Abstract

Behavior of Savings Account Holders in Malaysia: A Preliminary Study1

*Shabnam Mohamad Mokhtar, **Maswati Abd Talib, ***Asna Atqa Abdullah

Accounting and Finance Department, Faculty of Economics and Management, UPM, 43400 UPM Serdang, Selangor.

Tel (03) 8946 7618/7747/7635, Fax no: (03)89486188 E-mail:, *shabnam@econ.upm.edu.my **mas@econ.upm.edu.my,

*** asnaatqa@econ.upm.edu.my This paper runs a preliminary analysis on behavior of savings account holders. It was set out with the objective to identify factors that motivate people to save and also to examine the relationship between the decision to transfer fund (from Islamic savings account to conventional savings account) and religion of the account holders. A cross sectional data from a survey of 135 respondents suggest that saving for emergency (precautionary motive) is the most important reason that people save for while the least important reason is to obtain return on savings account provided by the bank. Besides that, setting a monthly savings target also helps people save more regularly. A chi-square analysis suggests that the decision to transfer fund from an Islamic savings account to a conventional savings account when the conventional account pays a higher interest rate differs significantly between Muslims and non-Muslims. Majority Muslims would not transfer to conventional accounts due to religious restrictions. Besides savings motives, this study also explores two multiple regression models to predict total savings and monthly savings. None of the variables included were able to predict total savings, however income and the number of children below 18 years old had significant effects in predicting monthly saving. All the results will however have to be interpreted with prudence because the preliminary study only involves a small number of respondents. Further improvement to the survey instrument and sampling methodology are needed before results could be said to be robust and represent the behavior of savings account holders in Malaysia. Keywords: Savings behavior, Al-Wadiah, Saving motives, Bank savings

1 Acknowledgement: Our sincere gratitude is extended to PM Dr. Murali Sambasivan and Dr. Taufiq Hassan from FEP, UPM who have contributed in making this paper possible. Our thanks are also dedicated to the respondents who have cooperated in completing the questionnaires

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Behavior of Savings Account Holders in Malaysia: A Preliminary Study 1.0 Introduction Savings decision is of importance to both an individual and a nation since savings provides an individual with financial security for possible hard times and provides a nation with a significant source of investment fund for economic development. Savings take many dimensions: public saving, private saving and household saving to name a few. Katona (1975) identified three types of savings not from an economist point view, but from an ordinary persons’ view: contractual saving (paying installments), discretionary savings (deliberate saving of spare income) and residual saving (money not yet spent and therefore saved by default). In this study we focus on discretionary and residual saving in the form of bank savings (i.e savings account). We define savings account as “money deposited at commercial banks (Maybank, BIMB,BCB,etc.) and savings institutions (Bank Simpanan Nasional, Bank Rakyat and Lembaga Tabung Haji) that could be withdrawn by the customer at any time and pays return on the amount deposited.” Therefore this study excludes other deposit accounts such as current account, fixed deposit accounts or other investment and unit trust accounts. East Asian countries including Malaysia is known for high savings rate (20-45% of GNP). According to the Central Bank’s governor Tan Sri Dato’ Dr. Zeti Akhtar Aziz, the consumption in Malaysia has increased from 56% in 1998 to 61% of GDP in 2002. The savings rate on the other hand has decreased (although remained high) from 42.2% in 1998 to 35% of GDP in 2002. She further explains that although household consumption and private investment is important to sustain domestic demand and economic development, the need to save should not be undermined. She also reminds the need for household to improve the management of their personal finance to sustain long-term financial security and living standard. (Aziz, Z.A. 2003, 2004) Various researches have been done to gain insights on savings behavior from an economic, demographic and even psychological point of view. Xiao and Noring (1994) highlights that although numerous studies have been carried out on savings behavior, only a few of these has aimed to investigate motivations for saving directly. Research on motivations to save is important because it can help financial advisors and educators to have a deeper understanding of the goals of people’s financial behavior (Canova et. al, 2005). Combining the call from Tan Sri Zeti and highlights from Xioa and Noring, we are exploring means to further understand what motivates people to save. To have a rich qualitative insight into savings motives we decided to pursue a survey of savings account holder to help us identify their savings motives. In addition, with the advent of Islamic Banking Scheme (IBS) in Malaysia, the impact of interest rate on savings is of prime importance.

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The growth of Islamic Banking Scheme in Malaysia establishes use of return not predetermined upfront (compared to conventional interest rate). This may give a new insight on the impact of return on savings especially on the level of deposits in bank savings accounts. Ariff (1988) explained that Islamic banks around the world offer three main types of depository services; current account, savings account and the investment account. Current account and savings account operate on the basis of Wadiah Yad Dhamanah (safekeeping plus guarantee) where the nominal value of the deposit is guaranteed but provide no guarantee on returns. The investment account operates on the basis of Mudharabah (profit sharing) where capital is not guaranteed neither any pre-fixed returns. Haron and Ahmad (2000) analyzed the relationship between total Islamic deposits and rate of returns offered under Islamic and conventional banking schemes in Malaysia from January 1984 to December 1999 on a monthly basis. They confirmed that customers who place their deposits at saving and investment account facilities are guided by profit motive since there is a negative relationship between interest rate of conventional banks and the amount deposited in interest free deposit facilities. This arises further interest for us to understand the qualitative insights of the behavior of savings account holder from both the conventional and Islamic banking facilities. In short, the main objectives of this study are twofold: to identify factors that motivate people to save, and to examine the relationship between the decision to transfer fund (from Islamic to conventional facility) and religion of savings account holder. In addition we also explore predictive variables of total and monthly savings. The findings from this study could benefit the banking industry by identifying factors that motivate individuals to save. The results from the survey could be incorporated in designing an innovative savings account that will stimulate individuals to make a committed savings plan. The paper is organized into five sections. The following section offers review of prior studies. Section three describes the data collection and research methodology. Section four presents the research findings and discussions. The final section provides conclusion of the study, its implication and suggestions for future research.

