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International Journal of Business and Society, Vol. 14 No. 1, 2013, 1 - 16

DETERMINANTS OF RISK TOLERANCE ON FINANCIAL ASSET OWNERSHIP: A CASE OF MALAYSIA

Jarita Duasa♣

International Islamic University Malaysia

Selamah Abdullah YusofInternational Islamic University Malaysia

ABSTRACT

The present study intends to shed new light on the issue of determinants of risk tolerance among Malaysians using data obtained from a survey. The main analysis is based on ordinary least square (OLS) method of regression using level of financial risk tolerance as the dependent variable and socio–economic factors as regressors. The preliminary analyses found that majority of the respondents prefer to keep their money in liquid assets, such as in saving account and cash. These assets definitely have the lowest level of risk compared to other forms of assets. A further analysis, using OLS regression reveals several significant determinants of risk tolerance among the sample respondents. Risk tolerance is higher among the young, males, those with higher level of education and those in non-public sector. In addition, the study also finds that Malays are more risk averse than Chinese and those in Kedah are more risk averse than those in Kuala Lumpur.

Keywords: Financial assets; Malaysians; Risk tolerance

1. INTRODUCTION

Risk pervades everyday life of human being. Risk, whether in form of the weather, price fluctuations, or illness, has been part of human society. Any definition of risk is likely to carry an element of subjectivity, depending upon the nature of the risk and to what it is applied. As such, there is no all encompassing definition of risk. The Royal Society (1983), for example, view risk as the probability “…that a particular adverse event occurs during a stated period of time, or results from a particular challenge”. Smith (1999) defines risk as a decision expressed by a range or possible outcomes with attached probabilities. When there is a range or possible outcomes but no assumed probabilities, there is only uncertainty. Hertz and Thomas (1984) have suggested that “…risk means uncertainty and the results of uncertainty….risk refers to a lack of predictability about problem structure, outcomes or consequences in a decision or planning situation”.

♣ Corresponding Author: Professor, Department of Economics, Faculty of Economics and Management Sciences, International Islamic University Malaysia, Jalan Gombak, 53100 Kuala Lumpur, Malaysia. Tel: 603-6196 4626, e-mail: [email protected].

2 Determinants of Risk Tolerance on Financial Asset Ownership: A Case of Malaysia

Beside the fact that risk is always postulated as the philosophy concerned with the understanding of the nature of harm associated with the hazard (The Royal Society, 1983), risk is often associated with many beneficial economic ventures especially for households. Households have variety of choices in allocating their savings/money in different types of financial forms. The choice could range from the lowest risk to the highest risk types of financial forms. This choice also implies different levels of risk tolerance among individuals. Some households are more risk averse than the others. The challenge for households who opt for risky financial assets, however, is basically how to manage the risks. In other words, households have to learn how to live with risk not through blithe acceptance but through managing risk by mitigating its consequences. Risk could be managed through precautionary accumulation of buffer stocks and savings, which can be drawn during times of economic hardship. This is called self-insurance. Or it could be managed through cross-insurance, which refers to implicit or explicit arrangements for households with positive income shocks to send resources to households with negative income shocks (Ariyapruchya et.al, n.d)

Knowing the fact that the propensity of households to diversify their portfolios is very relevant for policymakers and financial industry, it is crucial to know whether risk aversion is indeed the dominating factor in a household’s financial choices. The Markets in Financial Instruments Directive (MiFiD) of the European Commission, for example, requires financial advisors to identify customer risk preferences and to customize their advice according to these preferences (European Commission, 2006). Typically, this identification takes place by way of self-disclosure of the individual’s risk attitude. In the theoretical literature, it is generally thought that an investor with high risk aversion will maintain a more diversified portfolio in order to minimize the variance of returns (e.g. Friend and Blume (1975) and Morin and Suarez (1983)). However, empirical studies do not always support this view. For example, Campbell, Chan and Viceira (2003) find that demand for risky assets is a positive hump-shaped function of risk tolerance. Fellner and Maciejovsky (2007) show that self-declared risk tolerance has a positive effect on willingness to diversify into risky assets. Moreover, some studies investigating the question of how households diversify argue that classical portfolio theory is inapplicable in this context1

