 wereemployees 28 were self-employed 16 were students and 11 were retired These gures correspondreasonably well to those in the Dutch population as a whole though there is an under-representationof theover 80s who comprise 3 of the adult Dutch population (CBS 1996) but only 1 of this sample and aslight over-representation of the 65ndash79 age group (116 of this sample compared to 101 of the adultDutch population)There is an apparentlymore serious under-representationof the unemployed only 24of this sample are looking for work whereas the ofcial unemployment rate in the Netherlands in 1994 was77 This may be less signicant than it seems as some of those classied in other occupationalgroups (egwork as a volunteermdash14 othermdash32) may also ofcially be unemployed

QuestionnairesMembers of the panel completedve long questionnaires(similar but not identical from one year to the next)over several weeks each year These were entitled lsquoHealth and Incomersquo lsquoAccommodation and MortgagesrsquolsquoHousehold and Workrsquo lsquoAssetsrsquo and lsquoEconomic Psychologyrsquo The data used in this paper are drawn mostlyfrom the last (which includes questions on income income and price expectations attitudes to saving timepreferences time horizons money management assets and bequests) but also draws on the assetsquestionnaire (for information about arrears and credit arrangements) and the health and incomequestionnaire The selected variables were chosen as they were of theoretical interest though the waysome of the variables are measured was not ideal or tailored for this study as the questionnaireswere designedwith other purposes in mind Full details of all questionnaires can be obtained from CentER The itemsincluded in the analyses reported here are described below In most cases the description is brief more detailis provided by Webley and Nyhus (1999)


1 This is available in pdf format from httpcwiskubnl~ few5centerpubvsbprhtm

The independent variables

Net income Respondents supplied information about income in a number of forms On two separate occasionsthey provided information about estimated household net income (dened as lsquothe income of all members ofthe household after deduction of taxes taken as the sum over the past 12 monthsrsquo) once using six incomecategories once using 11 categories Information was missing from the six-categoryquestion on between 7and 9 of cases each year so missing values were replaced by alternative observations and by imputation

Change in income The net household income gures were used to calculate change in income measure (whichwas therefore available for changes between 1994 and 1995 and changes between 1995 and 1996) Of therespondents 70 reported being in the same income category from year to year This gave a 5-point scalefrom large drop (2 or more income categories) drop (1 category) no change increase (1 category) largeincrease (2 or more income categories)

Self-reported change in income Respondents had to indicate whether their income had increased remainedthe same or decreased over the past 12 months and if it had changed to indicate the percentage changeThe answers to these questions were used to create a 5-category self-reported income change measure foreach year from substantial increase (more than 11) increase no change decrease substantial (morethan 11) decrease with the vast majority of people (75ndash80) reporting no change from one year to thenext

Reported income change was fairly consistent almost half of the stayersrsquo income stayed the same acrossthe 3 years and for the majority of those that did experiencechange it was always in the same direction Only5 of this group experienced both an increase and a decrease in the 3-year period To try to capture thesechanges two additional measures were computed one being the average absolute percentage change inincome over the 3 years the other being the mean signed percentage change An individual experiencingchanges of 1 20 1 10 and 2 30 would therefore get a score of 20 for the rst measure and zero onthe second

Income expectations Income expectations for the next year and the next 5 years were assessed in the same wayrespondentshad to indicate whether their income was expected to increase remain the same or decrease andif they expected a change (whether an increase or decrease) to indicate the percentage change they expectedThe answers to these questions were used to create a 5-category expected income change measure fromsubstantial increase (more than 11) increase no change decrease substantial (more than 11) decreaseThe measures of 5-year income expectations showed considerable stability from year to year thus for thestayers 5-year expectations were averaged before being categorized The measures of 1-year incomeexpectations were less stable

Income uncertainty Three measures of income uncertainty were included two simple measures of certainty ofincome change over the 1- and 5-year periods (lsquohow certain do you feel about this change of incomersquo) withfour categories of response very certain rather certain not very certain not at all certain) and a detailedmeasure of income uncertainty in which respondentshad to indicate how likely were (on a 7-point scale fromlsquohighly unlikelyrsquo to lsquohighly likelyrsquo) seven possible changes in income in the next 12 months These were arisefall in income of more than 15 a risefall in income between 10 and 15 a risedrop in incomebetween 5 and 10 no signicant change in income A modied version of the procedure used by Das(1998) was used to derive a measure of income uncertaintythe likelihood ratings were standardizedand thenthe standard deviations of these likelihood ratings calculated (treating each possible income change asequivalent) This provides a measure of the spread of peoplersquos ratings Scores on this uncertainty indexcorrelated signicantly with the coarser measure of 1ndash7 year income uncertainty described above (1994r 5 24 1995 r 5 25 1996 r 5 24) Correlations between the same type of uncertainty measure acrossdifferent years were a little higher (for simple uncertainty measure 9495 r 5 27 9596 r 5 27 9496r 5 26 for uncertaintyindex 9495 r 5 34 9596 5 42 9496 r 5 34) The six uncertainty items takentogether formed a reasonable scale (a 5 65) and so an overall uncertainty scale was formed by standardizingand then summing these items


Socio-economic variables These included age gender education number of children housing and whetherthere was a partner in the household Respondents indicated their highest level of education using 13categories from primary to University education These were recoded into four categories high mediumlow and lsquootherrsquo education

Reported housing situation was very stable with only 3ndash4 of respondents changing housing situationeach year In view of this stability the housing situation reported when the participantrst joined the panelwas used in the analyses

Whether there was a partner in the household was also stable For example of the stayers 97consistentlyhad (or had not) a partner over the 3 years So whether a partner was present when the participantrst joined the panel was used in the analyses

Respondents had to report how many children were in the household (to be counted they had to eat andsleep in the household at least 4 days a week) Two variables were included in the analyses here rst thenumber of children in the household when the participant rst joined the panel and second the change inthe number of children across the years

Attitude to debt Previous analyses of the 21 lsquoattitude to savingrsquo items included in the 1993 and 1994 waves byWarneryd (1996) have identied ve orthogonal factors These factors were lsquothriftrsquo lsquono need to saversquo lsquosavinginvolvementrsquo lsquoshame of debtrsquo and lsquosaving habitsrsquo In total they accounted for 47 of the variance Theattitude to saving items were included in the 1994 and 1996 wave but not in the 1995 wave

In order to check how robust the reported factor structure was the authors carried out two independentfactor analyses on the 21 attitude items using the 1994 data and the 1996 data and using two mutuallyexclusive data sets These analyses provided some conrmation for the factor structure found by Warnerydand the factor of interest here (lsquothe shame of debtrsquo) emerged consistently Its three dening items were lsquoIngeneral it is a good idea to be able to buy a car on instalmentrsquo lsquoPeople who buy on instalment are wastersrsquo andlsquoIt is not a good idea to borrow money to go on holidayrsquo Separate raw score indices for the shame of debt in1994 and 1996 were constructed based on the relevant three items These two scales correlated 83 and 87with the relevant factor score and are used in the subsequent analyses

Time preferences These were explored using a number of paired questions (nine on the 1994 questionnaire 12on the 1995 questionnaire) in which the time period and size of the delayed reward were varied across threesituations (delayed receipt of money delayed payment speeding up receipt) A typical delayed payment itemasked the respondent to imagine that they had to settle a tax bill of D 1000mdashthey could opt to pay in fullnow or pay extra for the privilegeof settling the bill in 3-months time An example of the wording of just oneof these questions (translated from Dutch) is given here Question DRL3 (Delayed Receipt of money Smallamount Short time period)

lsquoImagine you win a cash prize in a lottery The prize is worth D 1000 and can be paid out AT ONCEImagine the lottery which is a nancially trustworthy organizationasks if you are prepared to wait 3months before you get the prize Would you agree to that proposal or would you ask for more moneyif you had to wait for 3 months

(1) I would agree to wait for 3 months without the need to receive extra money for that So after 3months I would receive D 1000

(2) I would agree to wait for 3 months but I would want to receive extra money for that If therespondent answered (2) they would be asked lsquolsquoHow much EXTRA money do you want to receiveAT LEAST in addition to the D 1000rsquo

An annual subjective discount rate (in percent) was computed using the answers to each of these questionsby using the formula r 5 ((VFVP) 2 1) 12t where VP is present value of the cash ow VF is the futurevalue of the cash ow t is the time period to be waited (in months) and r denotes the discount rate used inthe decision In the questions respondents are given t and either VF or VP and they are asked to ll in themissing value necessary to calculate r Nyhus (1997) pointed out that discount rates calculated from thesedifferent situations do not form a unitary scale and a factor analysis conrmed her nding that therewere distinct delayed payment and speeding-up factors all delayed payment items loaded on the secondfactor with loadings of between 42 and 79 and all speeding-upitems loaded on the rst factor with loadings


of between 44 and 67 with no cross-loadings of greater than 3 Therefore following Nyhus (1996) anannualizeddiscount rate was calculated for each setting and two scales constructedone using the six delayedpayment items (two from 1994 four from 1995) the other using the eight speeding receipt items Thesescales each had a values of 63

Self-control Four indices of self-control were used First spending styles which was measured with a single7-point item from lsquoI like to spend all my money immediatelyrsquo to lsquoI want to save as much as possiblersquo Theanswers to this question were fairly stable across the waves (9495 r 5 45 9596 r 5 48 9496 r 5 50)and the answers from the three years formed a good scale (a 5 75) The scores were therefore combinedto derive a single measure of self-characterization of preferred spending style Second two items from theHealth and Income questionnaireon height and weight were used to construct a body mass index (weight inkilograms divided by height in metres squared) The US National Institute of Health considers individualswith a body mass of 30 or more to be obese A simple two-fold categorizationwas therefore used those witha BMI of over 30 being classied as obese and those under 2999 as normal weight Note that both heightand weight measures from each correlated very highly across waves (all over 93 for all pairings) as did theBMI for each year (9495 r 5 87 9596 r 5 90 9496 r 5 87) Third three questions on smokingand drinking were used Heavy drinking was reasonably stable (86 of the stayers reported never drinkingheavily 59 reported heavy drinking in one year 38 heavy drinking in two years and 4 heavydrinking in all three years) A 4-point heavy drinking scale was used based on these categories

Time horizons Two questions were asked each year which asked lsquowhich of the following time horizons is most(or least) important with regard to planning expenditures and savingsrsquo The answers to these questions werenot very stable across the waves (for most important time horizonmdash9495 r 5 31 9596 r 5 31 9496r 5 26 for least important time horizonmdash9495 r 5 25 9596 r 5 25 9496 r 5 19) The answerswithin waves were also not particularly consistent for example 8 of respondents in 1994 specied the sametime period as both the most and the least important time horizon No attempt was therefore made toproduce an overall scale and the most appropriate time horizon was included in each analysis

