THE CORRELATION BETWEEN FINANCIAL LITERACY AND PERSONAL SAVING BEHAVIOR IN VIETNAM

Van Tuong Nguyen1+ --- Minh Duc Doan2

1 Senior Lecturer and Head of Monetary Department of Banking Academy, Vietnam.
2 Banking and Finance in Economic and Business University, Vietnam.

ABSTRACT

This study investigated factors impacting personal saving behavior in Vietnam. By using 692 respondents from a 63-province survey, the binary regression results showed  that financial literacy, finance major, married status, financial attitudes, and advanced financial behavior were factors positively and significantly influencing individual saving behavior while gender, student, and basic financial behavior were factors negatively and significantly impacting saving behavior. It highlighted the result that, women had a higher probability of saving than men. The implications for financial education in Vietnam from the policy makers as well as personal perspective are also discussed.

Keywords:Financial literacy, Financial inclusion, Financial behavior, Saving behavior, Financial education, Personal saving.

ARTICLE HISTORY: Received: 21 February 2020, Revised: 3 April 2020, Accepted: 7 May 2020, Published: 1 June 2020

Contribution/ Originality: This study contributes to the existing literature on the impact of financial literacy on financial behaviors. The study used 692 observations across a 63-province-level survey in binary logistic regression. The interesting result is that, in Vietnam, significantly, women were more frequent than men in saving. It is the first study to contribute findings consistent with the Vietnamese culture where women are the main financial decision makers in Vietnamese families.

1. INTRODUCTION

Asian Development Bank Institute (ADBI) 2017 and OECD’s 2016 studies have shown that financial literacy has a positive impact on personal saving behavior, but there is very little research on this issue in low-income or lower-middle income economies. These studies found that Vietnam’s financial inclusion index was significantly lower comparing to high-income countries, and that the use of formal savings tools was also quite low (ADBI, 2017). ADBI’s study showed that the correlation between financial literacy and general education also affects saving behavior. Yet this study had not discovered the relationships between other factors like other countries’ studies, such as comparing men and women’s effects in individual saving behavior, which will be described below.

To consolidate and extend previous studies, and to effectively, specifically support interdisciplinary policies (financial and education), this study analyzed the impact of financial literacy and related factors on Vietnamese’ individual saving behavior. As the result, policy recommendations can be made to harmoniously plan a financial literacy education strategy with current monetary and banking policies, as well as other development policies for Vietnam.

2. ESSENTIAL RESEARCH SITUATION

2017 ADBI’s study overview showed that previous studies on financial literacy focused on two main fields: (i) factors affecting financial literacy, including age, sex, academic level, profession; and, (ii) the impact of financial literacy on individual financial behavior, including saving behavior, credit usage and retirement planning behaviors.

2.1. Financial Literacy: Concepts and Measurement

(i) Concepts

In 2013, World Bank (WB) mentioned a quite broad implication of financial literacy as “financial management capacity” of each individual member of society. Authors like Hogarth (2002); Remund (2010); Mahdzan and Tabiani (2013); Lusardi and Mitchell (2014); OECD/INFE (2016)  have defined a more clarified meaning of the term: that financial literacy is formed through personal experience, expertise, and needs, and has a positive impact on individual involvement in the financial services market. Some organizations have defined financial literacy with universal admittance, such as OECD’s International Gateway for Financial Education (OECD/INFE). They defined financial literacy as

"...a combination of awareness, knowledge, skill, attitude, and behavior necessary to make sound financial decisions and ultimately achieve individual financial well-being."
Within this study, the authors’ summary about this term is: Financial literacy describes the status of knowledge and essential skill to make decision with potential financial consequence awareness, including (i) basic arithmetic skill; (ii) understanding of the benefit and risk tied with a specific financial decision; (iii) understanding of basic financial concepts; (iv) the ability of consulting, making accurate questions and understanding expert’s basic advisory.

