HOUSEHOLD'S PREPAREDNESS FOR INCOME SHOCK. YIING JIA LOKE 1 Universiti Sains Malaysia

Singapore Economic Review Conference 2013 6-8 August 2013. HOUSEHOLD'S PREPAREDNESS FOR INCOME SHOCK YIING JIA LOKE 1 Universiti Sains Malaysia Abstract The main purpose of the study is to investigate the significance of socio-demographic factors, financial behaviour and attitude and financial literacy on Malaysians preparedness for income shock. The study employs the data from the OECD International Network in Financial Education Pilot Study on Measuring Financial Literacy in 2010. It is found that the majority of respondents (79%) fail to meet the minimum guideline of having emergency funds equivalent to at least three months of living expenses if they lose their main source of income. Based on the ordered probit analysis, it is found that higher education, having reliable income and being financially knowledgeable contribute positively to an individual s financial preparedness for income shock. However, having prudent financial behaviour and attitude did not necessarily contribute to better financial preparedness for income shock. Lastly, it is found that savings by investment helps individuals to be more financially prepared for income shock compared to individuals who save conservatively through traditional savings channels only. Keywords: Personal finance, savings, financial emergencies, financial literacy, household JEL: D14; D91 1 Yiing Jia LOKE, School of Social Sciences, Universiti Sains Malaysia, 11800 Penang. Tel: +604 653 4615; Fax: +604 657 0918; Email: yjloke@usm.my. The author would like to thank Bank Negara Malaysia for the data used in the study. Opinions expressed are those of the author. Financial assistance from the Universiti Sains Malaysia Research University (RU) Grant (Grant No: 1001/PSOSIAL/816213) is acknowledged. 1

Household's Preparedness For Income Shock 1. Introduction Earning an income is crucial to sustain the welfare of an individual or a household. However, life uncertainties can have adverse effects on the income and welfare of a household. Economic crises which result in pay cuts or retrenchment, inability of the main breadwinner to work due to illness, or the death of the main breadwinner, are considered unplanned events that will require re-organization of household financial resources. This can strain the household's financial status if adequate liquid funds is not in place to help households to weather the sudden income shock. In recent years, some affected individuals have turned to illegal money lenders and unsecured loans such as loan sharks (Ah Long) and credit cards to help them cope with unexpected changes in family finances. While taking unsecured loans may help ease family finances temporarily, it may exacerbate the financial vulnerability of the household as the quick cash comes with exorbitant interest rate charges. As a result, ill-prepared individuals and households will fall into a vicious debt cycle. In the 2008 global economic crisis, UNDP (2009) reported that 33,599 individuals in Malaysia were retrenched and 34,383 had their pay cut in the period October 2008 to May 2009. The Sun (2011) reported that 10% of those who seek financial counselling and debt management from AKPK (Credit Counselling and Debt Management Agency) had done so due to retrenchments which resulted in being unable to manage their debt. While there is insurance and social security to help families cope with unexpected events such as death, illness or disability, there is no insurance that provides for a family or individual in the event of a job loss or pay cut. As such, it is important for households and individuals to prepare adequate emergency funds to help them survive a crisis. 2

Kapoor, Dlabay and Hughes (1996) have pointed out that a comprehensive financial plan should include emergency funds to accommodate for unplanned events such as an income shock. Johnson and Widdows (1985) defines emergency funds as financial holdings which are available to cover spending in the event of emergency (income shock) without drastically adjusting the household's current level of living. Johnson and Widdows (1985) tiered emergency fund holdings into three categories according to its degree of liquidity. Adequate emergency funds vary from two or three months to a year's worth of living expenses (Garman and Forgue, 1997, pp77-78; Greniger et al., 1996; Hanna and Wang, 1995; Dunman, 1994; DeVaney, 1994;). However, the rule of thumb is consumers should hold liquid assets sufficient to cover 3-6 months of living expenses as this is regarded as the average number of weeks of unemployment (Johnson and Widdows, 1985). Twelve weeks or 3 months is considered as the minimum and 6 months is considered as the maximum. The guideline is based on the assumption that a laid off worker would be re-employed in 3-6 months. The objective of this study is to investigate the factors that determine households' preparedness for income shock. This study is timely as no such studies have been conducted in Malaysia and moreover, the data were collected soon after the global crisis of 2007-2008 which resulted in a moderate widespread retrenchment and pay cuts among Malaysians. No such nationwide data on consumer finances have been available prior to this survey. Apart from this study being one of the first to examine on Malaysians ability to weather through income shock, the study extends existing literature on emergency funds holdings in several ways: Firstly, existing literature investigate holdings of emergency funds based on the types of emergency funds holdings held by households ( Anong and DeVaney, 2010; Bhargava and Lown, 2006; Chang et al., 1997; Huston and Change, 1997) instead of the actual period of individual's sustainability for income shock as used in this study. Measuring the actual 3

