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.
INTRODUCTION
Problem statements
Across both developing and developed nations, there is growing recognition that an inclusive financial system is a key driver of socio-economic development The Sustainable Development Goals (SDGs), adopted by the United Nations in 2015, address poverty and inequality as central challenges, and although financial inclusion is not named directly among the goals, it underpins eight of the 17 SDGs A well-functioning inclusive financial system reduces poverty traps and narrows income disparities, thereby fueling sustainable economic growth With improved access to financial services, individuals and households can manage risks, earn interest, and reduce the cost of credit, enabling better planning for the future and investments in health and education that lift living standards For businesses, online payment systems streamline core operations, expand consumption, and spur productive investment, creating a virtuous cycle that supports broader development outcomes.
An inclusive financial system is recognized as vital to overall development, prompting action by international organizations like the World Bank and IMF to support financial inclusion in emerging markets Governments are also working to remove barriers to financial services and raise residents’ financial literacy For example, Myanmar has rolled out the Basic Financial Literacy Booklet in rural areas to enhance household financial literacy, while Malaysia’s Financial Sector Master Plan has produced the Guideline for Basic Banking Services, enabling all population groups to obtain a basic bank account and access low-cost banking services.
Financial inclusion is a key driver of the development of financial markets and the economy, yet the 2017 Global Findex Database reports that about 1.7 billion people, or nearly 31 percent of the world’s population aged over 15, did not have an account with formal financial institutions To reduce the unbanked share, the World Bank Group launched Universal Financial Access by 2020 (UFA2020), aiming to add one billion new accountholders by 2020 and providing a practical framework for action to advance financial inclusion in 25 prioritized countries that together cover a substantial portion of the world’s financially excluded population.
Financial inclusion has long been embedded in Vietnam’s national strategies and government policies, and it features prominently in international discussions, such as the Asia-Pacific Economic Cooperation (APEC) meeting hosted by Vietnam in 2017, where the role of financial inclusion in agricultural development was a central topic (Le and Nguyen, 2017) As Vietnam advances toward upper-middle-income status, financial inclusion is seen as a means to achieve sustainable development and poverty alleviation However, the reach remains limited, with only 31 percent of Vietnamese adults holding an account at formal financial institutions according to the Global Findex Database 2017 To accelerate inclusion, the government is developing national strategies and a firm legal corridor With financial inclusion becoming an integrated part of Vietnam’s economic development, understanding its driving factors is essential before designing and implementing policy actions.
Relevance and justification of the research
In the last decade, financial inclusion has increasingly attracted attention in the academic literature Figure 1 illustrates the number of academic research on financial inclusion during the period of 1990-
An 2018 Google Scholar analysis of the term "financial inclusion" shows that the concept, first introduced in the 1990s, attracted only a small number of scholarly references in its early years By 2010, however, research output surged dramatically, with thousands of studies published annually, underscoring the topic's rising relevance to economic development.
Research shows a positive association between financial inclusion and economic growth and poverty reduction, yet the determinants of inclusion remain contested Proponents like Beck et al (2005) argue that the supply side, captured by indicators of banking penetration, is the primary driver of financial inclusion, while others such as Tuesta (2014) and Abel, Mutandwa, and Roux (2018) critique supply-side explanations and emphasize that demand-side factors shape the level of inclusiveness in financial markets In light of these debates, this paper analyzes financial inclusion in Vietnam from the user-side perspective, focusing on the period beginning in 2014.
By 2017, Vietnam had improved its financial system infrastructure, yet financial inclusion remained largely unchanged Assuming there are no constraints in accessing financial services, financial decisions would be driven primarily by demand-side factors, including socioeconomic characteristics, individual attitudes, and perceptions However, the paper concentrates only on these demand-side determinants of financial inclusion.
Individual characteristics have a meaningful effect on the likelihood of financial exclusion, making it reasonable to use these stable traits to explain and predict financial behavior Because these attributes tend to remain constant over long periods (Roe, 1984), they can reliably indicate who is at risk of exclusion This approach has been proposed and reinforced by numerous prior studies in this topic, which consistently emphasize the predictive value of personal demographics and socio-economic factors for financial inclusion outcomes.
Figure 1: Number of academic research of financial inclusion from 1990 to 2018
Source: Google Scholar (Accessed: July 28th 2019)
Research Objectives and Questions
This study analyzes demographic factors behind financial inclusion in Vietnam and assesses how these characteristics influence the use of formal financial services, including formal bank accounts, formal savings products, and access to formal credit By examining variables such as age, gender, education, income, urban-rural residence, and employment status, the research seeks to quantify the extent to which demographics determine engagement with formal financial institutions and to answer how these factors drive the adoption of formal accounts, savings, and credit in Vietnam.
How individual characteristics affect financial inclusion in terms of owning a formal account, formal saving and formal borrowing in Vietnam?
Scope of study
Using World Bank Global Findex Database data from 2017, this study analyzes 1,002 adults across 52 Vietnamese provinces and employs probit and multivariate probit estimations to examine how individual characteristics—age, gender, income, education, and employment status—are associated with financial inclusion indicators, defined as the probability of holding a formal account, engaging in formal saving, or securing formal credit.
Number google results of financial inclusion
Contribution of the study
Research on financial inclusion has grown in recent years, but studies detailing the determinants of using financial services in Vietnam remain limited This paper contributes to the literature as one of the first investigations into financial inclusion in the country By employing the same data source and methodology as previous studies, it also offers a cross-check of earlier findings.