(ii) Measuring Financial Literacy

- Financial Literacy Structure:  Researchers have always been developing methods for measuring financial literacy based on empirical tests, such as the Jumpstart Coalition for Personal Financial Literacy in 1997 (Mandell, 2009). Lusardi. and Mitchell (2006) also included a questionnaire to measure financial literacy in health and retirement study on Americans aged 50-and-above from 2004. These studies provided a baseline for many subsequent studies.

Three core questions in initial surveying were designed to evaluate the understanding on some significant financial concepts like compound interest, operating margin and risk diversification. Subsequent studies, like OECD/INFE (2016); OECD/INFE (2015) were constructed on this baseline and integrated questions about financial attitude, financial behavior and financial experiences. According to OECD/INFE (2016) there are three components similar to the basic structure of an individual's capacity, including: (i) Financial Knowledge; (ii) Financial Behavior; (iii) Financial attitude; the components and combination oftotal points of the three components with a total value ranging from 1 to 21 will give us an assessment of the financial literacy.

- Financial Literacy Measurement: Regarding questions or tools for measuring "financial literacy", OECD/INFE (2015) has proposed 19 key questions. This set of questions implicitly requires a combination of financial knowledge, attitude, and behavior. In addition to the core questions, the questionnaire also consisted of a series of demographic questions, including gender, age, family status, geographic location, occupation, and income. These questions were selected according to three criteria: (+) questions that have been tested and proven to be high quality and unbiased; (+) questions have been used in national surveys; (+) questions related to the concept being measured. The questions were modified to meet the requirements and suitable for Vietnamese people and economic situations.

2.2. Financial Literacy and Financial Behavior 

Financial literacy can be determined by many factors such as gender, age, career, education, financial socialization agents (Supinah, Japang, Amin, & Hwa, 2016) And then financial literacy impact individual’s saving behavior, many studies were conducted to prove this.

Lusardi and Mitchell (2014) gave an overview on previous studies about relating factors and showed that the age cycle and financial literacy have a tendency to form a hump-shaped pattern. The financial literacy level tended to increase with age, then periodically declines as an individual grew older. However elderly people with high confidence in their financial literacy level did not suffer from this decline. In general, women have a lower financial literacy level than men, with the cause still left open, although women still have the tendency to admit they do not know the answers, compared to men. One’s higher literacy compared to their parents also correlates with more positive financial literacy. These findings were once more affirmed in the OECD/INFE (2016) analysis of survey results conducted in 30 countries.

Lusardi and Mitchell (2008) argued that the financial literacy topic is particularly interesting for women who tend to live longer than men but have shorter work experience and lower earnings. By using the 785 respondents in the 2004 HRS module, their study points out that, in the United States,  only about 29% of the surveyed women can correctly answer all three financial literacy level evaluating questions and that the older women show very low levels of financial literacy and there is a large number of womenthat have not made any retirement plans. Financial literacy and planning are closely associated: women who display higher financial literacy are more likely to plan and be successful planners. These findings raise concerns about the ability of women to make sound saving and investment decisions over a long retirement period.

Fonseca, Mullen, Zamarro, and Zissimopoulos (2012) made the study by using data from the RAND American Life Panel (ALP). The ALP consists of over 2,500 respondents ages 18 and over who are interviewed periodically over the Internet. Their research has shown that financial illiteracy is widespread among women, and that many women are unfamiliar with even the most basic economic concepts needed to make saving and investment decisions. They argued that the gender gap in financial literacy may contribute to the differential levels of retirement preparedness between women and men.

Ernst & Young (EY, 2017) points out that women’s economic power and financial independence are growing rapidly around the world, making them an important market for the wealth management industry. However, many women view the investment industry as male-oriented and unwelcoming. It is concerning that, women commonly feel the wealth management industry is unwelcoming, patronizing, full of jargon and male dominated. A lot of men are uncomfortable with women wanting to work and earn and choose not to have a family or have other alternative lifestyle choices and a lot of men are often unaware of what a woman’s life involves in terms of what they are required to spend on and save for etc. Globally, 67% of female investors feel their wealth manager or private banker misunderstands their goals or cannot empathize with their lifestyle. So it is not surprising that they misunderstand but it is up to them not the women to make an effort to understand their clients, it relates to customer service.

