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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.19 No.3 pp.543-550
DOI : https://doi.org/10.7232/iems.2020.19.3.543

Economic Analysis of the Financial Literacy Effects in G20

Walaa Ismael Alnassar*
University of Baghdad, College of Administration & Economic, Iraq
*Corresponding Author, E-mail: alnassar.wala.ismael@gmail.com
May 6, 2020 June 22, 2020 June 30, 2020

ABSTRACT


This paper extends the literature on the elements and effect of financial literacy by investigating the elements of financial literacy and the impact of financial literacy on financial inclusion and savings. This research confirms the results of researches of other economies but exposes some dissimilarities as well. The principal factors of financial literacy are discovered to be government efficiency, educational level, income, economic performance and infrastructure. Both education levels and financial literacy are found to be meaningfully and positively linked to financial inclusion and savings in G20 economies.



초록


    1. INTRODUCTION

    Enlightening financial literacy is an international concern with many economies founding strategies and initiatives to assist citizens gain the financial knowledge that is supposed to be essential to ensure efficient controlling individual finances over a lifespan. With financial safety the eventual target of most financial literacy proposals (Blue et al., 2014), financial literacy education stimulates financial knowledge and skills. Consequently, an excess of financial education plans founded by industry, government and community are accessible, though there is concern regarding the efficacy and suitability of these plans (Blue et al., 2015). However, evaluations constantly display that the level of financial literacy is comparatively low even in developed economies (OECD INFE, 2016;Hessami, 2014;Luo et al., 2018). This points to the necessity to advance policies for financial education to enhance financial literacy.

    In what monitors, this study focuses on the Group of twenty (G20) countries, which involve a mix of the world’s largest developed and emerging economies. Members of the G20 are 19 countries (Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, the Republic of Korea, Mexico, the Russian Federation, Saudi Arabia, South Africa, Turkey, the United Kingdom, and the United States) along with the European Union.

    The G20 is a global forum held yearly that creates the world’s twenty largest economies. G20 economies has 85% share of the global economy, 80% of total world trade and two-thirds of the total population. The G20 is consequently a principal forum for constructing political will and commanding the execution of the United Nation’s (UN) motivated Agenda for Sustainable Development (Dutkiewicz and Ellis, 2018;Kodekova et al., 2018).

    The G20 targets at wider sharing and strengthening the advantages of globalization. Regarding this target, the expression “financial inclusion” defines the pursuit of delivering useful and reasonable access to financial services to all persons and businesses internationally. As such, financial inclusion can lead to higher level of employment and economic growth, furthermore it can lessen income inequality and might possibly escalate financial stability.

    Since the 2009 Pittsburgh Summit, financial inclusion shows a key role on the agenda of the G20. The G20 Leaders documented financial inclusion as one of the main elements of the worldwide development agenda at the G20 Summit in Seoul (2010) and recommended a “Financial Inclusion Action Plan (FIAP)” and “principles for innovative financial inclusion”. Following the Seoul Summit, the G20 started the “Global Partnership for Financial Inclusion (GPFI)” in December 2010. The GPFI is the leading coordinating and applying means for the FIAP and performs as an comprehensive platform for G20 and non-G20 countries for knowledge sharing, equal learning, policy coordination and advocacy. Especially, the GPFI assists countries applying the G20 principles for novel financial inclusion and targets at providing data for gauging financial inclusion (Timmermann and Gmehling, 2017).

    At 2020 summit in Los Cabos, the G20 leaders authorized the High-Level Doctrines on Nationwide Strategies for “Financial Education” advanced by the “Organization for Economic Cooperation and Development International Network on Financial Education” (OECD/ INFE), thus admitting the significance of organized policy methods to financial education (Morgan and Trinh, 2019).

    Generally, “financial literacy” is disaggregated into three sections: “financial knowledge”, “financial behavior”, and “attitudes toward longer-term financial planning”. This is in line with OECD INFE (2016), which outlines 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.” More exactly, the OECD/INFE idea of “financial literacy” is multidimensional, indicating not only understanding, but also attitudes, skills, and actual behavior.

    “Financial knowledge” is concepts and information that assists in comparing financial services and products and make suitable financial decisions. A fundamental knowledge of “financial concepts”, and the capability to apply mathematical skills to financial matters empower individuals to administer their “financial affairs” and act properly to occurrences that can have effects on their financial situation. “Financial knowledge” can be gauged either subjectively or objectively (through survey questions).

    “Financial behavior” indicates financial actions and decisions. Some sorts of behavior, such as not planning for potential expenditures, postponing bill payments, or selecting financial products without investigating the market, can unfavorably impact a person financial wellbeing and situation. “Financial behavior” can accordingly be different from “financial knowledge”, and it is essential to how financial knowledge can impact financial behavior.

