Accounting for the role of habit in regular saving

Journal of Economic Psychology 32 (2011) 581–592

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Journal of Economic Psychology

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Accounting for the role of habit in regular saving

Cäzilia Loibl a,⇑, David S. Kraybill b, Sara Wackler DeMay b

a Department of Consumer Sciences, The Ohio State University, 1787 Neil Avenue, Columbus, OH 43210, USAb Department of Agricultural, Environmental, and Development Economics, The Ohio State University, 2120 Fyffe Road, Columbus, OH 43210, USA

a r t i c l e i n f o

Article history:Received 27 May 2009Received in revised form 20 April 2011Accepted 25 April 2011Available online 7 May 2011

JEL classification:D14D91I32

PsycINFO classification:22293040

Keywords:HabitDecision makingConsumer behaviorSaving

0167-4870/$ - see front matter � 2011 Elsevier B.Vdoi:10.1016/j.joep.2011.04.004

⇑ Corresponding author. Tel.: +1 614 292 4226; faE-mail addresses: [email protected] (C. Loibl), kray

a b s t r a c t

The present study combines insights from social psychology and economics by examiningthe role of savings habits in regular saving. As frequently practiced, automatic, and goal-facilitated behaviors, savings habits play a critical role in everyday financial decisions.Using the Self-Report Habit Index developed by Verplanken and Orbell (2003), we collectedand analyzed survey data to (1) validate the role of habit in regular saving; (2) test whetherparticipation in a savings program, the Individual Development Account program, facili-tates habit formation; and (3) examine the role of habit in individual’s perception of finan-cial strain. The results showed that habit mattered for regular saving. It influenced savingsamounts above and beyond Theory of Planned Behavior and deposit frequency measures.Habit strength increased over time during program participation and savings habitsreduced the stress of financially difficult situations.

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1. Introduction

Household financial independence and stability, a goal universally recognized as good, can be achieved by mostpeople only through repeated acts of saving. In the prevailing economics literature, savings behavior is commonly addressedwith regard to highly conscious and deliberate financial decisions, such as the decision to allocate income for retirementsavings or the purchase of a major asset like a home (e.g., Li & White, 2009; Madrian & Shea, 2001). Yet many financialdecisions occur frequently throughout the day and tend to affect small amounts of money in the ‘‘peanuts’’ range(Markowitz, 1952; Prelec & Loewenstein, 1991). If these decisions are made in a budget-conscious manner and practicedoften enough to become habitual, they contribute towards the goal of household financial stability. Habits of saving canreduce ad hoc rationalizations, hassles, and moods that may lead to the decision not to save (Verplanken & Melkevik, 2008).

Forming good habits requires time and effort, as does the breaking of bad habits (Verplanken & Wood, 2006). Habit for-mation is a process of investment with opportunity costs and potential returns. Once established, habits vary in strength andevolve, requiring reinforcement if they are to be maintained. Savings habits in most people are formed in long-period, hiddenprocesses of socialization revolving around the household, school, and community (Wood, Tam, & Witt, 2005). Knowledgeabout how to reform deficient savings habits of adults is still rudimentary (Wood & Neal, 2007).

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582 C. Loibl et al. / Journal of Economic Psychology 32 (2011) 581–592

Using the Self-Report Habit Index developed by Verplanken and Orbell (2003), we collected and analyzed survey data to(1) validate the role of habit in regular saving; (2) test whether participation in a savings program, the Individual Develop-ment Account program, facilitates habit formation; and (3) examine the role of habit in individual’s perception of financialstrain. The analysis contributes to an understanding of habit formation and the role of habit in the financial decisions of low-income households.

2. Literature review

2.1. Role of habit

Definitions of habit in psychology and economics typically focus on three dimensions: repeated behavior in stable con-texts, restricted deliberation, and the interface with personal goals (Verplanken & Orbell, 2003; Wood & Neal, 2007). Repe-tition, even when frequent, does not necessarily make a behavior habitual; rather, repetition becomes habitual when presentbehavior is influenced by past behaviors (Becker, 1992; Ouellette & Wood, 1998). Past behavior will likely influence futurebehavior, if the behavior is repeated in stable, unchanging situations that allow for smooth initiation, execution, and com-pletion of the activity (Wood, Quinn, & Kashy, 2002). For instance, whether daily behaviors, such as watching TV, reading thenewspaper, or exercising, become habitual depends on whether they are triggered by the time of the day, a specific locale,actions of family and friends, or other cues.

Theories of habit differ in their treatment of goals. Some researchers view habits as goal-directed behavior in which goalsand actions are directly linked (Aarts & Dijksterhuis, 2000; Verplanken & Orbell, 2003). Others see goals as mediating theprocess of habit formation but not habit performance itself (Bargh, 1994). A contrasting view is that goals initiate behaviorprior to habit formation, goals are developed based on past habits, and goals and habits join forces to achieve a desired out-come (Wood & Neal, 2007). Whether goals affect habit performance directly or indirectly, they ultimately shape habitualbehaviors. The habit–goal interface consists of associative learning mechanisms through which individuals ‘‘learn whatworks’’ and through which social messages with potential to transform habits are transmitted.