* Financial Literacy Level and Individual Saving Behavior

Bernheim (1995); Bernheim (1998) pointed out efforts to measure financial literacy with other economy-financial behaviors. In America, correspond with higher diversification, Americans are interested more in their financial plan for retirement, especially after 2008-2009 global financial crisis. Hilgert, Hogarth and Beverly’s study in 2003 found close correlation between financial literacy and daily financial management skill, while Christelis, Jappelli, and Padula (2010); Van Rooij, Lusardi, and Alessie (2011) found that people with higher financial literacy and the ability to calculate usually take part in financial market, stock trading and have preventive savings. People with higher financial literacy can successfully realize their retirement plan and accumulate more (Lusardi & Mitchell, 2011). These findings were proven in Malaysia.

Regarding family loans, Moore’s study in 2003 pointed out that people with lower financial literacy levels have a higher probability of taking out more expensive mortgage loans. Campbell (2006) showed that people with low income and low education have less of a chance to restructure their mortgage during low interest rate periods. Stango and Zinman (2009) showed that people with limited arithmetic skills usually take out a loan more often and accumulate less.

* Personal Saving Behavior and Influencing Factors

* Personal Savings, National Savings, and Social Welfare
It is necessary to distinguish between saving behavior and personal savings results. Personal savings are the result of saving behavior and most likely reflecting personal financial management capacity that depends on many other things. In terms of personal savings related to the national economy and social welfare, there are some studies that show the following:

As a result, all studies together showed strict correlation between financial literacy and saving behavior. Higher financial literacy level, with people understanding, and being able to effectively establish and use financial services leads to stable savings and then contributes to the sustainable development of the economy.

2.3. Vietnam Related Studies

Regarding Vietnam, the most prominent study is ADBI’s study in 2017 because of its scale and range. The organization used OECD/INFE’s standardized survey tool about financial literacy for adults to conduct research in two relatively low-income economies: Cambodia and Vietnam. Through this, they analyzed the crucial factors on financial literacy, and the influence of financial literacy on financial behaviors in these two countries. Cambodia’s general total score (11.5) and Vietnam’s (12.0) are low ranking amongst 30 studied countries. However according to ADBI (2017), the score is considered normal regarding such low-income economies like Vietnam and Cambodia. Literacy, income, age, occupational status are the main decisive factors for the level of financial literacy. Both financial literacy and general education level have a positive effect on individual saving behavior.

3. RESEARCH METHODS AND RESULTS OF STATISTICAL HYPOTHESIS TESTING

3.1. Sampling

The author randomly selected 780 individuals from all provinces in Vietnam.700 questionnaires were distributed and 692 respondents provided answers (answer rate: 8.7%). Participants came from almost all provinces and economic regions (city, delta and mountainous area); and from all education levels, ages and gender.

3.2. Variables

3.2.1. Dependent Variable: Personal Saving Behavior

Personal saving behavior is considered a financial behavior on the basis that individuals have motivations and certain responsibilities for the future (social behavior) and, therefore, individuals often save rather than spending all their income; in other words that is the personal finance balancing ability. The research used saving regularity to measure personal saving behavior. Thus, the regularity of personal saving was in accordance with the frequency of saving variable or how frequent saving is undertaken to backup for uncertainty and for the future.

Subjects were questioned with a five point Likert scale of saving frequency namely: never, rarely, occasionally, often and very frequently. Behavioral thresholds were delineated with one group showing “saving behavior” when an individual carries out saving for the future from “occasionally” to “very frequently” (considered positive behavior) and one group who rarely or never saves for the future (limited or negative behavior). Thus it was possible to assign binary variables to these behaviors, in which the value of “1” was used when saving for the future was carried out from occasionally to very frequently while the value of “0” was assigned in cases where saving for the future was rarely or never carried out.