Money management There were 19 questions on this topic As well as general questions (eg a 5-point itemfrom lsquoI keep very bad track of my expenditurersquo to lsquoI keep very good track of my expenditurersquo) these itemsasked in some detail about ways in which people control their expenditure (eg using separate envelopes orboxes in which to keep money only withdrawing a limited amount of money at the beginning of each weekor month) if (and how) they put money aside for special purposes and if they used a bank card to pay in shopsAn attempt was made to construct a money management index but this was not successful there being nostrong links between many of these items The answers to the money management items were also not thatstable from one year to the next for example of the 357 claiming to use a housekeepingbook in 1994 over athird claimed not to be doing so in 1995 Kappa values (k) for money management items ranged from 23 to64 The highest k values as might be expected were for those items which concerned the most denite andformal money management techniques (eg automatic transfer of money each month to a savings account)

Economic socializationThree questions focused on respondentsrsquo experience as a teenager the rst asked if theywere regularly given money by parents or relatives the second if they regularly earned money and the thirdasked what percentage of their teenage income they spent immediately The median response for the teenageincome spent immediately question was 60 The answers to this were reasonably stable across the waves(9495 r 5 55 9596 r 5 56 9496 r 5 52) and the separate answers from the three years formed a goodscale (a 5 78) Answers were therefore averaged to derive a single measure

The answers to the money given and money earned questions were not consistent over the years 63 ofthe answers to the lsquogiven moneyrsquo questions were consistent as were 69 of the answers to the lsquoearned moneyrsquoquestion The answers to these questions were handled in an identical way to produce two 5-point scalesfrom denitely didnrsquot (givenworked) to denitely (givenworked)

Health The Health and Income questionnaire included 13 items on health Of these this analysis used ageneral characterization of health (5-point scale from excellent to poor) a question on changes in health


over the past year and a question on the subjective expectation of life relative to others of your ageTwo summary measures were constructed for use with the stayers the general characterization measureswere averaged across the three years and the best estimate of an individualrsquos subjective life expectation wascomputed based on the average of information supplied

Conscientiousness The questionnaires include items from the 16PA (16 personality adjectives Brandstatter1988) a 32-adjective list representing Cattellrsquos 16 personality factors The 16PA has been found to besufciently reliable to substitute for the 16PF (Brandstatter 1992) Form A (16 items) and Form B (also 16items) were included in the questionnaires distributed to the 1994 wave Form A was distributed in 1995and Form B in 1996 Previous analyses (Brandstatter 1996 Warneryd 1996) have identied factors labelledEmotional stability Extroversion and Conscientiousness

The procedure followed here was to carry out two separate factor analyses on all 32 items using the 199394 data and the 199596 data separately and using two mutually exclusivedata sets These analyses conrmedthat the factor structure found by Warneryd (1996) was extremely robust Six adjective pairs loaded on theconscientiousnessfactor (carefreendashmeticulousprincipledndashhappy-go-luckyanxiousndashunconcernedlittle self-controlndashdisciplined not easily hurtndasheasily hurt self-possessedndashchangeable) Raw score indices for con-scientiousness were constructed based on these items The 12-item raw score scale had an a of 77 andcorrelated 91 with the relevant factor score The two 6-item scales (using either 199394 data or 199596data) had a values of 61 and 60 and both correlated 84 with the relevant factor score Two 3-item scaleswere also constructed for those respondents who were only in the 1995 wave and only the 1996 wave Asingle score on conscientiousness for each individual was constructed using the scores from the appropriatescale thus conscientiousnessscores for the stayers are based on the 12-item scale (divided by 12) and based onthe second 6-item scale (divided by 6) for those who were in the sample in 199596

The dependent measure of debtPrevious studies have used a variety of measures of debt status Lea et al (1993) used ofcial records(information from a utility company) combined with self-report measures others have used informationabout peoplersquos credit standing (Tokunaga 1993) but most researchers have replied upon self-report alonesometimes upon a single measure

In order to identify which of six plausible candidate measures of debt should be used in the main analysesand how information from the chosen measures should be combined into a single index homogeneityanalysis using HOMALS (Van de Geer 1993a b) was carried out Homogeneity analysis is a form of non-metric multidimensional scaling and can be seen as a generalization of principal component analysis whichallows for non-interval variates that are non-linearly transformed during the process Separate HOMALSanalyses were carried out on the following three mutually exclusive groups (a) participantswho were only inthe panel in 1994 and those in the panel for the two years 1994 and 1995 (b) participants who were onlyin the panel in 1996 and those who were in the panel for the two years 1995 and 1996 (c) participants whowere only in the panel in 1995 and for all three years (199496) HOMALS treats all data as categoricalwhich is useful as it allows one to check any assumed ordinal relationships (eg that having three or morecredit arrangements is more related to indebtedness than having two credit arrangements)

The variables used were arrears bank debt extent of credit self-reported nancial situation perceivedcredit-worthiness and (for groups b and c) the stated relationship between income and expenditure (data forthis was collected only in 1995 and 1996) For group (a) data from 1994 were used for group (b) data from1996 and for group (c) data from 1995 The advantage of this procedure is that if similar solutions areobtained for three large independent samples we can be condent that the solution is robust and stable

In each of the initial analyses the rst dimension had an eigenvalue above 3 (33 32 33) and thesolutions (rotated if this was appropriate) were very similar The correlations between scores on dimension 1in 1994 1995 and 1996 ranged from 79 to 98 The solutions were very stable For example when thecategory lsquoarrearsrsquo was eliminated from the HOMALS analysis of the 1994 data set the pattern of scores wasalmost identical

The rst dimension of each solution was readily interpreted as ranging from lsquodebtrsquo through lsquomanagingrsquo tolsquosavingrsquo The second dimension was in each case uninterpretable Therefore category scores on the rstdimension were used to construct a debt index as described below (note that HOMALS solutions are nested


that is the rst dimension of a two-dimensional solution is the same as the one-dimensional solution) Thisindex correlates very highly with scores on HOMALS dimension 1 (r 5 92 for group (a) using the 1994data set r 5 93 for group (b) using the 1996 data set) The reason an index was constructed (rather thanthe alternative of simply using individual scores on HOMALS dimension 1 was rst that this ensured thatdebt scores were comparable across the years and second that the meaning of the index is more concrete andthus more interpretable The debt index was constructed as follows in arrears 3 points has bank debt 2points poor creditworthiness 1 point three or more credit arrangement 2 points 2 credit arrangements 1point self-report in debt 3 points self-reportedmanaging 1 point Scores could therefore range from zero toa maximum of 16 Those scoring 3 or more were classied as being debtors people in this group were eitherin arrears or self-reported in debt or had a bank debt or three or more credit arrangements and at least oneother indicator of debt or had three indicators of debt Those scoring 2 were classied as mild debtors andthose scoring 1 or 0 as non-debtors

Results

Static analyses

Differences between the debt groups In order to cross-validate the ndings (Bailey Hardingamp Smith 1989) the sample was initially split into two halves one group (N 5 1873)consisted of those in the 1994 wave only those in the 1994 and 1995 waves and 489 ofthose in all three waves the second group (N 5 1864) consisted of those in the 1996wave only those in the 1995 and 1996 waves those in the 1994 and 1996 waves and 644of those in all three waves Analyses were carried out using 1994 data for the rst groupand 1996 data for the second group Table 1 lists all the variables on which the debtgroups differed signicantly and describes the nature of the differences Though there area number of differences between the 1994 and 1996 data the broad pattern of results oneconomic and demographic variables is very similar to that found by previous researchersdebtors have fewer economic resources (lower incomes less likely to own their ownhomes less likely to have a partner) have greater economic needs (more children) andtend to be younger Results for the psychological variables extend our understandingDebtors emerge in both halves of the sample as being more present-oriented they have ahigher discount rate and their time horizon is shorter Their attitudes to debt are lessunfavourable The items used as indices of general self-control although not entirelyconsistent across the two halves of the sample suggest that debtors have less self-controlthan non-debtors and the economic socialization items suggest that they had differentchildhood experiences (the weakness of these effects is probably due to the unreliabilityof these measures) The results for the money management items (presented in Table 2)are not consistent from one half of the sample to the other although given the lack ofstability of these items and the lack of association between them this is perhaps notsurprising It is clear that debtors make more use of money-control techniquesmdashand thatthey denitely use the technique of not going shopping In order to use the moneymanagement items in the regression analyses a scale was created of the number of thosetechniques (with a maximum of four) listed in Table 2 that each individual used Thesescales when created for all individuals correlated well from year to year (42 39 43)and formed an acceptable scale with an a of 73 This scale is used in subsequent analyses

To test whether these rst-order effects were independent the data were analysedusing logistic regression contrasting non-debtors with mild debtors and debtors Ahierarchical form of logistic regression was used which explored the effects of a series



Tab

le1

Sum

mar

yof

rst

orde

rdi

ffer

ence

sbe

twee

nth

ede

btgr

oups

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

veli

nat

leas

ton

esa

mpl

ear

esh

own

1994

data

1996

data

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

x2

UN

plt

x2

UN

plt

Net

inco

me

Low

er16

0540

1807

001

1764

2918

510

01Se

lf-r

epor

ted

inco

me

chan

geD

ecre

ase

inin

com

e21

5626

1864

05

Five

year

inco

me

expe

ctat

ions

Hig

her

(mor

eop

tim

isti

c)20

1220

1862

02

08

Age

You

nger

1751

9418

620

0120

2310

1864

002

Gen

der

Mal

e4

3918

640

5H

ousi

ngM

ore

tena

nts

few

erow

nho

uses

193

1862

001

96

1857

002

Num

ber

ofch

ildr

enM

ore

2000

0018

620

119

6623

1864

001

Pre

senc

eof

part

ner

Few

erha

vepa

rtne

rs20

118

620

0110

218

640

01E

duca

tion

Hig

her

leve

l19

9983

1860

01

Tim

epr

efer

ence

sH

ighe

rdi

scou

ntra

te51

731

978

001

1729

862

20

05(d

elay

edpa

ymen

t)T

ime

hori

zon

Shor

ter

1939

7718

540

0220

1312

1864

001

Att

itud

esto

war

dsde

btLe

ssun

favo

urab

le15

4678

1705

001

1568

8416

920

01E

cono

mic

soci

aliz

atio

n(1

)P

refe

rred

tosp

end

imm

edia

tely

1195

2714

270

517

7094

1844

05

Eco

nom

icso

cial

izat

ion

(2)

Mor

eea

rned

mon

eyas

teen

ager

486

1864

05

Self

-con

trol

(1)

Pre

fer

tosp

end

imm

edia

tely

1615

7318

040

0117

5445

1844

001

Self

-con

trol

(2)

Mor

ear

eob

ese

53

1622

02

98

1798

002

Self

-con

trol

(3)

Mor

ean

dhe

avie

rci

gare

tte

smok

ers

1959

7718

440

5Se

lf-c

ontr

ol(4

)M

ore

heav

ydr

inke

rs7

0518

640

1Su

bjec

tive

life

expe

ctan

cyLo

wer

5546

499

80

1

Not

e T

hegr

oups

have

been

dich

otom

ized

wit

hno

n-de

btor

svs

mil

dde

btor

samp

debt

ors

and

x2

orM

ann-

Whi

tney

Ute

sts

used

asap

prop

riat

e

of groups of variables in turn The variables were entered into the analysis in the ordereconomic demographic psychological In the last category antecedent variables wereentered before current ones and internal variables before behavioural ones Thus psycho-logical variables were entered in the order economic socialization internal variables(time preferences attitudes) behavioural variables (self-control money management)The rationale behind this procedure was to provide the most conservative test for thesignicance of psychological variables the simplest explanation for debt is economicand it is possible that any correlation with other variables just reects mutual asso-ciations Only the variables which had been shown to have a signicant rst-order effectin both halves of the sample were included in the multivariate analysis To keep themodel as simple as possible only those variables in a group with signicant indi-vidual effects were retained at each stage This analysis was carried out initially on therst half of the sample and the nal model tested on the second half of the sampleNote that as information on the time preference (delayed payments) measure is onlyavailable for only a third of those in the other half of the sample it is not included in theseanalyses