What are the benefits of promoting habit development in the savings context? Firstly, habits create predictability androutineness that facilitate actions and provide an anchor in uncertain times (Twomey, 1999). Secondly, habits simplify ac-tions. By reducing the level of conscious control (e.g., by taking the customary route through the grocery store), mental re-sources are available for the more complex or unexpected choice situations (e.g., finding reduced-priced products). This roleof habits is associated with the concept of bounded rationality. First introduced by Simon (1955), it describes the extent towhich limitations in information processing capacity influence decision-making. Habitual behaviors, or heuristics, that func-tion with minimal cognitive effort help circumvent these capacity limits (Bettman, Luce, & Payne, 1998). Payne, Bettman, andSchkade (1999) describe the range and representations of automated behavior by linking it to stages in the decision makingprocess. The biases and limitations include myopic decision frames, insufficient problem representation, selectivity, and lim-ited tradeoffs, which ease consumer choice, but challenge corrective intervention.

Thirdly, the automatic processes instigated by habits connect and organize information in the mind. As a result, habitsinfluence the way a person perceives a situation, makes choices, and repeats the choice, so the associated action graduallybecomes automatic. The term ‘‘cognitive lock-in’’ aptly describes this kind of situation in the marketing literature (Johnson,Bellman, & Lohse, 2003). With an increasing amount of experience with a particular behavior, the mental resources associ-ated with thinking about, gathering information, and using the product decrease. When a behavior becomes increasinglyautomatic, an effort to change the behavior will create switching costs and, in avoiding these costs, habit strengthens(Murray & Häubl, 2007).

Interventions for habit change take advantage of the dependence of habit on a stable environmental context. A contextchange can be initiated actively, for instance, by avoiding exposure to situations that would trigger habitual response, count-erconditioning efforts, or active inhibition (Wood & Neal, 2007). In the savings context, typical strategies include avoidingthe trip to the mall or the coffee shop or suppressing thinking about the Friday evening dinner at a restaurant by directingone’s thought to the more important long-term saving goal. Active interventions are challenging, because they depend onpeople’s ability to constantly control behavior. A solution is provided by Verplanken and Wood’s (2006) concept of ‘‘down-stream-plus-context-change.’’ It suggests combining deliberate behavior change strategies with naturally occurring changesin the environment. If the behavioral context is changed (e.g., products rearranged in the grocery store), habitual behavior isinterrupted (e.g., the habitual route through the store). As a result, behavior is evaluated, new goals are set, appropriate ac-tions are taken, and new habits develop.

2.2. Habit in social-sector asset-building programs

A savings-related example of the ‘‘downstream-plus-context-change’’ approach is practiced by the Individual Develop-ment Account (IDA) program, a social-sector based matched savings program offered in the United States and Canada.The IDA program makes the development and reinforcement of savings habits among low-income clients a key goal (OCSDemonstration Division., 2000). It combines mandatory savings deposits with a supportive, high-touch environment. Weare using this savings program as the outlet for our study.

C. Loibl et al. / Journal of Economic Psychology 32 (2011) 581–592 583

IDAs are savings accounts that provide incentives for low-income individuals and families to save toward purchasing ahome, financing higher education, capitalizing a small business, or another program-permitted asset. The program was con-ceptualized as a life-time approach to asset-building by Sherraden and his team (Sherraden, 1991) and was tested in Amer-ican Dream Demonstrations (Sherraden, 2000). In 1998, the ‘‘Assets for Independence Act,’’ which directed federal funding tothe program, was enacted (105th United States Congress., 1998).

IDA program agencies vary widely in their mission statements and funding streams reflecting the diversity of socialneeds, and so do program design and rules. A certain degree of standardization has been achieved for recipients of federalprogram funding (105th United States Congress, 1998). In federally funded IDAs, participants save for up to 5 years and thesavings are matched using federal as well as state and private funds. The program is based on mandatory deposits, matchedsavings, intensive financial education, and regular one-on-one counseling (Office of Community Services., 2008).

The IDA program is a unique test bed, for several reasons, to study saving habits. It is a multi-year program that has theexplicit goal of creating savings habits among its low-income participants. It uses a network of social workers, banking, hous-ing, and small business professionals to establish a supportive context, which provides information, consultation, and finan-cial education (Sherraden, Schreiner, & Beverly, 2003). The IDA program requires regular savings deposits and expelsparticipants who fail to deposit three times in a row. Periods of inactivity and emergency withdrawal are possible for spec-ified reasons.

The IDA program agencies in our study offered a $2 match for every $1 saved. The savings goal was $1000, and the rec-ommended deposit frequency was once per month. The monthly savings target was $50. IDA program clients had to remainin the program for at least 6 months. The savings were held in a custodial account; the savings and the match were paiddirectly to the seller of a house, a business equipment store, or a community college for tuition, depending on the chosenuse of the funds.

3. Research questions

3.1. Does habit play a role for regular savings deposits?

The first research question examines the role of habit in regular saving. The Self-Report Habit Index was chosen as a mea-sure of habit strength. This scale of 12 items was developed by Verplanken and Orbell (2003) to reflect the different facets ofhabit, including the frequency, lack of awareness and control, and mental efficiency of behavior (Honkanen, Olsen, &Verplanken, 2005). Several scale items reflect each habit feature. Repetition of behavior is measured by items referring tofrequency and routine. Automaticity is composed of items measuring lack of awareness, difficulty to control, and uninten-tional actions. Mental efficiency is measured by items that reflect personal style and identity (Verplanken & Orbell, 2003).This multidimensional construct compares well to other published measures of habit (Verplanken, Myrbakk, & Rudi,2004, for a review) and has proved useful for many everyday behaviors, as shown in Table 1. The present study contributesto this literature by measuring habit formation in the quasi-experimental setting of a savings program intervention. Follow-ing standard procedures in the habit literature, the predictive importance of the habit measure is evaluated relative to theTheory of Planned Behavior variables and a behavioral frequency measure.