period of individual's sustainability for income shock gives a more definite notion on the individual's ability to weather income shock than types of emergency funds holdings. Further, each type of emergency funds holdings may vary in the amount available to buffer an individual from income shock. Secondly, so far in the existing studies, financial knowledge has not been considered as one of the plausible factors that can determine a household's or individual's financial emergency planning. Financial knowledge is found to play an effective role in shaping an individual financial decision such as retirement planning, stock market participation and investment (Mandell and Klein, 2009; Lusardi, 2008a & 2008b; Van Rooij et al., 2007; Fox et al., 2005; Hilgert et al. 2003). Similarly, while financial behavioural and attitudinal measurements have been incorporated in analysis for other types of financial decision making, these factors have so far not been considered in studies on financial emergency planning. This study will incorporate selected financial behavioural and attitudinal factors in the model on financial preparedness for income shock. Thirdly, in addition to income levels, this study include regularity and consistency of income receipt to the determinants of levels of financial preparedness for income shock. Notwithstanding the importance of income levels in affecting the ability for individuals to plan for financial emergencies, the flow of income receipt may also play a role in individuals financial planning. The plausible influence of the reliability of income receipt has largely being ignored in the existing studies. 2. Insights from the literature The widely adopted definition of emergency fund follows from Johnson and Widdows (1985) who argued that emergency funds are financial holdings that are available to cover living expenses without drastically altering the household standard of living in the event of income shock. Johnson and Widdows (1995) uses three measurements of emergency fund holdings - 4

quick, intermediate and comprehensive which differ according to their liquidity of assets. Quick funds encompass assets held in savings, checking and money market funds and accounts. Intermediate funds includes certificates of deposits and savings certificates and comprehensive funds consists of intermediate funds and value of stocks and bonds. There is debate on the adequate or minimum emergency funds that a household should have in the event of income disruption. The minimum adequacy is having funds equivalent to three months of living expenses and this is based on the average unemployment period of a worker (DeVaney, 1994). Greninger et al (1996) found strong consensus among financial planners and educators that liquid assets for emergencies should equal a minimum of two and half to three months of living expenses. Existing empirical studies on emergency funds holdings have mainly used sociodemographic and economic variables such as age, years of education, marital status, race, employment status, occupation and homeownership in modelling the determinants of emergency fund holdings (Bhargava and Lown, 2006, Worthington, 2004; Worthington, 2005; DeVaney, 2001; Hachter, 2000; Chang et al. 1997, Chang and Huston, 1995; and etc). In summary, these studies found that older households, higher income and education and larger household size are more likely to maintain emergency funds holdings. Apart from individuals' personal characteristics, emergency funds holdings is also affected by savings behaviour. Several types of variables are used to measure savings behaviour and this include as savings motive and savings planning horizon. Further, risk tolerance and income uncertainty (e.g. expectation of future income changes) have also been examined. These variables are incorporated into the analysis on emergency funds holdings by Huston and Chang (2010), Anong and DeVaney (201), Bhargava and Lown (2006), Bi and Montalto (2004). These studies found that those with longer saving planning horizon and those who are willing to take some financial risk are more likely to have adequate 5

emergency funds. Further, those who expect their future income to decline are less likely to have adequate emergency funds holding. 3. Methodology 3.1 The econometric model The dependent variable in this study is level of financial preparedness for income shock which is categorical and ordinal with a clear ordering. The categories of levels of financial preparedness are defined as strong, moderate and weak. A statistical model which is appropriate to explain the ordinal variations of the financial preparedness level is ordered probit (McCullaph 1980; McKelvey and Zavoina 1975). The categorisation of the levels of financial preparedness is based on Johnson and Widdows, (1985) who pointed out that an individual should at minimum has funds equivalent to 3 months of living expenses and maximum of 6 months. Therefore, a respondent who is able to sustain himself 3 months and more is categorised as having strong financial preparation for income shock while a respondent who is moderately prepared financially for income shock is able to sustain himself at least a month but less than 3 months. However, if a respondent is unable to weather through the income shock over a month, is categorised as being weak in his financial preparation for income shock. In short, the dependent variable of financial preparedness level for income which is also known as the outcome variable in the ordered probit model is characterized as follows: Strong: strong financial preparedness are able to sustain themselves 3 months of more Moderate: ability to sustain themselves for at least a month but less than 3 months Weak : ability to sustain themselves for less than a month 6