The results of these analyses to distinguish debtors from non-debtors are presented inTable 3 There were independent signicant effects for economic demographic andpsychological factors Two kinds of psychological measures are retained in the nalmodel namely attitudes (to debt) and two indices of self-control (preferred spendingstyle obesity) These measures are robust and have a signicant independent effect in thesecond half of the sample That the presence of a partner does not is unsurprising (it beingthe weakest of the variables in the model) but that age has no effect is certainly a littlepuzzling

As the prior probabilities of a respondent not being a debtor are so high the percentage


Table 2 Summary of rst-order differences between the debt groups for money managementitems Only differences that are signicant at the 05 level in at least one sample are shown

1994 data 1996 data

Variable Tendency of Debt groups x2 N p lt x2 N p lt

Use any of rst list of money More use of techniques 78 1864 01control techniques

Housekeeping book More likely to use one 41 1864 05

Credit cards Less likely to have them 161 1864 001

Ensure have limited amount More likely to use this 281 1864 001of money on person technique

Use any of second list of More use of techniques 39 1873 05money control techniques

Donrsquot go shopping Use technique more 274 1873 001 394 1864 001

Other ways Use (unspecied) techniques 488 1873 05more

Put money aside for special Do this more often 42 1873 05purposes in envelopes andjam jars

of cases correctly classied do not give an accurate impression of the power of the modelA sample of non-debtors of comparable size to the number of debtors was taken andthe regression analyses rerun with this sample and all the debtors The seven predictorvariables in the nal set correctly predict 673 of cases (although attitude to debt thenhas no signicant independent effect and the signicance of partner and obesity slip tolt 1) This gure of 673 compares favourably to the 56 correctly classied reportedby Lea et al (1995) since their strategy of oversampling debtors gave roughly equalsamples of serious debtors mild debtors and non-debtors this is a fair comparison

Differences between the debt groups using only the stayers A further static analysis was carriedout on the stayers only The reason for this is two-fold both the independent anddependent measures for this group are more reliable than for each wave alone (andtherefore are more sensitive) and some variables are available for only this group Thisanalysis uses individuals who were included in the static analyses above and is nottherefore independent of it Table 4 lists all of the signicant differences Broadlyspeaking this conrms the results of the earlier analysis with some interesting addi-tions As in the earlier analysis debtors have fewer economic resources but with thissubsample it is also clear that these are more likely to vary and thus debtors are moreuncertain about their incomes Debtors expect their incomes to increase more over thenext 5 years but this is not a matter of being generally optimistic since their meanrelative life expectancy is lower than that of non-debtors There are fewer signicantdifferences between the mild debtors and the debtors but the latterrsquos income varies morethey are younger prefer to spend their money immediately and nd it more difcult tocontrol their expenditure This analysis was repeated on the heads of household onlyand almost identical results were found To examine whether these effects were inde-pendent the data were analysed using hierarchical logistic regression with a nesteddichotomies approach (rst contrasting never-debtors with those who had been milddebtors or worse at least once and second contrasting those who had been mild debtorsat least once with those who had been debtors at least once) This approach was preferred


Table 3 Logistic regression model for predicting debt vs non-debt from the nal set of variableson 1994 and 1996 data

1994 data 1996 data

Independent variable Coefcient p lt Coefcient p lt

Constant 2 4411 0001 2 836 nsNet income 2 42 0001 2 44 0001Age 02 0001 005 nsNumber of children 18 01 18 005Partner present 2 35 07 2 16 nsAttitude to debt 2 17 005 2 25 0001Obesity 61 05 50 05Use of money management techniques 23 005 39 0001Prefer to spend immediately 2 26 0001 2 32 0001

Model x2 for 1994 data x

25 13538 8567 cases correctly classied for 1996 data model x

25 14042 8523 cases

correctly classied

to the use of the obvious alternative (ordered logit) as the latter assumes that variablesdiscriminate in the same way across all groups (Fox 1997)

The results of these analyses to distinguish mild debtors and debtors from never-debtors are presented in Table 5 There were independent signicant effects for economicdemographic psychological and behavioural factors There is considerable overlap withthe results presented in Table 3 in this analysis age is not a signicant factor and thereare three additional variables in the nal model one economic (variation in income)and two psychological (time preferences and difculty in controlling spending) althoughthe latter effectively replaces preferences to spend immediately Again as the priorprobabilities of a respondent never having been a debtor are high a sample of never-debtors of comparable size to the number of debtors was taken and the regression analysesrerun The nine predictor variables in the nal set correctly predict 6465 of cases(although mean income and partner present then have no signicant independent effect)

Regression analyses to distinguish mild debtors from debtors revealed that onlydifculty in controlling expenditure had an independent effect A model with only thisvariable had a x2 of 1175 ( p lt 001) and classied 6132 of cases

Dynamic analyses

Changes in debt status The main advantage of using panel data is that one can differentiatedebtors of different types and in particular between those for whom debt is a temporaryproblem and those for whom it is a chronic problem Of the 1650 who were in the panelboth in 1994 and 1995 120 (73) were in debt in 1994 Of these 56 (46) were stillin debt in 1995 while 15 (125) were mild debtors in 1995 To look at this anotherway of the 107 who were in debt in 1995 49 had been debtors and 49 non-debtors in1994 Of the 1635 who were in the panel in both 1995 and 1996 110 (67) were indebt in 1996 Of these 51 (46) were still in debt in 1996 while 18 (164) were milddebtors in 1996 Of the 119 who were in debt in 1995 51 had been debtors and 50 non-debtors in the previous year In each pairing of years only a minority (say 15ndash20) ofthose categorized as lsquomild debtorsrsquo were categorized as debtors 1 year on Data from thestayers provides further information Almost half (468) of this group never had anyindication of debt across the three years they had no loans were not in the red in the bankat the end of the year nor did they have any of the other indices of debt One in eight(119) were in debt at least once but being in debt once (rather than frequently) is morecommon (76 vs 43) If we look solely at those who were in debt in 1994 and seewhat happens to them over the next two years we get a slightly different impression Ofthe 66 who were in debt in 1994 18 stayed in debt for the next two years 20 were in debtin either 1995 or 1996 and 28 have got out and stayed out of debt These numbers arerather small to do effective analyses but some attempt is made here by contrasting thosewho were in debt in 1994 but out of debt in 1995 and 1996 (lsquotemporary debtorsrsquo) withthose who were in debt in 1994 and still in debt in 1995 or 1996 or both (lsquochronicdebtorsrsquo) Chronic debtors had lower net incomes (U 5 299 N 5 60 p lt 05) fewer hadpartners (x2

5 774 N 5 60 p lt 01) had a higher discount rate (U 5 266 N 5 55p lt 05) had a shorter time horizon in 1996 (U 5 242 N 5 60 p lt 01) preferredto spend money immediately (U 5 296 N 5 59 p lt 05) and found it more difcult to



Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar

control expenditure (U 5 306 N 5 60 p lt 05) The most striking nding here is thebig difference in the number of each group who have partners which largely explainsthe difference in net household income 21 of the 27 temporary debtors had partnersin 1994 (and one of the 6 who did not gained a partner in 1995) whereas 19 of the 33chronic debtors had no partner present in 1994 or subsequently) That time horizon in1996 differs between the groups (but time horizon in 1994 does not) suggests that thisdifference is a consequence of chronic indebtedness rather than a cause The timepreference measure on the other hand is based on data in the 1994 and 1995questionnaires and so is more likely to be partly responsible for the chronic debt Insummary these results suggest that chronic debtors have limited economic and socialresources (the absence of a partner reduces more than just household income) are morepresent-oriented than temporary debtors and nd it more difcult to control theirexpenditure

Cross-lagged analyses Cross-lagged correlational analysis takes a simple and super-cially straightforward approach to the issue of primacy of cause if the correlationbetween variable A at time 1 and variable B at time 2 is signicantly greater than thatbetween variable B at time 1 and variable A at time 2 this provides some evidence thatA causes B (rather than the reverse) However this approach makes a number of ratherrestrictive assumptions (Rogosa 1980) and it is certainly not an ideal approach (Cook ampCampbell 1979) The most important of the assumptions are that the synchronouscorrelations between the variables are similar the internal reliability of the measures arethe same and the auto-correlations do not differ for the two variables The rst and thirdof these assumptions are met for the two variables debt group and attitudes to debtmdashtherespective reliabilities are unknown Table 6 provides some evidence that being in debtmodies onersquos attitudes towards debt (the cross-lagged correlations are signicantlydifferent) and gives details for four more psychological variables In each case thecorrelation between debt group in 1994 and the psychological variable in 1996 is greaterthan the correlation between the psychological variable in 1994 and debt group in 1996This suggests that shifts in time horizon in preferred spending style in the use of money


Table 5 Logistic regression model for predicting never-debtors vs at least once a mild debtors orworse from the nal set of variables

Independent variable Coefcient p lt

Constant 2 69 nsNet income 2 25 05Variation in income 03 05Number of children 29 001Partner present 2 63 005Time preferences 10 005Attitude to debt 2 23 005Obesity 72 05Use of money management techniques 39 0005Find it difcult to control spending 17 0001

Model x2

5 13016 801 cases correctly classied

management techniques and in obesity may be more likely to be a result of being in debtrather than a cause of it

The prediction of debt from past variables As with the earlier static analyses the sample wassplit into two halves in order to cross-validate the ndings One group (N 5 1088)consisted of those in the 1994 and 1995 waves only and roughly half of those in all threewaves the second group (N 5 1087) consisted of those in the 1995 and 1996 waves onlyand the other half of those in all three waves Analyses were carried out using data on1995 debt and 1994 independent data for the rst group and data on 1996 debt and 1995independent data for the second group The procedure followed was to develop the bestlogistic regression model for predicting the following yearrsquos debt status using an iterativeprocedure using forward- and backward-step regression with all possible independentvariables in a wide range of combinations (this approximates to a best subsets regression)The same four signicant predictive variables emerged consistently (see Table 7)Unsurprisingly the best single predictor of debt in 1995 was debt status in 1994 inaddition net household income in 1994 preferred spending style in 1994 and use of moremoney management techniques in 1994 helped to predict 1995 debt status correctly in88 of cases The same model when applied to the prediction of 1996 debt status wassuccessful in predicting 87 of cases but the use of money management techniques madeno signicant independent contribution to this prediction

Debt status and differential panel attrition Hagenaars (1990) points out that lsquothere isusually little reason to expect that non-response occurs completely at random People will


Table 6 Correlations between debt group and psychological variables in 1994 and 1996

