FINANCIAL INCLUSION IN VIETNAM: THE IMPACTS OF DEMOGRAPHIC FACTORS ON THE USE OF FINANCIAL SERVICES

The research paper aims to understand the determinants of financial inclusion in Vietnam from the demand side. The empirical results are based on the World Bank’s 2017 Global Findex Database. Applying probit and multivariate probit estimations, it will analyse the correlation between individual characteristics (age, gender, income, education and employment status) and the use of formal financial services in Vietnam. Financial inclusion in this paper is defined as the probability of having a formal account formal saving formal credit. Subsample estimation for the year 2014 is also employed for robustness purpose. The main findings are that wealth and education have a positive and statistically significant relation with the level of financial inclusion in most of cases. While age and occupation are less likely to affect a person’s participation in financial system, gender shows no effect on the probability of having a formal account formal saving formal credit. The implication of these findings for policy is that the government should take the socioeconomic characteristics into consideration to address the unfavoured groups of population when establishing the financial inclusion policies.

FINANCIAL INCLUSION IN VIETNAM: THE IMPACTS OF DEMOGRAPHIC FACTORS ON THE USE OF FINANCIAL SERVICES A Research Paper presented by: NGUYỄN THỤY MINH CHÂU (VIETNAM) in partial fulfilment of the requirements for obtaining the degree of MASTER OF ARTS IN DEVELOPMENT STUDIES Major: Economics of Development (ECD) Members of the Examining Committee: Peter Van Bergeijk Pham Khanh Nam The Hague, The Netherlands November 2019 ii CONTENTS List of Figures iv List of Tables iv List of Acronyms v Abstract vi CHAPTER 1: INTRODUCTION 1.1 Problem statements 1.2 Relevance and justification of the research 1.3 Research Objectives and Questions 1.4 Scope of study 1.5 Contribution of the study 1.6 Limitations of the study 1.7 Chapter outline CHAPTER 2: CONTEXT OF VIETNAM CHAPTER 3: LITERATURE REVIEW 13 CHAPTER 4: METHODOLOGY AND DATA COLLECTION .21 4.1 Econometric models 21 4.2 Data collection 23 4.2.1 Data Source 23 4.2.2 Variable description 25 4.2.2.1 Dependent variables .25 4.2.2.2 Explanatory variables .25 CHAPTER 5: RESEARCH RESULTS .27 5.1 Descriptive Statistics .27 5.2 Main analysis 27 5.3 Robustness analysis .33 5.3.1 Probit model with 2014 dataset and 2017 sample but excluding employed variable .33 5.3.2 Multivariate probit model with the original sample .35 CHAPTER 6: CONCLUSIONS 37 6.1 Conclusion .37 6.2 Limitations and future research 37 REFERENCES 39 iii List of Figures Figure 1: Number of academic research of financial inclusion from 1990 - 2018 Figure 2: Account ownership and GDP per capita of Vietnam, in comparison with some countries Figure 3: Gaps in account ownership in Southeast Asia countries and Vietnam Figure 4: Reasons for unbanked Figure 5: Savings behaviour in Vietnam during 2011-2017 Figure 6: Distinction between access to and use of financial services .15 List of Tables Table 1: Summary table of literature review on the determinants of financial inclusion using demandside data .19 Table 2: Descriptive statistics 29 Table 3: Correlation matrix 30 Table 4: Demand-side determinants of financial inclusion indicators in Vietnam (2017) – Probit Regression 32 Table 5: Demand-side determinants of financial inclusion indicators in Vietnam (2014) – Probit Regression 34 Table 6: Demand-side determinants of financial inclusion indicators in Vietnam (2017) – Multivariate Probit Regression .36 iv List of Acronyms SDGs Sustainable Development Goals UFA2020 Universal Financial Access by 2020 GDP Gross domestic products ATM Automated teller machine SMEs Small-, medium-sized enterprises ADB Asian Development Bank v Abstract The research paper aims to understand the determinants of financial inclusion in Vietnam from the demand side The empirical results are based on the World Bank’s 2017 Global Findex Database Applying probit and multivariate probit estimations, it will analyse the correlation between individual characteristics (age, gender, income, education and employment status) and the use of formal financial services in Vietnam Financial inclusion in this paper is defined as the probability of having a formal account / formal saving / formal credit Sub-sample estimation for the year 2014 is also employed for robustness purpose The main findings are that wealth and education have a positive and statistically significant relation with the level of