Financial preparedness is given the score of 1, 2 and 3 which indicates strong, moderate and weak financial preparedness. The ordered probit (OP) model is usually justified on the basis of a latent variable, variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured). In general, the ordered probit model is written as: y * = β' x + ε (1) where y* = latent and continuous measure of financial preparedness levels coded as 0, 1, 2; β is the vector of estimated parameters and x is the vector of explanatory variables; ε is the error term. ε ~ N(0,1) with cumulative distribution denoted by Φ ( ) and density function denoted by φ ( ). The observed and coded discrete financial preparedness level, y, is determined from the model as follows: y* = 1 (strong) if y * < µ 1 (2) y* = 2 (moderate) if µ 1 < y * < µ 2 (3) y* = 3 (weak) if y * > µ 2 (4) µ 1 and µ 2 are threshold variables in the probit model. The threshold variables are unknown and determined the maximum likelihood estimation procedure for the ordered probit. Given the cumulative normal function, the probability for each level of financial preparedness is: Pr(y=0) = Φ ( µ 1 β' x) (5) 7

Pr(y=1) = Φ µ β' x) Φ ( µ ' x) (6) ( 2 1 β Pr(y=2) = 1 Φ ( µ 2 β' x) (7) Using the maximum likelihood estimates, the marginal effect of explanatory variables are derived by differentiating the equation (5) to (7) for the respective probabilities. Hence, the marginal effects of factors x on the probability of levels of financial preparedness can be evaluated in the following way: (Pr ob y= 0) = [ Φ ( µ 1 β' x) ]β x (8) (Pr ob y= 1) = [ Φ ( µ 2 β' x) Φ ( µ 1 β' x) ]β x (9) (Pr ob y= 2) = x [ Φ ( µ β' x) ]β 2 (10) 3.2 The Data The data is obtained from Bank Negara Malaysia. The data was collected for the OECD (International Network on Financial education) Pilot Survey on "Measuring financial literacy" which was conducted in 2010. Malaysia is among the 14 countries which have participated in this survey. The survey was carried out nationwide covering the Peninsular Malaysia and East Malaysia. The sample was stratified according to age, gender, income groups, states and ethnic groups. Of the total 1,046 sample size, 1,000 respondents are accounted for in this study. 46 respondents did not provide response or refuse to responds to the questions on the ability to sustain themselves following an income shock. The survey comprises of questions on financial knowledge, behaviour, attitudes relating to various aspects of financial literacy including budgeting, money management, short and long term financial planning and financial product choice. Questions on sociodemographic details of respondents are also included in the survey. 8

3.3 Variables The choice of explanatory variables is guided by existing empirical studies on preparedness for emergencies and holdings of emergency funds by Anong and DeVaney (2010), Bhargava and Lown (2006), Bi and Montalto (2004), Ding and DeVaney (2000), Huston and Chang (1997); Chang et al. (1997) and others. The variables are broadly divided into the socio-demographic variables, the financial attitudinal and behavioural variables and financial knowledge and behaviour. The sociodemographic variables include age groups, gender, household income, ethnicity, region and location of residence, household size and marital status. Region of residence is categorized by five dummy variables (northern, central, eastern and southern region of Peninsular Malaysia and East Malaysia) while location of residence refers to whether the respondent resides in urban or rural area. Financial attitudinal variables are measured by a 5 point Likert scale ranging from strongly agree to strongly disagree to the statements given: " I find it more satisfying to spend money than to save it for the long term (spend)", "I tend to live for today and let tomorrow take care of itself (live)", "Money is there to be spent (money)". Binary dummy variables are used to capture those who strongly agree or agree to each of the above statements. Similarly, financial behavioural variables are also measured by a 5 point Likert scale ranging from always to never to the statements given: " I keep a close watch on my personal financial affairs (closewatch)" and "I make sure that I have sufficient savings to cover emergency needs (emergency)". Binary dummy variables are created to capture those who responded always or frequently to the each of the statements. Respondents' financial knowledge is gauged using a set of ten questions which assess respondents' numeracy skills such as simple division, interest computation and compounding, knowledge of selected financial concepts such as time value of money, definition of inflation, 9

relationship between risk and returns and diversification. A full score would imply that a respondent has successfully answered all the ten questions while a zero score indicates that a respondent could not answer a single question correctly. The score obtained is used to proxy for respondents' financial knowledge. Respondents' savings habit is also considered in the model where a binary dummy variable is used to distinguish between those who save conservatively in traditional channels such as savings in the banks, cooperatives and with family members and those who diversify their savings by investing part of their savings funds. 3.4 Characteristics of survey respondents Descriptive statistics of variables used in the statistical model are presented in Table 1. Of the 1,000 respondents, 208(20.8%) are found to have strong financial preparation for income shock. 364(36.4%) are found to be moderately prepared financially whereby they are able to sustain themselves up to a month but less than 3 months if they lost their main income. On the other hand, 428 (42.8%) are able to sustain themselves for less than a month (weak) if they lost their main source of income. This group is considered to have weak financial preparation for income shock and are considered the most vulnerable group. The average household size for a respondent who has strong financial preparation for income shock is 3.45 while the average household size for a respondent has weak financial preparation is 2.82. A respondent with better financial knowledge has stronger financial preparation than a respondent with weaker financial knowledge. The average financial knowledge score of a person with strong financial preparation (7.11) is higher than the sample average (6.32) compared to a person with moderate (6.21) and weak financial preparation (6.02) for income shock. The sample stratification according to ethnic groups is reflected in the percentage of each ethnic group that makes up the total sample. The Malays make up the majority of the 10