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2.0 Literature Review Numerous studies have been done on the factors or determinants that affect savings. As savings is of interest to both the individual and the economy, researchers have tried to establish relationship of savings with not only the economic variables, but also psychological, sociological, behaviourial and demographical factors. Beverly & Sherraden (1999) made a comprehensive summary on the key theories of saving and assets accumulation. The life cycle hypothesis (LCH) and the permanent income hypothesis (PIH) are the two most well known neoclassical economic theories. The LCH predicts that consumption and saving reflect an individual’s stage in life cycle proxied by age. This model emphasizes saving for retirement as primary motivation for deferred consumptions or savings. Young households are expected to have negative saving because they have relatively low income and incur debt for education, purchase of home and other expenses. In the middle cycle, saving is expected to be positive since individuals pay their debt and save for retirement. Upon retirement dis-saving is expected to occur. The PIH, on the other hand, assumes that long-term income is the primary determinant of consumption. The model is based on 2 definitions of income; permanent income and transitory income. Permanent income is the present value of lifetime income while transitory income is the difference between current income and permanent income. The PIH predicts that household consumption will respond to changes in permanent income but not changes in transitory income. Thus, when an individual expects a temporary increase in income, she will increase her savings. But, if she expects a permanent increase in income she will increase her consumption. In recent years, an alternative model called the “buffer-stock” models of saving emphasize a precautionary motive for saving, particularly for younger households and for households facing greater income uncertainty. These households are expected to accumulate small stocks of assets (buffer stock) to face short-term income fluctuations and liquidity constraints. Although numerous studies have been carried out on savings determinants and behaviour, only a few of these have aimed to investigate motivations for saving directly (Xiao and Noring, 1994). Study on motivations tests directly the reasons why people save. This can help financial advisors and educators to have deeper understanding of the goals of people’s financial behaviour. Motivation to accumulate money to use for future retirement in LCH theory proposed by Modigliani and Brumberg (1954) is an example of savings motive. Keynes (1936) was the first one to conduct research on savings motives. He identified eight different motives; precaution, foresight, calculation, improvement, independence, enterprise, pride and avarice. Katona (1975) found that people save for emergencies, for serving necessities, for retirement and for children’s need. Few claimed to save for future income or bequest motive. Studies in Holland and Sweden (Alessie et al., 1997; Lindqvist et. al, 1978) indicate that precautionary motive is one of the most important reasons for

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saving. Webley et al. (2000) compared the saving motives of Italian, English and Israeli respondent and found that Italians were more inclined to save as much as possible. They save for their children’s education and for medical care. For the English respondent, saving for future purchase was an important motivation. Warneryd (1995, 1999) distinguished four motives; saving as a continuous habit, precautionary motive, bequest motive and profit motive. The results of a multiple regression indicate that saving as a continuous habit and precautionary motive explained significant variance of the total sum of money saved. Lindqvist (1981) interviewed 429 randomly selected households in Sweden to analyze four different saving estimates; bank savings measured by changes over a three-month period in amounts saved up in the bank, repayment of debts, total savings and a liquidity estimate measured by how much of their savings people could easily use. He applied a stepwise multiple regression design to those variables. Both economic variables and softer variables like attitude and expectation were used as explanatory variable. The variables failed to explain bank savings. However, Lindqvist did find effects on savings by income, stage in family life-cycle, economic satisfaction, consumer sentiment and economic activity. Lunt & Livingstone (1991) conducted an in-depth survey to 241 ordinary residents in Oxford and added another 60 respondent through snowballing technique to obtain a total of 279 workable questionnaires for analysis. They analysed psychological, social and economic variables to explain savings. They used discriminant analysis to distinguish between those who do not save regularly and have no savings (non-savers), those who save regularly and have savings (savers) and those who do not save regularly but have savings (nonsavers with savings). The results of multiple regression show that economic variables (48% variation) seem to predict recurrent (savers) but not demographic variables (0% variation). Among the significant positive economic variables are disposable income, spending on clothes, and total savings. Results on attitude, value, discussion and economic behaviour variables (17% variation) show that the more importance people assign to enjoyment in lives, the less they save. The same goes to argument about money with partners, discussion about money with friends, and frequency of shopping around for best buy. It is also interesting to note that the more people save, the more inclined they are to disagree with the notion that being in debt means people do not manage their money properly. The result of multiple regression on total savings and the studied variables, on the other hand, show a different outcome. Unlike recurrent savings, demographic variables tend to explain 11% of total savings. A further 42% variance is explained by economic variables; disposable income, total investments, and spending on insurance. Only 4% of variance is explained by psychological variables. The more importance people place on achievement as value in lives, the less their total savings and the more they save, the more they attributed their financial problems to bad luck and the less they attributed them to occurrence of unexpected repairs.

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Canova, Rattazzi and Webley (2005) analysed super-ordinate goals motivating decisions to save using network analysis. They surveyed 97 British adults through questionnaire asking on personal reasons why they wanted to save, after which they explained why these reasons were important and were then asked to give further justification. The results were 15 salient goals functioned hierarchically with concrete goals (purchase, holidays and money availability) at the bottom of the hierarchy, abstract goals (self-esteem, self-gratification) at the top of the hierarchy and intermediate goals which channel the more concrete to the more abstract in the intermediate position of the hierarchy. A lot of studies have also analysed the link between demographic factors and saving. They are important in determining savings motives. The evidences are however mixed. Lunt & Livingstone (1991) showed that high level of savings (total savings) are associated with participants who are male, older, have more children. Attanasio & Paiella (2001) studied the savings behavior of USA households using the micro data from Consumer Expenditure Survey from 1982 to 1995. They employed synthetic cohort techniques to characterize the life cycle profile of savings rate. They found important difference in saving behavior across cohorts, with the youngest cohort born after 1940 displaying the lowest saving rate and those born before 1930 with the highest saving rate. The level effect in life cycle theory predicts that the higher the dependency ratio the lower the savings ratio. However, having more children may induce a bequest motive and is expected to result in a positive dependency-saving relationship (Baharumshah et al., 2003). Weiss et al. (2006), in analysing savings determinants of low-income household with children found that race, level of education, type of employment, level of income and asset ownership are highly associated with level of savings. They found that participants who are African American save less than other races and participants who graduated from college save more than those who did not complete high school. Burney & Khan (1992) utilized a micro-level data of the Household Income and Expenditure Survey in Pakistan to examine both the nature of income-saving relationship and the impact of various socio-economic and demographic factors on household savings including the dependency ratio, education, occupation, residence location and the secondary earner. The dependency ratio was found to have a negative influence on household savings. Education, unlike in study by Weiss et al. (2006), seems to have a negative relationship with saving since educated parents will have a higher children education expenses. They did not find any significant relationship between occupation, secondary earner and savings. Jain and Joy (1997) did a study involving Asian ethnic in Canada by. They interviewed 36 professional South Asian (Indian) families in a metropolitan city in Canada to understand how consumption, saving and investment behaviors are influenced by culture. Although the research did not directly look at savings alone, the result indicated that their investment decisions were focused on planning for education of children, purchase of a house, investments in jewellery and other assets, which were influenced by the temporal view of life, the performance of one’s duty towards children and the protection of the family as part of their Hindu religion concern. Saving, to them, was ‘to keep money in