    Financial attitudes concerning longer-term “financial planning” comprise properties such as period preference and readiness to make intentional savings. Such likings promote behaviors that may cause condensed financial resilience and well-being.

    This research is ordered as follows. Section 2 concisely argues the literature on factors of financial literacy and its impacts. The data collection and methodology are provided in Section 3. Sections 4 shows empirical outcomes, followed by discussion and conclusions in Section 5.

    2. LITERATURE REVIEW

    The researches on financial literacy concentrate on two key aspects: (i) the factors of financial literacy such as education level, income and etc.; and (ii) the impact of financial literacy on “financial behavior”, such as saving.

    One of the primitive to progress quantifiable indicator of “financial literacy” was that of the Jumpstart Partnership for “Personal Financial Literacy program” for college and high school students in 1997 in the US explained in (Mandell and Klein, 2009;Mwaniki and Ondiek, 2018). Lusardi and Mitchell (2011a) enhanced it by adding a set of “financial literacy” questions to the “2004 Health and Retirement Study (HRS)”, an evaluation of “US households” aged 50 and older, which served as a base for following surveys. The three essential queries in the initial survey were targeted at classifying respondents’ apprehension of some fundamental financial ideas: multifactorial interest, actual rates of return, and diversification of risk. Subsequent surveys, involving the “OECD/INFE survey”, enhanced the “financial knowledge” questions but also additional questions about “financial attitudes”, “financial behavior”, and “financial experience” (Morgan and Trinh, 2019).

    Lusardi and Mitchell (2014) deliver a detailed assessment of the literature on elements related to “financial literacy”. “Financial literacy” results tend to track a hump-shaped outline regarding age of individual, first increasing and then decreasing in old age. Nevertheless, senior persons’ confidence in their own “financial literacy” shows no comparable reduction, showing a perceptual gap. Higher stages of education of individual and their parents are positively affected financial literacy. These outcomes were largely supported in the analysis of the outcomes of the “OECD/INFE survey” in the aforementioned sample of 30 countries in OECD INFE (2016).

    An important issue for policy makers is whether “financial education” programs may advance “financial literacy”. A large number of researches have been performed, but the outcomes are questionable. The outcomes rely on many exclusive features of the programs, such as knowledge of the teachers, course content. Fernandes et al. (2014) implement a meta-analysis of 188 researches and discover that “financial education” has a meaningful but very small impact of only 0.1% on linked “economic behaviors”. Following that, Lusardi and Mitchell (2014) and Walstad et al. (2010) revealed meaningful effects from a financial literacy program. Later, in their assessment, Hastings et al. (2013) claim that the proof on the success of “financial education programs” on financial literacy is “ ... at best conflicting.”

    Many studies tried to connect indicators of financial literacy with other financial and economic behaviors since Bernheim (1998) in the US. Regarding this aim, Hilgert et al. (2003) reveal a robust connection between “financial literacy” and daily financial managing skills, though other researches reveal that the more accomplished and financially literate are more probable to partake in financial markets such as investing in stocks or making cautionary savings (de Bassa Scheresberg, 2013;Morgan and Trinh, 2019). The more financially knowledge are likewise more likely to accept “retirement planning”, and those who apply “financial plans” likewise tend to collect more wealth (Lusardi and Mitchell, 2011a). Jonubi and Abad (2013) uncover analogous proof in Malaysia.

    Regarding “household borrowing”, Hekmatpour et al. (2017) shows that those with lesser “financial literacy” are more probable to have more pricy mortgages. Campbell (2006) disclosed that those with less education and lesser income were less probable to refinance their loans through periods of declining interest rates. Also, Stango and Zinman (2009) showed that those who could not properly determine interest rates commonly accumulated less wealth and borrowed more.

    3. DATA AND METHODOLOGY

    3.1 Data Collection

    This research applied the result of the year 2017 from the coordinated OECD/INFE questionnaire of adult financial literacy for G20 and Netherland and Norway. The questionnaire includes questions about individual information (such as income) and questions about financial literacy and financial inclusion. Some countries have missing data such as Australia, Japan, Mexico and USA. Hence, these countries are removed from date of the study. Data of saving and income are extracted and checked with World Bank database. Government efficiency, infrastructure and economic performance data were collected from “IMD world competitiveness yearbook database”. It should be noted that for the first time in this field of study, this research uses individual saving for saving variable (rather than dummy values), also it applies disposable income in comparing with previous studies in this field. Furthermore, it employs new controls variables including government efficiency, infrastructure, and economic performance.