3.2. Does IDA program participation enable the formation of savings habits?

Participation in the IDA program may affect savings habits for two reasons. The first reason is participants’ self-controlexerted by self-regulation (Wood & Neal, 2007). People would not participate in this time-consuming and controlling

Table 1Literature using the Self-Report Habit Index.

References Focus Object SRHI/HINT

Verplanken andOrbell (2003)

Test–retest reliability; validity compared to frequencymeasure, to reflect behaviors that differ in frequency

Using bicycle, bus; watching TV, eating candy,listening to music; list of self-declared habits

12 items;a = 0.85–0.95

Verplanken et al.(2004)

Intercorrelation of four habit measures; correlation ofbehaviors for each measure

Using bus, car 12 items;a = 0.93

Honkanen et al.(2005)

Habit-intention relationship; moderation effect of habiton attitude–intention relation

Eating seafood 4 items; a = 0.82

Verplanken(2006)

Validate discriminant validity over past frequencymeasures

Eating snack food 12 items;a = 0.88

Verplanken et al.(2007)

Influence of the habit of negative self-thinking onselected self-worth measures

Negative thinking 12 items;reliability:0.91–0.96

Verplanken andMelkevik(2008)

Construct stability via internal consistency and atdifferent time points; distinction of habit and frequency

Exercising 12 items;a = 0.93, 0.92

Verplanken andVelsik (2008)

Influence of the habit of negative body image thinking onself-esteem and eating disturbance propensity

Negative body image thinking 12 items;a = 0.94

Note: SRHI = Self-Report Habit Index; HINT = Habit Index of Negative Thinking, an adapted version of the SRHI; a = Cronbach’s alpha reliability measure.


program if it were not for the overwhelming desire to achieve an important personal goal. The second reason may be found‘‘upstream of the habit cue,’’ based on program observation (e.g., Im & Busette, 2010), noting that clients’ interaction with theIDA program over the length of their participation, usually 2–3 years, tends to be their first prolonged engagement withfinancial services organizations. Such interaction is intended to increase experience and confidence in interacting within anetwork of case managers, financial professionals, and fellow program participants. As Wood et al. (2005) suggest, this con-text may ‘‘create new circumstances that bring about behavior change by activating new intentions and goals’’ (p. 920).

To test whether the savings program intervention is successful in creating savings habits, we compare program partici-pants to a general population sample. We matched the general population sample, consisting of persons who had not par-ticipated in an IDA program, with program participants on demographic and socio-economic characteristics.

Three results are expected. First, the strength of savings habits is expected to be similar initially for new IDA programparticipants and non-participants. The first 6 months of program participation are generally considered the most challengingby program providers because participants adjust their financial behaviors to accommodate regular savings deposits andtransaction costs associated with fulfilling program requirements. Program attrition is highest during this time (Schreiner& Sherraden, 2005).

Second, with increasing length of time in the program, we expect to measure an increase in habit strength among par-ticipants, compared to non-participants and early participants. It is also expected that the change in habit strength gainedfrom program participation decreases with additional time spent saving in an IDA, for several reasons. The environmentalcues that enable thrifty behavior are limited in number and strength, disposable income is limited among the low-incomeIDA clientele, and changes in participants’ economic environment and personal situation can cause a revision of savings goalsduring participation in the multi-year program. In fact, recent program evaluations have indicated a decline in averagemonthly deposits as participants move from their first year to their second and their third year of IDA program participation(Mills, Lam, DeMarco, Rodger, & Kaul, 2008).

3.3. Do savings habits alleviate the perception of financial strain?

Relating the role of habit to the perception of financial strain adds a psychological outcome measure to the analysis ofhabit strength. Literature on the mental aspects of habitual behavior documents the relationship between habit and mentalprocesses, including emotional responses (Verplanken, 2006; Verplanken, Friborg, Wang, Trafimow, & Woolf, 2007). In diarystudies, Wood et al. (2002) show that habit, as an ‘‘efficient and nontaxing mode of initiating and controlling daily activities’’(p. 1282), alleviates feelings of stress, loss of control, and helplessness. Habit is explained as an adoption mechanism inwhich the influence of emotions is alleviated as behaviors are repeated and become habitual.

With regard to savings habits, financial strain was chosen as an emotional measure. In this study, financial strain gauges‘‘an individual’s perception of financial inadequacy as well as her/his financial concerns and worries’’ (Mills, Grasmick, StoutMorgan, & Wenk, 1992, p. 441). These perceptions have been shown inversely related to a household’s financial resources(Hilton & Devall, 1997; Voydanoff, Donnelly, & Fine, 1988). Savings habits are expected to reduce the perception of financialstrain. People with strong savings habits have been shown to develop a sense of independence as they feel more able to man-age their finances (Wärneryd, 1999). They appear less inclined to financial concerns and worries than those with less practicein these behaviors (Livingstone & Lunt, 1993). We use a 12-item economic strain scale developed for low-income single- andtwo-parent families (Hilton & Devall, 1997). The results of this analysis are expected to confirm the discriminant validity ofhabit versus two measures of household financial resources, household income and household total savings, when examin-ing perceived financial strain.