The study’s main findings offer an overview of the participants in the financial markets, highlighting who remains unfavoured or underserved These insights are valuable for community educators, financial institutions, and policymakers, enabling them to identify targeted groups and tailor responses and supports accordingly to address disparities and improve market access.
Limitations of the study
These limitations shape the findings: the analysis exploits within-country variation and changes over time within a cross-sectional framework; other potential determinants are excluded due to data constraints; and the study does not cover all aspects of financial inclusion.
Chapter outline
The remaining of the paper is organized as follows:
Chapter 2 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 3 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 4 provides the econometrics model underpinning the estimations and the data collection
Chapter 5 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 6 draws the conclusion from the main findings Then, it will point out some limitations of the paper to give suggestions for future research
CONTEXT OF VIETNAM
Vietnam stands out as a prominent subject in this research field, offering substantial opportunities to expand financial inclusion GDP per capita has risen dramatically, from about $100 in the 1990s to more than $2,300 in 2017, according to World Bank data The financial sector contributes significantly to the economy as financial intermediation deepens By 2016, the country boasted more than 10,000 commercial bank branches and over 1,000 People’s Credit Funds The reach of financial services has expanded further, with ATMs increasing from about 2,000 in 2006 to 17,000 by the end of 2016 (Le and Nguyen).
In Vietnam, four licensed microfinance institutions operate more than 150 programmes to deliver financial services to the rural poor The financial system has rapidly embraced digital technology, with internet use exceeding 48% of the population and about 125 million people subscribing to mobile services, fueling widespread online and mobile financial activities Most commercial banks now offer internet banking and mobile banking, and several institutions have partnered with fintech firms to develop e-wallets According to the Vietnam Microfinance Working Group (2018), the reach of mobile money is expanding, and the World Bank’s Findex Database shows the share of adults with a mobile money account rising from 0.15% to 3.50% between 2014 and 2017.
Financial inclusion in Vietnam remains constrained by a cash-based economy, with roughly two-thirds of payment transactions still settled by cash-on-delivery even as e-commerce grows Financial services are concentrated in urban areas, leaving rural towns with limited access to banking and financial institutions Digital payments via mobile accounts hold promise for broader inclusion, but progress is hampered by an underdeveloped legal framework and the common requirement that a mobile account be linked to a credit or debit card, which means many potential users do not have formal accounts Currently, there is only one M-Service product under the GSMA’s Mobile Money for the Unbanked program that lets users deposit cash, make payments, and transfer money from a mobile account without a bank card.
With these above difficulties, formal account ownership in Vietnam remains at a low rate The share of adults with a formal account in 2017 remained stable at 30.80 percent, compared to the year
In 2014, this proportion was lower than the Southeast Asia regional average of 50.64 percent and significantly below the global average of 68.50 percent However, this comparison may be biased due to differences in data sources, sampling methods, and the set of countries included, which can affect the comparability of regional and world figures.
Demirgüç-Kunt and Klapper (2013: 290) point out that differences in GDP per capita across countries help explain variations in account penetration A fairer evaluation compares Vietnam’s formal account ownership with that of other lower-middle-income countries in Southeast Asia and around the world Figure 2 shows that the gaps have narrowed When Singapore, Malaysia, Thailand, and Brunei are excluded, the Southeast Asia average for account ownership drops to 31.83% Relative to the remaining countries in the region, Vietnam trails the Philippines and Indonesia but leads Cambodia and Myanmar in account ownership This pattern aligns with the idea that higher GDP per capita is associated with higher account penetration, with Lao PDR as the sole exception: although it has a higher income than Vietnam, its account ownership is 29.05%, slightly below Vietnam’s 30.80%.
In comparison with lower-middle-income countries, account ownership in Vietnam is still below the average level of the group (43.34 percent)
Figure 2: Account ownership and GDP per capita of Vietnam, in comparison with some countries in 2017
Source: The Global Findex Database & World Development Indicators (Accessed: April 2nd 2019)
In Vietnam, account ownership varies across individual characteristics, as shown in Figure 3 People in the labor force are more likely to own an account than those who are unemployed, indicating a gap in financial inclusion by employment status These differences highlight how employment status influences access to financial services among different groups in Vietnam.
Lower middle- income countries Southeast asia
GDP per capital (current US$)
Account ownership gaps widened by five percentage points since 2011, with the income-driven disparity standing at about 17.50 percentage points and persisting over time: 37.80% of the richest 60% own a formal account, compared with 20.28% of the poorest 20% Education also matters, as adults with a higher level of education are about three times more likely to have a formal account (42.43%) than less-educated adults (13.04%) Employed, wealthier, and well-educated individuals consistently show higher formal account ownership, a pattern that is also observed in other lower-middle-income countries.
Figure 3: Differences in account ownership between Vietnam and other lower-middle income countries by individual characteristics in 2017
Source: Global Findex Database 2017(Accessed: April 2nd 2019)
Account ownership in Vietnam diverges from the pattern seen in other lower-middle-income countries, revealing distinct gender and age dynamics that shape financial inclusion In lower-middle-income countries, men are more likely to hold formal accounts (47.09%) than women (39.70%), while Vietnam shows no significant gender gap; women's ownership grows from about 19.00% in 2011 to 30.40% in 2017, and men's from 24.54% to 31.20% over the same period, narrowing the gap Age matters too: in LMICs, account penetration tends to be lower among younger adults (34.39%) than older adults (46.94%), a pattern that differs from Vietnam's experience.