Our study intends to shed new light on this issue by exploring individual’s risk attitude among Malaysians using data obtained from a survey. Additionally, we investigate the factors contribute to this financial risk attitude or risk tolerance. The main analysis is based on ordinary least square method of regression using level of financial risk tolerance as the dependent variable and socio–economic factors as regressors. Risk tolerance is measured in two ways: firstly, it is defined as the proportion of risky assets to total financial assets owned, and secondly, from the response to the question, “Which of these statements best describes the amount of financial risk that you are willing to take when you save or make investment?” The higher the proportion of risky assets owned, the higher is the level of risk-taking. The results show that younger respondents are willing to take more risk, compared to older ones. In addition, males compared to females, those with higher level of education and those in non-

1 Guiso, Haliassos and Jappelli (2002), p.2.

3Jarita Duasa and Selamah Abdullah Yusof

public sector are risk-takers in financial strategy. Malays are found to be more risk averse than Chinese and those in Kedah are more risk averse than those in KL.

The remainder of the paper is organized as follows. In the next section, we review the existing empirical literature on determinants of households’ financial portfolios. The second section describes the data, methodology and variables used. The fourth section presents the results and section five concludes with policy recommendation.

2. LITERATURE REVIEW

Academic research on the determinants of portfolio diversification is not new and it began with the mean-variance analysis of Markowitz (1952), who showed how investors would select assets if they cared only about the mean and variance of portfolio returns. In this context, Markowitz theorized that risk aversion would play a major role in determining investment decisions. The prediction with respect of portfolio diversification is that investors with high risk aversion will hold more diversified portfolios in order to minimize the risk associated with variance of returns.

Empirical research on the issue begins with studies by Friend and Blume (1975) and Morin and Suarez (1983) who, in line with predictions of classical portfolio theory, hypothesize that risk aversion is positively related to the level of diversification. Similarly, Gomes and Michaelides (2005) argue that more risk-averse people will have more diversified portfolios. Their explanation of this relationship, however, differs from classical mean-variance argumentation. They show that risk-prone households accumulate very little wealth and, correspondingly, most of them do not hold stocks. In contrast, more risk-averse investors achieve higher wealth levels and, therefore, have a stronger incentive to pay the market entry costs and acquire more assets. In contrast, King and Leape (1998) find that risk-averse individuals are more likely to limit their portfolios to safe assets, such as saving accounts and government bonds. Correspondingly, they are less likely to diversify into risky assets. Campbell et al. (2003) argue that demand for stocks is a hump-shaped function of risk tolerance. Demand is strongly positive at intermediate levels of risk tolerance, but negative for extremely risk-averse and extremely risk-loving investors. The authors explain this idea by noting that stocks can be used to hedge against the fluctuations in their own future returns. This hedging feature should be attractive for investors with intermediate levels of risk aversion, forming the middle of the demand “hump.” On either side of this hump are the very conservative investors, who tend to avoid any risk, and the extremely risk-tolerant investors, who have little interest in hedging intertemporally.

Despite the role of risk aversion, it is not the sole determinant of investment behavior. There is a wide agreement in the empirical literature that the socioeconomic and demographic characteristics of investors also have a significant influence on portfolio decisions. In particular, Uhler and Cragg (1971) find that differences in income, age, and education explain a great deal of the variation in the number of different financial assets held by U.S. households. Evidence from a wide variety of other countries supports this finding, such as Henry, Odonnat and Ricart (1992) on France; Hochguertel, Alessie and Van Soest (1997) and Alessie, Hochguertel and