ethnic group while minorities refer to respondents from Indian or East Malaysian natives ethnicity. In terms of income groups, 12% of the sample belongs to the low income group with household income of less than RM1,000, while only 5% are categorised as high income earners with household income of above RM7,000. The mid income group makes up the majority of the total sample with 49% from the lower mid income while 34% belongs to the upper mid income group. The total sample is made up of 60% male and 40% female. Males are slightly more financially prepared than females as 64% of those who have strong financial preparation are males. The breakdown of respondents in terms of level of financial preparedness and their region of residence generally reflects the proportion of each region of residence in the overall total sample. However, respondents from the northern region tends to be more weakly financial prepared compared to respondents from other region of Malaysia. In terms of marital status, the breakdown of the respondents in terms of level of financial preparedness and marital status corresponds to the proportion of marital status in the overall total sample. In other words, there is no clear distinction between marital status in terms of their level of financial preparedness for income shock. Respondents with regular and predictable income are more likely to have strong financial preparation for income shock as 65% of those with strong financial preparation have regular income. On comparing the breakdown of respondents according to education level in the total sample, it is found that though only 18% of the respondents have tertiary education, 32% of those with strong financial preparation have tertiary education while only 10% of those who have weak financial preparation have tertiary education. This suggests that tertiary education contributes positively to financial preparation for income shock. The total sample consists of 21% of those aged between 18--24 years old, 12% aged between 25-29 years old, 25% aged between 30-39 years old, 20% aged between 40-49 years old, 12% aged between 50 to 59 years old and 10% aged 60 years old and above. The 11

majority of the respondents save through traditional channel such as savings in banks and cooperatives, only 22% of the total respondents diversify their savings into investment portfolios such as stocks, properties and gold holdings. It is also found that respondents whose savings include investment are more likely to have strong financial preparation as they make up 44% of those with strong financial preparation. The majority of the respondents appears to have prudent and responsible financial attitude whereby the majority disagree that it is more satisfying to spend money than to save for it long term, to live for today and let tomorrow take care of itself and that money is there to be spent. Similarly, 80% and 72% of the total respondents claim that they keep close watch on their personal financial affairs and have sufficient savings for emergency needs respectively, but only 67% and 74% of them respectively have strong financial preparation. It is perplexing to find prudent financial behaviour and attitude does not necessarily translate to better financial preparation to income shock. 4. Empirical Results Table 2 presents the estimates of the ordered probit (column 2) and the marginal effects of the explanatory variables on the levels of financial preparedness for income shock (columns3-5). The ordered probit model has identified several significant variables associated to the different levels of financial preparation. The likelihood ratio of -962.77 and pseudo R 2 for the ordered probit of 0.0892 indicate that the model is fit. A positive coefficient in the estimate for the ordered probit indicates a higher probability of membership in the highest category (weak financial preparedness) and a lower probability of membership in the lowest category (strong financial preparedness). The positive coefficient estimates as detailed in Table 2 show that a respondent who lives in the urban area, has seasonal income, non-tertiary education (diploma, secondary and primary), aged 50 years old and above, agree that money is there to be spent and always 12

ensure that there is sufficient savings for emergency needs are more likely to be weakly financially prepared for income shock. On the other hand, the negative coefficient estimates in Table 2 (column 2), indicate that a respondent with larger household size, higher financial knowledge score and saves by investment are less likely to be weakly prepared financially for income shock. However, based on the ordered probit estimates alone, the effects of changes in the explanatory variables on the probability of membership in the intermediate group (moderate financial preparedness) is ambiguous. For this reason, the discussion of estimated coefficients of the ordered probit is kept general and the discussion of the ordered probit will focus on the marginal effects of each explanatory variable on the respective financial preparedness levels (Columns 3-5). A positive value indicates that an increase in the magnitude of the explanatory variable increases the probability that a respondent will be of specific level of financial preparedness. Overall it is found that ethnicity, income levels, region of residence and marital status have no significant effect on a respondent's level of financial preparation for income shock. A respondent with larger household size is more likely to have stronger financial preparation for income shock. For example, for every additional household member that a respondent has, it increases the probability of being in the strong and moderate financial preparedness groups by 0.81% and 0.03% respectively while, reduces the probability of being in the weak financial preparedness group by 1.14%. The result contradicts with the findings by Chang (1995), Change and Huston (1995) and DeVaney (1995) who found that larger households were less likely to meet the minimum guidelines of having at least 3 months of funds for emergency purposes. Compared to the previous studies conducted in America, the finding here suggests that a respondent with larger household size have better preparation for financial emergencies such as income shock. Having larger household size appears to 13