Account ownership ( % adults 15+)Vietnam Lower middle income countries
On the contrary, younger Vietnamese are more likely to have an account than their older counterparts The age gap in account ownership in Vietnam is only 4.30 percentage points, compared with an average 12.55 percentage-point gap across other lower-middle-income countries.
Figure 4: Self-reported reasons for not having a formal account in Vietnam and lower middle-income countries in 2017 (% of unbanked population)
Source: Global Findex Database 2017(Accessed: April 2nd 2019)
To understand why nearly 70.00 percent of Vietnamese population remained unbanked, figure 4 provides the eight reasons cited by respondents without a bank account in the Global Findex Survey
In 2017, 44.25% of individuals reported lack of money as the main barrier to account ownership, underscoring a significant financial inclusion challenge in Vietnam This finding reinforces that Vietnam remains a lower-middle-income country, where affordability of basic financial services continues to limit access for a substantial portion of the population.
World Bank (2018a) estimates about 9 million people in Vietnam live in poverty, with the majority coming from ethnic minority groups or rural areas This poverty-driven barrier to bank-account penetration is not unique to Vietnam; it is also identified as the leading obstacle to financial inclusion globally (Demirguc-Kunt et al., 2018) The second most cited barrier is that household members already have an account, which explains about 22.75% of the unbanked population Other common reasons include lack of documentation and a perceived lack of need for financial services, each contributing roughly 14% of the unbanked.
Lack of trust in financial institutions
Too expensive Too far away
No need for financial services
Someone in the family has an account
Lower middile-income countries Vietnam
Distance (12.10%), high transaction costs (11.70%), and distrust in financial institutions (7.66%) are less important barriers to the use of financial services Only a very small portion of adults without a formal account (0.59%) report being financially excluded because of religious reasons In lower-middle-income countries, “insufficient funds” (62.17%), “someone in the family has an account” (24.11%), and “too expensive” (24.09%) are the most cited barriers to participating in financial systems among the unbanked, while “no need for financial service” (4.27%) is the least cited reason.
Figure 5: Savings behaviour in Vietnam during 2011-2017 (% of total population)
Source: Global Findex Database 2011, 2014, 2017(Accessed: April 2nd 2019)
Figure 5 tracks Vietnamese saving behavior from 2011 to 2017 In 2017, about 57.4% of Vietnamese adults reported saving money in the past year, down from 63.3% in 2014 Formal savings at financial institutions rose from 17.74% in 2011 to 23.08% in 2014 and 25.23% in 2017, according to the Global Findex Survey Beyond formal channels, many households rely on saving clubs or saving with acquaintances; saving clubs have long been a common semi-formal saving channel that challenges financial inclusion, with semi-formal savers rising from 11.62% in 2011 to 25.02% in 2017 Still, a sizable share of savers use other methods, accounting for nearly half of savers, although this share has fallen since 2011, based on the Vietnam Access to Resources Household Survey.
Saved formally Saved semi-formally
Saved using other method only Did not save
Newman et al (2008) show that in 12 Vietnamese provinces, home and informal savings constitute a large portion of household savings, with most kept at home as cash, gold, or jewelry, while formal savings behavior varies by age, wealth, and education These individual characteristics also shape formal saving decisions in the Global Findex Survey, which finds that women, wealthier and younger adults, those with higher education, and the employed are more likely to save formally and tend to save more overall.
Figure 6 : Formal savings in Vietnam and other countries in 2017 (% of total population)
Source: Global Findex Database 2017(Accessed: April 2nd 2019)
Vietnam's formal savings rate is 14.08%, higher than Cambodia (5.33%), Myanmar (8.07%), and the Philippines (11.93%) This places Vietnam close to the global average for lower-income countries Within Southeast Asia, Indonesia — a lower-middle-income country — records the highest share of people saving at a financial institution, at 21.53%.
LITERATURE REVIEW
Financial inclusion has long been a focus of research, with evidence that wider access to financial services can drive development through multiple channels It boosts saving and investment, supporting production and economic welfare For example, Dupas and Robinson (2013: 164) find that rural Kenyan business households that frequently used transaction accounts accumulated more savings and investment and had higher expenditures than those in the control group By mobilizing saving, financial inclusion also helps vulnerable groups—such as women and the poor—access financial services, further fueling production and economic welfare (Dupas and Robinson, 2013: 164).
Financial inclusion can raise the poor’s income and consumption, contributing to poverty alleviation The IMF (2018) finds that higher levels of financial inclusion are associated with a substantial reduction in income inequality Insurance products help the poor manage risks from price shocks, health costs, and natural disasters, while affordable credit expands production and income and improves access to education and healthcare (Copestake, 2010) Moreover, Burgess and Pande (2005) show that expanding bank penetration and saving and lending activities in rural India boosted non‑agricultural output, increased employment, and reduced poverty in those areas Together, these findings suggest that expanding financial services—through savings, credit, and risk management—drives inclusive growth.
Thirdly, financial inclusion creates value to SMEs (Demirgüç-Kunt, Beck and Honohan, 2008: 9-
Firms that constitute a large share of the nation’s labor force have a significant impact on overall development (Ayyagari, Demirguc-Kunt and Maksimovic, 2011: 21) By reducing financial constraints, these firms can boost productivity and investment, thereby creating more job opportunities (Blancher et al., no date: 5).