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Van Soest (2000) on Netherlands; Guiso and Jappelli (2000) on Italy; Banks and Smith (2000) and Burton (2001) on the UK; and Himmelreicher (1998) on Germany. One of the studies by Sunden and Surette (1998) on workers suggests that there exist gender differences in the allocation of assets in retirement savings plans. Using data from the 1992 and 1995 Surveys of Consumer Finances, they examine whether workers differ systematically by gender in the allocation of assets in a defined contribution plans. They state that previous researchers have reported that many workers tend to invest their retirement assets too conservatively, and in particular that women are less likely than men to invest in risky assets such as stocks. In the presence of an equity premium, a lower propensity by women to invest in stocks could translate into large differences in the accumulation of financial wealth for retirement. The authors establish that gender differences in investment decisions exist, though they are more complicated than previous studies have suggested. They show that these differences are not completely explained by differences in individual or household characteristics.

Wealth also plays a significant role in a household asset allocation. As found by Brunnermeier and Nagel (2006), changes in liquid wealth have a significant positive effect on the probability of stock market entry and a negative effect on the probability of exit. However, changes in liquid wealth essentially play no role in explaining changes in asset allocation for households that participate in the stock market.

The level of risk-taking also depends on the age and wealth of an individual. Milligan (2004) observes that empirical evidence across many countries suggests the age profile of risk-bearing tends to follow a hump shape pattern, with risk tolerance first increasing with age then decreasing in later life. The evidence is stronger on the extensive margin (the ownership rate) than on the intensive margin (portfolio shares). The share of wealth held in financial assets increases with age, particularly in bank accounts. In addition, Milligan found that direct holding of stocks goes down with age while holding of fixed income securities goes up. This may suggest increasing preferences for liquidity and increasing risk aversion with age.

Riley and Chow (1992) compares investment risk aversion indexes from actual asset allocations. A model developed to examine the hypothesized relationships between risk tolerance and given variables indicates that relative risk aversion decreases as one rises above the poverty level and decreases significantly for the very wealthy. It also decreases with age, but only up to a point. The study looked at four classes of assets –personal property, real estate, bonds and risky assets. Risk aversion appears to decrease with education. However, education, income and wealth are all highly correlated, so the relationship may be a function of wealth rather than education. The differences in risk aversion and marital status, race and gender, however, appear to be small.

Another study by Gomes and Michaelides (2005) shows that a life-cycle model with realistically calibrated uninsurable labor income risk and moderate risk aversion can simultaneously match stock market participation rates and asset allocation decisions conditional on participation. They find that households with low risk aversion smooth earnings shocks with a small buffer stock of assets, and consequently most of them (optimally) never invest in equities. Therefore,

Determinants of Risk Tolerance on Financial Asset Ownership: A Case of Malaysia

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the marginal stockholders are (endogenously) more risk averse, and as a result they do not invest their portfolios fully in stocks. Therefore, one definitely needs to control for the socioeconomic profile of households when analyzing their portfolio decisions.

3. DATA AND METHODOLOGY

Given all these findings, the present model used in our study will attempt to capture the important factors used in earlier studies as well as additional factors which are thought relevant on the present data used, in particular, data on Malaysians.

3.1. Data

The sample in this study consisted of 2653 randomly selected respondents at selected companies in five states – Kedah, Selangor, Johor, Kelantan, Sarawak, and Kuala Lumpur. The survey conducted from November, 2007 to February, 2008 for a bigger project on determining the expenditure and investment patterns of Malaysians. A random selection of companies/organizations was made to include private and public organizations from every economic sector in the six mentioned states. Basically, the respondents were chosen randomly regardless of age, gender, marital status, ethnic group, occupation and level of education.