4. Methodology

4.1. Study conditions

The study sample is small. However, it is a good-sized sample compared to the number of participants in the typical IDAprogram. Even after 10 years of federal support, most implementation sites have relatively few participants. Cumulative pro-gram data show an average of 51 program participants per site (Office of Community Services, 2008). At the time of datacollection, the collaborator in the current study was the fourth largest IDA program provider in the country. Working witha statewide network of 17 agencies for the current study, the sample represented the program well with respect to assetchoices, matching structure, and participant demographic characteristics.

A second potential bias relates to sample attrition, which may influence the estimated effects of habit development.Ideally, all clients who enroll in the program complete it. In reality, one-third to one-quarter of IDA participants drop out,typically during the first 6 months of program participation (Schreiner & Sherraden, 2005). To test whether participant char-acteristics were different for those who have been in the IDA program for a longer time and those who joined more recently,we conducted pairwise means comparison tests of demographic (listed in Table 2) and psychological characteristics (Theoryof Planned Behavior variables, financial strain) across the program cohorts. The measures were stable and did not indicateattrition bias. These results suggest that attrition was random and is likely not a severe problem because it is unrelated topsychological and demographic variables.

Table 2Sample characteristics.

Variable Range IDA participants General public AllMean (SD) Mean (SD) Mean (SD)

Gender (female = 1) 0–1 0.87 (0.340) 0.86 (0.347) 0.87 (0.341)Age (cont.) 19–77 38 (9.6) 39 (13.5) 38 (10.8)Race (white = 1) 0–1 0.55 (0.500) 0.62 (0.492) 0.57 (0.497)Education (more than HS = 1) 0–1 0.73 (0.449) 0.68 (0.475) 0.71 (0.455)Marital status (married/lwp = 1) 0–1 0.29 (0.454) 0.38 (0.492) 0.31 (0.465)Household size (cont.) 1–7 3.14 (1.304) 2.97 (1.518) 3.09 (1.366)Employment status (in workforce = 1) 0–1 0.98 (0.147) 0.97 (0.164) 0.98 (0.152)Location of residence (metro = 1) 0–1 0.77 (0.424) 0.73 (0.450) 0.76 (0.430)Monthly household income $160–5250 $1725 ($886) $1770 ($895) $1738 ($886)N 91 37 128

Note: The sample characteristics did not differ between IDA participants and the general public; lwp = living with a partner.


Small-scale and exploratory field studies, such as ours, of social sector programs can illuminate the role of decision-making processes in ways that are impossible to achieve in controlled lab settings. We regard the findings of this studyof habit formation in the IDA programs as tentative in nature, yet directive for future research.

4.2. Procedure

Data were collected by survey from two groups of low-income individuals: a treatment group of IDA program participantsin a US Midwestern state and a comparison group of individuals from the general population in counties served by the IDAprogram, but who were not IDA program participants.

The treatment group data were collected in spring 2008 through paper questionnaires given by IDA program case man-agers to their clients during financial education and counseling meetings. In total, 94 questionnaires were returned from apossible 186 current IDA program participants, resulting in a response rate of 52%. Case managers were given a $10 gift cardfor their help in collecting the surveys.

The comparison group data were collected by mail, also in spring 2008. The sample targeted census tract block groupswhose income and location of residence meet IDA program requirements. Addresses of 2200 persons were purchased anda mail survey was sent to these individuals using recommended survey practices (Dillman, 2007); a monetary incentivewas not provided. Of these, 237 contacts were invalid; 447 individuals responded (22.8%), 291 with completed questionnaire(14.8%). Missing value analysis indicated 22 questionnaires with more than 50% missing items, which reduced the sample to269 responses. Separate variance t-tests for this sample showed that missing values were completely at random. They wereestimated and imputed by using the expectation maximization and maximum likelihood algorithm. Before estimating miss-ing values, the data were screened for extreme values, which were set to missing.

With respect to matching the comparison group sample with the treatment and comparison group samples, we con-ducted means comparison tests to determine whether significant differences existed between program participants andnon-participants. The tests showed significant differences existed between program participants and non-participants forgender, age, race, education, household size, employment status, household income, and location of residence at thep < 0.05 significance level; marital status was the exception. We then used a propensity score matching approach to accountfor the demographic differences. Propensity score matching has been recognized as a useful tool for evaluating public policyprograms in the absence of an experimental design (Heckman, Ichimura, & Todd, 1997) and has been used previously to eval-uate the IDA program (Mills et al., 2008). It uses a logistic regression model to estimate probabilities of participation in theIDA program with the independent variables being the nine demographic factors listed above. Once the predicted probabil-ities of participation were determined, nearest neighbor matching without replacement was used to pair comparison groupobservations with treatment group observations. A total of 43 non-participants could be matched to program participants;226 comparison group observations were excluded from the matched sample. The difference in propensity scores betweenparticipant and non-participant observations was relatively small (Caliendo & Kopeinig, 2008), ranging between 0.01 and0.92; the average propensity score distance (caliper) was 0.49. The size of the matched sample was 137, including 94 treat-ment and 43 comparison group observations. The matching procedure mirrors the fact that a much larger number of treat-ment group individuals had high propensity scores but only few comparison individuals had high propensity scores.

In a final step, three treatment group and seven comparison group observations were removed because they used directdeposit or payroll deduction for regular savings. The final sample of 128, 91 in the treatment group and 37 in the comparisongroup, depended completely on their own initiative for each savings deposit, which creates similar conditions amongprogram participants and non-participants.