1994 1996

debt att time h spend m-m obese debt att time h spend m-m

1994 Attitude 08Time horizon 12Spending style 12 12 13Money-manage 13 03 07Obesity 03 07

Debt group 35 06 07 09 101996 Attitude 10 35 06 13 09

Time horizon 09 22 11 12Spending style 13 15 08 43 10 16 14Money manage 13 04 39 13Obesity 07 07 06 64 06 09

All correlations are Kendallrsquos t (signs are omitted) Synchronous correlations are in the top (1994) and bottom (1996)triangular sections Dychronous correlations are in the square Bold gures give auto-correlations the cross-laggedcorrelations between debt group in 1994 and psychological variables in 1996 are italicized and the cross-laggedcorrelations between psychological variables in 1994 and debt group in 1996 are underlined Only correlationssignicantly different from zero are displayedNote that though Kendallrsquos t is the most appropriate it is not possible to calculate the signicance of the difference betweencorrelations so for attitude and debt group the difference between Pearsonrsquos correlations is tested using Steigerrsquos (1980)Z2 The synchronous Pearsonrsquos correlations were 13 and 12 the dychronous correlations 44 and 46 and the cross-laggedcorrelations 08 and 16

have denite reasons and motives not to participate and these may be directly relatedto the variables under studyrsquo (p 249) Given that the CentER data set is concerned with awide range of nancial matters we might expect to nd differential rates of attritionbetween those of different debt status although it is worth noting that participants werenot recruited specically to give information about nancial matters and regularlycompleted questionnaires on a wide range of subjects

There are differences between those who were in the panel for 1 year only (whether1994 1995 or 1996) and those who have stayed in the panel for all three years Asignicantly higher proportion of those who stayed in the panel for 1 year only were indebt For example of those who were in the panel only in 1994 74 were in debtwhereas 58 of the stayers were in debt in 1994 (x2

5 58 p lt 05) and of those whowere in the panel only in 1995 88 were in debt (compared to a debt gure in 1995of 53 for the stayers x2

5 102 p lt 05) Given this differential attrition rate onemust be cautious in extrapolating the results of analyses on the stayers to the generalpopulation

Discussion

The rst conclusion to be drawn from the analyses reported in this paper is that thepicture of debt and debtors painted by previous researchers appears to be conrmedAlthough the measure of debt used here is not exactly comparable to those used inprevious studies (where the debt samples have consisted of people seeking assistance withdebt problems people known to be in arrears with utility bills or people who have ledfor bankruptcy) and the structural constraints on credit are different in the Netherlandsfrom other countries the results obtained are strikingly similar As in all previousstudies of debt economic factors turn out to be very important and can do quite a goodjob of predicting who is in debt and who remains in debt But more important is thatpsychological variables are implicated both those variables that have been examined herefor the rst time and those that have demonstrated predictive power in previous studies(such as attitudes) play an important role despite the use of rather different measurementand data collection methods

The most interesting of the new psychological variables are time preferences and self-control It was expected that those with a present orientation and a lack of self-control


Table 7 Logistic regression model for predicting debt vs non-debt from the previous yearrsquosvariables

Debt in 1995 Debt in 1996


Constant 2 65 ns 2 95 nsDebt in previous year 225 0001 234 0001Net income 2 35 0005 2 20 05Use of money management techniques 22 05 12 nsPrefer to spend immediately 2 21 05 2 18 05

Model x2 for 1995 data chi-square 5 1488 886 cases correctly classied N 5 1025 for 1996 data model

x2

5 1562 8721 cases correctly classied N 5 1048

would be more likely both to get into debt and to stay in debt Our results providesome evidence that this may be the case The measures of present orientation (time pre-ferences and time horizon) are consistently associated with debt in both the 1994 and1996 data distinguish never-debtors (type I) from others and differ between chronic(type IV) and temporary (type III) debtors Similarly all of the measures of self-control(spending obesity smoking drinking) were associated with debt in some of the analysesand two of them (spending and obesity) were robustly associated with debt status in aparticular year and distinguished lsquonever-debtorsrsquo (type I) from the others Preference forspending immediately also differed between chronic (type III) and temporary (type IV)debtors However while spending immediately cigarette smoking and obesity mayindicate lack of self-control they may also be a consequence rather than a cause of debtObesity for example may be a result of comfort eating and smoking may reduce thestress of being in a poor nancial state It is also possible that for some people gettinginto debt may decrease both their desire and their ability to control spending (the kind ofreasoning typied by the English proverb lsquoin for a penny in for a poundrsquo) So while ourresults are consistent with the dispositional route into debt described in the introductionthey do not provide unambiguous evidence for it

Those variables grouped under the label of money management are also interestingOur initial attempt to construct an index of money management items was unsuccessfulperhaps not surprisingly as these techniques represent to some extent alternativesolutions to the problem of monitoring and controlling onersquos expenditure If thetechnique of setting up a monthly automatic transfer of money to a savings accountworks there is no need to use other methods Nonetheless the use of certain moneymanagement techniques (using a housekeeping book having a limited amount of moneyon onersquos person avoiding shopping) does seem to be associated with debt These tech-niques share the characteristic of being lsquolow techrsquo and perhaps of not being medium andlong-term commitment techniques (such as overpaying tax and withdrawing a xedamount of money each month) The use of more of these techniques consistentlydifferentiated debtors from non-debtors and lsquonever-debtorsrsquo from the others But itwould be reasonable to conclude simply that the use of money management techniquesincreases when they are necessary

Most of the psychological variables that previous researchers report as being correlatedwith debt are also associated with debt in this sample even if they did not contributeto the ability to predict debt group Attitudes found to be relevant by Livingstoneand Lunt (1992) and Lea et al (1993) though not by Lea et al (1995) are associated withdebt in both the 1994 and 1996 data and are predictive of never being in debt Theproblem here is that the evidence also suggests that this association may be an adaptivepreference getting into debt (as Davies amp Lea 1995 report) makes debt seem not quiteso bad It was expected that conscientiousness would be strongly associated with debtbut this proved not to be the case Being conscientious was associated with never havingbeen in debt but generally the relationship between conscientiousness and debt statuswas not strong This is somewhat surprising but we suspect it is a result of the relativeunreliability of the measure of conscientiousness employed Only for those individualswhere full data were available (from 1994 and 1996) was it possible to construct a reliableconscientiousness scale and it is with this group that the relationship was found

The nal kind of variables worth noting are those concerned with expectations (eg of


future income) Expectations play an important role in current economic models ofhousehold consumer behaviour and we now have some evidence of their signicance toindebtedness The rst thing to note is the robust nding that debtors are more likelyto expect their incomes to increase in the medium-term but not the short-term This isentirely consistent with a life-cycle approach to borrowing and consequently debt It alsoappears that debtors experience more variation in their incomes and are less certain oftheir future income This seems very reasonable though having an uncertain futureincome might be expected to lead to attempted saving (for a buffer) rather than debt Itis difcult to say more based on the evidence available the measures of income expec-tations are fairly crude (most individuals report expecting no change in their income)and the income uncertainty measures are also far from ideal as Das (1998) hascommented

Although the introduction to this paper stressed the disadvantages of mail surveysand the relative advantages of using a representative panel the latter is not without itsproblems The initial quota selection of households and the relatively high non-responserate does introduce unknown variation into the data and means that the claim that thepanel is truly representative must be treated with caution It could be the case that whilethe proportions in different socio-economic categories correspond to the Dutch popula-tion the individuals included in those categories are atypical It is possible for instancethat many elderly people would be put off by the thought of answering questions on acomputer and so the older people who do participate might be more exible in betterhealth and so on than is usual for their age group However Ritzema and Homan (1991)point out that the procedure used to recruit the members of the panel minimizes thedrop-out rate that can be attributed to the use of computers which are only mentioned inthe last part of the initial recruitment interview Only 51 of those who did notparticipate took this decision because a computer had to be used Ritzema and Homan(1991) also comment that on a very wide variety of socio-economic characteristicsthe panel is comparable to the Dutch norm and so conclude that it is legitimate to see thepanel as representative of the population

While this may be so the non-response rate for particular questions and thedifferential attrition rate are all potential threats to validity Both may distort the resultssomewhat and given that debt is a private and sensitive issue it is likely that thenumbers admitting debts are less than those who actually have debts Nonetheless onedoes not have to be too apologetic about the use of self-report measures of debt Workby Lea Webley and Bellamy (1995) suggests that studentsrsquo reports on their bankbalances are fairly accurate (in that the aggregate estimates correspond very well withthose based on bank records) and Lea et al (1993) report that 72 of a group thataccording to a Utility companyrsquos records had debts admitted to thesemdashand many ofthe remainder may well have moved out of debt between categorization by the companyand the administration of the questionnaire

This paper argues that both economic and psychological factors are needed to explainconsumer debt Economic factors and structural constraints obviously have a very largepart to play in indebtedness the large differences in the proportion of those in debt inThe Netherlands and the UK for example can in the main be explained by referenceto differences in the standard of living the deregulation of the credit market in the UKand better social security arrangements in Holland But psychological variables also


matter some people in dire nancial circumstances keep out of debt and others with goodnancial positions get into serious difculties So it is crucial that future studies looksimultaneously at both economic and psychological factors We also believe that we havedemonstrated the value of investigating debt longitudinally which has enabled us todifferentiate between two of the consumer groups mentioned in the introduction(temporary and chronic debtors) and provided some indication of likely causal factorsThe existing literature gives us plenty of information about what variables are asso-ciated with debt but very little about the direction of causality What is needed now issome integrated theoretical development we do not need yet more elaborate contingentempirical generalizations nor more elegant economic models but a model that combinesthe rigour and simplicity of the best economic models with the insights of psychologistsSuch an integrated theory is very hard to construct but it may be that the kind ofeconomic psychological model of tax paying proposed by Cullis and Lewis (1997) whichtakes social conventions into account is process oriented but still deductive will providethe basis for a similar model of debt behaviour

Acknowledgements

The work for this study was carried out while the rst author was a research visitor and the second authora research fellow at CentER (the Center for Economic Research University of Tilburg) The authors are verygrateful for their hospitality and the use of their facilities They would also like to thank Henk Elffers for hishelpful advice on the homogeneity analysis Harry Garst for providing useful SPSS macros Karl-ErikWarneryd for his very valuable comments on an earlier draft Michael Wood for his perceptive feedback onhow to describe the complicated sampling procedure and the anonymous referees for their helpful commentsFinancial support from the TMR grant project lsquoSavings and Pensionsrsquo (ERB 4061 PL 95-0274) and aEuropean Community Marie Curie Fellowship (ERB FMBICT 961080) is gratefully acknowledged

References

Bailey R A Harding S A amp Smith G L (1989) Cross-validation In S Kotz amp N L Johnson (Eds)Encyclopedia of statistical sciences (supplement volume pp 39ndash44) New York Wiley

Bandura A amp Mischel W (1965) Modication of self-imposed delay of reward through exposure to liveand symbolic models Journal of Personality and Social Psychology 2 698-705

Bernheim B D Garrett D M amp Maki D M (1997) Education and saving The long-term effects of high schoolnancial curriculum mandates (NBER Working Paper 6085) Cambridge MA National Bureau of EconomicResearch