In specific, the participants are Malaysians residing in various parts of the country and representing various segments of the society. Due to the unavailability of a complete sampling frame of all Malaysians, we restrict the selection of sample to employed Malaysians only. In this regard, we apply several stages of stratification. First, several states or territory are chosen to represent various regions in Malaysia. They are Kedah, Kelantan, Selangor, Johor, Sarawak and Kuala Lumpur. Next, within each state, with the exception of Sarawak, at least one urban (or more urban) and one rural (or less urban) area are selected. The specific areas are given below:

i. Kedah – representing northern peninsular Malaysia. The two areas selected are Alor Setar and Kulim.

ii. Kelantan – representing eastern peninsular Malaysia. The two areas selected are Kota Bharu and Pasir Mas.

iii. Selangor – representing central peninsular Malaysia. The areas selected are Petaling Jaya, Shah Alam, Kelang, Subang Jaya, Seri Kembangan, Puchong, Kuala Selangor and Sungai Besar.

iv. Johor – representing southern peninsular Malaysia. The areas included are Johor Bahru, Muar and Batu Pahat.

v. Sarawak – representing east Malaysia; and the area is Kuching.

vi. Kuala Lumpur2 – the capital city of Malaysia.

2 Hereafter referred to as KL.

Jarita Duasa and Selamah Abdullah Yusof

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We further stratify according to economic sectors and selections are made within the economic sectors. Based on the proportion of population of the various states, of urban and rural, and of various sectors in the economy, the target size of each sub-sample is determined accordingly. For the non-agriculture private sector, most of the companies are randomly selected from the list given in the Yellow Pages (http://www.yellowpages.com.my). Others are randomly chosen from Times Business Directory of Malaysia 20043. In the case of the public sector, a random selection of the organizations is made from a list of government institutions located in the selected areas. For the agricultural sector, a list of associations of farmers and another of fishermen are obtained from various sources. We first randomly choose the associations and then randomly select members of the chosen associations. Two sets of identical questionnaires are prepared for data collection. One is in the Malay language and the other is in English.

Majority of respondents are Malays (88.3 percent), while the rest are made up of Chinese (7.7 percent), Indians (2.3 percent), and other (1.7 percent) ethnicities. Although the percentage for Malays is disproportionately high, we suspect that the actual percentage may be lower since a significant number of respondents did not disclose their ethnicity. The sampling procedure was constructed in such a way that the ethnic groups would be proportionately represented. The population estimates for 2007 are 66.7% Malay, 24.9% Chinese, 7.5% Indians and 1.2% others (Malaysia, 2008).

3.2. Data Analysis Techniques

This study utilizes several methods to analyze the results. Descriptive measures such as frequencies, proportions and mean are used to provide a general summary of the findings. We also use t-test to test a hypothesis that a variable is equal to a specific value. Correlation analysis is applied to determine the existence of significant relationships between variables. For more in-depth analyses, ordinary least squares (OLS) regressions are estimated to determine factors that may have an impact on a particular variable.

3.3. Measurement Of Variables

For the purpose of analyzing using the above methods, several variables are formed as follows: 3.3.1. Socio-economic factors

a. Economic sectors

Respondents are categorized according to the economic sector in which the company they worked for belongs to, which are: (i) agriculture and fishing; (ii) manufacturing; (iii) electricity, gas or water; (iv) construction; (v) wholesale trade, retail trade, restaurant and hotel; (vi) transport and communication; (vii) financing, insurance, real estate and business services;

3 We rely more on the Yellow Pages for the selection since the information of the companies listed are more current. Information on companies listed in Times Business Directory are somewhat outdated.


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and (viii) government. We construct dummy variables also for the various sectors, which are govt (government), manuf (manufacturing), electric/gas/water, construction, wholesale/ retail/rest/hotel (wholesale trade, retail trade, restaurant and hotel), transport/comm. (transport and communication), finance/real estate/business service (finance, real estate and business services) and agric (agriculture).

b. Currently married

A binary number is used and assigned the value of one for respondents who are currently married and zero, otherwise.

c. Household size

Total household size is defined as the total number of persons living in the same home. We further subdivide the total size into three: (i) the number of young household members is equal to the number of household members aged between 0 to 17 years old; (ii) the number of working age household members are the number of members between 18 to 64 years old; and (iii) the number of aged household members are the number of members aged 65 and above.