Scholars have increasingly scrutinized the concepts and measurements of financial inclusion, recognizing that there is no single official definition Definitions vary across countries and regions, shaped by different social, economic, and political contexts Among early contributors, Leyshon and Thrift (1995) described financial exclusion as the set of processes that prevent poor and other disadvantaged social groups from gaining access to the formal financial system Building on this line, Carbó and colleagues (2005) offered another perspective, defining financial inclusion as the process that ensures individuals and firms can obtain appropriate financial services and products under accessible and reasonably priced conditions.
14 exclusion as “the inability and/or reluctance of particular societal groups to access mainstream financial services.”
Financial inclusion means ready access for households and firms to affordable, useful financial services, contrasting with financial exclusion The Asian Development Bank defines it as broad access to reasonably priced financial products that support everyday needs The World Bank's current definition expands this concept to include individuals and businesses having access to useful and affordable financial products and services—covering transactions, payments, savings, credit, and insurance—delivered in a responsible and sustainable way to meet their needs.
Many definitions emphasize the importance of broader access to financial services, but the actual use of those services has often been neglected (Singh and Roy, 2015:12) Stone (2005: 8-9) notes that in some developed economies, such as the United States and the United Kingdom, access to finance can be treated as equivalent to usage By contrast, developing countries frequently face physical or institutional barriers that restrict access, making it more appropriate to frame financial inclusion in terms of usage rather than access.
Demirguc-Kunt, et al (2008: 26-29) stress the distinction between access to financial services and their use in illustrating financial inclusion (Figure 6) Access is determined by the supply side, while use involves both demand and supply, capturing whether people not only have services available but also actively utilize them To clarify these differences, the authors divide non-users of financial services into voluntary exclusion and involuntary exclusion, highlighting that exclusion can arise from personal choice or external constraints.
Voluntary exclusion describes individuals who opt out of participating in financial systems even when they have access, often for ethical or religious reasons Some people engage with financial services only indirectly, such as by using a relative’s account for transactions, a pattern known as self-exclusion From the policymakers’ perspective, little can be done to draw these groups into the formal system because exclusion in this case stems from low demand rather than a lack of supply (Demirgüç-Kunt et al., 2008: 26).
Another group of nonusers remains involuntarily excluded from financial services From the supply side, these unbankable groups consist of individuals and households that lack sufficient income or exposure to the high lending risk faced by lenders Government failures or market imperfections may also drive exclusion in this category Contributory factors include unattractive financial products, limited information, high transaction costs, weak risk-management systems, and ill-informed regulations, all of which constrain this population from accessing financial services.
15 access financial products and services These barriers of financial inclusion are worth to be addressed in the policy framework (Demirgüç-Kunt, et al., 2008: 26-29)
Figure 8: Distinction between access to and use of financial services
Access to financial services No access to financial services
Source: Demirgüç-Kunt, Beck and Honohan, (2008: 29)
Honohan and King (2009:20) argue that the distinction between access and usage remains unclear due to varying definitions of access, and therefore the focus should shift to measuring usage, since usage statistics tend to more accurately reflect access The World Bank (2013:16) notes that voluntary exclusion within the system complicates observing and measuring actual access relative to actual use, and accordingly their report favors assessing financial inclusion from the dimension of financial usage.
Other studies have sought to measure financial inclusion from different dimensions From the providers’ perspective, Beck et al (2005) proposed traditional indicators of banking-system penetration based on physical access, affordability, and eligibility for deposit and lending services These indicators capture the geographic and demographic penetration of branches and ATMs, the number of loan accounts per capita, and the loan-to-GDP ratio.
Users of formal financial services
Non-users of formal financial services
Not to use Indirect access
Insufficient income High risk Discrimination
Contractual / Informational frameworkPrice / Product features
Evidence shows that countries with denser banking networks and greater use of loan services tend to have higher financial inclusion, as reflected in measures such as deposit accounts per capita and the deposit-to-GDP ratio Yet, relying on these indicators in isolation cannot accurately capture the extent of financial inclusion; Sarma (2008) notes that separate metrics can misstate inclusion because people may hold multiple accounts or foreign accounts distort the share of the banked population Moreover, differences across indicators can paint only a faint picture of the current state, and combining them may be problematic; Amidžić et al (2014) illustrate this with Albania, where a high debt-to-income ratio coexists with a disproportionate number of bank branches.
To capture multiple facets of inclusive financial markets, Sarma (2008) proposed an index of financial inclusion that aggregates banking penetration, availability, and usage, constructed in the same way as UNDP indicators; higher values signal a more inclusive financial system While this index effectively tracks changes in financial inclusion over time and across economies, Amidžić et al (2014) warn that assigning equal, exogenous weights to the three dimensions can distort its interpretation, prompting a new composite index with a more appropriate weighting scheme Yet, like other indicators, it remains largely supply-side and country-level Cámara and Tuesta (2014) contend that greater access points or better governance may facilitate entry into financial markets but do not automatically raise the level of inclusiveness, since individual financial decisions are shaped by socio-economic characteristics and behavior; consequently, the determinants of financial inclusion arise from both supply-side and demand-side factors (Abel, Mutandwa and Roux, 2018).
Little is known about the driving forces behind financial inclusion from the user’s perspective One of the first attempts to study financial inclusion from the demand side was by Demirguc-Kunt and Klapper (2012), who offered a qualitative overview of financial behaviors across 148 nations using the World Bank’s Global Financial Inclusion (Global Findex) Database Building on this, Allen et al (2012) developed an empirical model to examine the determinants of financial inclusion in 123 countries, drawing on data from over 120,000 participants at both the individual and country levels.