3.2.2. Independent Variables

These variables measured“financial literacy”, based on a numberof questions applied by Lusardi and Mitchell (2007a); Lusardi and Mitchell (2007b); Lusardi and Mitchell (2007c); Lusardi and Mitchell (2008). Financial literacy was divided into two groups of indicators inaccordance with the 02 corresponding levels: (+) Basic (Basic_Literacy); (+) Advanced (Adv_Literacy):

The research used the “FL_ps” variable as an inspection threshold: FL_ps got the value of 0 if the total financial literacy score was less than 50% of the total score (below average); FL_ps got the value of 1 when the total financial literacy score was equal or greater than the average (knowledgeable about finance, or above average score).

*Risk tolerance behavior (Risk_Tolerance). Risk tolerance behavior was measured from 1 to 5: from point of being completely unwilling to accept any financial risks to the extent of being willing to accept very high financial risks. High level corresponded to high risk tolerance and vice versa.

*Demographic variables: These variables are included in the detailed Table 1.

3.3. Research Hypothesis on Saving Behavior

The study predicts the relationship between dependent and independent variables according to Appendix 1; Variables to discover "information channels / knowledge source" to achieve "financial literacy level" from the average point or above are also included to test (conditional) in the model such as: from high school study, from radio, press and other materials.

Research hypotheses were developed based on the analysis framework as described in Figure 1.

Figure-1. Analysis framework: relationship between education, financial social environment, money attitudes, financial literacy and behaviors.

Source: Albeerdy and Gharleghi (2015).

Based on the research overview and the analysis framework, the study tested the following research hypotheses:
Hypothesis 1: Financial literacy has the same directional impact on personal saving behavior (+).
Hypothesis 2: Individual demographic characteristics that affect personal saving behavior.

Hypothesis 2 can be divided into:

  1. Age has a positive relationship to personal saving behavior (+).
  2. Different gender possess different personal saving behavior.
  3. The number of children has a positive relationship to personal saving behavior (+).
  4. The number of work-years has a positive relationship to personal saving behavior (+).
  5. The income level has a positive relationship to personal saving behavior (+).
  6. Education level has a positive relationship to personal saving behavior (+).
  7. The level of risk tolerance is inversely related to personal saving behavior (-).

3.4. Analysis Model

The study is based on econometric models assessing the impact of "people's knowledge about finance" on "saving behavior" (Effects of Financial Literacy on Saving Behavior) accordingto ADBI’s study (2017) (Peter J. Morgan & Long Q. Trinh, ADBI, 2017); and with reference to Mahdzan and Tabiani (2013) to determine the impact of financial literacy on personal saving behavior, the research will verify according to the following equation:
=                                     +                +                              +              +        (*)

In which: Saveiis a dummy variable, which takes the one “1” value if the individual carries out saving money for the future (savings) from occasionally to very frequently and if he or she rarely or never saves money for in the future, this variable takes the zero “0” value;

FLi measures financial literacy (point).
β1 measures impacts of financial literacy on personal savings.
Incomei is income of each person.
Xi is controlling vector.
i is each error.
Xi includes gender, age, marital status, occupation, number of children (note that in principle, we test every information collected and provide as much as possible if that ratio is statistically meaningful).
β0 is constant or free factor.
β2, β3 is correlation coefficients of impacts of income on savings; is error.

As mentioned above, since the dependent variable is a variable that takes two different values (dichotomous, duality), the research used the Binary Logistic analysis model. Binary Logistic analysis model uses binary regression or binary received value (obtain two values: 1 and 0). The test model was based on the theory to bring the impact factors (statistically significant, 5%) to personal saving behavior.