motivate a respondent to be more prepared for emergencies so that his family members will not be too affected should the respondent is laid off from work. A respondent with higher financial knowledge increase the probability of being in the strong and moderate financial preparedness by 1.42% and 0.60% but decreases the probability of being weakly financially preparedness by 2.02%. The finding corroborates with existing studies that show financial knowledge contributes positively to financial planning (Mandell and Klein, 2009; Lusardi, 2008a & 2008b; Van Rooij et al., 2007; Fox et al., 2005; Hilgert et al. 2003). A respondent who resides in urban area reduces the probability of having strong and moderate financial preparedness by 3.87% and 1.64% respectively while it increases the probability of having weak financial preparedness by 5.51%. While income does not have significant effect on the levels of financial preparedness for income shock, the consistency and regularity of income receipt is found to have significant effect on the levels of financial preparedness. A respondent whose income varies seasonally reduces the probability of having strong and moderate financial preparedness but increases the probability of having weak financial preparedness compared to a respondent, whose receipt of income is regular and predictable. It appears that though a respondent is aware that his receipt of income varies seasonally and is be expected to make better preparation for income shock, the finding seem to suggest that the unpredictability of income receipt is found to hinder respondents from being financially prepared for income shock. Education level is found to matter in financial preparation for income shock. A respondent with non-tertiary education (diploma/vocational, secondary and primary) is found to reduces the probability of being in strong and moderate financial preparedness and increases the probability of being in weak financial preparedness compared to a respondent with tertiary education. In other words, those who have tertiary education are able to weather 14

through income shock better than those with non-tertiary education. In corroborating with Bhargava and Lown (2006), Chen and DeVaney (2001), Ding and DeVaney (2000), Huston and Chang (1997), this result suggest that those with tertiary education are more aware of the need to have emergency funds to buffer against income shock. In terms of age effect on financial preparedness, it is found that a respondent who is 50 years old and above is less financially prepared than those a respondent who is aged between 30-39 years old. A respondent who is aged between 50-59 years old and aged between 60 years old and above increases the probability of having weak financial preparation by 9.92% and 12.19% respectively. The finding contradicts Bhargava and Lown (2006), Chen and DeVaney (2001), Ding and DeVaney (2000), Huston and Chang (1997) who found that older respondents are were most likely to have adequate fund holdings. A respondent who diversify his savings channel to include investments of stocks, properties, commodities and etc., is found to increase the probability of having strong and moderate financial preparation and reduces the probability of having weak financial preparation compared to a respondent who saves through traditional channels. In fact, it is found that a respondent who saves by investing his funds will reduce the probability of having weak financial preparation by 27.01% compared to a respondent who only saves through the traditional channels. A respondent who diversifies his savings channel to include holding investment portfolio can be considered as willing to take some financial risk which a respondent who saves through traditional channels only is considered as not willing to take any financial risk. This result corroborates with Bhargava and Lown (2006) and Huston and Chang (2010) who found that households which are willing to take some financial risk are more likely to have adequate emergency funds holdings than households which are not willing to take any financial risk. 15

Among the three financial attitudinal variables only the statement on "money is there to be spent" is found to be significant. Contrary to our expectations, it is found that a respondent who agrees that money is there to be spent reduces the probability of being weakly financially prepared and increases the probability of being strongly financially prepared. On the other hand, only one of the financial behavioural variable is found to be significant and this refer to the statement that "I ensure that I have sufficient savings for emergency needs". However, the result is contradictory as a respondent who claims that he always or frequently ensure that he has sufficient savings for emergency needs have higher probability of being ill prepared financially. This appears to indicate that the respondent may have not been truthful in his response or the respondent's understanding of having sufficient savings for emergency needs fall below the minimum guideline for emergency funds. 5. Conclusion The existing empirical literature on emergency funds holdings do not differentiate the levels of financial preparedness among individuals but instead on whether individuals have emergency funds or otherwise and the type of emergency funds that individuals hold. By analysing the level of financial preparedness, it helps to distinguish between the vulnerable ones from those who are moderately financial prepared. Overall, the results here indicate that most of the socio-demographic variables with the exception of household size, education level, age groups and location of residence have significant effects on individuals' level of financial preparedness. In addition to the aforementioned factors, better financial knowledge and savings by investment contributes positively to a respondent's financial preparation for income shock. However, it is found that having prudent financial attitude and behaviour did not necessarily contribute to better financial preparedness for income shock. The findings in this study highlights several important implications. 16