4.3. Measurement of the constructs

Theory of Planned Behavior variables included measures of attitude, intention, social norm, and perceived behavioralcontrol (Ajzen, 2006). Attitude toward regular saving was measured with a scale consisting of five items, including, ‘‘For


me, saving some of my income every month is important/useful/responsible/positive/pleasant.’’ Internal scale reliability washigh at a = 0.893 (full sample); 0.867 (comparison); 0.899 (treatment). Intention to save regularly was measured with theitem, ‘‘I intend to save some money every month;’’ social norm with the item, ‘‘Most people who are important to me thinkthat I should save some money every month;’’ and perceived behavioral control with the item, ‘‘For me, saving some of myincome every month is possible.’’ Responses were coded on a scale ranging from 1 = strongly disagree to 5 = strongly agree.

Savings habits were measured by using the Self-Report Habit Index (Verplanken & Orbell, 2003). The wording of the indexwas adjusted slightly to fit the savings context and a reading level of 8th grade (see Appendix A). Internal scale reliability washigh at a = 0.942 (full sample), 0.937 (comparison, treatment).

Past deposit frequency was measured with two frequency measures. For the treatment group, the first measure was open-ended, ‘‘Adding up all deposits, how often did you deposit into your IDA savings account in the past 12 months?’’ For savingsoutside the IDA, respondents were asked to state ‘‘Adding up all deposits, how often did you put money aside for savingsoutside your IDA in the past 12 months?’’ The answers to both questions were added up. The second measure wasclosed-ended and served as a back-up measure in case the first measure was not answered, ‘‘Thinking about the past12 months, what was your schedule for IDA savings deposits?’’ For non-IDA savings, it inquired ‘‘Thinking about the past12 months, what was your schedule for putting money aside for savings outside of your IDA? Please do not count job-relatedsavings, such as 401ks or IRAs.’’ Response options were monthly deposits, biweekly deposits, weekly deposits, and other(specified by respondent).

For the comparison group, the open-ended question stated, ‘‘Adding up all deposits, how often did you put money asidefor savings in the past 12 months?’’ The closed-ended back-up question stated, ‘‘Thinking about the past 12 months, whatwas your schedule for putting money aside for savings? Please do not count job-related savings, such as 401ks or IRAs.’’

Sum of savings deposits was measured by multiplying deposit frequency with the answer to an open-ended question. Itstated for IDA savings, ‘‘On average, how much money did you deposit each time?’’ and for non-IDA savings, ‘‘About howmuch money did you usually put aside in these savings in the past 12 months? Again, please do not count job-related sav-ings, such as 401ks or IRAs.’’

Financial strain was measured with a 12-item scale developed by Hilton and Devall (1997). The wording of the originalscale was revised slightly to a reading level of 8th grade (see Appendix A). Internal scale reliability was high at a = 0.900 (fullsample); 0.851 (comparison); 0.888 (treatment).

Household savings were measured with the question, ‘‘What is the total amount of money that you and the other membersof your household have in savings and investments? Please include retirement savings.’’ For IDA participants, the questionstarted with, ‘‘Outside your IDA program, ...’’ Responses were open-ended (‘‘dollars total’’).

Demographic characteristics of the sample were measured by nine variables, see Table 2. Variable coding followed stan-dard procedures, except for information on annual household income. It was acquired in two ways, copying an approach thatwas used in the partnering IDA agencies’ program enrollment sheets. The first measure inquired in detail about every sourceof income in a typical month. The second measure inquired generally about the total amount of income of all householdmembers. Responses to both measures were open-ended; responses were compared and adjusted for calculation errors,where needed.

5. Results

5.1. Testing whether habit contributes to regular saving

Table 3 shows means and correlations for the sum of savings deposits in the past 12 months, savings habits, study groupmembership, past deposit frequency, and the Theory of Planned Behavior variables. The table indicates that the sum of sav-ings deposits in the past 12 months averaged $1140. The past frequency of deposits measure shows that a deposit was madeabout once a month. Intention to save, attitudes toward saving, subjective norm, and perceived behavioral control werestrong with values above the scale midpoint (3.59–4.27). In contrast, the habit measure ranked right below the scale mid-point (mean = 2.73, on a scale from 1 to 5), suggesting only moderate levels of habitual behavior. Savings habits were sig-nificantly correlated with the sum of deposits (r = 0.417), past frequency of deposits (r = 0.331), attitude toward saving(r = 0.415), perceived control over the ability to save (r = 0.513) and the intention to save (r = 0.420). The sum of savingsdeposits was unrelated to social norm, as were sum of savings deposits, and past frequency of deposits. The strongest cor-relation was between attitude and intention (r = 0.660).

The contribution of habit to the sum of savings deposits was tested by means of hierarchical multiple regressions. Follow-ing the pattern of analysis in previous habit research (Verplanken, 2006; Verplanken et al., 2007), the regression analysisexamined whether savings habits independently accounted for individual savings deposits, above and beyond past fre-quency of deposits. Hierarchical multiple regression was conducted with the sum of deposits as dependent variable. Thisvariable was regressed on attitude, subjective norm, perceived control and intention in Step 1, past deposit frequency andstudy group membership in Step 2, and savings habits in Step 3. The results are presented in Table 4, including the full modelwith all variables. The Variance Inflation Factor ranged between 1.234 to 2.220, indicating a lack of excessive correlation ofthe predictor variables. Savings habits contributed to the prediction of the amount of savings deposits over and above thepreviously entered variables. They increased the variance accounted for in the deposit amount by 2.6%. Confirming previous

Table 3Means, standard deviations, and correlations of sum of savings deposits, habit, group membership, and Theory of Planned Behavior variables.