    3.2 Construction of Financial Literacy Scores

    This research applies the methodology in Guest and Brimble (2018) to compute scores for the various indexes of “financial literacy” and “financial inclusion”. The result for “financial knowledge” is computed from answers to seven questions showing the subject’s digesting of basic knowledge (or responsiveness) of finance such as computation of “interest rates” and “compound interest rates”, “risk” and “return evaluation”, the impact of inflation, and the advantage of “financial diversification”. This index varies between 0 and 7 based on the sum of correct answers. The “financial behavior” point is computed from nine sections relating to household budget planning, considered purchases, saving, care about financial affairs, bill payments, borrowing and long-term financial goals has a value between 0 and 9. The point for “financial attitude” shows the respondent’s answers to five sections about wealth, saving, and expenditure, and value from 1 to 5. A higher point shows more considered and conservative behavior. The overall value for “financial literacy” is the sum of abovementioned three sections, and henceforth gets values between 1 and 21. The value for “financial inclusion” is calculated from seven indexes, involving savings, payment products, credit products, insurance, product choice, and emergency financial support by family, and has value from 0 to 7.

    This research transformed the above-mentioned indicator scores into z standard-score values:

    S c o r e z = S c o r e S c o r e ¯ S c o r e s d
    (1)

    where Scorez is the transformed z-normal score, Score is the mean of score, and Scoresd is the standard deviation of the score.

    3.3 Methodology

    The current research estimates the following equations to assess the relationship between income and “financial literacy”:

    F L i =   α 0 + α 1 I n c o m e i + α 2   X i + ε i
    (2)

    where FLi “financial literacy”, “financial knowledge”, “financial behavior”, and “financial attitude” score of individual iFLi alternatively; Incomei is the natural logarithm of individual i’s disposable income; Xi are control variables; and εi is the error term. The control variables involve education, government efficiency, infrastructure and economic performance.

    Impact of Financial Literacy on Saving Behavior: To measure the impact of “financial literacy” on “saving”, the subsequent equation is assessed:

    S a v e i = β 0 + β 1 F L i + β 2 I n c o m e i + β 3   X i + ε i
    (3)

    where Savei is a natural logarithm of saving, FLi and Incomei are the “financial literacy” value and “disposable income” accordingly, and finally Xi control variables same as previous equation.

    Effect of Financial Literacy on Financial Inclusion: To measure the impact of “financial literacy” on “financial inclusion”, the subsequent equation is projected:

    F I i = γ 0 + γ 1 F L i + γ 2 I n c o m e i + γ 3   X i + ε i
    (4)

    where FIi is the “financial inclusion” value, FLi and Incomei are the “financial literacy” value and “disposable income” accordingly, and finally Xi control variables same as previous equation.

    4. FINDINGS and DISCUSSION

    In this section, we estimate the factors of “financial literacy”, and the effects of “financial literacy” on the savings and “financial inclusion” in abovementioned sample.

    4.1. Finding of EQUATION I (factors of financial literacy):

    The analysis section starts with diagnosing multicollinearity issue for equation I. This research utilizes variance inflation factors (VIF) to assess this issue. VIF unveils how much the estimated coefficient variance fluctuates in the case of no correlation among all explanatory variables (Gujarati and Porter, 2009). According to the outcome of VIF in table 1, all computed VIF values are lower than 10, so there is no problem of multicollinearity (Gujarati and Porter, 2009).

    Following the above section, this research also tests model I for autocorrelation, heteroscedasticity and normality of residual and their outcomes are provided in Table 2. Regarding autocorrelation detecting, the study applied Durbin-Watson (DW) method with the null hypothesis of no autocorrelation and alternative of existing autocorrelation. Based on the result of DW test in table 2, the value is 2.134 (between 1.5 to 2.5) test which is not significant and hence the null hypothesis of no serial correlation cannot be rejected. So, there is no issue of autocorrelation in the model.

    Following that, Table 2 illustrates the result Cameron & Trivedi’s decomposition of IM-test for detecting heteroscedasticity issue of the equation I. In the result of heteroscedasticity test, probability of Chi-square is insignificant, so the null hypothesis of homoscedasticity (not heteroscedasticity) effect is not rejected for this model. Hence, this model does not have the issue of heteroscedasticity. Finally, the model I is tested for the normality of residuals. This research used the Doornik-Hansen test to assess the normal distribution of residuals. If the P-value of Doornik- Hansen test is significant, the distribution of residuals is not normal and otherwise it is normally distributed (Gujarati, 2003). Table 2 displays the outcome of normality testing. The insignificant P-value leads to accept null hypothesis of normal distribution of residuals. Therefore, there is no issue of non-normality of residual.

    Ultimately, Table 3 shows the results of regression analysis of equation I. According to this table, income (lnIncome), education and economic performance have significant and positive impact on financial literacy (FL) with 5%, 1% and 10% level of significance. On the other hand, infrastructure has meaningful negative impact on financial literacy (FL). Therefore, it can be concluded that the higher income, level of education and economic performance lead to higher financial literacy while better infrastructure interestingly leads to lower financial literacy.