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cash’ whereas investment was ‘to make more money’. Saving acted as a reserve of excess funds for future use and for emergency purpose like traveling, buying a car or furniture or repairing the house. It is also important to note that besides economic, psychological and social determinants, institutional variables (institutionalized saving mechanism, targeted financial education, attractive saving incentives and facilitation) promote individual and household saving (Beverly & Sherraden, 1999). These factors, that may influence savings motive, stems out from critics of LCH and PIH theories as they assume that individuals have (or act as if they have) almost-perfect vision regarding future income flows, prices, household consumption, and life span and that they manifest rationality and self-control as they prepare for retirement. According to Beverly and Sherraden, this is particularly true for low-income households. They later identify these 4 institutional variables as 5; namely access, information, incentives, facilitation and expectations (Sherraden, Schreiner and Beverly, 2003). They argue that individuals who have access to institutionalised mechanisms are more likely to have higher saving rates than those who lack access. As people understand and have more information on process and rewards of savings, they are more likely to engage in savings. The same goes to savings incentives and facilitation. Interest rates, match rate, and the use of direct deposits are normally considered as part of factors that gives incentives and facilitate savings. Expectation is also another factor that may motivate household savings. Weiss, Wagner, and Ssewamala (2006) confirmed the previous study by Sherraden, Schreiner and Beverly (2003). They operationalized these institutional variables and found that they are highly associated with the performance of Individual Development Account (IDA). As cited in Weiss et. Al (2006), IDAs are matched savings accounts, targeted to low-income people that provide institutional structures including incentives for savings. Account holders receive matching funds as they save for assets that promote long-term well-being and financial self-sufficiency such as homeownership, post-secondary education and microenterprise. Given the access of low-income household to IDA, IDA participant is also required to attend free financial education and asset specific classes as well as peer group meetings as part of the program. The result of study shows that each additional hour of financial education is associated with a $0.86 increase in savings and each additional dollar in monthly saving target is associated with a $0.32 increase in savings. It is also interesting to note that participants who have either a checking or savings account (other than their IDA accounts) in other institutions are associated with a $3.16 higher savings than participants with no accounts. Interest rates or return on savings as an incentive to savings is always predicted as the determinants of savings. However, previous studies on this issue provide rather mixed results (e.g. Kim, 2001, Attanasio & Paiella (2001) and Athukorala & Sen (2003). Beverly & Sherraden (1999) explains that an increase in return on savings would have two possible effects. First, individuals may choose to save more because the price of

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current consumption increases relative to the price of future consumption (the substitution effect). Second, individuals may choose to save less and still enjoy the same amount of future consumption (the income effect). They further argued that interest rate changes may not change the level of savings but simply result in reshuffling of assets, with no net savings, which is more likely to happen in high income households. The outcomes of interest rate effect on savings are not conclusive as most empirical studies found generally small and often insignificant positive coefficient of interest rate, which may be due to the uncertainty of substitution effect and the income effect (Kim, 2001). Studies have shown that religion is not the only factor that influences customers to choose Islamic banks (Erol & El-Bdour, 1989). Studies in Malaysia and Singapore find both religion and profit as the reason for relationship with Islamic banks (Haron et al., 1994; Gerrad and Cunningham, 1997). Haron & Shanmugam (1995) try to link the rates of profit to total Islamic bank’s deposits. Using the Pearson Correlation and First Order Autoregressive model, they found a strong negative relationship between interest rates and the total Islamic deposits. Kaleem & Isa (2003) studied the relationship between Islamic and conventional term deposit returns from January 1994 to December 2002 on a monthly basis. They applied unit-root test and Granger causality test. The findings indicate that the conventional TDRs Granger cause Islamic TDRs. They concluded that Islamic banking considers interest rate before adjusting its deposits returns.

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3.0 Methodology This study used data obtained from a survey distributed to about 200 respondents in November – December 2006 around Klang Valley area. We obtained responses for 161 questionnaires (80%). A total of 135 questionnaires (67.5%) were usable. The questionnaire was typically structured to ease the data collection process, as respondents are usually unwilling to spend long time to answer survey questions. However some open-ended questions were still included to solicit reasons for a particular decision or behavior. The 6 pages questionnaire consists of multiple-choice questions, Yes or No questions and Likert Scale questions. It had an introduction page (explaining the definition of savings under study and the objective of the study) followed by 3 pre-arranged sections:

Part A – Savings Behavior Part B – Returns on Deposits Part C – Respondent’s Background

Questions in part A were structured with the objective to identify factors that motivate people to save and also their savings motives. Part B was structured to help examine (qualitatively) the relationship between the decision to transfer fund and religion. Part C collected all the demographic variables of the respondents. A quota sampling method was used in this study to ensure we had data representing each sector and age group we chose to analyze. Below is the sampling distribution that we utilized in the study: Sampling distribution GovernmentPrivate

companies Self Employed

Student Retiree Total

Student 18 – 24 20 20 Early working 25 – 40 10 10 10 30 Retirement 41-55 10 10 10 30 Retiree >55 20 20 20 20 20 20 20 100 We then administered the survey using a convenience sampling technique to achieve the quota. We distributed the survey using various methods including personal interview, e-mail and snowballing technique through workplace contacts to obtain the following final usable sample:

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Table 1: Final Valid Sample (n=135)

Sector

Total Government Private Self Employed Student Retiree Age group

18-24 0 4 1 33 0 3825-40 11 36 12 0 0 5941-55 9 11 7 0 0 27> 55 0 0 0 0 11 11

Total 20 51 20 33 11 135 We were able to obtain more samples than planned, however certain quota seem to have a surplus with some other showing deficits. Those working in the private sector dominated the sample (38%) followed by students (24%). We obtained 14% sample from both the government sector and those who were self employed although the distribution of the age seem to differ slightly from our planned distribution. We only manage to obtain 8% of those in the retiree age group. We also had some difficulties initially to obtain responses especially form those in the age of 41-55. However after the utilization of snowball technique, the response seems to improve. In general, 53% of our sample was female. We had 68% Malay respondents, 19% Chinese respondents, 10% Indian respondents and 4% from other races.2 We had all races in our sample because we wanted to explore whether there was any significance difference between Muslims and non-Muslims when evaluating the relationship between the decision to transfer fund and race. 50% of the respondents were married with 35% (from total respondent) having children below the age of 18 (dependent child). With regards to monthly income, 10 respondents did not disclose their income. About 49% of the respondent earned less than RM 2,000. 40% earned RM 2,000 – RM 6,000 and 11% earned RM 6,000-RM10,000. We initially had one respondent that earned more than RM 10,000 in our sample, however we excluded the response to avoid any outlier problem. The majority (75%) of our respondent had a high level of education (degree – Phd). 20% with middle level of education (STPM – Diploma) and the balance 6% with a low level of education (PMR and lower).