3.5. Testing Results and Results of Binary Logistic Regression Testing on Savings Behaviors

(a) Regression testing

References showed that the Wald test revealed that 08 variables in the model (Gend; Marr; Major; Student_y; FL_ps; AA; Bp and Bh with Sig respectively <0.05) had a statistically significant correlation with the dependent variable "Saving" with 95% confidence.

3.6. Binary Logistic Regression Results and Analysis  

According to information collected from 692 respondents the regression ratio test (Wald test) was conducted and showed that there were 7 variables (Gend; Marr; Major; Student_y; Bh; FL_ps; Rsk_behavior) with corresponding Sigs<0,05) with statistically meaningful correlation with the dependent variable " Saving Behavior" with 95% confidence.

Table-1. Variables in the equation.

 
 
B
S.E.
Wald
df
Sig.
Exp(B)
Step 1a
Gend
-,401
,194
4,289
1
,038
,670
 
Marr
,527
,211
6,208
1
,013
1,694
 
Major
,930
,203
20,999
1
,000
2,535
 
Student_y
-1,144
,373
9,406
1
,002
,318
 
FL_ps
,393
,196
4,006
1
,045
1,482
 
AA
,681
,294
5,382
1
,020
1,976
 
Bp
-,765
,360
4,516
1
,034
,465
 
Bh
,904
,268
11,354
1
,001
2,470
 
Constant
-,108
,243
,198
1
,657
,898

a. Variable(s) entered on step 1: Gend, Marr, Major, Student_y, FL_ps, AA, Bp, Bh.

b) Model fit test:

(+) Forecast accuracy level of the model:

Table 2 (Classification Table), with 43 individuals rarely saving (see columns), the model correctly predicted 22 cases (see rows), so the accuracy rate was 12.4%. While 596 people often save, the model predicted 440 people, the accuracy rate was 95.4%. Therefore, the forecasting accuracy rate of the whole model (Overall Percentage) was 72.3%

Table-2. Prediction level of the model classification table a.

 
 
 
Predicted
 
 
 
Frequency level of saving
 
 
Observed
Rarely and
never
Occasionally and frequently
Percentage Correct
Step 1
Frequency of
saving
Rarely and never
22
156
12,4
Occasionally and
frequently
21
440
95,4
Overall Percentage
72,3

Note: a. The cut value is, 500

(+) The relevance of the model:

The Omnibus test from Table 3 showed that sig <0.01 (99% confidence level), it was concluded that independent variables were linearly related to the dependent variable overall. Or that the chosen model was a good fit.

Table-3. Omnibus tests of model coefficients.

 
 
Chi-square
df
 
Sig.
Step 1
Step
68,698
8
,000
Block
68,698
8
,000
Model
68,698
8
,000
Model Summary
Step
-2 Log likelihood
Cox & Snell R Square
Nagelkerke R Square
1
687,352a
,102
,147

Note: Estimation terminated at iteration number 5 because parameter estimates changed by less than ,001.

(c) Simulation of the probability of changing personal saving behavior

In Table 4, results from the regression coefficient column "B" and column [Exp (B)=eB] were used, forming the probability scenario that changes when then initial probability is 10%, 20%,30%,40% and 50%.
If set: P0 : Initial probability; P1 : Changed probability. Then, P1 was calculated by the following formula:

The results were shown in "Table 4". This table simulates how the probability of personal saving behavior changes.

Table-4. Simulation of the probability of changing personal saving behavior.