People with insufficient income or those living in rural areas are particularly prone to financial exclusion, while the distance to financial service points and high transaction costs are reported as the main barriers to accessing financial services.
METHODOLOGY AND DATA COLLECTION
Econometric models
To identify the determinants of financial inclusion, the paper relies on the threshold decision model introduced by Hill and Kau (1973) and further developed by Akudugu (2012) The core idea is that a binary choice hinges on crossing a response threshold, beyond which the decision may flip Individual heterogeneity shifts this threshold, influencing observed inclusion outcomes In formulating the theoretical framework, Y is treated as a latent decision variable that determines the observed binary outcome when the threshold is crossed.
Under a threshold model of action, an individual either takes action or does not, with the decision driven by exogenous factors X that are distributed around a critical threshold X* When X is below X*, adoption does not occur; as X approaches and reaches the threshold X*, the likelihood of adoption rises and a positive reaction is triggered This relationship can be described by a threshold equation linking the adoption outcome to X and X*.
𝑋 𝑖 is a set of exogenous factors affecting the decision of individual i th
The probability of individual taking action is given as:
Financial inclusion is modeled as a binary outcome variable representing an individual's decision to participate in the financial system It is measured by three indicators: account ownership, usage of savings products, and use of bank credit These outcomes are analyzed as functions of individual characteristics Given the available data, the explanatory variables include age, gender, income level, labor status, and education.
As people age, their demand for financial products may remain high, even as they face increasing challenges in accessing financial services Ageing can bring loss of mobility, physical impairments, and cognitive decline (AgeUK, 2016: 7) and decreasing financial literacy (MacLeod et al., 2017: 3), which can hinder engagement with traditional financial institutions Some may find it troublesome to commute to banks due to distance or to keep up with digital banking technology Others may no longer have the ability to manage their finances or to remember passwords and PINs (Bleijenberg et al., 2017: 1).
AgeUK (2016:7) notes that in Vietnam the elderly are more likely to lose independence and face social exclusion Many seniors prefer saving cash or storing gold and jewelry at home for their lifetime, rather than using formal financial services As a result, this reliance on informal saving can create a threshold at which age itself becomes a barrier to financial inclusion for older people.
Research by Demirguc-Kunt and Klapper shows persistent gender gaps in account penetration in developing countries, regardless of income, education, age, or region Legal discrimination against women and prevailing gender norms, including domestic violence and child marriage, hinder women’s ability to open bank accounts and to use saving and credit products The United Nations Capital Development Fund adds that women face disadvantages related to mobility, as well as physical and financial capacity, making men more likely to receive bank loans As a result, gender bias in financial inclusion is evident, particularly in developing economies like Vietnam.
Income levels strongly influence financial inclusiveness Sinha and Subramanian (2007) show that households with low incomes are less likely to hold formal savings accounts because they have limited incentives to save or may be ineligible for certain financial products Demirgüç-Kunt and Klapper (2013) add that, in many countries, the transaction costs of banking make account ownership seem unnecessarily expensive for low-income individuals These insights together explain how low income creates barriers to formal financial services by reducing saving incentives and increasing the affordability hurdle of banking.
Employment status is a key determinant of access to banking and financial services The absence of formal employment is linked to lower income, which can make it difficult for those outside the workforce to obtain a bank account In contrast, employed adults typically have greater demand for financial services, since wages are commonly received through bank accounts and savings from earnings support financial inclusion As Demirgüç-Kunt et al (2018) note, working individuals are more likely to engage with banking products, reinforcing the link between employment and financial access Therefore, employment status should be a central consideration when assessing financial inclusion strategies.
A sizable share of adults—about 23%—is expected to be financially included, yet having a job does not automatically translate into an active bank account In many developing countries, employers still pay wages in cash, and informal workers tend to spend earnings in cash, which limits the use of formal financial services (Botric and Broz, 2017: 217; Sinha and Subramanian, 2007: 17).
Education has a consistently positive relationship with financial inclusion, as shown by several studies (Akudugu, 2013: 4; Cámara and David, 2015: 25; Fungáčová and Weill, 2014: 196; Allen et al., 2012: 25) Atkinson and Messy (2013: 18) emphasize the role of education, especially financial literacy, in promoting financial inclusion Less-educated individuals are more likely to have lower financial literacy or be unaware of financial products, which discourages their involvement in a broad range of financial practices.
In a discrete choice model, both logit and probit estimations can be used because each method computes the probability that a respondent chooses one option over alternatives They tend to produce similar results, so they are often employed interchangeably in practical applications The primary distinction lies in their cumulative distribution functions: the logit model uses a logistic distribution, while the probit model uses the standard normal distribution I prefer the probit specification to facilitate comparability with results reported in the literature (Demirgüüç-Kunt and Klapper, 2013; Fungácová and Weill, 2014; Cámara and David, 2015; Fungácová and Weill, 2016).
Using a multivariate probit model, I estimate correlated outcomes simultaneously Introduced by Ashford and Sowden (1970), this approach allows the error terms across equations to be pairwise correlated and predicts financial inclusion indicators jointly rather than one at a time This enables assessing the joint determinants of multiple financial inclusion indicators and their relationship with individual characteristics There may be few studies using this method to examine such relationships, making this analysis a notable contribution to the literature on financial inclusion.
Data collection
Data from the World Bank's Global Findex Database offers the most up-to-date insights into how people around the world access financial services and into the individual characteristics that accompany this access (World Bank, 2018b) The dataset is collected by Gallup, Inc through nationally representative surveys of more than 150,000 individuals across 148 countries This source supports analysis of global financial inclusion trends and the factors that shape how people interact with financial systems.