financial inclusion in most of cases While age and occupation are less likely to affect a person’s participation in financial system, gender shows no effect on the probability of having a formal account / formal saving / formal credit The implication of these findings for policy is that the government should take the socio-economic characteristics into consideration to address the un-favoured groups of population when establishing the financial inclusion policies Relevance to Development Studies An inclusive financial system is believed to be a key element for poverty reduction and inequalities Several studies have found the positive relationship between financial inclusion and economic growth Indeed, many governments have been attempting to facilitate financial inclusion and access to financing Although the access and the use of financial services are common in developed countries, the level of financial inclusion in developing countries, like Vietnam, is still quite low To encourage the population participating in financial system, it is essential to understanding the determinants of being financial included It will help to shape the deliberate policies, enhance the country’s economy Keywords financial inclusion, account ownership, the use of financial services, Vietnam, individual characteristics, formal accounts, formal savings, formal credit vi CHAPTER 1: INTRODUCTION 1.1 Problem statements In recent years, there has been a growing interest in the linkage between an inclusive financial system and socio-economic development in both developing and developed countries Sustainable Development Goals (SDGs), adopted by all United Nations Member States in September 2015, has addressed poverty and inequality as major challenges the world is facing Despite not being directly mentioned in SDGs, financial inclusion is considered as an important enabler to achieve eight of 17 goals (Klapper et al., 2016: 1) A well-functioning inclusive financial system will help to reduce the poverty traps and unequal income distribution, thus boosts the economic growth (World Bank, 2013: 1; Demirgüç-Kunt et al., 2008: 106) With access to financial services, individuals and households can manage risk, earn interest, reduce the cost of credit (Ghosh, 2013: 3), make plans for their future, invest in better healthcare and education system (Klapper et al., 2016: 5), thus improve their living standards For business, the online payment system will support core business operations and increase consumption and productive investment (Blancher et al., no date: 6-8) Recognizing the prominence of the inclusive financial system to the overall development, many international organizations, such as the World Bank and International Money Fund, have taken action on supporting financial inclusion initiatives in emerging markets Also, governments have been endeavouring to remove constraints which exclude people from accessing financial services, as well as to increase the residents’ financial literacy For example, in Myanmar, an educational program, named Basic Financial Literacy Booklet, has been executed in rural areas to improve households’ financial literacy (Chen, 2019: 2) Through the Financial Sector Master plan, the Malaysian government has issued Guideline for Basic Banking Services which enables all segments of the population to have a basic account and access to banking services at low costs (De Luna-Martinez, 2017: 26-27) While financial inclusion plays such a significant part of the development of financial markets and the economy, it is estimated that, in 2017, 1.7 billion people, accounting for nearly 31 percent of the world population aged over 15, don’t own an account in the formal financial institutions, according to 2017 Global Findex Database They are referred to as the “unbanked” population With the purpose of scaling down the proportion of the unbanked, through the Universal Financial Access by 2020 (UFA2020), the World Bank Group set the goals of achieving one billion new accountholders by 2020 They also provide a framework for action to promote financial inclusion in 25 prioritized countries which constitute a large portion of the world’s financially excluded population (World Bank, 2018c) In Vietnam, financial inclusion is not a new concept It has lain in the root of national strategies and government policies for many years Also, it has been mentioned as one of the key subjects in international conferences For instance, in Asia-Pacific Economic Cooperation 2017, which was hosted by Vietnam, the role of financial inclusion to agricultural development