2 The proportion of the difference races seems to conform to the distribution of Malaysian population. According to ethnic composition in the 2000 census, there are 65.1% Bumiputra, 26% Chinese and 7.7% Indian. Religion distribution of Malaysian population is highly related with ethnicity; Muslims (60.4%), Buddhism (19.2%), Christianity (9.1%) and Hinduism (6.3%).

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4.0 Findings and Discussions Descriptive Statistics This study was carried out to achieve two main objectives:

a. To identify factors that motivates people to save. b. To examine the relationship between the decision to transfer fund and religion.

We only focus on bank savings in this study because we were particularly interested to obtain a qualitative insight into the findings by Haron and Ahmad (2000) where they found a negative relationship between interest rate of conventional banks and the amount deposited in Islamic deposit facilities i.e if conventional interest rate increases the amount deposited in the Islamic facilities would decrease (customer would transfer their fund from Islamic facilities to conventional facilities). Table 2 presents the type of savings account held by the respondents.

Table 2: Account Type

Frequency Percent Valid Percent Cumulative

Percent Valid Conventional 74 54.8 54.8 54.8

Islamic 26 19.3 19.3 74.1 Both 35 25.9 25.9 100.0 Total 135 100.0 100.0

About 55% from our sample had only the conventional savings account. While 19% had only Al-Wadiah savings account and the remaining 26% had both conventional and Islamic savings account. To achieve the first objective we conducted the following descriptive analyses: i. Savings motives We asked the respondent the purpose of their saving (i.e Why do you save?) and they were given the option of choosing more than one answer. The summary of our findings is presented in Table 3:

Table 3: Saving Motives

Saving motives % Yes 1. Emergency 90.4 2. Children's education 43.7 3. Asset purchase 42.2 4. Retirement 38.5 5. Increase wealth 36.3 6. Return on savings 26.7 7. Inheritance 11.1

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Precautionary motive seems to be the primary reason for saving. 90% of our respondent agreed that they save for emergency purpose. Bequest motive (inheritance) seems to be the least contributing purpose for saving. In order to access the priority of the savings motives, we asked the respondents to only choose the most important and the least important purpose of their savings. The results are presented in Table 4:

Table 4: Priority of Saving Motives Most Important Purpose Least Important Purpose

Frequency Percent Frequency Percent

Emergency 84 62.22 4 2.96 Asset purchase 6 4.44 11 8.15 Children education 10 7.41 9 6.67 Retirement 14 10.37 10 7.41 Wealth 10 7.41 28 20.74 Return 4 2.96 38 28.15 Inheritance 1 0.74 31 22.96 Others 5 3.70 2 1.48 Total 134 99.26 133 98.52 NR 1 0.74 2 1.48 Grand Total 135 100.00 135 100.00

The two most important purpose of saving is for emergency purpose (62%) and to save for retirement (10%). The least important reason for saving is for the return on savings account (28%) and for inheritance purpose (23%). Our findings are thus consistent with the Katona’s (1975) findings where people save for emergency and retirement purpose and few claimed to save for future income (interest or dividend) and bequest (inheritance) motive. Other studies have also found that precautionary motive is one of the most important reasons for saving (Kotlikoff 1989, Alessie et. al 1997, Johnson 1999). Horioka & Watanabe (1997) found that Japanese families mainly put away savings for retirement and precautionary reasons. The hierarchical financial needs categorization by Xiao and Anderson (1997) may help explain why return on savings account is the least important reason. They ran a tobit analysis and found that checking account and savings account follow the pattern of survival need asset i.e the most basic financial need. Checking and savings account (especially those savings accounts that are not of substantial amount) are meant for regular income deposit and daily expenses payment purposes and not for investment purposes. Thus the return (interest, dividend) on savings serves as the least important purpose that people save for. Harris et. al (2002) reported that bequest motive is a relatively less important motive in Australian families. Further insights that supports our findings were provided by a few respondents (during the interview) when they explained that they would provide well education for their children to ensure the survival of the child rather than providing them

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with a bulk of money as this proverb goes “Teach the person to catch a fish (rather than just giving him a fish everyday) so the person can catch his own fish in future.” ii. Factors that motivates saving In addition to savings motive we also tested how the 4 institutional mechanisms (facilitation, incentive, information, expectation) identified by Sherraden et. al (2003) would motivate our respondents to save more often. The respondents were given a list of the mechanisms and they were asked to rank how these factors would motivate them to save more often according to the following ratio: 1 – Not motivating 2- Somewhat motivates 3-Motivates 4- Most motivating The mean of each factors were calculated. We also calculated the overall mean for each mechanism using the compute function in SPSS. A simple descriptive analysis was carried out to analyze which mechanism gives the highest motivation for regular saving. Results are presented in Table 5:

Table 5: Institutional Mechanisms Descriptive Statistics N Sum Mean Facilitation 125 327 2.62 Deposit machine 132 323 2.45 Direct debit 127 357 2.81 Incentive 129 364 2.82 High return 134 388 2.90 Tax exempt 129 354 2.74 Information 130 349 2.69 Save wisely 132 352 2.67 Create budget 133 359 2.70 Manage money 134 380 2.84 Buy asset 133 344 2.59 Expectation 129 367 2.84 Savings target 133 391 2.94 Buy asset goal 130 360 2.77

The top two mechanisms that motivate the respondents to save regularly are the expectation mechanism (2.84) and incentive mechanism (2.82). Having a monthly savings target seems to be the most motivating factor that helps respondents save more often (highest mean = 2.94). According to a few respondent that were interviewed, having a savings target either formally (written down budget) or informally (mere planning in mind) help discipline them to make a committed savings each month.