Variables
B
Exp(B)
1- E
10%
20%
30%
40%
50%
Student_y
-1.144
0.318
0.682
3.4%
7.4%
12.0%
17.5%
24.1%
Bp
-0.765
0.465
0.535
4.9%
10.4%
16.6%
23.7%
31.7%
Gend
-0.401
0.67
0.33
6.9%
14.3%
22.3%
30.9%
40.1%
Major
0.93
0.535
0.465
5.6%
11.8%
18.7%
26.3%
34.9%
FL_ps
0.393
1.482
-0.482
14.1%
27.0%
38.8%
49.7%
59.7%
Marr
0.527
1.694
-0.694
15.8%
29.8%
42.1%
53.0%
62.9%
AA
0.681
1.976
-0.976
18.0%
33.1%
45.9%
56.8%
66.4%
Bh
0.904
2.47
-1.47
21.5%
38.2%
51.4%
62.2%
71.2%
Constant
-0.108
0.898
0.102
9.1%
18.3%
27.8%
37.4%
47.3%

The above test showed the factors that influence (statistically significant) the behavior of personal saving in order: "Major in undergraduate study" (Major); "Advanced social behavior" which involves responsibilities for the future (Bh), "Financial attitude" which involves both attitude to risk, attitude to money (AA) ; "Marital status" (Marr); "Financial literacy" (FL_ps);

(vi) "Gender" (Gend); "Basic social behavior" covering responsibilities for current resources (Bp);

"Student’s academic year" (Student_y).
Meanwhile, the test did not find (not statistically significant) the impact of "age" (age), "job experience" (Exp_job), "number of children" (Child), "income" (Income)," education level" (Edu),
... and basic channels (such as high school study; short-term training; mass media such as television, radio and press; official research papers ...) to personal saving behavior.

(d) Personal saving behavior prediction model

After eliminating non-statistically significant variables, performing Binary Losigstic regression analysis, results were obtained and displayed in "Table 1" (above section). And therefore:
=++++_+_+ + + (**).
Replacing the coefficients in Table 2 (above) into the equation (**):
= −  . −   . +   . +   . −   . +   . +   . −   . +   .
The equation estimates the probability of having personal savings as follows:

E(Y/X): is the probability where Y =1 (positive personal saving behavior exists) when the independent variable X has a specific value of Xj

Table-5. Predicting factors affecting personal saving behavior according to scenarios.

   
Regression
Scenario  1
Scenario  2
  Variable name (if correct, value =1)
coefficient
(KB1)
(KB2)
  Gender Male
-0.401
0
-0.401
Married
0.527
0
0.527
Undergraduate study major: Banking and finance
0.93
0
0.93
Student of the second year or above
-1.144
0
-1.144
The level of financial literacy is at average or higher
0.393
0
0.393
Have  good  attitude  (average)  towards  banking  and
Finance
0.681
0
0.681
Basic  social  behavior  related  to  banking  activities
from an average level upwards
-0.765
0
-0.765
Advanced social behavior related to banking activities
from the average level upwards
0.904
0
0.904
Block coefficient (intersecting the vertical axis)
-0.108
-0.108
-0.108
P(Y/Xj)
47.3%
73.4%

There were hypotheses on variables to predict saving behaviors. “Predicting factors affecting personal saving behavior according to scenarios” (Table 5) was based on results of variable hypotheses, which provide dus predictions as follows:

The first scenario (KB1): If the individual had the following characteristics (elements): notmale sex; not married; the major in undergraduate education is not banking and finance; being a student under the second year; the level of financial literacy is below average; the attitude towards banking and finance is below average; basic social behavior related to banking activities is below average; and, advanced social behavior related to banking activities is below average; then the probability of having positive saving behavior was 47.3%.

The second scenario (KB2): If the individual had the following characteristics (elements):male gender; married; the major in undergraduate education is banking and finance; being a second year student or above; having financial literacy level of above average; attitude towards banking and finance is average or higher; basic social behavior related to banking activities is average or above; and, advanced social behavior related to banking activities is average or above; then the probability of having positive saving behavior was 73.4% (i.e. at least occasionally, regularly and very frequently save for protective needs or for the future).