Predictor Range Mean (SD) 2 3 4 5 6 7 8

1. Natural log of sum of deposits, past12 mo.

$0–17,550

$1140($2059)

0.417*** 0.479*** 0.545*** 0.284*** 0.080 0.411*** 0.335***

2. Savings habits 1–5 2.73 (1.031) 0.319*** 0.331*** 0.415*** 0.133 0.531*** 0.420***

3. Treatment group 0–1 .71 (.455) 0.474*** 0.329*** 0.138 0.486*** 0.403***

4. Past behavioral frequency 0–72 12.24(10.949)

0.248*** �0.037 0.416*** 0.251***

5. Attitude 1–5 4.27 (.809) 0.421*** 0.574*** 0.660***

6. Subjective norm 1–5 4.03 (1.126) 0.191** 0.326***

7. Perceived behavioral control 1–5 3.59 (1.371) 0.610***

8. Intention 1–5 4.24 (1.065)

N = 128.⁄ Significant at p < 0.10.

** Significant at p < 0.05.*** Significant at p < 0.01.

Table 4Hierarchical multiple regression analysis predicting the amount of savings deposits.

Predictor B SE B b DR2 Final b

Step 1Attitude .091 .397 .027 .181*** �.025Subjective norm �.086 .217 �.036 .020Perceived behavioral control .633 .213 .321*** .018Intention .336 .299 .133 .075

Step 212-month deposit frequency .095 .021 .384*** .199*** .360***

Treatment group 1.276 .526 .215** .212**

Step 3Savings habits .514 .223 .196** .026** .196**

Dependent measure is the amount of money deposited during the past 12 mo. into a savings account. R2 = 0.407; adjusted R2 = 0.372; N = 128; VIF range:1.234–2.220.⁄ Significant at p < 0.10.



literature (Verplanken & Orbell, 2003), these results support the discriminant validity of savings habits as a multi-dimensional construct for savings deposits.

5.2. Examining program influence on habit formation

In order to examine the influence of program participation on habit formation, the length of participation was dividedinto five 6-month intervals plus a ‘‘0 month’’ measure for the non-participants. Respondents were divided into cohortsaccording to the length of time participating in the IDA program at the time of the survey. The five cohorts consisted of1–6 months, 7–12 months, 13–18 months, 19–24 months, and 25 and more months of program participation at the timeof the survey. The Least Significant Difference (LSD) test proved the best fit for pair wise comparison of treatment and com-parison group because of its sensitivity to small sample sizes when looking for differences between groups. The demographicvariables were added as covariates to control for their influence on savings behavior.

Examining Column (4) in Table 5, the difference in habit strength of �0.237 between the general public comparison group(not in IDA) and recently enrolled IDA participants (1–6 mo.) is not significant, but the negative sign indicates a positive ten-dency toward habit building. The finding confirms observations by IDA program case managers that the first 6 months in theprogram are a period of adjustment. These months typically have the highest dropout rates, which tend to be significantlyreduced after the 6-month threshold has passed. Examining the next four results in the first row of Table 5, Columns (5–8),shows that, compared to non- IDA participants, the habit strength of program participants increased over time (�0.237 to�1.392), peaked at the 19–24 month group (�1.392, p < 0.01), and then dropped to a medium level (�.0727, p < 0.05).The results confirm the value of the average program participation of 2 years and indicate diminishing gains in habit strengthfor those who stay longer in the program. Feedback from IDA case managers suggests that longer program participation iscaused by reasons unrelated to savings, such as low credit scores that prevent an asset purchase at an earlier time.

Within the group of IDA participants, significant differences in savings habits emerged only when comparing the19–24-month cohort in Column (7) to the other four cohorts: 1–6 months (�1.155, p < 0.01), 7–12 months (�0.650,

Table 5Pair wise comparison of savings habits among study participants.

N Mean (SD) 1–6 mo. 7–12 mo. 13–18 mo. 19–24 mo. 25+ mo.(1) (2) (3) (4) (5) (6) (7) (8)

1. Not in IDA 37 2.22 (0.967) �0.237 �0.742*** �0.558* �1.392*** �0.727**

2. 1–6 mo. 15 2.55 (1.074) �0.505 �0.321 �1.155*** �0.4893. 7–12 mo. 30 2.99 (0.768) 0.184 �0.650** 0.0164. 13–18 mo. 17 2.72 (0.888) �0.834** �0.1685. 19–24 mo. 14 3.53 (1.238) 0.666*

6. 25+ mo. 15 2.92 (0.992)

Covariates in the model are evaluated at the mean value; N = 128; Dependent variable: savings habits; equal variances across groups assumed (Levene’stest): F = 0.938, n.s.

* Significant at p < 0.10.** Significant at p < 0.05.

*** Significant at p < 0.01.

Table 6Means, standard deviations, and correlations of the perception of financial strain and income, wealth and habit.