d. Income

Apart from monthly income obtained from main occupation, total monthly income is computed, which is equal to the income obtained from the main occupation as well as from other sources. In addition, for respondents who are married, we also compute the total household monthly income which is defined as the sum of the total monthly income of the respondent and income of the spouse.

e. Gender

Gender is a dummy variable equal to 1 if the respondent is a male, and 0 otherwise.

f. Ethnicity

In this analysis, ethnicity is restricted to the three main ethnic groups in Malaysia, which are Malay, Chinese and Indian. Thus, two dummy variables are constructed: (i) Malay is equal to 1 if the respondent is Malay, 0 otherwise; and (ii) Indian is equal to 1 if the respondent is Indian, 0 otherwise. Chinese is selected as the base category.

g. State or Territory

The base category is KL. Thus, five dummy variables are constructed to represent the five states – Kedah, Kelantan, Selangor, Johor and Sarawak.

h. Population

The areas included in the sample are also categorized in terms of their population density. Pasir Mas, Kuala Selangor and Sungai Besar are classified as areas with populations less


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than 100,000; Kulim, Batu Pahat and Muar as areas with populations between 100,000 and 200,000; and Alor Setar and Kota Bharu having populations between 200,000 and 500,000. The other areas belong in the category of populations more than 500,0004.

i. Financial assets

Total financial assets are the total value (in ringgit) of all financial assets owned. Proportion of risky assets owned is the ratio of the value of stocks, shares and bonds owned to total value of all financial assets owned.

3.3.2. Risk tolerance

Risk tolerance is measured in two ways: firstly, it is defined as the proportion of risky assets to total financial assets owned, and secondly, from the response to the question, “Which of these statements best describes the amount of financial risk that you are willing to take when you save or make investment?” The higher the proportion of risky assets owned, the higher is the level of risk-taking. For the second measure, the response to the question can be “… take substantial risks …” (score of 1) or “… not willing to take any financial risks” (score of 5), or any response in between. Thus, a lower score implies a higher level of risk taking.

The first measure of risk tolerance is based on actual behavior, while the second is based on reported behavior by the respondent. Both methods have their advantages and disadvantages. Any measurement based on actual behavior is usually more accurate than that based on what is reported, provided the information required is given accurately by the respondents. However, not all respondents provided this information, and for those who did, the information given may not be accurate. Thus, the findings using this measure should be interpreted with caution.

The second method provides an alternative way to measure risk tolerance especially for those who did not complete the questions on financial assets. For others who responded to both parts of the questionnaire, we found that the two measures are highly correlated (p-value=0.014). Thus, either method provides an indicator of a person’s level of tolerance to risk.

4. EMPIRICAL FINDINGS AND ANALYSIS

This section is divided into two. One, on the analysis of the data obtained descriptively and second, the analysis using OLS method of estimation on selected data for determinants of risk tolerance.

4.1. Descriptive Analysis

In this study, respondents are asked on their ownership of financial assets. The financial assets listed in the survey question include cash, saving account, current account, fixed deposits, cash value of insurance, company stocks/shares, mutual fund/unit trust, bond fund, Employees

4 The populations for the various towns and cities selected are obtained from http://population.wn.com/country/Malaysia, retrieved on 10 July, 2007.


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Provident Fund (EPF), Tabung Haji (Pilgrimage Fund), retirement fund and other financial assets. Table 1 below displays the percentage of respondents who own the various types of financial assets.

Table 1: Ownership of Financial Assets Frequency Percent

Cash 1574 59.3Savings account 1791 82.4Current account 541 20.4Fixed deposits 270 10.2Cash value of insurance 750 28.3Company stocks/shares 210 7.9Mutual fund/unit trust 751 28.3Bond fund 36 1.4Employee Provisional Fund (EPF) 1434 54.1Tabung Haji (Pilgrimage Fund) 862 32.5Retirement fund 156 5.9Others 113 4.3

Majority of the respondents prefer to keep their money in liquid assets such as savings account and cash. These assets have the lowest level of risk compared to others. Risky assets such as company stocks or shares and bonds are less attractive forms of financial assets and the numbers of respondents who own these assets are less than 10 percent.