3.7. Discussion on factors influence Personal Saving Behaviors in Vietnam

Predictions, simulation of above hypotheses and, particularly, results of Binary Logistic Regression on data collected from 692 respondents in different areas showed that there were 7 factors affecting personal saving behaviors in Vietnam and that:

4. POLICY IMPLICATIONS

Based on the test results and the context of low level financial literacy compared to developing countries, official financial instruments at low levels (ADBI, 2017), black credit in the country, the Government’s effort towards a comprehensive financial strategy and education reform and practice of financial education in Vietnam, the authors make some suggestions as below relating to financial education to increase personal savings, and thereby enhance national savings and achieve macro-economic objectives for Vietnam's socio-economic development:

Funding: This study received no specific financial support.

Competing Interests: The authors declare that they have no competing interests.

Acknowledgement: Both authors contributed equally to the conception and design of the study.

REFERENCES

ADBI. (2017). Determinants and impacts of financial literacy in Cambodia and Viet Nam. DBI Working Paper, No. 754; Asian Development Bank Institute (ADBI), Tokyo.

Agnew, J. R. (2000). Do behavioral biases vary across individuals. Journal of Financial and Quantitative Finance, 41(4), 939-962.

Albeerdy, M. I., & Gharleghi, B. (2015). Determinants of the financial literacy among college students in Malaysia. International Journal of Business Administration, 6(3).

Bernheim, D. (1995). Do households appreciate their financial vulnerabilities? An analysis of actions, perceptions, and public policy. Tax Policy and Economic Growth, 3, 11-13.

Bernheim, B. (1998). Financial literacy, education, and retirement saving. In Living with Defined Contribution Pensions: Remaking Responsibility for Retirement, edited by Olivia S. Mitchell and Sylvester J. Schieber (pp. 38–68). Philadelphia: University of Pennsylvania Press.

Burnes, K., & Schultz, J. (2000). Older women and private pensions. Waltham, Massochusetts: National Centrer For Women and Aging, Brandeis University.

Campbell, J. Y. (2006). Household finance. NBER, Working Paper 12149.

Christelis, D., Jappelli, T., & Padula, M. (2010). Cognitive abilities and portfolio choice. European Economic Review, 54(1), 18-38. doi: https://doi.org/10.1016/j.euroecorev.2009.04.001

Clark, R., & d’Ambrosio, M. (2008). Adjusting retirement goals and saving behavior: The role of financial education. Overcoming the Saving Slump: How to Increase the Effectiveness of Financial Education and Saving Programs, 237-256.

Embrey, L. L., & Fox, J. J. (1997). Gender different in the investment decision-making process. Financial Counseling and Planning, 8(2), 33-40.

EY. (2017). Women and wealth the case for a customized approach. Retrieved from: https://www.ey.com/Publication/vwLUAssets/EY-women-investors/$FILE/EY-women-and-wealth.pdf .

Fonseca, B. R., Mullen, K. J., Zamarro, G., & Zissimopoulos, J. (2012). What explains the gender gap in financial literacy? The role of household decision-making. Journal of Consumer Affairs, 46(1), 90-106. doi: https://doi.org/10.1111/j.1745-6606.2011.01221.x

Gottschalck, A. O. (2008). Net worth and the assets of household saving in Australia. The Economic Record, 78(241), 207-223.

Hogarth, J. M. (2002). Financial literacy and family and consumer sciences. Journal of Family and Consumer Sciences, 94(1), 15-28.

Levine, P. B., Mitchell, O. S., & Moore, J. F. (2000). Women on the verge of retirement: Predictors of retiree well-being. Forecasting Retirement Needs and Retirement Wealth, 167-207.

Lusardi, A., & Mitchell, O. (2011). Financial literacy and planning: Implications for retirement well-being. In Financial Literacy: Implications for Retirement Security and the Financial Marketplace, edited by Olivia S. Mitchell, and Annamaria Lusardi, (pp. 17–39). Oxford and New York: Oxford University Press.