Berthoud R amp Kempson E (1992) Credit and debt The PSI report London Policy Studies InstituteBrandstatter H (1988) Sechzehn personlichkeits-adjektivskalen (16PA) als forschunginstrument anstelle

des 16PF (Sixteen personality adjective scales as a substitute for the 16PF) Zeitschrift fur Experimentelle undAngewandte Psychologie 35 370ndash391

Brandstatter H (1992) Zur validitat der 16 personlichkeits-adjektivskalen (On the validity of the 16Personality Adjective scales) Zeitschrift fur Differentielle und Diagnostische Psychologie 13 53ndash67

Brandstatter H (1996) Saving income and emotional climate of households related to personality structure (ProgressReport 38) VSB-CentER Savings Project Tilburg University The Netherlands

Browning M amp Lusardi A (1996) Household savings Micro theories and micro facts Journal of EconomicLiterature 34 1797ndash1855

Canner G B amp Luckett C A (1991) Payment of household debts Federal Reserve Bulletin 77218ndash229

Centraal Bureau voor de Statistiek (1996) Statistisch jaarboek 1996 rsquos Gravenhage CBS-publikatiesCook T D amp Campbell D T (1979)Quasi-experimentation Design and analysis issues for eld settings Boston

Houghlin Mifin


Cullis J G amp Lewis A (1997) Why people pay taxes From a conventional economic model to a modelof social convention Journal of Economic Psychology 18 305ndash321

Das M (1998) On income expectations and other subjective data A micro-econometric analysis Tilburg UniversityCentER

Das M amp van Soest A (1997) Expected and realised income changes Evidence from the Dutch socio-economic panel Journal of Economic Behavior and Organization 32 137ndash154

Davies E amp Lea S E G (1995) Student attitudes to student debt Journal of Economic Psychology 16663ndash679

Dessart W amp Kuylen A (1986) The nature extent causes and consequences of problematic debt Journalof Consumer Policy 9 311ndash334

Fisher I (1930) The theory of interest London MacmillanFord J (1988) The indebted society London RoutledgeFox J (1997) Applied regression analysis linear models and related methods London SageHagenaars J A (1990) Categorical longitudinal data Newbury Park CA SageHulshof M E (1993) De integrale aanpak van problematische schuldern (The integrated approach to

problematic debts) rsquos Gravenhage Ministerie van Sociale Zaken en WerkgelegenheidJohnson P (1985) Saving and spending The working-class economy in Britain 1870ndash1939 Oxford Clarendon

PressLawrance E C (1995) Consumer default and the life-cycle model Journal of Money Credit and Banking 27

939ndash954Lea S E G (1999) Credit debt and problem debt In P Earle amp S Kemp (Eds) The Elgar companion to

consumer research and economic psychology (pp 139ndash144) Cheltenham Edward ElgarLea S E G Webley P amp Bellamy G (1995) Student debt A psychologicalanalysis of the UK experience

In E K Nyhus amp S V Troye (Eds) Frontiers in economic psychology (pp 430ndash444) Bergen NorwegianSchool of Economics and Business Administration

Lea S E G Webley P amp Levine R M (1993) The economic psychology of consumer debt Journal ofEconomic Psychology 14 85ndash119

Lea S E G Webley P amp Walker C M (1995) Psychological factors in consumer debt Moneymanagement economic socialization and credit use Journal of Economic Psychology 16 681ndash701

Livingstone S amp Lunt P (1992) Predicting personal debt and debt repayment Psychological social andeconomic determinants Journal of Economic Psychology 13 111ndash134

Lunde T K (1990) Ny-fattigdommen i Norge Betalingsproblemer i levekarsperspektiv New poverty in Norwaypayment problems in life perspective Arbeidsrapport nr 11mdash1990 Lysaker Statens Institutt forForbruksforskning

Lunt P K amp LivingstoneS M (1992) Mass consumption and personal identity BuckinghamOpen UniversityPress

Maital S amp Maital S L (1977) Time preference delay and gratication and the intergenerationaltransmission of economic inequality A behavioral theory of income distribution In O Ashenfelter ampW E Oates (Eds) Essays in labor market analysis in memory of Yochanan Peter Comay New York Wiley

Maital S amp Maital S L (1991) Is the future what it used to be A behavioral theory of the decline of savingin the West In G AntonidesW Arts amp W F van Raaij The consumption of time and the timing of consumptionToward a new behavioral and socio-economics (pp 195ndash214) Amsterdam North-Holland

Mischel W (1961) Father-absence and delay of gratication Cross-cultural comparisons Journal ofAbnormal and Social Psychology 63 116ndash124

Mischel W Shoda Y amp Rodriguez M L (1992) Delay of gratication in children In G Loewenstein ampJ Elster (Eds) Choice over time (pp 147ndash164) New York Russell Sage Foundation

Nyhus E K (1996) The VSB-Center savings project Data collection methods questionnaires and samplingprocedures (Progress Report no 42) VSB-CentER savings project Tilburg CentER Tilburg University

Nyhus E K (1997) On the measurement of time preference and subjective discount rates In IQuintanilla (Ed) The XXII international colloquium of economic psychology (pp 1095ndash1111) Pardo ValenciaPromolibro

Parker G (1988) Indebtedness In R Walker amp G Parker (Eds) Money matters (pp 212ndash226) LondonSage

Ritzema H J amp Homan M E (1991) Schulden and Bezittingen in Nederland Debts and Assets in theNetherlands rsquos Gravenhage SWOKAmdashInstituut voor consumenteonderzoek


Rogosa D (1980) A critique of cross-lagged panel correlation Psychological Bulletin 88 245ndash258Routh D A amp Burgoyne C B (1989) Absent-mindedness with money Its incidence classication and

correlates In T Tyszka amp P Gasparski (Eds) Homo oeconomicus Facts and presumptions (pp 619ndash633)Warsaw Polish Academy of Science

Routh D A amp Burgoyne C B (1990) Further development of the absent-mindedness with moneyquestionnaire (AWMQ) Aspects of construct validity In S E G Lea P Webley amp B M Young (Eds)Applied economic psychology in the 1990s (pp 487ndash500) Exeter Washington Singer Press

Schor J B (1998) The overspent American New York Basic BooksSexton D E (1977) Determining good and bad credit risks among high and low income families Journal

of Business 50 236ndash239Steiger J H (1980) Tests for comparing elements of a correlation matrix Psychological Bulletin 87

245ndash251Stradling S (1998) Psychological effects of student debt End-of-grant report Swindon ESRCStrotz R H (1956) Myopia and inconsistency in dynamic utility maximisation Review of Economic Studies

23 165ndash180Thaler R H amp Shefrin H M (1981) An economic theory of self-control Journal of Political Economy 89

392ndash406Tokunaga H (1993) The use and abuse of consumer credit Application of psychological theory and

research Journal of Economic Psychology 14 285ndash316Tucker D M (1991) The decline of thrift in America Our cultural shift from saving to spending New York

PraegerVan de Geer J P (1993a) Multivariate analysis of categorical data Theory Newbury Park CA SageVan de Geer J P (1993b) Multivariate analysis of categorical data Applications Newbury Park CA SageVermeulen H Dirven H J Kersten J amp Euwals R (1992) Financiele problemen schulden en problematishce

schuldsituaties in Nederland Omvang verdeling determinenten en dynamie Financial problems debt andproblem debt in the Netherlands Scope distribution determinants and dynamics rsquos GravenhageMinisterie van Sociale Zaken en Werkgelegenheid

Walker C M (1996) Financial management coping and debt in households under nancial strain Journalof Economic Psychology 17 789ndash807

Walker C M (1997) The psychology of debt in the 1990s Unpublished PhD thesis University of ExeterWarneryd K-E (1996) Saving attitudes and saving behaviour In C Roland-Levy (Ed) Social and economic

representations (pp 798ndash811) Paris Universite Rene DescartesWarneryd K-E (1999) The psychology of saving A study on economic psychology Cheltenham Edward ElgarWebley P amp Nyhus E K (1999) A dynamic approach to consumer debt TMR Progress Report no 1 Tilburg

CentER Tilburg UniversityWebley P Burgoyne C B Lea S E G amp Young B M (2001) The economic psychology of everyday life

London Psychology Press

Received 21 May 1999 revised version received 28 April 2000


with a marked increase in debt problems according to Parker (1988) approximately 2million people were experiencing such problems in the UK in the late 1980s with forexample 7 of domestic electricity users having difculty in paying their bills InNorway the growth of borrowing and debt problems in the late 1980s and early 1990sled to a change in the law to enable households to declare bankruptcy In the Netherlandsdebt problems have not been so severe Nonetheless more than 100 000 households hadproblematic debt in the late 1980s (Hulshof 1993)

The academic research that this social problem has inspired has not been atheoreticalbut it has not been theoretically driven It would be fair to characterize it as eclecticempiricism As a consequence we can now paint a reasonable though incompletepicture of the correlates of consumer debt (Lea 1999) Debt is associated with lowerincomes and larger outgoings (so the prototypic debtor is a young single parent livingin rented accommodation) and economic variables alone do a reasonable job of predict-ing who will be in debt A number of psychological factors are associated with debtand in some studies provide predictive power over and above that given by economicvariables For example attitudes towards debt and towards credit have been found to berelevant by both Livingstone and Lunt (1992) and Lea et al (1993) (although not by Leaet al 1995) and absent-mindedness with money has been found to be associated bothwith self-reported tendency to get into debt (Routh amp Burgoyne 1989) and self-reportedactual debt (Routh amp Burgoyne 1990) There is some evidence that external locus-of-control is associated with debt (Dessart amp Kuylen 1986 Livingstone amp Lunt 1992Tokunaga 1993) and rather more that reference groups particularly comparing onersquosconsumption with a group with higher income than onersquos own are implicated (Lea et al1995 Schor 1998)

Although the results seem very plausible it is not clear what the direction of causalityis between these psychological variables and debt For example poor money managementtechniques were found to be useful independent predictor variables of debt by Lea et al(1995) This could be the result of a causal link in one direction (not knowing how tomanage money leads one into debt) the reverse (chronic nancial insecurity inducinga form of learned helplessness) that money management if poor keeps people in debtor if good helps them to get out of it or be artifactual (being in debt leads one to deducethat one must be bad at managing money) Our natural history of debt is currently a verystatic one This is very important both theoretically and practically short-termindebtedness may be explained by rational expectations about future income and is lesslikely to be a serious social problem Long-term or chronic indebtedness probablyrequires alternative explanations and certainly is an important social issue Such littleevidence as there is (Vermeulen et al 1992) suggests that for most people being in debtmay be a short-term problem although these results may simply reect the generalimprovement in the economy during the period of their study

There are also major problems with most of the studies mentioned above Firstconsumer debt has been treated as a separate phenomenon and not integrated with thestudy of nancial behaviour generally nor seen in a life-cycle perspective (Lawrance 1995is a notable exception to this rule) There has been no use for example of the concept offuture expectations which plays a central role in economic models of household nancialdecision making (eg Das amp van Soest 1997) nor has there been much use of thosepsychological variables that have been demonstrated to be relevant in the linked area of


