As of the amount of financial assets owned by the respondents, Table 2 displays the mean, minimum and maximum amounts of each type of financial asset. On average, larger amounts are held in insurance, EPF and retirement fund accounts. Bonds, although owned by relatively

Table 2: Amount of Financial Assets

Cash 1100 10 200,000 1,838.33Saving account 1252 10 140,000 6,235.46Current account 366 30 250,000 11,302.26Fixed deposits 193 50 200,000 15,320.08Cash value of insurance 413 30 500,000 41,618.91Company stocks/shares 152 100 300,000 15,072.37Mutual fund/unit trust 555 20 358,000 14,800.96Bond fund 27 50 200,000 26,594.44EPF 887 33 300,000 30,409.59Tabung Haji 602 10 100,000 4,567.90Retirement fund 77 50 200,000 30,283.67Other financial assets 57 100 10,000,000 192,985.09

Mean (RM)

Maximum (RM)

Minimum (RM)

n


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few respondents, have a mean value of about RM 27,000, which is relatively high compared to other financial assets. The amount for the other category is very large, with a mean of almost RM 200,000. Those who responded that their financial assets fall into this category may probably not wish to disclose which specific types of financial assets they invest in. In general, the results suggest that the respondents are more inclined to keep their money in the form of non-risky assets, and less likely in risky assets. However, caution must be exercised in generalizing the results since a significant number of respondents do not disclose the value of the financial assets they owned.

4.2. Determinants Of Risky Financial Assets Ownership

Further analysis is done on the relationship of respondents’ income and age with amount owned in risky and non-risky assets. We first group financial assets into two categories, namely, risky assets and non-risky assets. Risky assets include company stocks or shares and bonds, whereas non-risky assets include other types of financial assets in the list given. The proportions of risky and non-risky assets out of total assets are obtained and correlation coefficients of these proportions with income and age variables are then computed, controlling for state, population size, ethnicity and employment sector.

Table 3: Partial Correlation Coefficients

Age 0.252*** -0.079* 0.075Main income 0.453*** -0.123*** 0.117**

Prop.of Risky Assets

Prop.of Non-risky Assets

Total Financial Assets

Notes: *, **, *** significant at 10%, 5% and 1% levels, respectively.

The results in Table 3 show that income from main occupation is significant and negatively related to the proportion of non-risky assets but positively to risky assets. This indicates that high-income earners are more willing to keep their money in risky assets and less on non-risky assets compared to low-income earners. Looking at the significant relationship between age and proportion of non-risky assets, it tells us that age matters in the selection of financial assets. The negative coefficient implies that younger respondents hold a bigger proportion in non-risky assets compared to older respondents. The smaller fraction of non-risky assets held by older respondents probably because they are more stable financially and, thus, can afford to apportion some of their investment in risky assets. These findings on the differences in income, age, and education that explain a great deal of the variation in the number of different financial assets held by U.S. households were also found by Uhler and Cragg (1971). Similar evidence obtained from a wide variety of other countries supports this finding, such as Henry, Odonnat and Ricart (1992) on France; Hochguertel, Alessie and Van Soest (1997) and Alessie, Hochguertel and Van Soest (2000) on Netherlands; Guiso and Jappelli (2000) on Italy; Banks and Smith (2000) and Burton (2001) on the UK; and Himmelreicher (1998) on Germany.