Lusardi, A., & Mitchell, O. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44. doi: http://dx.doi.org/10.1257/jel.52.1.5

Lusardi, A., & Mitchell, O. S. (2007). Financial Literacy and Retirement Planning: New Evidence from the Rand American Life Panel. Annamaria Lusardi (Dartmouth College) and Olivia S. Mitchell (University of Pennsylvania).  Retrieved from http://www.mrrc.isr.umich.edu/

Lusardi, A., & Mitchell, O. S. (2007a). Financial literacy and retirement planning: New evidence from the rand american life panel. Annamaria Lusardi (Dartmouth College) and Olivia S. Mitchell (University of Pennsylvania). Retrieved from: http://www.mrrc.isr.umich.edu/ .  

Lusardi, A., & Mitchell, O. S. (2007b). Financial literacy and retirement preparedness: Evidence and implications for financial education. Business Economics, 42(1), 35-44. doi: 10.2145/20070104

Lusardi, A., & Mitchell, O. S. (2007c). Household saving behavior: The role of financial literacy, information, and financial education programs. Paper presented at the Implications of Behavioral Economics for Economic Policy, Federal Reserve Bank of Boston.

Lusardi, A., & Mitchell, O. S. (2006). Financial literacy and planning: Implications for retirement wellbeing, Pension Research Council Working Paper No. 1. Philadelphia, PA: The Wharton School, University of Pennsylvania.

Lusardi, A., & Mitchell, O. S. (2008). Planning and financial literacy: How do women fare? American Economic Review, 98(2), 413-417. doi: https://doi.org/10.1257/aer.98.2.413

Mahdzan, N. S., & Tabiani, S. (2013). The impact of financial literacy on individual saving: An exploratory study in the Malaysian context. Transformations in Business and Economics, 12(1), 28.

Maki, D. (2004). Financial literacy and private pension" trong private pension and public policies. Washington D.C Brooking Institution Press.

Mandell, L. (2009). The financial literacy of young American adults: Results of the 2008 national jump$tart coalition survey of high school seniors and college students. Washington, DC: Jump$tart Coalition.

Modigliani, F., & Brumberg, R. (1954). Utility analysis and the consumption function: An interpretation of cross-section data. Franco Modigliani, 1(1), 388-436.

OECD/INFE. (2015). 2015 OECD/INFE Toolkit for measuring financial literacy and financial inclusion (E. I. S. SecretariatINFE@oecd.org., Trans.): Organisation for Economic Co-operation and Development, 2 rue André-Pascal, 75775 Paris cedex 16, France.

OECD/INFE. (2016). OECD/INFE International survey of adult financial literacy competencies. Paris: OECD.

Remund, D. L. (2010). Financial literacy explicated: The case for a clearer definition in an increasingly complex economy. Journal of Consumer Affairs, 44(2), 276-295. doi: https://doi.org/10.1111/j.1745-6606.2010.01169.x

Stango, V., & Zinman, J. (2009). Exponential growth bias and household finance. The Journal of Finance, 64(6), 2807-2849. doi: https://doi.org/10.1111/j.1540-6261.2009.01518.x

Stango, V., & Zinman, J. (2013). Borrowing high vs. borrowing higher: Sources and consequences of dispersion in individual borrowing costs (No. w19069). National Bureau of Economic Research.

Sunden, A. E., & Surette, B. J. (1998). Gender differences in the allocation of assets in retirement savings plans. The American Economic Review, 88(2), 207-211.

Supinah, R., Japang, M., Amin, H., & Hwa, M. A. C. (2016). The role of financial socialization agents on young adults' financial behaviors and attiudes. Paper presented at the 2016 WEI International Academic Conference Proceeding, Rome, Italy.

Tang, C. F., & Chua, S. Y. (2009). The saving growth nexus in Malaysia: Evidence from nonparametric analysis. The IUP Journal of Financial Economics, 7(3 & 4), 83-94.

Van Rooij, M., Lusardi, A., & Alessie, R. (2011). Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449-472.

Views and opinions expressed in this article are the views and opinions of the author(s), Asian Economic and Financial Review shall not be responsible or answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content.