Method













































Results

Static analyses





Tab

le1

Sum

mar

yof

rst

orde

rdi

ffer

ence

sbe

twee

nth

ede

btgr

oups

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

veli

nat

leas

ton

esa

mpl

ear

esh

own

1994

data

1996

data

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

x2

UN

plt

x2

UN

plt

Net

inco

me

Low

er16

0540

1807

001

1764

2918

510

01Se

lf-r

epor

ted

inco

me

chan

geD

ecre

ase

inin

com

e21

5626

1864

05

Five

year

inco

me

expe

ctat

ions

Hig

her

(mor

eop

tim

isti

c)20

1220

1862

02

08

Age

You

nger

1751

9418

620

0120

2310

1864

002

Gen

der

Mal

e4

3918

640

5H

ousi

ngM

ore

tena

nts

few

erow

nho

uses

193

1862

001

96

1857

002

Num

ber

ofch

ildr

enM

ore

2000

0018

620

119

6623

1864

001

Pre

senc

eof

part

ner

Few

erha

vepa

rtne

rs20

118

620

0110

218

640

01E

duca

tion

Hig

her

leve

l19

9983

1860

01

Tim

epr

efer

ence

sH

ighe

rdi

scou

ntra

te51

731

978

001

1729

862

20

05(d

elay

edpa

ymen

t)T

ime

hori

zon

Shor

ter

1939

7718

540

0220

1312

1864

001

Att

itud

esto

war

dsde

btLe

ssun

favo

urab

le15

4678

1705

001

1568

8416

920

01E

cono

mic

soci

aliz

atio

n(1

)P

refe

rred

tosp

end

imm

edia

tely

1195

2714

270

517

7094

1844

05

Eco

nom

icso

cial

izat

ion

(2)

Mor

eea

rned

mon

eyas

teen

ager

486

1864

05

Self

-con

trol

(1)

Pre

fer

tosp

end

imm

edia

tely

1615

7318

040

0117

5445

1844

001

Self

-con

trol

(2)

Mor

ear

eob

ese

53

1622

02

98

1798

002

Self

-con

trol

(3)

Mor

ean

dhe

avie

rci

gare

tte

smok

ers

1959

7718

440

5Se

lf-c

ontr

ol(4

)M

ore

heav

ydr

inke

rs7

0518

640

1Su

bjec

tive

life

expe

ctan

cyLo

wer

5546

499

80

1

Not

e T

hegr

oups

have

been

dich

otom

ized

wit

hno

n-de

btor

svs

mil

dde

btor

samp

debt

ors

and

x2

orM

ann-

Whi

tney

Ute

sts

used

asap

prop

riat

e






1994 data 1996 data














1994 data 1996 data





25 14042 8523 cases

correctly classied




Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin





































































Method













































Results

Static analyses





Tab

le1

Sum

mar

yof

rst

orde

rdi

ffer

ence

sbe

twee

nth

ede

btgr

oups

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

veli

nat

leas

ton

esa

mpl

ear

esh

own

1994

data

1996

data

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

x2

UN

plt

x2

UN

plt

Net

inco

me

Low

er16

0540

1807

001

1764

2918

510

01Se

lf-r

epor

ted

inco

me

chan

geD

ecre

ase

inin

com

e21

5626

1864

05

Five

year

inco

me

expe

ctat

ions

Hig

her

(mor

eop

tim

isti

c)20

1220

1862

02

08

Age

You

nger

1751

9418

620

0120

2310

1864

002

Gen

der

Mal

e4

3918

640

5H

ousi

ngM

ore

tena

nts

few

erow

nho

uses

193

1862

001

96

1857

002

Num

ber

ofch

ildr

enM

ore

2000

0018

620

119

6623

1864

001

Pre

senc

eof

part

ner

Few

erha

vepa

rtne

rs20

118

620

0110

218

640

01E

duca

tion

Hig

her

leve

l19

9983

1860

01

Tim

epr

efer

ence

sH

ighe

rdi

scou

ntra

te51

731

978

001

1729

862

20

05(d

elay

edpa

ymen

t)T

ime

hori

zon

Shor

ter

1939

7718

540

0220

1312

1864

001

Att

itud

esto

war

dsde

btLe

ssun

favo

urab

le15

4678

1705

001

1568

8416

920

01E

cono

mic

soci

aliz

atio

n(1

)P

refe

rred

tosp

end

imm

edia

tely

1195

2714

270

517

7094

1844

05

Eco

nom

icso

cial

izat

ion

(2)

Mor

eea

rned

mon

eyas

teen

ager

486

1864

05

Self

-con

trol

(1)

Pre

fer

tosp

end

imm

edia

tely

1615

7318

040

0117

5445

1844

001

Self

-con

trol

(2)

Mor

ear

eob

ese

53

1622

02

98

1798

002

Self

-con

trol

(3)

Mor

ean

dhe

avie

rci

gare

tte

smok

ers

1959

7718

440

5Se

lf-c

ontr

ol(4

)M

ore

heav

ydr

inke

rs7

0518

640

1Su

bjec

tive

life

expe

ctan

cyLo

wer

5546

499

80

1

Not

e T

hegr

oups

have

been

dich

otom

ized

wit

hno

n-de

btor

svs

mil

dde

btor

samp

debt

ors

and

x2

orM

ann-

Whi

tney

Ute

sts

used

asap

prop

riat

e






1994 data 1996 data














1994 data 1996 data





25 14042 8523 cases

correctly classied




Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin























































Method













































Results

Static analyses





Tab

le1

Sum

mar

yof

rst

orde

rdi

ffer

ence

sbe

twee

nth

ede

btgr

oups

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

veli

nat

leas

ton

esa

mpl

ear

esh

own

1994

data

1996

data

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

x2

UN

plt

x2

UN

plt

Net

inco

me

Low

er16

0540

1807

001

1764

2918

510

01Se

lf-r

epor

ted

inco

me

chan

geD

ecre

ase

inin

com

e21

5626

1864

05

Five

year

inco

me

expe

ctat

ions

Hig

her

(mor

eop

tim

isti

c)20

1220

1862

02

08

Age

You

nger

1751

9418

620

0120

2310

1864

002

Gen

der

Mal

e4

3918

640

5H

ousi

ngM

ore

tena

nts

few

erow

nho

uses

193

1862

001

96

1857

002

Num

ber

ofch

ildr

enM

ore

2000

0018

620

119

6623

1864

001

Pre

senc

eof

part

ner

Few

erha

vepa

rtne

rs20

118

620

0110

218

640

01E

duca

tion

Hig

her

leve

l19

9983

1860

01

Tim

epr

efer

ence

sH

ighe

rdi

scou

ntra

te51

731

978

001

1729

862

20

05(d

elay

edpa

ymen

t)T

ime

hori

zon

Shor

ter

1939

7718

540

0220

1312

1864

001

Att

itud

esto

war

dsde

btLe

ssun

favo

urab

le15

4678

1705

001

1568

8416

920

01E

cono

mic

soci

aliz

atio

n(1

)P

refe

rred

tosp

end

imm

edia

tely

1195

2714

270

517

7094

1844

05

Eco

nom

icso

cial

izat

ion

(2)

Mor

eea

rned

mon

eyas

teen

ager

486

1864

05

Self

-con

trol

(1)

Pre

fer

tosp

end

imm

edia

tely

1615

7318

040

0117

5445

1844

001

Self

-con

trol

(2)

Mor

ear

eob

ese

53

1622

02

98

1798

002

Self

-con

trol

(3)

Mor

ean

dhe

avie

rci

gare

tte

smok

ers

1959

7718

440

5Se

lf-c

ontr

ol(4

)M

ore

heav

ydr

inke

rs7

0518

640

1Su

bjec

tive

life

expe

ctan

cyLo

wer

5546

499

80

1

Not

e T

hegr

oups

have

been

dich

otom

ized

wit

hno

n-de

btor

svs

mil

dde

btor

samp

debt

ors

and

x2

orM

ann-

Whi

tney

Ute

sts

used

asap

prop

riat

e






1994 data 1996 data














1994 data 1996 data





25 14042 8523 cases

correctly classied




Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin


















































Method













































Results

Static analyses





Tab

le1

Sum

mar

yof

rst

orde

rdi

ffer

ence

sbe

twee

nth

ede

btgr

oups

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

veli

nat

leas

ton

esa

mpl

ear

esh

own

1994

data

1996

data

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

x2

UN

plt

x2

UN

plt

Net

inco

me

Low

er16

0540

1807

001

1764

2918

510

01Se

lf-r

epor

ted

inco

me

chan

geD

ecre

ase

inin

com

e21

5626

1864

05

Five

year

inco

me

expe

ctat

ions

Hig

her

(mor

eop

tim

isti

c)20

1220

1862

02

08

Age

You

nger

1751

9418

620

0120

2310

1864

002

Gen

der

Mal

e4

3918

640

5H

ousi

ngM

ore

tena

nts

few

erow

nho

uses

193

1862

001

96

1857

002

Num

ber

ofch

ildr

enM

ore

2000

0018

620

119

6623

1864

001

Pre

senc

eof

part

ner

Few

erha

vepa

rtne

rs20

118

620

0110

218

640

01E

duca

tion

Hig

her

leve

l19

9983

1860

01

Tim

epr

efer

ence

sH

ighe

rdi

scou

ntra

te51

731

978

001

1729

862

20

05(d

elay

edpa

ymen

t)T

ime

hori

zon

Shor

ter

1939

7718

540

0220

1312

1864

001

Att

itud

esto

war

dsde

btLe

ssun

favo

urab

le15

4678

1705

001

1568

8416

920

01E

cono

mic

soci

aliz

atio

n(1

)P

refe

rred

tosp

end

imm

edia

tely

1195

2714

270

517

7094

1844

05

Eco

nom

icso

cial

izat

ion

(2)

Mor

eea

rned

mon

eyas

teen

ager

486

1864

05

Self

-con

trol

(1)

Pre

fer

tosp

end

imm

edia

tely

1615

7318

040

0117

5445

1844

001

Self

-con

trol

(2)

Mor

ear

eob

ese

53

1622

02

98

1798

002

Self

-con

trol

(3)