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As mentioned earlier, the use of the proportion amount of risky to total assets owned as a measure of risk tolerance might not be reliable if not all respondents provided this information and the information given may not be accurate. For this reason, we proceed with the second measurement of risk tolerance in which respondents are asked on their level of risk tolerance. Using this measurement, it is found that individuals are risk averse, in general. They prefer to take less than average financial risk. Only 13.1 percent indicate that they are willing to take substantial risks, and 19.5 percent are willing to take above average financial risks. The findings also show that 39.1 percent of the respondents are not willing to take any financial risk. Using the other measure of risk-averseness, that is, the proportion of risky assets to total financial assets holding, we find that the mean for the sample is 1.51 percent, which is a very low value. These two results are consistent with one another, and as mentioned earlier, there is a significant positive correlation between these two measures.

Subsequent analyses use the measure of risk tolerance based on the response to the question on risk tolerance (self reported behavior) rather than the proportion of actual holdings of risky assets to total financial assets5.

5 See the earlier discussion on the two measures.

6 For the question to measure risk-tolerance there are five responses, which are assigned the value of 1 to 5. A low value indicates the respondent is a risk-taker, while a high value indicates a risk-averse person. The value of 3 is considered to be risk-neutral. Thus we conducted t-tests on whether the true mean value is equal to 3, or different (less than, or more than) from 3 for each ethnic group, as well as for males and females, and for each educational groups.

Table 4: Level of Risk Tolerance

Level of risk-toleranceMeanEthnicity Malay (1604) 3.5318 Risk-averse Chinese (155) 2.9161 Risk-neutral Indian (68) 3.1324 Risk-neutralGender Male (1119) 3.3360 Risk-averse Female (1303) 3.4658 Risk-averseHighest No formal education (34) 4.4412 Risk-averseeducation Primary school (154) 4.2857 Risk-averselevel Secondary school up to form 3 (110) 3.8455 Risk-averse Secondary school up to form 5 (767) 3.4785 Risk-averse Diploma/STPM (827) 3.3216 Risk-averse Bachelor degree (415) 3.0434 Risk-neutral Masters degree (71) 2.9577 Risk-neutral PhD degree (10) 2.9000 Risk-neutral

Notes: (.) indicates number of observations

General comparisons between ethnic groups indicate that Malays are risk averse, while Indians and Chinese are risk neutral6. The study also finds that both males and females are risk-averse, but females are more risk-averse than males. The results also show that the level of risk-


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aversion decreases as the level of formal education one has increases. Those will less formal education tend to be risk-averse, while those with more education are risk-neutral. See table 4 for a summary of the results.

Further analysis is done to determine the impact of socio-economic factors on risk-tolerance using OLS method of regression. Estimates from the regression model (Table 5) show that younger respondents are willing to take more risk, compared to older ones7. Similar results were found by Milligan (2004) which suggests the age profile of risk-bearing tends to follow a hump shape pattern, with risk tolerance first increasing with age then decreasing in later life. Consistent with the earlier findings by Riley and Chow (1992) and Sunden and Surette (1998), those with more formal education or males take more risk than their counterparts. Similar to findings by Riley and Chow (1992), we also do not find any significant difference in level of risk taking between married and unmarried respondents. Though wealth or income level is expected to contribute to level of financial risk, we found it is not among the significant factors in the analysis.

A significant contribution made in this study is that, in the case of Malaysian, it is found that there is no difference between Chinese and Indians on level of risk taking, but Malays are less willing to take risk compared to Chinese. The results were expected as Malays are well-known as risk-averse group in the society. The former Prime Minister, Abdullah Badawi was once stated that the Malays should develop a “new mindset and a fresh spirit” which are based on education and training in science and technology, and an approach that was not premised on “excessive reliance on the government”. He also urged diversification and risk-taking and stressed the necessity of developing a new culture of “excellence based on merit and performance among Bumiputeras” (Bennet, 2005)

In addition, the results also indicate that Kedahans are more risk averse compared to those in Kuala Lumpur. Finally, those in construction, wholesale/ retail/rest/hotel (wholesale trade, retail trade, restaurant and hotel), transport/comm. (transport and communication) and finance/real estate/bus. serv (finance, real estate and business services) sectors are willing to take more risk than those in government sector.