1,000 respondents in each country However, the sample size can be increased in proportion to country size
The study relies on survey data collected in 2011, 2014, and 2017, with each year's information gathered through randomly selected individuals for face-to-face interviews Although data exist for all three years, a panel dataset is not available to track changes in financial inclusion over time because the observed individuals differ across years Consequently, this paper uses the 2017 cross-sectional data—the most recent update—to provide a snapshot of the current state of financial inclusion in Vietnam The 2017 sample comprises 1,002 respondents across 52 provinces.
From the Global Findex 2017 baseline of 1,002 observations, I dropped three cases, leaving 999; these were respondents who did not have a bank account and cited religious norms as their sole barrier to financial services Among them, Muslims who refrain from earning or paying interest are voluntarily excluded to comply with Islamic beliefs, independent of other characteristics Brekke (2018:5) observed that demographic factors such as age, level of education, and origins do not affect financial decisions among Muslims surveyed in Norway in 2015–2016 Because this group represents only a very small share of the data, their exclusion is unlikely to alter the results I retained observations where respondents self-excluded for reasons of “no need” or “someone in the family has one,” since individual characteristics could influence these reasons Although prior studies are limited, there is a noticeable disproportion in these exclusion reasons by personal attributes, with women, employed individuals, and those who have completed secondary education or less more likely to cite these two reasons than their peers.
Global Findex data exclude eleven Vietnamese provinces: An Giang, Dac Lak, Dien Bien, Gia Lai, Ha Giang, Ha Tinh, Kien Giang, Kon Tum, Nghe An, Quang Binh, and Thanh Hoa These excluded areas account for about 19 percent of Vietnam's population, according to World Bank data from 2018 (World Bank, 2018b).
Building on prior research, this study analyzes financial inclusion along three dimensions: account ownership, savings, and credit An account serves as a gateway to financial markets, while savings and credit provide essential services that expand and deepen financial inclusion.
Account penetration was measured by asking respondents whether they hold a formal bank account or any other financial institution account, and by identifying those who used mobile money services in the past year Mobile money, covered under the GSMA’s program for the Unbanked, operates independently from a traditional bank account and provides a practical path to financial services; therefore, mobile money account holders are treated as part of the banked population in this study The account variable is coded as 1 if a respondent reports having either a formal account or a mobile money account, or both, and 0 otherwise.
Saving behaviour is the second dimension of financial inclusion Merely having an account does not guarantee that owners will use it to save; many save through informal channels such as a savings club or a circle of acquaintances To capture this dimension, respondents are asked whether they save formally at a financial institution If they answer "yes," the saving variable is coded as 1; otherwise, it is coded as 0.
Credit behaviour is the third dimension of financial inclusion Like saving, borrowing comes from a range of sources People may borrow formally from financial institutions such as banks, credit unions, and microfinance institutions, or rely on informal lenders—family members or friends—or savings clubs Access to diverse credit options strengthens financial inclusion by supporting households and small businesses to manage cash flow, investment needs, and resilience against shocks.
If they reported borrowing from a formal institution in the past year, the dummy variable (credit) will take a value of one: and zero otherwise
The determinants of financial inclusion and socioeconomic characteristics, namely gender, age, income, level of education and employment status – are represented as the following variables:
Gender is coded as a dummy variable, with female equal to 1 for respondents who identify as women, enabling analysis of gender effects on financial inclusion The hypothesis is that there is a negative correlation between being female and the level of financial inclusion, suggesting that women may have lower access to or use of financial services.
Age is treated as a continuous variable representing the number of years and is typically expected to have a positive effect on financial inclusion up to a certain point Beyond that threshold, however, the relationship reverses and becomes negative To capture this inverted U-shaped pattern, the model includes age squared (age2) in addition to age, allowing the effect of age on financial inclusion to increase at younger ages and then decrease as individuals grow older.
The relative income is divided into five dummy variables for poorest 20 percent (income_1 st ), second
Four income quintile dummy variables—income_2nd, income_3rd, income_4th, and income_5th—are used to indicate whether a respondent’s income falls into the second, third, fourth, or richest quintile, respectively, with each variable coded as one if the respondent belongs to that quintile and zero otherwise The regression omits the poorest 20 percent as the reference category, meaning the poorest quintile serves as the baseline against which the other quintiles are compared It is expected that the higher income level relates to the greater use of financial services.
For the education background, the paper uses three dummy variables for completed primary or less (edu_pri), secondary (edu_sec) and completed tertiary or more (edu_ter) Edu_pri is the excluded variable in the estimations Each variable takes a value of one if respondents reach that level of education and zero otherwise Those with higher education are expected to have a higher probability of being financially included since they comprehend the products and their benefits The paper looks for the positive relationship between the level of education and financial inclusion
Employment status is a dummy variable (employed), which captures whether the respondents are in the workforce or not The value of employed equals one if the person has a job and zero elsewise Since more and more employers prefer to pay salaries into a bank account, employed individuals are more likely to be included in financial systems than their counterparts Therefore, the expected sign of this variable is positive
RESEARCH RESULTS
Descriptive Statistics
Table 2 shows the summary statistics for all variables used in the estimations and their expected signs The sample comprises Vietnamese adults aged 15 to 91, with a mean age of 43 About 74.17% of respondents are employed, and women make up 57.4% of the sample Formal financial inclusion is observed in 33.4% of Vietnamese adults, 29.4% reported borrowing money formally in the past year, and 16.5% saved money at formal financial institutions.