Predictor Range Mean (SD) Household income (ln) Household savings (ln) Savings habit

Financial strain 1–5 2.82 (0.963) �0.231*** �0.316*** �0.497***

Household income $160–5250 $1738 ($886) 0.166* �0.027Household savings $0–100,000 $5594 ($15,537) 0.265***

Savings habits 1–5 2.73 (1.031)

N = 128.* Significant at p < 0.10.⁄⁄ Significant at p < 0.05.

*** Significant at p < 0.01.


p < 0.05), 13–18 months (�0.834, p < 0.05) and 25-plus months of program participation (0.666, p < 0.10; in Column (8)).This finding indicates a peak in habit strength at the time when most participants were nearing the end of their participationin the program. Taken together, the findings support the notion of habit formation during IDA program participation andindicate that the IDA participants did not start out at a higher habit level, at program entry, compared to a general publicsample.

5.3. Examining the influence of habit on financial strain

Table 6 shows correlations between savings habits, financial strain, household income and total household savings. Thetable indicates moderate experiences of financial strain (2.82 on a scale from 1 to 5). Average monthly household incomewas $1738, slightly above the federal poverty line ($1467) in 2008 for a family of three, which was the sample average.Household savings averaged $5594 for the matched sample, which were higher than the average holdings of financial assetsat this income level in the US ($1700; Bucks, Kennickell, Mach, & Moore, 2004). Habit was significantly negatively related tofinancial strain (r = �0.497), which was the strongest correlation among these variables, and positively to total householdsavings (r = 0.265).

Hierarchical regression analysis in Table 7 examined the influence of savings habits on the perception of financial strain.Household income and savings were entered in Step 1 and savings habits in Step 2. The demographic variables listed in Table2 served as control variables. Savings habits increased the variance accounted for in financial strain 20% over and above thecontributions of the two financial variables. Household savings, a significant predictor in Step 1, did not retain a significantbeta weight when savings habits were entered in Step 2. The results document the discriminant validity of savings habitsagainst perceptions of financial strain and its mediating role for the influence of household savings on perceptions offinancial strain.

6. Discussion

6.1. Habit influences savings

The present study combines insights from social psychology and economics by examining the role of savings habits inregular saving. As frequently practiced, automatic, and goal-facilitated behaviors, savings habits play a critical role ineveryday financial decisions in several regards. Savings habits thus complement highly conscious and deliberate savingsdecisions, which are characteristic of retirement savings and asset purchase decisions. Once savings habits form, they directbehavior largely unaffected by mental activity, removing the ‘‘immediate cost of mental anguish’’ involved in financial

Table 7Hierarchical multiple regression analysis predicting the perception of financial strain.

Predictor B SE B b DR2 Final b

Step 1Household income (ln) �0.368 0.142 �0.222** 0.211** �0.265**

Household savings (ln) �0.057 0.020 �0.247*** �0.107

Step 2Savings habits �0.448 0.071 �0.479*** 0.199*** �0.479***

Dependent measure is the perception of financial strain; R2 = 0.411; adjusted R2 = 0.355; N = 128; VIF range: 1.076–1.329; control variables in the modelinclude (Beta): gender 0.105; age 0.007; race 0.171; education �0.297�; marital status 0.301�; household size 0.056; employment status �0.642; residence0.074.⁄ Significant at p < 0.10.



decision making (De Meza, Irlenbusch, & Reyniers, 2008, p. 30). Because savings habits happen effortlessly, below the level ofawareness and beyond individual control, they eliminate ad hoc rationalizations, hassles, and moods that may lead to thedecision not to save (Verplanken & Melkevik, 2008). In this role, savings habits can provide an element of stability in finan-cially stressful situations. On the downside, spending habits and inertia are equally robust to intervention and capable ofsabotaging the intention to save, a likelihood that has been shown to increase with the strength of competing habits (Ver-planken, 2005). The tendency in financial decisions to follow the path of least resistance (Choi, Laibson, Madrian, & Metrick,2006) will benefit the established habits at the cost of those that are developing (Wood & Neal, 2007). A suggestion for futureresearch would be to relate saving and spending habits to investigate the extent to which the strength of one habit promotesor inhibits another habit. Goal systems theory (Fishbach, Friedman, & Kruglanski, 2003; Kruglanski et al., 2002) might pro-vide a framework to further explore how habits develop when competing for limited mental resources.

6.2. Savings program participation supports habit formation

The beneficial aspects of savings habits for financial behaviors have made them a target behavior in financial educationefforts (e.g., FDIC Money Smart curriculum, America Saves campaigns, and Individual Development Account programs). Thehighly automatic nature of habits, however, requires a specific type of intervention to create savings habits or break coun-terproductive ones. The literature on status quo bias describes the resistance to change habitual financial behaviors as one ofthe strongest of a number of cognitive biases that affect the savings domain (Choi, Laibson, & Madrian, 2004; Madrian & Shea,2001; Samuelson & Zeckhauser, 1988).

The behavioral economics literature suggests exploiting current habits, in particular inertia, rather than attempting tochange these habits. Procedures like automatic enrollment, automatic contribution rate increases, and automatic asset allo-cation take advantage of people’s reluctance to change current habits. On the other hand, the ‘‘upstream-oriented’’ proce-dures of financial institutions are generally not available for everyday financial behaviors. In the case of the IDA program,simple devices, such as direct deposit, are rejected by 80–90% of participants because individuals feel unable to commit afixed savings amount at program enrollment, a sign of both tight budgets and a desire for flexibility in reallocating resources(Heath & Soll, 1996). In these contexts, the habit literature suggests a combination of four ‘‘downstream’’ and ‘‘upstream’’ingredients to actively establish new habits (Verplanken, 2005; Verplanken & Wood, 2006). Motivation and good intentionstoward saving is implemented by the IDA program’s efforts to actively provide a supporting network of professionals(Schreiner & Sherraden, 2005). A related aspect is a person’s ability to save. A suggestion for future research would be to con-sider whether times of financial stress and loss, which may require families to adapt to changes in employment, householdcomposition, and family structure and functioning influence whether savings habits begin, endure, grow, or break duringfinancial shocks.