Firstly, it is disturbing to find that those entering into retirement age (age 50 and above) are poorly prepared for financial emergencies compared to younger individuals such as those aged between 30 to 39 years old. As the older generation have weak buffer against income shock, it signals another problem at hand. In other words, these individuals are probably unlikely to be well prepared to sustain themselves through their golden age. Secondly, education attainment and financial knowledge play a key role in helping individuals to be financially prepared. Those with tertiary education are better prepared than those with diploma, secondary and primary education. Higher education helps to expand an individual s horizons and thinking skills, which encourage them to plan ahead. As a result, individuals with tertiary education are better prepared for financial emergencies such as income shock. Further, apart from attaining higher education, individuals with better financial knowledge have shown to be better financially prepared. Higher educated individuals may find it easier to grasp basic financial concepts and numeracy skills and hence be more competent in applying the financial knowledge and skills to financial affairs. As the majority of respondents did not attained tertiary education, it is important to intensify financial education programs at the school level. This is to minimize individuals from being marginalized due to education attainment. Thirdly, prudent financial behaviour does not guarantee that individuals are financially prepared for income shock. Contrary to a priori expectation, the study found that those who frequently ensure that they have sufficient savings to cover emergency needs are less financially prepared than those who rarely or never plan sufficient savings to cover emergency needs. This suggests that households need to be educated on the minimum amount of emergency funds required to help households cushion for financial emergencies such as income shock. On the other hand, those who did not agree that money is there to be spent, their action seem to contradict their way of thinking as these individuals are found to 17

be more likely to be ill prepared for income shock than those who think otherwise. This finding could imply that measuring an individual's financial behaviour and attitudes through likert scale statements such as the one conducted in this survey may not capture the actual financial psychology traits of an individual. Insights from behaviour economics may be helpful to examine the underlying cognitive and behavioural biases of an individual. Fourthly, it is found that individuals who diversify their savings by investing either in investment products, property, business or foreign currencies are in better position to prepare them from income shock. Although greater risks may be involved in investing savings, such savings may be able to generate higher returns and help individuals to create wealth. As long as an individual engages with good financial planners and fund managers, they are better prepared financially for income shock. Lastly, several findings in this study contradicts existing literature on individuals' preparedness for emergency funds such as age, household size and the non-significance of income. While existing studies show that older individuals and individuals with smaller household size are more likely to meet the minimum guideline for emergency funds holdings, the findings of this study show otherwise. This suggest that households in different countries may behave differently and may be affected by different factors. Existing findings which were mostly conducted using the U.S.A data cannot be generalized to developing countries or an Asian country such as Malaysia. References Bhargava, V., and J.M. Lown. 2006. Preparedness for financial emergencies: Evidence from the Survey of Consumer Finances. Financial Counseling and Planning, 17(2), 16-26. Chang, Y.R., S.E. Hanna and J.X. Fan. 1997. Emergency fund levels: Is household behaviour rational? Financial Counseling and Planning, 8(1), 47-55. Chen, C., and S.A. DeVaney. 2001. The effect of life cycle stages and saving motives on the probability of emergency fund adequacy. Proceedings of the Association for Financial 18

Counseling and Planning Education (pp.176-185). Columbus, OH: Association of Financial Counseling and Planning Education. DeVaney, S.A. 1994. The usefulness of financial ratios as predictors of household insolvency: Two perspectives. Financial Counseling and Planning, 5, 5-24. Ding, J., and S.A. Devaney. 2000. Predictors of emergency funds adequacy. Proceedngs of the Asociation for Financial Counseling and Planning Education (pp.56-63). Columbus, OH: Association of Financial Counseling and Planning Education. Fox, J., S. Bartholomae, and J. Lee. 2005. Building the case for financial education. The Journal of Consumer Affairs, 39(1), 195-214. Greninger, S.A., V.L. Hampton, K.A. Kitt and J.A. Achacoso. 1996. Rations and benchmarks for measuring financial well-being of families and individuals. Financial Services Review, 5(1), 57-70. Hatcher, C.B. 2000. Should household establish emergency funds?, Financial Counseling and Planning, 11(2), 77-83. Hilgert, M., J. Hogarth, S. Beverly. 2003. Household financial management: The connection between knowledge and behaviour. Federal Reserve Bulletin pp. 309-322. Huston, S., and Y.R. Chang. 1997. Adequate emergency funds holdings and household types. Financial Counseling and Planning, 8(1). 37-46. Johnson, D.P., and Widdows, R. 1985. Emergency fund levels of households. In K.P. Schnuttgrund (ed.) Proceedings of the 31st Annual Conference of the American Council of Consumer Interests, 235-41. Kapoor, J.R., Dlabay, L.R., and Hughes, R.J. 1996. Personal finance, 4th edition, Chicago: Irwin. Lusardi, A. 2008a. Financial literacy: An essential tool for informed consumer choice? Paolo Baffi Center Research Paper No. 2009-35. Available at SSRN: http://ssrn.com/absract=1336389. Lusardi, A. 2008b. Household saving behaviour: The role of literacy, information and financial education programs. NBER Working Paper n. 13824. Mandell, L. and L.S. Klein, 2007, Motivation and financial literacy. Financial Services Review, 16, pp.106-116. McCullaph, P., 1980, Regression models for ordinal data. Journal of the Royal Statistical Society. Series B. Methodological, 42, pp.109 142. McKelvey, R. D. and Zavoina, W. 1975, A Statistical model for the analysis of ordinal level dependent variables. Journal of Mathematical Sociology, 4, pp. 103 120. The Sun, (2012). AKPK sees a drop in counseling cases, 7 September 2012. 19