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The next motivating factor is high return on savings (mean=2.90). This would be a rational outcome to be expected since people would be more motivated to save if they earn a higher return from their saving. The result however has to be observed with prudence because the term “high” is relative and different respondent may have different interpretation of it. In addition, financial education or information on money management seems to motivate the respondents to save more often (mean = 2.84). This is one area where consumer education could play its role. Just out of curiosity we included additional question to a few respondents that seems to be highly motivated (response of 3-4) by the financial education/information factor. We asked whether they would attend these classes if banks were to have it over the weekend or any other specific time. The common answer among few elderly was “No. The bank’s are far and I do not have time to go”. We then further asked if they would participate if the banks were to do a mobile financial education class around their housing area or shopping complexes. The response seems to be more positive and encouraging. Therefore, even though the information factor seems to motivate respondents, the best medium of information transfer would have to be explored because it would be costly for banks to handle financial education classes with low attendance rate. Finally, the direct debit facility or automatic deduction from salary also seems to motivate people to save more often (mean = 2.81) because committed savings is pre-arranged before salary could be spent. One point has to be taken into account when interpreting these results. We used a 4-point Likert scale to measure the level of motivation. This measurement may have limitations because it does not give the respondent the opportunity to take a neutral position. Future studies may use a 5-point, 7-point or even a 9-point Likert scale to have better measurement of the motivation level. Hypothesis Testing The relationship between the decision to transfer fund and religion In order to achieve the second objective, Part B of our questionnaire was specifically designed to obtain further insights into findings by Haron and Ahmad (2000). We included a question asking if the respondents would transfer their fund from Islamic account into conventional account if the conventional account pays a higher interest. They were given a simple “Yes” and “No” answer option with a follow up question asking reasons for their responses. Since our dependent variable (transfer fund) is a nominal data, we chose to run a chi-square test to the following hypothesis:

Ho: The decision to transfer fund is independent of religion Ha: The decision to transfer fund is dependent on religion

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Initially we cross-tabbed transfer fund with religion (measured in 4 categories) and the results are as following: Transfer * Religion Crosstabulation Count

Religion

Total Islam Buddhism Hinduism Others Transfer Yes 19 24 12 3 58

No 73 2 1 1 77 Total 92 26 13 4 135

Chi-Square Tests

Value df Asymp. Sig.

(2-sided) Pearson Chi-Square 59.116(a) 3 .000 Likelihood Ratio 65.102 3 .000 Linear-by-Linear Association 42.146 1 .000

N of Valid Cases 135

a. 2 cells (25.0%) have expected count less than 5. The minimum expected count is 1.72. The pearson chi-square statistic is highly significant (.000) however there are 25% of the cells that have an expected count less than 5. This violates one of the assumptions of chi-square test (minimum expected count must be 5). Thus we reduced the number of categories in religion to have a more valid test result. We therefore created a dummy variable for religion (1= Muslim, 0 = otherwise). We then, re-ran the chi-square test and obtained the following results:

Transfer * RR_Muslim Crosstabulation

RR_Muslim

Total 0 1 Transfer Yes Count 39 19 58

% within RR_Muslim 90.7% 20.7% 43.0% No Count 4 73 77

% within RR_Muslim 9.3% 79.3% 57.0% Total Count 43 92 135

% within RR_Muslim 100.0% 100.0% 100.0%

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Chi-Square Tests

Value df Asymp. Sig.

(2-sided) Exact Sig. (2-sided)

Exact Sig. (1-sided)

Pearson Chi-Square 58.672(b) 1 .000 Continuity Correction(a) 55.849 1 .000

Likelihood Ratio 64.138 1 .000 Fisher's Exact Test .000 .000Linear-by-Linear Association 58.238 1 .000

N of Valid Cases 135 a. Computed only for a 2x2 table b. 0 cells (.0%) have expected count less than 5. The minimum expected count is 18.47.

The Pearson Chi-Square statistic is highly significant (.000) with no cells violating the chi-square assumption. We therefore could reject the null hypothesis and conclude that there is a significant relationship between the decision to transfer fund and religion of the account holder. The observed cell frequencies show that about 80% of the Muslims would not transfer their fund to the conventional account even when it pays higher interest. The reasons indicated by the respondents are summarized below:

Reason for non-transfer Frequency Percent (%) Islamic Law 24 37Support IB 15 23Not for return 10 15Hassle 7 11Other 5 8No sig. dif 4 6Total 65 100

65 respondents (out of 77 that indicated “No” as their answer) provided their reasons for not transferring their fund. We grouped their answer into 6 categories. The main reason for non-transfer is prohibition in Islamic law (37%). We included a respondent under this category if they provided answers like; riba is prohibited, it’s my duty as a muslim, there is a prohibition in my religion, halal return, return in this world and hereafter and for zakat purposes. Other reasons include; to provide support for Islamic banking industry (23%), return is not main purpose of savings (15%), it is a hassle to switch between accounts (8%) and there is no significant difference in the amount (6%).

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Symmetric Measures

Value Approx. Sig. Nominal by Nominal

Phi .659 .000 Cramer's V .659 .000

N of Valid Cases 135 a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis.

We also calculated the Phi and Cramer’s V statistic to capture the strength of the correlation between transfer fund decision and religion. The Phi and Cramer’s V statistic is 0.659 (more than 0.50) and significant. This shows that there is a high correlation between the two variables.3 We can therefore conclude that there is a significant relationship between the decision to transfer fund and religion. This finding is also consistent with the findings in the first part where our results show that return on savings is the least important savings motives. The question to examine this issue was however worded generally: “Assume you have both conventional and Islamic savings accounts. If the conventional savings account pays higher interest, will you transfer your money from the Islamic account to conventional savings account?” The question did not provide any magnitude of savings nor differences in the return rate. Since savings account pay a relatively low return for small amount of savings, the findings may not reflect the behavior of respondents that have a substantial amount of savings. We therefore ran another chi-square test to test the following hypothesis:

Ho: The decision to transfer fund is independent of savings amount Ha: The decision to transfer fund is dependent on savings amount

We have collected the information on total savings (in their savings account) for each respondent. The answer for this question initially had 8 categories. To avoid the violation of chi-square assumption (minimum expected count must be 5) we recoded the total savings variable into three new categories:

1 = Low savings = below than RM 2,000 2 = Medium savings = RM 2,001 – RM 8,000 3 = High Savings = RM 8,001 and above

3 This rule of thumb is based on Green, S.B., Salkind, N.J. and Akey, T.M. (1997), Using SPSS for Windows: Analyzing and Understanding Data: Prentice Hall, New Jersey, p. 406. His rule of thumb indicates that:

Phi and Cramer’s V < .01 No correlation Phi and Cramer’s V = .01 – 0.29 Small correlation Phi and Cramer’s V = .029-0.50 Moderate correlation Phi and Cramer’s V > 0.50 High correlation

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The result of the chi-square test is as follows:

Transfer * Savings_new Crosstabulation

Savings_new

Total 1 2 3 Transfer Yes Count 40 7 11 58

% within Savings_new 40.8% 38.9% 57.9% 43.0%No Count 58 11 8 77

% within Savings_new 59.2% 61.1% 42.1% 57.0%Total Count 98 18 19 135

% within Savings_new 100.0% 100.0% 100.0% 100.0%

Chi-Square Tests

Value df Asymp. Sig.