    In order to double confirm the outcome of this study, the robust model has been investigated. In the robust equation, financial literacy has been replaced with financial knowledge as dependent variable. Similar to main outcome of equation I, the result of robust model approved the significant positive impact of income, education on the dependent variable, however, infrastructure and economic performance do not show significant impact in this model.

    4.2 Finding of EQUATION II (Impact of Financial Literacy on Savings)

    Similar to the process for equation I, the first test for Equation II diagnoses multicollinearity issue by VIF method. According to the outcome of VIF in Table 5, all computed VIF values are lower than 10, so there is no issue of multicollinearity for Equation II.

    Following that, Table 6 provides the outcomes of testing for autocorrelation, heteroscedasticity and normality of residual. Regarding autocorrelation detecting, based on the result of DW test in table 6, the value is 2.272 (between 1.5 to 2.5) test which is not significant and hence the null hypothesis of no serial correlation cannot be rejected. So, there is no issue of autocorrelation in the model.

    The outcome of heteroscedasticity test shows insignificant probability of Chi-square, so the null hypothesis of homoscedasticity is not rejected for this model and the model does not have the issue of heteroscedasticity. Also, Table 6 displays the outcome of normality testing which is insignificant. Consequently, residual of this equation has normal distribution.

    Later, Table 7 shows the outcomes of regression analysis of the equation II. According to this table, financial literacy and education have meaningful and positive effect on saving (lnSaving) with 10% and 5% level of significance respectively. The coefficient of income becomes insignificant, proposing that the correlation of this variable with the savings has been captured by the financial literacy value. The rest of variables do not have significant impact on saving in this equation.

    4.3 Finding of EQUATION III (Impact of Financial Literacy on Financial Inclusion)

    Following the same procedure, the first test for Equation III diagnoses multicollinearity problem. Based on the outcome of VIF in Table 8, all computed VIF values are lower than 10, so there is no issue of multicollinearity for Equation III.

    Also, Table 9 provides the outcomes of testing for autocorrelation, heteroscedasticity and normality of residual. In case of autocorrelation detecting, the result of DW test in table 9 is 2.445 (between 1.5 to 2.5) test which is not significant and hence the null hypothesis of no serial correlation cannot be rejected. Hence, there is no issue of autocorrelation in the model.

    The finding of heteroscedasticity test shows insignificant probability. So, the model does not have the problem of heteroscedasticity. Additionally, Table 9 shows the resultf of normality testing which is insignificant. Accordingly, residual of this equation has normal distribution.

    Subsequently, Table 10 illustrates the findings of regression analysis of the equation III. Based on this table, financial literacy, income and government efficiency shows significant and positive impact on financial inclusion with 5%, 5% and 10% level of significance accordingly. Furthermore, infrastructure has significant probability with negative coefficient which implies a reverse impact on financial inclusion. The rest do not have meaningful effect on financial inclusion.

    5. DISCUSSION AND CONCLUSION

    This study of financial literacy in G20 has novelty in four ways: (i) it analyses the model in cross-nationals for G20 GROUPS. (ii) it employs individual saving for saving variable (instead of dummy values), (iii) it applies disposable income for the first time. (iv), finally it includes government efficiency, infrastructure, and economic performance in this field of analysis.

    Commonly, this research corroborates the outcomes of studies of other countries but exposes some differences as well. This investigation displays that that the level of education normally is highly meaningful and positively associated with “financial literacy” in both G20. This remains also for savings as well. However, the education level was not significant for financial attitudes. These outcomes were coherent with the results of the other 30 countries reported in (OECD INFE, 2016). These outcomes additionally are in the same line with those of (Lusardi and Mitchell, 2011b;Morgan and Trinh, 2019), which apply different index of financial literacy.

    The outcome with the most essential macroeconomic consequences is that both education levels and financial literacy are significantly and positively affected savings. Furthermore, financial literacy, income and government efficiency are found to be meaningfully and positively linked to the index of “financial inclusion”. Amplified financial inclusion implies that boosted savings may be made more willingly accessible for investment in those economies. Again, this emphasizes the prominence of enhancing policies to improve both financial literacy and education.

    Figure

    Table

    Result of multicollinearity test VIF for equation I

    Diagnostics Tests for equation I

    Source: Research calculations.

    Regression outcomes of equation I. (DV: Financial Literacy)

    Source: Research calculations.

    Regression outcomes for robust model (DV: Financial knowledge)

    Source: Research calculations.

    Result of Multicollinearity test VIF for equation II.

    Diagnostics Tests for equation II.

    Source: Research calculations.

    Regression outcomes of equation II. (DV: lnSaving)

    Result of Multicollinearity test VIF for equation III

    Source: Research calculations.

    Diagnostics Tests for equation III

    Source: Research calculations.

    Regression outcomes of equation II (DV: financial inclusion)

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