Mor

ean

dhe

avie

rci

gare

tte

smok

ers

1959

7718

440

5Se

lf-c

ontr

ol(4

)M

ore

heav

ydr

inke

rs7

0518

640

1Su

bjec

tive

life

expe

ctan

cyLo

wer

5546

499

80

1

Not

e T

hegr

oups

have

been

dich

otom

ized

wit

hno

n-de

btor

svs

mil

dde

btor

samp

debt

ors

and

x2

orM

ann-

Whi

tney

Ute

sts

used

asap

prop

riat

e






1994 data 1996 data














1994 data 1996 data





25 14042 8523 cases

correctly classied




Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin













































Method













































Results

Static analyses





Tab

le1

Sum

mar

yof

rst

orde

rdi

ffer

ence

sbe

twee

nth

ede

btgr

oups

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

veli

nat

leas

ton

esa

mpl

ear

esh

own

1994

data

1996

data

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

x2

UN

plt

x2

UN

plt

Net

inco

me

Low

er16

0540

1807

001

1764

2918

510

01Se

lf-r

epor

ted

inco

me

chan

geD

ecre

ase

inin

com

e21

5626

1864

05

Five

year

inco

me

expe

ctat

ions

Hig

her

(mor

eop

tim

isti

c)20

1220

1862

02

08

Age

You

nger

1751

9418

620

0120

2310

1864

002

Gen

der

Mal

e4

3918

640

5H

ousi

ngM

ore

tena

nts

few

erow

nho

uses

193

1862

001

96

1857

002

Num

ber

ofch

ildr

enM

ore

2000

0018

620

119

6623

1864

001

Pre

senc

eof

part

ner

Few

erha

vepa

rtne

rs20

118

620

0110

218

640

01E

duca

tion

Hig

her

leve

l19

9983

1860

01

Tim

epr

efer

ence

sH

ighe

rdi

scou

ntra

te51

731

978

001

1729

862

20

05(d

elay

edpa

ymen

t)T

ime

hori

zon

Shor

ter

1939

7718

540

0220

1312

1864

001

Att

itud

esto

war

dsde

btLe

ssun

favo

urab

le15

4678

1705

001

1568

8416

920

01E

cono

mic

soci

aliz

atio

n(1

)P

refe

rred

tosp

end

imm

edia

tely

1195

2714

270

517

7094

1844

05

Eco

nom

icso

cial

izat

ion

(2)

Mor

eea

rned

mon

eyas

teen

ager

486

1864

05

Self

-con

trol

(1)

Pre

fer

tosp

end

imm

edia

tely

1615

7318

040

0117

5445

1844

001

Self

-con

trol

(2)

Mor

ear

eob

ese

53

1622

02

98

1798

002

Self

-con

trol

(3)

Mor

ean

dhe

avie

rci

gare

tte

smok

ers

1959

7718

440

5Se

lf-c

ontr

ol(4

)M

ore

heav

ydr

inke

rs7

0518

640

1Su

bjec

tive

life

expe

ctan

cyLo

wer

5546

499

80

1

Not

e T

hegr

oups

have

been

dich

otom

ized

wit

hno

n-de

btor

svs

mil

dde

btor

samp

debt

ors

and

x2

orM

ann-

Whi

tney

Ute

sts

used

asap

prop

riat

e






1994 data 1996 data














1994 data 1996 data





25 14042 8523 cases

correctly classied




Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin



















































































Results

Static analyses





Tab

le1

Sum

mar

yof

rst

orde

rdi

ffer

ence

sbe

twee

nth

ede

btgr

oups

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

veli

nat

leas

ton

esa

mpl

ear

esh

own

1994

data

1996

data

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

x2

UN

plt

x2

UN

plt

Net

inco

me

Low

er16

0540

1807

001

1764

2918

510

01Se

lf-r

epor

ted

inco

me

chan

geD

ecre

ase

inin

com

e21

5626

1864

05

Five

year

inco

me

expe

ctat

ions

Hig

her

(mor

eop

tim

isti

c)20

1220

1862

02

08

Age

You

nger

1751

9418

620

0120

2310

1864

002

Gen

der

Mal

e4

3918

640

5H

ousi

ngM

ore

tena

nts

few

erow

nho

uses

193

1862

001

96

1857

002

Num

ber

ofch

ildr

enM

ore

2000

0018

620

119

6623

1864

001

Pre

senc

eof

part

ner

Few

erha

vepa

rtne

rs20

118

620

0110

218

640

01E

duca

tion

Hig

her

leve

l19

9983

1860

01

Tim

epr

efer

ence

sH

ighe

rdi

scou

ntra

te51

731

978

001

1729

862

20

05(d

elay

edpa

ymen

t)T

ime

hori

zon

Shor

ter

1939

7718

540

0220

1312

1864

001

Att

itud

esto

war

dsde

btLe

ssun

favo

urab

le15

4678

1705

001

1568

8416

920

01E

cono

mic

soci

aliz

atio

n(1

)P

refe

rred

tosp

end

imm

edia

tely

1195

2714

270

517

7094

1844

05

Eco

nom

icso

cial

izat

ion

(2)

Mor

eea

rned

mon

eyas

teen

ager

486

1864

05

Self

-con

trol

(1)

Pre

fer

tosp

end

imm

edia

tely

1615

7318

040

0117

5445

1844

001

Self

-con

trol

(2)

Mor

ear

eob

ese

53

1622

02

98

1798

002

Self

-con

trol

(3)

Mor

ean

dhe

avie

rci

gare

tte

smok

ers

1959

7718

440

5Se

lf-c

ontr

ol(4

)M

ore

heav

ydr

inke

rs7

0518

640

1Su

bjec

tive

life

expe

ctan

cyLo

wer

5546

499

80

1

Not

e T

hegr

oups

have

been

dich

otom

ized

wit

hno

n-de

btor

svs

mil

dde

btor

samp

debt

ors

and

x2

orM

ann-

Whi

tney

Ute

sts

used

asap

prop

riat

e






1994 data 1996 data














1994 data 1996 data





25 14042 8523 cases

correctly classied




Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin











































































Results

Static analyses





Tab

le1

Sum

mar

yof

rst

orde

rdi

ffer

ence

sbe

twee

nth

ede

btgr

oups

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

veli

nat

leas

ton

esa

mpl

ear

esh

own

1994

data

1996

data

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

x2

UN

plt

x2

UN

plt

Net

inco

me

Low

er16

0540

1807

001

1764

2918

510

01Se

lf-r

epor

ted

inco

me

chan

geD

ecre

ase

inin

com

e21

5626

1864

05

Five

year

inco

me

expe

ctat

ions

Hig

her

(mor

eop

tim

isti

c)20

1220

1862

02

08

Age

You

nger

1751

9418

620

0120

2310

1864

002

Gen

der

Mal

e4

3918

640

5H

ousi

ngM

ore

tena

nts

few

erow

nho

uses

193

1862

001

96

1857

002

Num

ber

ofch

ildr

enM

ore

2000

0018

620

119

6623

1864

001

Pre

senc

eof

part

ner

Few

erha

vepa

rtne

rs20

118

620

0110

218

640

01E

duca

tion

Hig

her

leve

l19

9983

1860

01

Tim

epr

efer

ence

sH

ighe

rdi

scou

ntra

te51

731

978

001

1729

862

20

05(d

elay

edpa

ymen

t)T

ime

hori

zon

Shor

ter

1939

7718

540

0220

1312

1864

001

Att

itud

esto

war

dsde

btLe

ssun

favo

urab

le15

4678

1705

001

1568

8416

920

01E

cono

mic

soci

aliz

atio

n(1

)P

refe

rred

tosp

end

imm

edia

tely

1195

2714

270

517

7094

1844

05

Eco

nom

icso

cial

izat

ion

(2)

Mor

eea

rned

mon

eyas

teen

ager

486

1864

05

Self

-con

trol

(1)

Pre

fer

tosp

end

imm

edia

tely

1615

7318

040

0117

5445

1844

001

Self

-con

trol

(2)

Mor

ear

eob

ese

53

1622

02

98

1798

002

Self

-con

trol

(3)

Mor

ean

dhe

avie

rci

gare

tte

smok

ers

1959

7718

440

5Se

lf-c

ontr

ol(4

)M

ore

heav

ydr

inke

rs7

0518

640

1Su

bjec

tive

life

expe

ctan

cyLo

wer

5546

499

80

1

Not

e T

hegr

oups

have

been

dich

otom

ized

wit

hno

n-de

btor

svs

mil

dde

btor

samp

debt

ors

and

x2

orM

ann-

Whi

tney

Ute

sts

used

asap

prop

riat

e






1994 data 1996 data














1994 data 1996 data





25 14042 8523 cases

correctly classied




Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin































































Results

Static analyses





Tab

le1

Sum

mar

yof

rst

orde

rdi

ffer

ence

sbe

twee

nth

ede

btgr

oups

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

veli

nat

leas

ton

esa

mpl

ear

esh

own

1994

data

1996

data

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

x2

UN

plt

x2

UN

plt

Net

inco

me

Low

er16

0540

1807

001

1764

2918

510

01Se

lf-r

epor

ted

inco

me

chan

geD

ecre

ase

inin

com

e21

5626

1864

05

Five

year

inco

me

expe

ctat

ions

Hig

her

(mor

eop

tim

isti

c)20

1220

1862

02

08

Age

You

nger

1751

9418

620

0120

2310

1864

002

Gen

der

Mal

e4

3918

640

5H

ousi

ngM

ore

tena

nts

few

erow

nho

uses

193

1862

001

96

1857

002

Num

ber

ofch

ildr

enM

ore

2000

0018

620

119

6623

1864

001

Pre

senc

eof

part

ner

Few

erha

vepa

rtne

rs20

118

620

0110

218

640

01E

duca

tion

Hig

her

leve

l19

9983

1860

01

Tim

epr

efer

ence

sH

ighe

rdi

scou

ntra

te51

731

978

001

1729

862

20

05(d

elay

edpa

ymen

t)T

ime

hori

zon

Shor

ter

1939

7718

540

0220

1312

1864

001

Att

itud

esto

war

dsde

btLe

ssun

favo

urab

le15

4678

1705

001

1568

8416

920

01E

cono

mic

soci

aliz

atio

n(1

)P

refe

rred

tosp

end

imm

edia

tely

1195

2714

270

517

7094

1844

05

Eco

nom

icso

cial

izat

ion

(2)

Mor

eea

rned

mon

eyas

teen

ager

486

1864

05

Self

-con

trol

(1)

Pre

fer

tosp

end

imm

edia

tely

1615

7318

040

0117

5445

1844

001

Self

-con

trol

(2)

Mor

ear

eob

ese

53

1622

02

98

1798

002

Self

-con

trol

(3)

Mor

ean

dhe

avie

rci

gare

tte

smok

ers

1959

7718

440

5Se

lf-c

ontr

ol(4

)M

ore

heav

ydr

inke

rs7

0518

640

1Su

bjec

tive

life

expe

ctan

cyLo

wer

5546

499

80

1

Not

e T

hegr

oups

have

been

dich

otom

ized

wit

hno

n-de

btor

svs

mil

dde

btor

samp

debt

ors

and

x2

orM

ann-

Whi

tney

Ute

sts

used

asap

prop

riat

e






1994 data 1996 data














1994 data 1996 data





25 14042 8523 cases

correctly classied




Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin























































Results

Static analyses





Tab

le1

Sum

mar

yof

rst

orde

rdi

ffer

ence

sbe

twee

nth

ede

btgr

oups

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

veli

nat

leas

ton

esa

mpl

ear

esh

own

1994

data

1996

data

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

x2

UN

plt

x2

UN

plt

Net

inco

me

Low

er16

0540

1807

001

1764

2918

510

01Se

lf-r

epor

ted

inco

me

chan

geD

ecre

ase

inin

com

e21

5626

1864

05

Five

year

inco

me

expe

ctat

ions

Hig

her

(mor

eop

tim

isti

c)20

1220

1862

02

08

Age

You

nger

1751

9418

620

0120

2310

1864

002

Gen

der

Mal

e4

3918

640

5H

ousi

ngM

ore

tena

nts

few

erow

nho

uses

193

1862

001

96

1857

002

Num

ber

ofch

ildr

enM

ore

2000

0018

620

119

6623

1864

001

Pre

senc

eof

part

ner

Few

erha

vepa

rtne

rs20

118

620

0110

218

640

01E

duca

tion

Hig

her

leve

l19

9983

1860

01

Tim

epr

efer

ence

sH

ighe

rdi

scou

ntra

te51

731

978

001

1729

862

20

05(d

elay

edpa

ymen

t)T

ime

hori

zon

Shor

ter

1939

7718

540

0220

1312

1864

001

Att

itud

esto

war

dsde

btLe

ssun

favo

urab

le15

4678

1705

001

1568

8416

920

01E

cono

mic

soci

aliz

atio

n(1

)P

refe

rred

tosp

end

imm

edia

tely

1195

2714

270

517

7094

1844

05

Eco

nom

icso

cial

izat

ion

(2)