was chosen to be the main topic of discussion (Le and Nguyen, 2017) It is because Vietnam has been in the transformation process into an upper-middle-income country and financial inclusion is believed to be a means to achieve sustainable development and poverty alleviation However, the level of inclusiveness in Vietnam remains quite low with only 31 percent of Vietnamese adults reported to have an account at formal financial institutions, according to Global Findex Database 2017 With the attempt to increase financial inclusion, the Vietnamese government is on its way to building up the national strategies and a firm legal corridor Since promoting financial inclusion becomes an integrated part of Vietnam’s economic development, it raises the necessity to comprehend the driving factors of financial inclusion before designing and implementing any policies 1.2 Relevance and justification of the research In the last decade, financial inclusion has increasingly attracted attention in the academic literature Figure illustrates the number of academic research on financial inclusion during the period of 19902018, which is derived from Google Scholar for the keyword “financial inclusion” Since the first appearance of this concept in the 1990s, there had been little scientific references addressing it However, the year 2010 witnessed a skyrocket in the number of studies in this field, up to thousands per year This emphasises the importance of the topic to economic development While a variety of research identified the positive linkages between financial inclusion and economic growth/ poverty alleviation, there is still an ongoing debate on its determinants Many researches (e.g Beck et al., 2005) supposed that the supply (measured by the indicators of banking penetration) is the primary driving force of financial inclusion However, others (e.g Tuesta, 2014; Abel, Mutandwa and Roux, 2018) criticized the limitations of supply-side approaches and pointed out that the level of inclusiveness in financial markets is determined by demand-side factors Following these arguments, this paper approaches financial inclusion in Vietnam from user-side During 20142017, the infrastructure of financial systems in Vietnam have been improved, yet the rate of financial inclusiveness remains unchanged Assumed that there is no constraint in access to financial services, then the financial decision will be solely affected by demand elements It includes socio-economic characteristics, individual attitudes and perceptions However, the paper concentrates only on the effect of an individual’s characteristics on their possibility of being financially excluded It is reasonable to use an individual’s characteristics to explain and predict an action since these traits are constant for a long period (Roe, 1984) Also, this approach was proposed by many prior studies in this topic Figure 1: Number of academic research of financial inclusion from 1990 to 2018 Number google results of financial inclusion 8000 7000 6000 5000 4000 3000 2000 1000 1990 1995 2000 2005 2010 2015 2018 Source: Google Scholar (Accessed: July 28th 2019) 1.3 Research Objectives and Questions The main objective of the research paper is to analyse demographic factors behind financial inclusion and the extent to which these factors influence the use of a formal account, formal saving and formal credit in Vietnam.by addressing the following question:  How individual characteristics affect financial inclusion in terms of owning a formal account, formal saving and formal borrowing in Vietnam? 1.4 Scope of study The paper employed the individual-level data from World Bank’s Global Findex Database in 2017, which covers 1,002 adults in 52 provinces of Vietnam Using probit and multivariate probit estimations, it will examine the correlation between individual characteristics (age, gender, income, education and employment status) and financial inclusion indicators Financial inclusion in this paper is defined as the probability of having a formal account / formal saving / formal credit 1.5 Contribution of the study Although the number of research on financial inclusion has been increasing in recent years, there are little studies about the determinants of the use of financial services in Vietnam This paper can contribute to the literature as one of the first researches on financial inclusion in the country Moreover, by using the same data source and the same method, it provides a cross-check on the findings of earlier studies The main findings of the paper may provide an overview of the participants in the financial