UNDP (2009), UNDP Report; The Global Financial Crisis and The Malaysian Economy, Impact and Responses, Kuala Lumpur: UNDP (pp. 28, Table 16) Van Rooij, M., A. Lusardi and R. Alessie. 2007. Financial Literacy and Stock Market Participation. Worthington, A. C. 2005. A note on households' choice of emergency finance. Economics Bulletin, 4(4): 1-7. Worthington, A.C. 2004. Emergency fund in Australian households: An empirical analysis of capacity and sources. Financial Counseling and Planning, 15: 21-30. 20

Table 1: Variables Definitions and Sample Statistics for Explanatory Variables Variable Definition Continuous variables Household size Number of persons in household 3.45 (6.94) FK Score Score of financial knowledge 7.11 (2.09) Binary explanatory variables (yes=1; no=0) Ethnicity Malay Chinese Indians Bumi Income group Low income Lower mid income Upper mid income High income Male Region of residence Northern Central Southern Eastern East Malaysia Ethnicity is Malay Ethnicity is Chinese Ethnicity is Indian Ethnicity is East Malaysian natives Monthly household income less than RM1,000 Monthly household income RM1,000-RM3,500 Monthly household income RM3,501-RM7,000 Monthly household income above RM7,000 Gender is male Resides in the northern region of Malaysia Resides in the central region of Malaysia Resides in the southern region of Malaysia Resides in the eastern region of Malaysia Resides in East Malaysia Financial Preparedness Strong Moderate Weak 0.47 0.31 0.08 0.14 0.08 0.48 0.39 0.05 0.64 0.16 0.27 0.17 0.13 0.16 2.87 (1.92) 6.21 (2.04) 0.52 0.31 0.09 0.08 0.15 0.46 0.33 0.07 0.57 0.25 0.30 0.13 0.17 0.15 2.82 (1.72) 6.02 (2.19) 0.50 0.30 0.10 0.10 0.12 0.51 0.32 0.04 0.59 0.29 0.29 0.16 0.11 0.15 Full Sample 2.97 (3.56) 6.32 (2.15) 0.50 0.30 0.09 0.11 0.12 0.49 0.34 0.05 0.60 0.25 0.30 0.15 0.13 0.17 21

Urban Resides in urban area 0.61 0.64 0.64 0.63 Marital status Married Single Divorce Marital status is married Marital status is single Marital status is divorce or widow 0.54 0.38 0.08 0.55 0.34 0.11 0.60 0.32 0.08 0.57 0.34 0.09 Regularity of income Regular Seasonal Irregular Education level Tertiary Income is regular and predictable Income varies from season to season Income is irregular Tertiary as highest education level 0.65 0.28 0.06 0.32 0.57 0.35 0.08 0.21 0.56 0.38 0.05 0.10 0.58 0.35 0.06 0.18 Diploma Secondary Primary Diploma as highest education level Secondary school as highest education level Primary education level or no formal education 0.10 0.50 0.08 0.09 0.59 0.11 0.11 0.61 0.18 0.10 0.58 0.13 Age groups Age1824 Age2529 Age30s Age40s Age50s Age60s SaveInvest Age is 18-24 years old Age is 25-29 years old Age is 30-39 years old Age is 40-49 years old Age is 50-59 years old Age is 60 years old and above Savings includes investment 0.24 0.11 0.28 0.17 0.10 0.10 0.44 0.23 0.12 0.25 0.18 0.12 0.10 0.23 0.19 0.13 0.23 0.21 0.14 0.10 0.10 0.21 0.12 0.25 0.20 0.12 0.10 0.22 Spend Live Money Closewatch Emergency More satisfying to spend money than to save for it long term Tends to live for today and let tomorrow take care of itself Money is there to be spent Keeps close personal watch on personal financial affairs Have sufficient savings to cover emergency needs 0.29 0.19 0.36 0.74 0.67 0.36 0.28 0.42 0.81 0.70 0.37 0.26 0.52 0.82 0.75 0.35 0.25 0.45 0.80 0.72 Sample size 208 364 428 1000 22