Mor

eea

rned

mon

eyas

teen

ager

486

1864

05

Self

-con

trol

(1)

Pre

fer

tosp

end

imm

edia

tely

1615

7318

040

0117

5445

1844

001

Self

-con

trol

(2)

Mor

ear

eob

ese

53

1622

02

98

1798

002

Self

-con

trol

(3)

Mor

ean

dhe

avie

rci

gare

tte

smok

ers

1959

7718

440

5Se

lf-c

ontr

ol(4

)M

ore

heav

ydr

inke

rs7

0518

640

1Su

bjec

tive

life

expe

ctan

cyLo

wer

5546

499

80

1

Not

e T

hegr

oups

have

been

dich

otom

ized

wit

hno

n-de

btor

svs

mil

dde

btor

samp

debt

ors

and

x2

orM

ann-

Whi

tney

Ute

sts

used

asap

prop

riat

e






1994 data 1996 data














1994 data 1996 data





25 14042 8523 cases

correctly classied




Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin














































Results

Static analyses





Tab

le1

Sum

mar

yof

rst

orde

rdi

ffer

ence

sbe

twee

nth

ede

btgr

oups

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

veli

nat

leas

ton

esa

mpl

ear

esh

own

1994

data

1996

data

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

x2

UN

plt

x2

UN

plt

Net

inco

me

Low

er16

0540

1807

001

1764

2918

510

01Se

lf-r

epor

ted

inco

me

chan

geD

ecre

ase

inin

com

e21

5626

1864

05

Five

year

inco

me

expe

ctat

ions

Hig

her

(mor

eop

tim

isti

c)20

1220

1862

02

08

Age

You

nger

1751

9418

620

0120

2310

1864

002

Gen

der

Mal

e4

3918

640

5H

ousi

ngM

ore

tena

nts

few

erow

nho

uses

193

1862

001

96

1857

002

Num

ber

ofch

ildr

enM

ore

2000

0018

620

119

6623

1864

001

Pre

senc

eof

part

ner

Few

erha

vepa

rtne

rs20

118

620

0110

218

640

01E

duca

tion

Hig

her

leve

l19

9983

1860

01

Tim

epr

efer

ence

sH

ighe

rdi

scou

ntra

te51

731

978

001

1729

862

20

05(d

elay

edpa

ymen

t)T

ime

hori

zon

Shor

ter

1939

7718

540

0220

1312

1864

001

Att

itud

esto

war

dsde

btLe

ssun

favo

urab

le15

4678

1705

001

1568

8416

920

01E

cono

mic

soci

aliz

atio

n(1

)P

refe

rred

tosp

end

imm

edia

tely

1195

2714

270

517

7094

1844

05

Eco

nom

icso

cial

izat

ion

(2)

Mor

eea

rned

mon

eyas

teen

ager

486

1864

05

Self

-con

trol

(1)

Pre

fer

tosp

end

imm

edia

tely

1615

7318

040

0117

5445

1844

001

Self

-con

trol

(2)

Mor

ear

eob

ese

53

1622

02

98

1798

002

Self

-con

trol

(3)

Mor

ean

dhe

avie

rci

gare

tte

smok

ers

1959

7718

440

5Se

lf-c

ontr

ol(4

)M

ore

heav

ydr

inke

rs7

0518

640

1Su

bjec

tive

life

expe

ctan

cyLo

wer

5546

499

80

1

Not

e T

hegr

oups

have

been

dich

otom

ized

wit

hno

n-de

btor

svs

mil

dde

btor

samp

debt

ors

and

x2

orM

ann-

Whi

tney

Ute

sts

used

asap

prop

riat

e






1994 data 1996 data














1994 data 1996 data





25 14042 8523 cases

correctly classied




Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin














































Tab

le1

Sum

mar

yof

rst

orde

rdi

ffer

ence

sbe

twee

nth

ede

btgr

oups

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

veli

nat

leas

ton

esa

mpl

ear

esh

own

1994

data

1996

data

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

x2

UN

plt

x2

UN

plt

Net

inco

me

Low

er16

0540

1807

001

1764

2918

510

01Se

lf-r

epor

ted

inco

me

chan

geD

ecre

ase

inin

com

e21

5626

1864

05

Five

year

inco

me

expe

ctat

ions

Hig

her

(mor

eop

tim

isti

c)20

1220

1862

02

08

Age

You

nger

1751

9418

620

0120

2310

1864

002

Gen

der

Mal

e4

3918

640

5H

ousi

ngM

ore

tena

nts

few

erow

nho

uses

193

1862

001

96

1857

002

Num

ber

ofch

ildr

enM

ore

2000

0018

620

119

6623

1864

001

Pre

senc

eof

part

ner

Few

erha

vepa

rtne

rs20

118

620

0110

218

640

01E

duca

tion

Hig

her

leve

l19

9983

1860

01

Tim

epr

efer

ence

sH

ighe

rdi

scou

ntra

te51

731

978

001

1729

862

20

05(d

elay

edpa

ymen

t)T

ime

hori

zon

Shor

ter

1939

7718

540

0220

1312

1864

001

Att

itud

esto

war

dsde

btLe

ssun

favo

urab

le15

4678

1705

001

1568

8416

920

01E

cono

mic

soci

aliz

atio

n(1

)P

refe

rred

tosp

end

imm

edia

tely

1195

2714

270

517

7094

1844

05

Eco

nom

icso

cial

izat

ion

(2)

Mor

eea

rned

mon

eyas

teen

ager

486

1864

05

Self

-con

trol

(1)

Pre

fer

tosp

end

imm

edia

tely

1615

7318

040

0117

5445

1844

001

Self

-con

trol

(2)

Mor

ear

eob

ese

53

1622

02

98

1798

002

Self

-con

trol

(3)

Mor

ean

dhe

avie

rci

gare

tte

smok

ers

1959

7718

440

5Se

lf-c

ontr

ol(4

)M

ore

heav

ydr

inke

rs7

0518

640

1Su

bjec

tive

life

expe

ctan

cyLo

wer

5546

499

80

1

Not

e T

hegr

oups

have

been

dich

otom

ized

wit

hno

n-de

btor

svs

mil

dde

btor

samp

debt

ors

and

x2

orM

ann-

Whi

tney

Ute

sts

used

asap

prop

riat

e






1994 data 1996 data














1994 data 1996 data





25 14042 8523 cases

correctly classied




Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin


















































1994 data 1996 data














1994 data 1996 data





25 14042 8523 cases

correctly classied




Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin

















































1994 data 1996 data





25 14042 8523 cases

correctly classied




Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin
















































Dynamic analyses





Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin














































Tab

le4

Sum

mar

yof

rst

-ord

erdi

ffer

ence

sbe

twee

nth

eth

ree

grou

ps

neve

r-de

btor

sat

leas

ton

cea

mil

dde

btor

at

leas

ton

cea

debt

or

Onl

ydi

ffer

ence

sth

atar

esi

gni

cant

atth

e0

5le

vel

are

show

n

Com

pari

son

1ne

ver-

debt

ors

vs

Com

pari

son

2m

ild

mil

dde

btor

san

dde

btor

sde

btor

svs

de

btor

s

Ove

rall

Var

iabl

eTe

nden

cyof

Deb

tgr

oups

plt

x2

UN

plt

x2

UN

plt

Mea

nne

tin

com

eLo

wer

001

8995

111

280

01M

ean

abso

lute

ch

ange

inin

com

eM

ore

vari

atio

nin

inco

me

001

9698

611

280

0168

0625

40

5A

vera

ged

5ye

arin

com

eH

ighe

r(m

ore

opti

mis

tic)

05

9924

211

280

1ex

pect

atio

nsM

ean

inco

me

unce

rtai

nty

Mor

eun

cert

ain

05

5955

086

20

1A

geY

oung

er0

0191

258

1128

001

6908

254

05

Num

ber

ofch

ildre

nM

ore

001

9508

711

280

01P

rese

nce

ofpa

rtne

rFe

wer

have

part

ners

001

155

1128

001

Edu

cati

onH

ighe

rle

vel

ns

1014

3311

280

5T

ime

pref

eren

ces

(del

ayed

paym

ent)

Hig

her

disc

ount

rate

001

8924

810

920

01T

ime

hori

zon

Shor

ter

005

9722

411

280

02A

ttit

udes

tow

ards

debt

Less

unfa

vour

able

001

7130

910

050

01C

onsc

ient

ious

ness

Less

cons

cien

tiou

s0

196

736

1125

01

Eco

nom

icso

cial

izat

ion

(2)

Mor

ere

port

earn

ing

mon

eyas

teen

ager

005

9777

211

280

02Se

lf-c

ontr

ol(1

)P

refe

rto

spen

dim

med

iate

ly0

0188

114

1126

001

6788

252

05

Self

-con

trol

(2)

Mor

ear

eob

ese

05

54

1113

05

Mon

eym

anag

emen

tU

sem

ore

tech

niqu

es0

0185

639

1118

001

Con

trol

ofex

pend

itur

eFi

ndit

mor

edi

fcu

lt0

0167

857

1061

001

5513

243

001

Mea

nsu

bjec

tive

life

expe

ctan

cyLo

wer

ns

5261

583

60

5

Not

e (1

)T

heov

eral

lsi

gni

canc

eis

deri

ved

eith

erfr

oma

Kru

skal

-Wal

liste

st(w

here

Man

n-W

hitn

eyU

are

repo

rted

inth

eco

mpa

riso

nco

lum

ns)

ora

x2

(whe

rech

i-sq

uare

sar

ere

port

edin

the

com

pari

son

colu

mns

)T

heco

mpa

riso

nsar

epl

anne

dan

dar

eth

eref

ore

carr

ied

out

even

whe

nth

ere

are

nosi

gni

cant

diff

eren

ces

betw

een

the

grou

psov

eral

l(2

)Fi

gure

sre

port

edar

efo

r19

96fo

rti

me

hori

zon

and

atti

tude

tode

bt

gure

sfo

r19

94ar

eve

rysi

mil

ar







Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin



















































Model x2







1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin


















































1994 1996










Discussion









x2













Acknowledgements


References











Houghlin Mifin

















































Discussion









x2













Acknowledgements


References











Houghlin Mifin
























































Acknowledgements


References











Houghlin Mifin



















































Acknowledgements


References











Houghlin Mifin














































Acknowledgements


References











Houghlin Mifin










































































































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Life-cycle and Dispositional Routes Into Problem Debt

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