markets It could be useful for community educators, financial institutions and policymakers to identify which groups of the population are un-favoured Thus, they will have more appropriate responses to and supports for each group 1.6 Limitations of the study There are some restricted points which are important to be taken into the consideration Firstly, the analyses exploit within-country variation and changes over time when using cross-sectional sample Secondly, other potential determinants are excluded from the sample due to the lack of data Lastly, it does not cover all aspects of financial inclusion 1.7 Chapter outline The remaining of the paper is organized as follows: Chapter gives the contextual setting of financial inclusion in Vietnam, including comparisons with other countries It provides a general view on financial activities (bank account, savings and borrowing) of Vietnamese people and the barriers of the unbanked population Chapter dedicates to the literature on financial inclusion It gives many existing definitions of the concept, as well as how other studies approach the topic from different dimensions Thence, it will shape the research paper Chapter provides the econometrics model underpinning the estimations and the data collection Chapter presents the main empirical results of estimations to determine the drivers of financial inclusion in Vietnam A robustness test is applied to check whether the results are consistent with the database of 2014 Chapter draws the conclusion from the main findings Then, it will point out some limitations of the paper to give suggestions for future research Table 2: Descriptive statistics Variable Definition Obs Mean Std Dev Min Max Expected sign A Indicators of financial inclusion account = if respondent has a formal account 999 0.334 0.472 saving = if respondent saves formally 985 0.164 0.371 borrowing = if respondent borrow formally 991 0.294 0.456 B Determinants of financial inclusion female = if respondent is female 999 0.574 0.495 (–) age = age in number of years 999 42.388 16.213 15 91 (+) age2 = the squared of age (in years) 999 2059.288 1522.361 225 8281 (–) 999 0.178 0.383 999 0.180 0.385 (+) 999 0.186 0.389 (+) 999 0.207 0.406 (+) 999 0.248 0.432 (+) (+) income_1st income_2nd income_3rd income_4th income_5th = if respondent’s income belongs to the first quintile = if respondent’s income belongs to the second quintile = if respondent’s income belongs to the third quintile = if respondent’s income belongs to the fourth quintile = if respondent’s income belongs to the fifth quintile employed = if in the workforce 999 0.742 0.438 edu_pri = if completed primary or less 999 0.338 0.473 edu_sec = if secondary education 999 0.511 0.500 (+) edu_ter = if completed tertiary or more 999 0.151 0.358 (+) 29 Table 3: Correlation matrix female age age2 edu_pri edu_sec edu_ter inc_1st inc_2nd inc_3rd inc_4th inc_5th employed account saving credit A Correlation among individuals’ characteristics female 1.000 age 0.054* 1.000 age2 0.054* 0.981*** 1.000 edu_pri 0.120*** 0.348*** 0.341*** 1.000 edu_sec -0.116*** -0.210*** -0.212*** -0.730*** 1.000 edu_ter 0.002 -0.166*** -0.154*** -0.301*** -0.431*** 1.000 inc_1st 0.047 0.138*** 0.150*** 0.231*** -0.151*** -0.094*** 1.000 inc_2nd 0.041 -0.0353 -0.036 -0.016 0.079* -0.089* -0.218*** 1.000 ince_3rd -0.029 0.036 0.032 -0.011 0.067** -0.079** -0.223*** -0.224*** 1.000 inc_4th 0.001 -0.015 -0.024 -0.010 -0.008 0.025 -0.238*** -0.239*** -0.245*** 1.000 inc_5th -0.053*** -0.109*** -0.107*** -0.171*** 0.0111 0.210*** -0.268*** -0.269*** -0.275*** -0.294*** 1.000 -0.069** -0.233*** -0.289*** -0.144*** 0.0582* 0.109*** -0.102*** -0.069** 0.018 0.003 0.133*** 1.000 employed B Correlation matrix between individuals’ characteristics and three indicators of financial inclusion account -0.019 -0.258*** -0.263*** -0.336*** 0.078** 0.334*** -0.141*** -0.050 -0.044 0.019 0.192*** 0.161*** 1.000 saving 0.0263 -0.104*** -0.105*** -0.227*** 0.089*** 0.179*** -0.136*** -0.029 -0.058* 0.018 0.181*** 0.073** 0.449*** 1.000 credit 0.0112 -0.156*** -0.163*** -0.015 0.003 0.015 -0.009 0.015 0.007 0.004 -0.016 0.097*** 0.086*** -0.015 (*), (**), (***) denotes statistically significant level at 10 percent, percent, percent, respectively 30 1.000 Unexpectedly, employment status has no significant relationship with having a formal account and formal saving This finding differs from the results of Allen et al., (2012) and (Ampudia and Ehrmann, 2017) Nevertheless, there is a positive effect on formal credit at 10 percent significant level Those who are employed are