Before assessing how individual characteristics influence the use of financial products, it is necessary to perform a correlation analysis to understand the signs of the coefficient estimates As presented in Part A, Table 3, there is no high intercorrelation among the predictor variables, which mitigates multicollinearity The second part of Table 3 forecasts significant relationships between individuals’ characteristics and three indicators of financial inclusion The age coefficient is unexpectedly negative, while the female variable shows no significant association with any financial inclusion indicator Dummy variables for education and income exhibit a positive association with formal saving and formal accounts The correlation between the credit variable and education- or income-related variables appears unstable, suggesting that the results may not fulfill all expectations.
Main analysis
Table 4 presents probit estimations of the relationship between individual characteristics and financial inclusion in Vietnam, showing how demographic factors affect the probability of holding a formal account, engaging in formal saving, and obtaining formal credit—the three indicators used to measure financial inclusion The analysis indicates that these demographic attributes significantly influence access to formal financial services in Vietnam, underscoring the role of targeted policy measures to broaden formal banking, saving, and credit across different population groups.
It is surprising that gender does not have a significant effect on any of the three financial inclusion indicators, a result that aligns with Allen et al (2012), who found no global link between gender and financial inclusion Similar findings are reported in country studies, including Akudugu (2013) for Ghana and Cámara and David (2015) for Peru This lack of gender disparity may reflect the rising participation of women in economic activities and public life, with more women becoming the primary income earners in their households In many contexts, men migrate domestically or overseas for work, leaving the wife to become the household breadwinner.
28 the receiver of the remittances and domestic money transfers (United Nations Capital Development Fund, 2006: 25) This helps to weaken gender bias in financial inclusion
Age positively influences the likelihood of holding an account at formal financial institutions, while age squared has a negative effect, indicating an inverted U-shaped relationship between age and formal account ownership As people grow older, they tend to be more financially included due to rising demand for financial services, with each additional year increasing the probability of owning a formal account by 1.30 percentage points, though this probability eventually declines after a certain age as physical disabilities hinder access to financial institutions Fungáčová and Weill (2014: 202) observed the same pattern in China and attributed it to a generational effect determined by both demand and supply sides By contrast, this nonlinear relationship does not hold for formal saving and formal credit, since both age and age^2 do not significantly relate to these two indicators.
Education level is positively associated with the likelihood of having a formal account or saving, with both education dummies (edu_sec and edu_ter) significant at the 1% level and tertiary education yielding the larger effect Specifically, individuals who have completed post-secondary education or higher are 55.60 percentage points more likely to have a formal saving account than those with only elementary education, while those with completed secondary education are 30.49 percentage points more likely These findings underscore the role of education in improving financial inclusion and are consistent with existing literature; however, education shows no effect on formal credit.
Income influences the probability of owning a formal saving account or using formal financial services, while there is no significant difference in the likelihood of obtaining formal credit across income groups The richest 20 percent are more likely to hold a formal saving account, with an increase of 15.30 percentage points (18.29 percentage points in another specification) compared to the poorest 20 percent This suggests that higher income enhances financial inclusion by expanding the ability to save In addition, education consistently emerges as a robust determinant of financial inclusion across studies.
Variable Definition Obs Mean Std Dev Min Max Expected sign
A Indicators of financial inclusion account = 1 if respondent has a formal account 999 0.334 0.472 0 1 saving = 1 if respondent saves formally 985 0.164 0.371 0 1 borrowing = 1 if respondent borrow formally 991 0.294 0.456 0 1
B Determinants of financial inclusion female = 1 if respondent is female 999 0.574 0.495 0 1 (–) 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 (–) income_1st = 1 if respondent’s income belongs to the first quintile 999 0.178 0.383 0 1 income_2nd = 1 if respondent’s income belongs to the second quintile 999 0.180 0.385 0 1 (+) income_3rd = 1 if respondent’s income belongs to the third quintile 999 0.186 0.389 0 1 (+) income_4th = 1 if respondent’s income belongs to the fourth quintile 999 0.207 0.406 0 1 (+) income_5th = 1 if respondent’s income belongs to the fifth quintile 999 0.248 0.432 0 1 (+) employed = 1 if in the workforce 999 0.742 0.438 0 1 (+) edu_pri = 1 if completed primary or less 999 0.338 0.473 0 1 edu_sec = 1 if secondary education 999 0.511 0.500 0 1 (+) edu_ter = if completed tertiary or more 999 0.151 0.358 0 1 (+)
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 employed -0.069** -0.233*** -0.289*** -0.144*** 0.0582* 0.109*** -0.102*** -0.069** 0.018 0.003 0.133*** 1.000
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 1.000
(*), (**), (***) denotes statisticallysignificant level at 10 percent, 5 percent, 1 percent, respectively
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 measures how well the explanatory variables explain the variation in each financial inclusion indicator In regression (1), the adjusted R-squared is highest at 18.3 percent, reflecting the statistical significance of age, income, and education The value declines to 10.80 percent when formal saving is the dependent variable, and it further drops to 3.20 percent for formal credit, where only one variable can explain the variation in the dependent variable.
Overall, the results show a mixed pattern of financial inclusion in Vietnam Being older, well-educated, and wealthier increases the likelihood of account ownership and, in turn, the use of other financial services For formal saving, a higher level of income and education emerge as the main determinants, while employment status is the sole driver of formal credit in Vietnam.