Technically, while using this method of regression, we found the existence of heteroskedasticity problem in residuals in the original regression. However, it then be corrected for heteroskedasticity problem using Newey-West test. Overall, the regression is free from a problem serial correlation in residuals. However, the residuals are not normally distributed as shown by Jarque-Bera statistic. Due to this caveat, the inferences made from the estimates, again, should be interpreted with caution.

7 The present study attempts to include variable age square into the model. However, the estimate is not significant. The variable is then dropped from the model.


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Table 5: Regressions of Financial Risk

(Constant) 3.916*** (2.234)

population 0.034 (0.633)

age 0.009*** (2.234)

gender -0.220*** (-2.828)

currently married 0.159* (1.722)

highest education level -0.156*** (-3.533)

log(main income) -0.039 (-0.540)

household size -0.022 (-1.396)

dummy malay 0.233** (2.274)

dummy indian 0.176 (0.862)

dummy kelantan 0.210 (1.496)

dummy kedah 0.335** (2.327)

dummy selangor 0.043 (0.370)

dummy johor -0.218 (-1.599)

dummy sarawak 0.119 (0.745)

dummy agricultural sector 0.021 (0.116)dummy construction -0.413** (-2.534)

Dependent variable: Level of financial riskIndependent variable


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dummy electricity/gas/water -0.468 (-1.212)

dummy financing/insurance -0.554*** (-4.579)

dummy wholesale trade/retail -0.313*** (-3.049)

dummy manufacturing -0.182 (-0.581)

dummy transport/communication -0.445** (-2.506)

Adjusted R squared 0.087F-statistic 8.732***

Diagnostic tests: Jarque-Bera Stat. (normality) 95.733***Breusch-Godfrey F-stat. (serial correlation) 0.312

Notes: *, **, *** significant at 10%, 5% and 1% levels, respectively. T-statistic is in the parentheses.

5. CONCLUSION AND POLICY RECOMMENDATION

This study aims to analyze the determinants of financial tolerance among Malaysian. Using survey data on a sample of Malaysians, the preliminary analyses found that majority of the respondents prefer to keep their money in liquid assets, such as in saving account and cash. These assets definitely have the lowest level of risk compared to other forms of assets.

A further analysis, using OLS regression reveals several significant determinants of risk tolerance among the sample respondents. In specific, risk tolerance is higher among the young, males and those with higher level of education. Most of these findings seemed to be consistent with those found in previous studies. In addition, the study also finds that Malays are more risk averse than Chinese, those in Kedah are more risk averse than those in Kuala Lumpur and government servants are more risk averse than those in non-government sectors.

Base on the results obtained, the study proposes several policy recommendations. First, the sensitivity of individuals/households to loss suggests that promoting financial allocation with potential gains should be combined with insurance or other supporting measures. Probably, this could be temporary. One success has convinced the households that allocating funds in risky assets are viable, risk aversion will decline.

Table 5: Regressions of Financial Risk (cont.)

Dependent variable: Level of financial riskIndependent variable


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Second, individuals are willing to take on more risk in exchange for higher returns. While early success seems to be important, they should also be allowed to accumulate assets before they are challenged or tempted to take on more risky ventures. This is especially important to young individuals who are just started their working life and hardly possess basic assets.

Third, there is a need for basic education about risks and alternatives among Malaysians especially among the Malays, those with less formal education and even females. Government may come under growing public pressure to intervene in support of the household sector, such as, in the form of added public expenditure pressures. The objective may be to strengthen basic financial education programs at school and at workplace. Though it is true that providing information could probably be sufficient, household often make limited use of financial information provided. Information cannot substitute for greater household financial literacy.

Finally, in the long run, of course, the development of private market to spread risk is crucial. Indeed, the development of credit and insurance markets is the most certain way to reduce levels of risk aversion among households in the country.

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