more likely to borrow from a formal institution, compared to their counterparts Adjusted R-squared indicates how well the explanatory variables can explain the variation in each of financial inclusion indicators The adjusted R-squared in the regression (1) has the highest value (18.3 percent) since most of the variables (age, income, education) are statistically significant The value decreases to 10.80 percent when formal saving is substituted as the dependent variable It bottoms out at 3.20 percent in case of formal credit as only one variable can explain the variation in the dependent variable To sum it up, the results show a mixed pattern of financial inclusion in Vietnam Being older, welleducated, wealthier favour the possibility of having an account, hence increase the probability to use other financial services For formal saving, a higher level of income and education are the main determinants while employment status is found to be the only driven of formal credit in Vietnam 31 Table 4: Demand-side determinants of financial inclusion indicators in Vietnam (2017) Probit Regression Variables female age Formal account (1) Formal saving (2) 0.014 0.035 0.0185 (0.0313) (0.0218) (0.02945) 0.0029 0.0027 (0.0042) (0.00527) -0.000035 -0.000087 (0.00005) (0.00006) 0.013 ** (0.0063) age2 -0.0002 *** (0.00007) edu_sec 0.246 *** 0.1588 *** (0.0366) edu_ter (0.0293) 0.556 *** inc_2nd inc_3rd inc_4th 0.3049 *** -0.0391 (0.03488) -0.0367 (0.045) (0.05901) (0.0463) 0.028 0.0748 -0.0022 (0.0571) (0.0533) (0.04934) 0.0416 0.0518 -0.00699 (0.0514) (0.05051) (0.04852) 0.079 0.1177 ** (0.056) inc_5th Formal credit (3) (0.05367) 0.1530 *** 0.1829 *** -0.01189 (0.04717) -0.04359 (0.0559) (0.05394) 0.050 0.01209 (0.0389) (0.02839) (0.03517) 999 985 991 R-squared 0.183 0.108 0.032 Adjusted R-squared 0.166 0.083 0.014 employed Observations (0.04575) 0.0664* Noted: (*), (**), (***) denotes statistically significant level at 10 percent, percent, percent, respectively Marginal effects are reported in the table Standard errors are in parentheses 32 5.3 Robustness analysis To check the robustness, I applied the same probit model with (1) 2014 dataset and (2) 2017 sample but excluding employed variable Also, taking into consideration the potential correlation caused by unobserved individual characteristics, a multivariate probit model is employed This approach is expected to give a better analysis since it allows error term of each equation correlating in pairs 5.3.1 Probit model with 2014 dataset and 2017 sample but excluding employed variable Table illustrates the results of the robustness test with probit model The regression (4) to (6) estimate the possibility of each financial inclusion indicators with the Global Findex Database in 2014 Since the data for the employed variable is not available for the year of 2014, it is not included in the regression Still, being a female does not have an effect on the formal account and formal saving However, it has a positive relationship with having a formal credit It is implied that in 2014, women are more likely to be involved in the financial system through credit activities This finding appears to contradict the results of Fungáčová and Weill, (2014), which indicated that being a female reduces the possibility of borrowing at a formal institution Another difference in the robustness analysis is the association between age and three financial inclusion indicators Age and the age squared have no effect on the probability of formal account This result goes against most of the literature The only study which supported this finding is (Cámara and David (2015) for the case of Peru Meanwhile, the year 2014 witnessed a nonlinear relation between formal saving / formal credit and age, which could not be found in 2017 The association between the education variable and formal account/ formal saving remains unchanged in 2014 A remarkable difference is that the dummy variable for tertiary education is found to be negative with formal credit It indicates that those who have higher education will less likely to borrow money from a formal institution There is no considerable difference in the relationship between income and three indicators of financial inclusion in 2014 To make the robustness test more comparative, I run regression (7) to (9) on the 2017 dataset but without employed variable When excluding the employed variable, there is no difference in the results of formal account and formal saving, compared to the main analysis However, in the regression (9), there is a negative relationship between formal credit and the