(2-sided) Pearson Chi-Square 2.035(a) 2 .362 Likelihood Ratio 2.014 2 .365 Linear-by-Linear Association 1.397 1 .237

N of Valid Cases 135

a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 7.73. The significance level of the Pearson Chi Square statistic is more than the alpha level (0.362 > 0.05). Thus we could not reject the null hypothesis. Therefore the decision to transfer fund is independent of the amount of savings the respondents hold. We can therefore conclude that the decision to transfer fund is significantly different between Muslims and non-Muslims regardless of their savings amount. Generally, Muslims are guided not only by profit motive but also religious factor. This finding is therefore not consistent with findings by Haron and Ahmad (2000). One possible explanation for the contradicting result is the context of study. Our study is only focusing on savings account where return on savings may not be a significant factor to the respondent’s transfer decision. However Haron and Ahmad included all deposits (including Mudharabah investment account) in their study. Investment account holder may have a different savings motive and may therefore have a different decision. Future research in the area is needed to provide empirical evidence on behavior of investment account holders.

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Exploratory Predictive Model In Part A of our questionnaire we collected information on total savings (TS) and monthly savings (MS). To obtain maximum response we had to measure these variables within 8 categories of ranges. With this limitation in mind, we explored whether we could predict the two dependent variables using a multiple regression model. From the review of past studies, the following variables (economic and demographic) have been used to predict savings; income, no. of children, gender, race, education, age, type of employment and asset ownership (Lunt & Livingstone 1991, Weiss et. Al 2006). We have collected these variables (except asset ownership) in our survey and thus it could be incorporated in a regression model to predict TS and MS. Besides these variables, we also asked the respondents their saving attitude (1 = save then consume, 2= consume then save balance). We were also interested to examine whether this variable would play a significant role in predicting savings. Since the model includes a few demographic factors we had to create dummy variables to represent race (3 dummies), gender (1 dummy) and attitude (1 dummy). We specify our models as follows: TS = a + b1Y + b2Age + b3Child + b4Educ + b5Savtime + b6R1 + b7R2 + b8R3 + b9G +

b10Att + e ………….(1) MS = a + b1Y + b2Age + b3Child + b4Educ + b5R1 + b6R2 + b7R3 + b8G + b9Att + e …………(2) Where, TS = Total savings MS = Monthly savings Age = Age of respondents Child = Number of children below 18 years old (dependent child) Savtime = Saving time (how long have accumulated total saving)

Educ = Education level (1= Low, 2= middle, 3= high) R1 = Dummy for Malay (1= Malay, 0 otherwise) R2 = Dummy for Chinese (1= Chinese, 0 otherwise) R3 = Dummy for Indian (1= Indian, 0 otherwise) G = Dummy for gender (1 = Male, 0 otherwise) Att = Dummy for saving attitude (1 = save first, 0 otherwise) e = disturbance term to capture the unobservable effect We had 10 independent variables in Model 1 and 9 independent variables in Model 2. Model 1 had saving time as an additional variable because we asked the respondent the length of time they have kept their total savings. The results of the linear regression for Model 1 are presented below:

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Model Summary(b)

Model R R Square Change Statistics Durbin-Watson

R Square Change F Change df1 df2 Sig. F Change

1 .468(a) .219 .219 1.434 10 51 .193 2.122a. Predictors: (Constant), Save first, RR_Indian, Gender_Male, saving time, RR_Chinese, No. of child, Educ, Income, Age group, RR_malay b. Dependent Variable: saving

ANOVA(b)

Model Sum of

Squares df Mean Square F Sig. 1 Regression 76.259 10 7.626 1.434 .193(a)

Residual 271.229 51 5.318 Total 347.488 61

a. Predictors: (Constant), Save first, RR_Indian, Gender_Male, saving time, RR_Chinese, No. of child, Educ, Income, Age group, RR_malay b. Dependent Variable: saving

Coefficients(a)

Model Unstandardized

Coefficients t Sig. Collinearity Statistics

B Std. Error Tolerance VIF 1 (Constant) 1.537 2.780 .553 .583 saving time .237 .293 .812 .421 .795 1.258 Age group .174 .525 .331 .742 .392 2.553 No. of child -.289 .214 -1.350 .183 .720 1.388 Income .364 .227 1.608 .114 .549 1.820 RR_malay .794 1.826 .435 .666 .120 8.365 RR_Chinese .715 1.917 .373 .711 .151 6.605 RR_Indian .825 2.021 .408 .685 .244 4.105 Educ -.555 .619 -.896 .375 .677 1.476 Gender_Male .358 .622 .576 .567 .900 1.111 Save first .386 .685 .563 .576 .794 1.259

a. Dependent Variable: saving The findings indicate that Model 1 is not significant (p-value of the F statistic = 0.193 > 0.05). The examination of the co-efficient also shows that none of the variables included has a significant co-efficient. Income and no. of child seems to have the lowest significance however it still exceeded the 10% cut off point. The Variance Inflation Factor (VIF) for the quantitative variables (saving time, age group, child, income and education) did not exceed 2.553 so we can say there was no serious collinearity

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problem.4 The descriptive analysis of the residuals shows that the skewness and kurtosis conform to normality.5 The descriptive analysis of residual is shown in Appendix 1. One factor that may contribute to this insignificance is the different interpretation that the respondents may have made in understanding the question. The question was worded as following: “How much is your TOTAL savings (in your savings account) not intended for current consumption (within 1 month)?” The objective of the question was to measure total savings; however some respondents may have understood it as savings in one month. For some of the respondents where an interview method was utilized, the confusion could have been clarified but if it was only distributed and re-collected later, there was room for misunderstanding of this question. Thus total savings may face some measurement problem. Further refinement of the questionnaire is vital for improvement of the measurement of total savings. The result of the linear regression of Model 2 is presented below:

Model Summary(b)

Model R R Square Change Statistics Durbin-Watson

R Square Change F Change df1 df2 Sig. F Change

1 .866(a) .750 .750 6.991 9 21 .000 1.893a. Predictors: (Constant), Save first, RR_Chinese, Educ, Gender_Male, Income, RR_Indian, No. of child, Age group, RR_malay b. Dependent Variable: monthly saving

ANOVA(b)

Model Sum of

Squares df Mean Square F Sig. 1 Regression 14.036 9 1.560 6.991 .000(a)

Residual 4.685 21 .223 Total 18.721 30

a. Predictors: (Constant), Save first, RR_Chinese, Educ, Gender_Male, Income, RR_Indian, No. of child, Age group, RR_malay b. Dependent Variable: monthly saving

4 The VIF indicates a problem if the factor exceeds 10 (Neter et. al., 1983). 5 Kurtosis less than 3

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Coefficients(a)

Model Unstandardized

Coefficients t Sig. Collinearity Statistics

B Std. Error Tolerance VIF 1 (Constant) 2.717 .989 2.748 .012 Age group -.101 .142 -.707 .487 .592 1.690 No. of child -.358 .072 -4.994 .000 .675 1.481 Income .369 .071 5.232 .000 .609 1.643 RR_malay -.091 .732 -.124 .902 .066 15.156 RR_Chinese -.151 .750 -.201 .843 .100 9.998 RR_Indian -.383 .767 -.500 .622 .115 8.669 Educ -.065 .221 -.295 .771 .844 1.185 Gender_Male .314 .194 1.617 .121 .779 1.284 Save first -.345 .195 -1.764 .092 .832 1.202

a. Dependent Variable: monthly saving Only 65 respondents in our sample had a monthly saving allocation thus we only used 65 observations to fit this model. The findings indicate that Model 2 is a significant exploratory predictor (p-value of the F statistic = .000). The R2 = 0.75. This shows that the model may be able to explain 75% of the variance in monthly savings. The examination of the co-efficient indicate that 2 variables are highly significant; income and no. of child. The attitude (save first then consume) may be significant at 10% level. Gender slightly exceeded the 10% cut off point. Other variables are not significant. Income has a positive correlation indicating that the higher the income then the higher will be the monthly saving. Child has a negative coefficient indicating that the more dependent child an individual has, the lower will be the monthly saving because they have more expenses to incur. Attitude has a negative co-efficient. (The dummy used was 1= save first). We expected a positive sign. A negative co-efficient would mean that those who save first would have a lower monthly saving. One possible explanation would be, those who save first may provide a smaller amount of saving compared to those who consume first and save the balance. Looking at the positive sign of the gender co-efficient, males may be saving more compared to the female respondents. Race, education level and age were not significant. The signs of the co-efficient for age and education does provide a reasonable explanation; The older the respondent is, the lower will be the monthly saving because they will have a larger family and higher household expenses. As for the negative co-efficient of education; this indicates that the higher the education then the lower will be the monthly savings. Since we are looking at bank savings account, the higher educated individuals may be investing elsewhere where they may be earning a higher return than return on savings account. Race does not seem to have any effect on monthly savings. Thus no one race had a significantly higher monthly saving compared to the other races.

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The Variance Inflation Factor (VIF) for the quantitative variables (age group, child, income and education) did not exceed 1.690 so we can say there was no serious collinearity problem. The descriptive analysis of the residuals shows that the skewness and kurtosis also conform to normality. The descriptive analysis of residual is shown in Appendix 1. This exploratory finding thus indicate that income and number of dependent children plays a significant role in predicting monthly savings while none of the variables were able to predict total saving. However the results have to be interpreted with prudence because only 65 respondents had a monthly saving. This is quite a small number of observations to obtain a reliable model. With only 2 significant variables, 75% R2 is just too high. Since the study only involves a small number of observations, the R2 may be biased towards a particular group of respondents that produced similar responses. The constant of the model is quite high (2.717) with a significance level around 10%. This means that there maybe a lot of missing variables that is not included in the model. Future work in this area is needed with a larger sample size (maybe N>300) and more dispersed respondent to obtain a more conclusive and strong predictive model. Then we may be able to build a model with a much more reasonable R2 and more accurate co-efficient signs. 5.0 Conclusion Savings behavior is usually examined using a time-series (longitudinal) data, however the findings and discussions in this study are based on a cross-sectional survey of 135 respondents within Klang Valley area. This limitation should be first highlighted before the findings are summarized. In addition, the findings are also limited in generalization as we adopted a convenience sampling method due to time constraint. Nevertheless, since this is a preliminary study, the above methodology has helped us identify problems that may occur in future studies and thus provided us with an opportunity to further refine the questionnaire and improve our sampling technique. The study was carried out to achieve two main objectives; to identify factors that motivate people to save and to examine the relationship between the decision to transfer fund and religion of account holders. The results suggest that savings account holders primarily save for precautionary motives and retirement purposes. The least important reason to save was for return on savings account and bequest motive. Our findings are consistent with previous studies (Katona 1975, Kotlikoff 1989, Alessie et. al 1997, Horioka & Watanabe 1997, Johnson 1999 and Harris et.al 2002) and provide some evidence that checking and savings account (especially those savings accounts that are not of substantial amount) serves as a cash management tool rather than an investment account. Besides the savings motives, we also explored which institutional mechanism (identified by Sherraden et. al 2003) motivates people to save more often. Our results indicate that the expectation mechanism (i.e. having a monthly savings target) gives the highest motivation to help people save regularly.

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We ran a chi-square test to explore whether respondents would transfer their fund from Islamic savings account to a conventional savings account given that the conventional account pays a higher interest. Our results suggest that the decision to transfer is related to religion of the account holders and not related to the amount of savings. Majority of Muslims would not transfer their fund due to religious restriction. This finding is inconsistent with the finding of Haron and Ahmad (2000) maybe due to differences in the context of study. We only focused on savings account while Haron and Ahmad included savings and investment account in their study. Future studies may need to include the behavior of investment account holder to improve the comparability of the results. On top of the two main objectives, we explored two models to predict the amount of total savings and monthly savings. The preliminary results suggest that income, number of dependent child and savings attitude may possess significant effect in determining monthly saving. None of the variables included in the exploratory model (including age, race and education) were able to predict total savings. These results however need to be taken in view of the limitation we have highlighted above. These exploratory models were intended to identify possible variables for further investigation. Thus the results do not provide conclusive evidence of prediction. The highlighted limitation of this study should be taken as an opportunity to further improve the study rather than a stopping point because a study of behavioral finance is able to give new ideas about how “real” people make economic decisions. The results from an improved behavioral finance study may give valuable insights on how people decide to save, invest and consume (Mitchell and Utkus, 2006). Future research in this area could include a survey of household financial need and whether the current financial instrument has met this need. This information could then serve as empirical evidence that financial institutions may use to design new or improved financial instruments. Empirical studies of savings and investment behavior could also provide valuable information for financial advisors and educators. It can help them understand why people save and what they invest in and thus provide valuable insights into the financial planning of personal finance.