*Standard deviations in parenthesis 23

Table 2: Maximum likelihood estimation of ordered probit for financial preparedness and marginal effects of explanatory variables on the probabilities of financial preparedness Variables Estimates Marginal effects of explanatory variables on the probabilities of financial preparedness + (1) (2) Strong (3) Moderate (4) Weak (5) Household size -0.033** (0.014) 0.81** (0.004) 0.03** (0.001) -1.14** (0.005) FK Score -0.057*** (0.019) 1.42*** (0.005) 0.60*** (0.002) -2.02*** (0.007) Ethnicity (reference = Malay) Chinese -0.082 (0.096) 2.07 (0.024) 0.88 (0.010) -2.95 (0.034) Indian 0.074 (0.136) -1.86 (0.034) -0.79 (0.014) 2.65 (0.048) Bumi 0.005 (0.192) -0.01 (0.048) -0.05 (0.020) 0.17 (0.068) Income (reference = low income) Lower mid -0.048 1.20 0.05-1.70 income (0.128) (0.032) (0.014) (0.046) Upper mid -0.143 3.54 1.50-5.04 income (0.144) (0.036) (0.015) (0.051) High income -0.115 (0.207) 2.86 (0.052) 1.21 (0.022) -4.07 (0.074) Male -0.090 (0.079) 2.26 (0.020) 0.96 (0.008) -3.21 (0.028) Region of residence (reference = central) Northern 0.149 (0.110) -3.73 (0.028) -1.59 (0.012) 5.32 (0.039) Southern -0.035 (0.123) 0.88 (0.031) 0.37 (0.013) -1.26 (0.044) Eastern -0.091 (0.139) 2.28 (0.035) 0.97 (0.015) -3.25 (0.049) East Malaysia -0.034 (0.161) 0.86 (0.040) 0.36 (0.017) -1.22 (0.057) Urban 0.154* (0.087) -3.87* (0.022) -1.64* (0.009) 5.51* (0.031) Marital status (reference = married) Single -0.034 (0.120) 0.84 (0.030) 0.36 (0.013) -1.19 (0.043) Divorce -0.140 (0.141) 3.48 (0.035) 1.47 (0.015) -4.96 (0.050) Regularity of income (reference = regular) Seasonal 0.217*** (0.083) -5.43*** (0.021) -2.30*** (0.009) 7.73** (0.030) Irregular -0.039 (0.163) 0.96 (0.041) 0.41 (0.017) -1.37 (0.058) Education (reference = tertiary) 24

Diploma 0.406*** (0.146) -10.12*** (0.036) -4.30*** (0.016) 14.42*** (0.052) Secondary 0.296*** (0.104) -7.39*** (0.026) -3.14*** (0.011) 10.52*** (0.037) Primary 0.482*** (0.149) -12.02*** (0.037) -5.10*** (0.016) 17.13** (0.052) Age (reference= age30s) Age1824-0.009 (0.140) 0.22 (0.034) 0.09 (0.015) -0.31 (0.050) Age2529 0.095 (0.137) -2.38 (0.034) -1.01 (0.015) 3.39 (0.049) Age40s 0.148 (0.114) -3.69 (0.028) -1.57 (0.012) 5.26 (0.040) Age50s 0.279** (0.133) -6.96** (0.033) -2.96** (0.014) 9.92** (0.047) Age60s 0.343** (0.152) -8.56* (0.038) -3.63* (0.016) 12.19* (0.054) SaveInvest -0.750*** (0.097) 18.97*** (0.023) 8.05*** (0.013) -27.01*** (0.033) Spend -0.051 (0.091) 1.27 (0.023) 0.54 (0.010) -1.81 (0.032) Live -0.005 (0.099) 0.01 (0.025) 0.05 (0.011) -0.02 (0.036) Money 0.333*** (0.080) -8.31*** (0.020) -3.53*** (0.089) 11.84*** (0.028) Closewatch -0.100 (0.103) -2.49 (0.026) -1.05 (0.011) 3.54 (0.037) Emergency 0.159* (0.089) -3.96* (0.022) -1.68* (0.009) 5.64* (0.032) μ 1-0.798 μ 2 0.330 + All probabilities are multiplied by 100 and therefore are in percentage terms. Asymptotic standard errors are in parentheses * p < 0.10; ** p < 0.05, *** p < 0.01. 25