squared of age, which is not witnessed in the regression (3) As can be seen from table 5, education and income are two factors which remain the effect on financial inclusion indicators through all regression 33 Table 5: Robustness check with 2014 dataset and 2017 sample without employed variable - Probit Regression 2014 Variables female age age2 edu_sec edu_ter inc_2nd inc_3rd inc_4th inc_5th Observations R-squared Adjusted R-squared Formal account (4) 0.0103 (0.0309) -0.0001 (0.0049) -0.0001 (0.0001) 0.1149 *** (0.0351) 0.5587 *** (0.0492) 0.0296 (0.0571) 0.0930 * (0.0561) 0.1801 *** (0.0586) 0.2553 *** (0.0578) 994 0.182 0.166 Formal saving (5) -0.0299 (0.0221) 0.0139 *** (0.0041) -0.0002 *** (0.0001) 0.0375 (0.0259) 0.2943 *** (0.0592) 0.0983 ** (0.0526) 0.0424 (0.0467) 0.1289 ** (0.0531) 0.1993 *** (0.0556) 988 0.121 0.098 2017 Formal credit (6) 0.0517 ** (0.0243) 0.0363 *** (0.0051) -0.0004 *** (0.0001) -0.0297 (0.0269) -0.0933 ** (0.0342) -0.0516 (0.0346) -0.0389 (0.0349) -0.0508 (0.0349) -0.0348 (0.0364) 992 0.076 0.056 Formal account Formal saving (7) (8) 0.0112 0.0353 (0.0313) (0.0218) 0.0156 *** 0.0035 (0.0060) (0.0039) -0.0002 *** -0.0000 (0.0001) (0.0000) 0.2481 *** 0.1593 *** (0.0365) (0.0293) 0.5606 *** 0.3071 *** (0.0447) (0.0588) 0.0273 0.0740 (0.0570) (0.0532) 0.0444 0.0521 (0.0572) (0.0505) 0.0816 0.1177 * (0.0564) (0.0537) 0.1588 *** 0.1841 *** (0.0558) (0.0539) 999 0.182 0.166 985 0.108 0.085 Noted: (*), (**), (***) denotes statistically significant level at 10 percent, percent, percent, respectively Marginal effects are reported in the table Standard errors are in parentheses 34 Formal credit (9) 0.0155 (0.0294) 0.0056 (0.0050) -0.0001 ** (0.0001) -0.0355 (0.0348) -0.0289 (0.0466) -0.0047 (0.0492) -0.0031 (0.0486) -0.0103 (0.0472) -0.0372 (0.0459) 991 0.029 0.012 5.3.2 Multivariate probit model with the original sample Multivariate probit model, proposed by Ashford and Sowden (1970), is the generalization of probit model It estimates correlated binary outcomes jointly In the univariate probit model, the potential cross-commodity correlation among financial inclusion indicators for the same adults are not observed Unobserved factors may be related in the error term In the multivariate probit model, this shortcoming can be overcome Instead of calculation the probability of each financial inclusion indicators, it will predict these choices jointly It allows the error term of each equation correlating in pairs Table analyses the determinants of three financial services by multivariate probit model When jointly predicting these financial inclusion indicators, age still has an inverted-U shaped relationship solely with the possibility of having a formal account Education and wealth remain as important factor of financial inclusion with high level of statistical significance This confirms that being well-educated and richer increases the probability to be financially included via deposit services On the contrast, there is no association between these individual characteristics with the use of formal credit Only employment status is found to have a significant relationship with formal credit This suggests that those who have a job are more likely to borrow from a formal financial institution These findings are consistent with the results of probit model In addition, the last three row of Table presents the variance-covariance matrix of the error terms in the use of formal financial services Taking into consideration the impacts of unobserved factors, this indicates whether the participation of a person in these services is substitutes or complementary The correlation coefficient between formal account and formal saving is positive and statistically significant at the 1% level, appearing that the participation of a person in formal account will promote their participation in formal saving However, the covariance between formal credit and formal account (formal saving) is not significant, suggesting that these financial inclusion indicators are not related 35 Table 6: Demand-side determinants of financial inclusion indicators in Vietnam (2017) Multivariate Probit Regression Variables female age age2 edu_sec edu_ter inc_2nd inc_3rd inc_4th inc_5th employed Constant Formal account (10) Formal saving (11) 0.0374 (0.0916) 0.0385 ** (0.0182) -0.000609 *** (0.000211) 0.686 *** (0.114) 1.509 *** (0.154) 0.0521 (0.162) 0.131 (0.161) 0.256 * (0.155) 0.409 *** (0.150) 0.122 (0.119) -1.795 *** (0.389) 0.141 (0.102) 0.0147 (0.0192) -0.000178 (0.000216) 0.726 *** (0.139) 1.013*** (0.168) 0.304 (0.198) 0.147 (0.202) 0.475 ** (0.187) 