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References: Alessie, R., Lusardi, A. and Aldershof, T. (1997), Income and wealth over the life cycle: Evidence from panel data. In Canova, L., Rattazzi, A.M.M and Webley, P (2005), The hierarchical structure of savings motives, Journal of Economic Psychology, 26, p. 21-34. Ariff, M. (1988), Islamic Banking, Asian-Pacific Economic Literature, Vol 2, p.46-62 Athukorala, P., and Sen, K. (2004), The determinants of private saving in India, World Development Vol.32, No.3, p. 491-503 Attanasio, Orazio P., and Paiella, M. (2001), Household Savings in the U.S.A., Research in Economics, 55, p. 109-132. Aziz, Z.A. (2003). Outlook and Prospects for Asia, Governor’s keynote address at the Asia Pacific Bankers Club Conference, 15 September 2003, Kuala Lumpur, [online]. Available: http://www.bnm.gov.my/ Aziz, Z.A. (2004). Governor’s opening remarks at the launching of the Merdeka Savings Bond, 2 January 2004, Kuala Lumpur, [online]. Available: http://www.bnm.gov.my/ Baharumshah, A.Z., Thanoon, M.A.,Rashid, S. (2003), Savings dynamics in the Asian countries, Journal of Asian Economics 13, p. 827-845 Beverly, S.G., and Sherraden, M. (1999), Institutional determinants of saving: Implications for low income households and public policy, Journal of Socio-Economics 28, p. 457-473. Burney, N.A. and Khan, A.H. (1992). Socio-economic characteristics and household savings: an analysis of the households’ savings behaviour in Pakistan, Pakistan Development Review, 31(1), p. 31-48 Canova, L., Rattazzi, A.M.M and Webley, P (2005), The hierarchical structure of savings motives, Journal of Economic Psychology, 26, p. 21-34. Erol & El-Bdour (1989), Attitudes, Behaviour and Patronage Factors of Bank Customers towards Islamic Banks. International Journal of Bank Marketing, Vol.17,no.6, p.31-39. Gerrard, P. and Cunningham, J.B. (1997), Islamic Banking: A study in Singapore, International Journal of Bank Marketing 15/6, p. 204-216. Green, S.B., Salkind, N.J. and Akey, T.M. (1997), Using SPSS for Windows: Analyzing and Understanding Data. Prentice Hall, New Jersey, p. 406 Haron, S., Ahmad, N. and Planisek, Sandra L. (1994), Bank Patronage Factors of Muslim and Non-Muslim Customers, International Journal of Bank Marketing Vol.12, No.1, p. 32-40.

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Haron, S. and Ahmad, N. (2000), The Effects of Conventional Interest Rates and Rate of Profit on Funds Deposited with Islamic Banking System in Malaysia, International Journal of Islamic Financial Services, Vol.1 No.4. Haron, S. and Shanmugam, B. (1995), The Effects of Rates of Profit on Islamic Bank’s Deposits: A Note, Journal of Islamic Banking and Finance, Vol. 12, No.2, p.18-28. Harris, M.N., Loundes, J. and Webster, E. (2002), Determinants of household savings in Australia. In Canova, L., Rattazzi, A.M.M and Webley, P (2005), The hierarchical structure of savings motives, Journal of Economic Psychology, 26, p. 21-34. Horioka, C.Y. and Watanabe, W. (1997), Why do people save? A micro-analysis of motives for household saving in Japan. In Canova, L., Rattazzi, A.M.M and Webley, P (2005), The hierarchical structure of savings motives, Journal of Economic Psychology, 26, p. 21-34. Jain, A.K. & Joy, A. (1997), Money matters: An exploratory study of the socio-cultural context of consumption, saving, and investment patterns, Journal of Economic Psychology 18, p. 649-675 Kaleem, A. and M-Isa, M (2003), Causal relationship between Islamic and conventional banking instruments in Malaysia, International Journal of Islamic Financial Services, Vol.4, No.4. Katona, G. (1975), Psychological economics. In Canova, L., Rattazzi, A.M.M and Webley, P (2005), The hierarchical structure of savings motives, Journal of Economic Psychology, 26, p. 21-34. Keynes, J.M. (1936). The general theory of employment, interest and money. London: Mac Millan. Kim,Y.H. (2001), The Asian crisis ,private sector saving, policy implications. Journal of Asian Economics 12, p. 331-351. Kotlikoff, L.J (1989), What determines savings? In Canova, L., Rattazzi, A.M.M and Webley, P (2005), The hierarchical structure of savings motives, Journal of Economic Psychology, 26, p. 21-34. Lindqvist, A. (1981), A Note on Determinants of Household Saving Behavior, Journal of Economics Psychology 1, p 39-57. Lunt, P.K. and Livingstone, S.M.(1991), Psychological, social and economic determinants of saving: Comparing recurrent and total savings, Journal of Economics Psychology 12, p. 621-641.

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Appendix 1:

Table 1: Descriptive Statistics for Residual of Total Savings

N Mean Std.

Deviation Skewness Kurtosis

Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error Unstandardized Residual 61 .2161935 2.51568533 .639 .306 -.766 .604Deleted Residual 61 .2979411 3.14509029 .687 .306 -.588 .604Standardized Residual 61 .0937475 1.09087101 .639 .306 -.766 .604Studentized Residual 61 .1100038 1.21656381 .656 .306 -.708 .604Valid N (listwise) 61

Table 2: Descriptive Statistics for Residual of Monthly Savings

N Mean Std.

Deviation Skewness Kurtosis

Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error Unstandardized Residual 30 -.0072971 .54785473 -.325 .427 .444 .833Deleted Residual 30 -.0225064 .79952255 -.272 .427 .835 .833Standardized Residual 30 -.0154491 1.15989701 -.325 .427 .444 .833Studentized Residual 30 -.0287336 1.39349468 -.318 .427 .613 .833Valid N (listwise) 30