Context cues that trigger new savings habits are provided in the IDA program in budgeting and one-on-one counselingsessions. These meetings aim to develop savings strategies and concrete action plans. If action plans are specific with regardsto time, location, and action, savings behavior is likely to be initiated successfully (Gollwitzer, 1993) and may develop intohabits, if they prove functional for the individual and are practiced repeatedly (Verplanken & Faes, 1999). Incentives thatencourage new actions are presented by matched savings and social pressure from the program’s case managers in the caseof the IDA program (Schreiner, 2004). Repetition of new actions to build associations between cues and actions occurs duringthe IDA program, which average 2 years in length of participation. As the results show, habit formation peaks at around2 years of participation. According to stage models of behavior change, the learning of new behaviors is not necessarily alinear process, but may include relapse and recycling to the initial, pre-action stages (Prochaska, Norcross, & DiClemente,1994). The final stage of the program attempts to address the long-term maintenance of the newly acquired behavior as wellas reinforcement strategies (Littell & Girvin, 2002; Petrocelli, 2002). This stage appears the most relevant for establishingsavings behaviors that have the three elements of habit. Our finding of diminished habit formation after 2 years indicateda prolonged maintenance period.


Taken together, our findings suggests that the structure of the IDA program provides an effective model for creating sav-ings habits through a combination of both downstream and upstream processes that are based on individual self-regulationand action planning, on the one hand, and monetary incentives and a sufficiently long period for practicing the new behavior,on the other hand.

6.3. Habit eases financially stressful experiences

Wärneryd (1999) suggests that savings habits are behaviors that generate intrinsic motivation by relating saving to ‘‘asense of independence and the power to do things.’’ This interpretation relates well to the functionality component of habits,which indicates that habits develop in response to a behavior that works well for a person and has proven to be effective.With respect to savings habits, our findings suggest that habits provide a sense of accomplishment that counteracts feelingsof financial distress over and above the security provided by income and household savings. This finding also corresponds tothe argument of Livingstone and Lunt (1992) that regular savings behavior is closely related to a person’s self-confidence,which in turn strengthens habit and ensure its continuation. A suggestion for future research would be to employ a morecomprehensive and focused analysis of how personal goals relate to savings habits and the type and strength of reinforcersthat support habit formation for these individual goals. The research of Wood and colleagues indicates that life-changing sit-uations are particularly effective for habit development (Wood & Neal, 2007; Wood et al., 2002, 2005). With respect toencouraging savings habits, personal life events, such as the first job, marriage, a new child, or challenging situations, suchas illness, job loss, or divorce, can present effective moments for behavior change interventions that encourage new habitformation. Major life events open windows of opportunity for behavior change that rearrange established behavior patternsand can provide a means to better understand how and to what extent habits govern financial decisions.

7. Conclusion

Savings habits have been addressed in behavioral economics and social psychology. The current study draws from bothliteratures to examine the role of habit in regular savings. We find evidence that savings habits contribute to saving, can beacquired over time in targeted interventions, and ease feelings of financial strain. This information is useful for behavioralinterventions that aim to increase asset accumulation by turning attention to the importance of well functioning dailybehaviors.

Acknowledgements

The generous financial support of the Center for Urban and Rural Analysis (CURA) at The Ohio State University and OhioState University Extension is gratefully acknowledged.

Appendix A

Self-Report Habit Index (Verplanken & Orbell, 2003)The next few questions ask what you think about saving money. Please rate the following statements.Response options: 1 = strongly disagree to 5 = strongly agree.

Saving money is something I do frequently.Saving money is something I do automatically.Saving money is something I do without having to consciously remember.I feel uncomfortable if I don’t make an effort to save.Saving money is something I do without thinking.Saving money is effortless for me.Saving money is something that belongs in my monthly routine.Saving money is something I start doing before I realize I’m doing it.I find it difficult to stop saving money.Saving money is something I have no need to think about doing.Saving money is something that’s typically ‘‘me.’’Saving money is something I have been doing for a long time.

Financial strain scale (Hilton & Devall, 1997)The following statements describe some of the ways that families experience economic strain. For each statement, pleasecircle the number that best matches your situation.Response options: 1 = strongly disagree to 5 = strongly agree.


In general, it is hard for me and my family to live on our present income.I have money problems.Money problems interfere with my work and daily routine.I worry about money.Money problems interfere with my relationships with other people.I worry that my children are disappointed because I can’t give them things they want.I worry about having money to celebrate holidays and special occasions.I put off family activities (vacations, movies, special events) because of the expense.I feel frustrated because I cannot afford the education or training I need to get ahead.I have to put off getting medical care for family members because of the expense.I have to put off getting dental care for family members because of the expense.I feel bad that I cannot afford to buy my children the brand name clothing that other children their age are wearing.

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Accounting for the role of habit in regular saving