0.692 *** (0.180) 0.0244 (0.133) -2.325 *** (0.431) Observations Formal credit (12) 0.0511 (0.0879) 0.0105 (0.0157) -0.000286 (0.000175) -0.104 (0.103) -0.0812 (0.145) -0.0190 (0.146) -0.0383 (0.145) -0.0435 (0.141) -0.155 (0.141) 0.186 * (0.111) -0.473 (0.342) 978 Correlation coefficient rho21: Formal account x Formal saving 0.6005 *** rho31: Formal account x Formal credit 0.0597 rho32: Formal saving x Formal credit -0.04055 Noted: (*), (**), (***) denotes statistically significant level at 10 percent, percent, percent, respectively Coefficients are reported in the table Standard errors are in parentheses 36 CHAPTER 6: CONCLUSIONS 6.1 Conclusion Since financial inclusion has a positive influence on economic growth and poverty alleviation, it is essential to investigate the extent to which an individual’s characteristics will affect their decision to participate in the financial system Using the Global Findex database in 2017 and the probit model, the paper address the issues for the case of Vietnam For formal account and formal saving, educational attainment and level of wealth are found to the major determinants of financial inclusion As expected, those who are well-educated and wealthier have a higher probability to be financially included These findings are almost identical with the results of Allen et al (2012), Akudugu, (2013), Cámara and David, (2015) Other factors have a mixed pattern The nonlinear effect of the respondents’ age is only significantly statistic with the likelihood of formal account in the main analysis while this relationship can be observed for formal saving or formal credit in 2014 Gender and employment status play a less important role in the individuals’ decision to engage in the financial system, but only in formal credit Being a woman and having a formal job may increase the possibility of borrowing at a formal institution This reflects a difference from previous research The main findings in this paper helps to identify groups of population which need to be targeted to enhance financial inclusion in Vietnam For example, vulnerable groups such as less-educated and poor adults are less likely to be financially included Community educators and policymakers may design proper strategies to improve people’s financial literacy as well as to increase the living standard Financial institutions can consider to lower the requirements to facilitate the access to financial services Customized services, such as microfinance products for low-income individuals, can be developed to meet the needs of target groups 6.2 Limitations and future research Financial inclusion is a multi-dimensional concept, which is not straightforward to observe and measure Therefore, it is unavoidable for the paper to expose some limitations Firstly, while the survey took place in three years, it can only be analysed at one point in time It limits the research in the cross-sectional model, which is not suitable to examine the changes in individuals’ usage of financial services over time, as well as to exploit within-country variation As stated by Allen et al., (2012: 25), the results of cross-sectional analysis only indicate the significant correlations between variables, not causality 37 Secondly, it is important to note that there are other factors which are major drivers of financial inclusion For example, Cámara & David, (2015) pointed out that the regional variations may affect the household’s financial decision Those who live in the urban area have wider access to financial services while rural areas have a low density of bank branches and weak political institutions According to the General Statistics Office of Vietnam, in 2017, it was estimated that about 65 percent of the total population live in the countryside The differences between urban and rural population may have a significant effect on the overall level of inclusiveness, thus need to be addressed in the analysis Also, Allen et al., (2012) and Ampudia and Ehrmann (2017) considered marital status as a determinant of financial inclusion Due to the lack of data, the impact of these factors on the level of inclusiveness is neglected in this paper Another caveat of the paper is that it does not cover all aspects of financial inclusion While defining financial inclusion as the usage of financial services, it takes no account of other dimensions, such as the outreach and quality of financial services Moreover, the paper only concentrates on the usage of deposit and credit whereas it sets aside other financial products, 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