Table 4: Demand-side determinants of financial inclusion indicators in Vietnam (2017)
Noted: (*), (**), (***) denotes statistically significant level at 10 percent, 5 percent, 1 percent, respectively
Marginal effects are reported in the table Standard errors are in parentheses
Robustness analysis
To check robustness, I applied the same probit model to two variations: a 2014 dataset and a 2017 sample with the employed variable excluded To address possible correlation arising from unobserved individual characteristics, I also estimated a multivariate probit model, which allows the error terms across equations to be correlated pairwise This approach is expected to provide a more reliable analysis by capturing cross-equation correlations in the error terms and offering a joint view of the determinants.
5.3.1 Probit model with 2014 dataset and 2017 sample but excluding employed variable
Table 5 presents the robustness-test results obtained from a probit model Regressions (4)–(6) estimate the probability of each financial inclusion indicator using the 2014 Global Findex Database, and since the data for the variable used in the analysis are not available for 2014, it is not included in the regression.
Gender does not affect access to formal accounts or formal savings, but it is positively associated with having formal credit In 2014, women were more likely to participate in the financial system through credit activities This finding contrasts with Fungácová and Weill (2014), who reported that being female reduces the likelihood of borrowing from formal institutions.
Robustness analyses show that age and age squared do not affect the probability of holding a formal account, a finding that stands in contrast to much of the literature across three financial inclusion indicators The only study supporting this pattern is Cámara and David (2015) for Peru By contrast, the year 2014 reveals a nonlinear relationship between age and formal saving and formal credit, a pattern that was not observed in 2017.
The association between the education variable and formal account/ formal saving remains unchanged in
In 2014, tertiary education was negatively associated with formal credit, indicating that individuals with higher education are less likely to borrow from formal financial institutions There is no substantial difference in how income relates to the three indicators of financial inclusion in 2014.
To strengthen the robustness of the findings, I re-estimated regressions (7) to (9) on the 2017 dataset with the employed variable dropped The exclusion leaves the results for formal account and formal saving essentially unchanged from the main analysis, indicating robustness to this specification By contrast, in regression (9) the coefficient on formal credit shows a negative relationship with age squared, a pattern that is not present in the main analysis.
(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
Table 5: Robustness check with 2014 dataset and 2017 sample without employed variable - Probit Regression
Noted: (*), (**), (***) denotes statistically significant level at 10 percent, 5 percent, 1 percent, respectively
Marginal effects are reported in the table Standard errors are in parentheses
5.3.2 Multivariate probit model with the original sample
The multivariate probit model, proposed by Ashford and Sowden (1970), generalizes the probit model to estimate correlated binary outcomes jointly In the univariate probit model, potential cross-commodity correlations among financial inclusion indicators for the same adults are not observed, and unobserved factors may appear as correlated error terms The multivariate probit model overcomes this limitation by predicting these choices jointly rather than calculating the probability of each indicator separately It allows the error terms across equations to be correlated in pairs, capturing dependencies among financial inclusion indicators and improving inference.
Table 6 uses a multivariate probit model to analyze determinants of three financial services, finding that age has an inverted-U relationship only with the probability of holding a formal account when the indicators are predicted jointly Education and wealth remain strong, highly significant predictors of financial inclusion, indicating that higher education and greater wealth increase the likelihood of financial inclusion via deposit services By contrast, these characteristics show no association with the use of formal credit Instead, employment status is the only characteristic significantly related to formal credit, suggesting that employed individuals are more likely to borrow from a formal financial institution Overall, the findings are consistent with the probit model results.
The last three rows of Table 6 present 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 in a 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.
Table 6: Demand-side determinants of financial inclusion indicators in Vietnam (2017)
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, 5 percent, 1 percent, respectively
Coefficients are reported in the table Standard errors are in parentheses
CONCLUSIONS
Conclusion
Financial inclusion fuels economic growth and poverty reduction, making it essential to understand how individual characteristics influence the decision to participate in the financial system Using the 2017 Global Findex dataset and a probit model, this study investigates Vietnam to identify the determinants of financial participation The findings show that factors such as education, income, age, gender, and urban-rural status significantly affect the likelihood of financial inclusion, providing evidence-based guidance for policies aimed at expanding access to and use of financial services in Vietnam.
Educational attainment and wealth emerge as the dominant drivers of financial inclusion for both formal accounts and formal savings, with higher-educated and wealthier individuals more likely to be financially included These findings align with prior studies by Allen et al (2012), Akudugu (2013), and Cámara and David (2015) Other determinants display a mixed pattern The age effect is nonlinear and significantly associated with the likelihood of holding a formal account in the main analysis, while this relationship is evident for formal saving or formal credit in 2014 Gender and employment status have a smaller overall impact, but they matter for formal credit, where being a woman and having formal employment can increase the probability of borrowing from a formal institution, a result that differs from some previous research.
This study identifies target populations to boost financial inclusion in Vietnam, highlighting that vulnerable groups, especially the less-educated and poor adults, are less likely to be financially included By turning these findings into action, community educators and policymakers can design strategies to improve financial literacy and raise living standards To broaden access to financial services, financial institutions should lower entry barriers and offer customized solutions, such as microfinance products for low-income individuals, aligned with the needs of these groups.
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
Although the survey spanned three years, the analysis was conducted at a single point in time, restricting the study to a cross-sectional framework This design limits the ability to track changes in individuals’ use of financial services over time and to exploit within-country variation As Allen et al (2012: 25) note, cross-sectional results indicate significant associations between variables but do not establish causality.
Secondly, several other factors are major drivers of financial inclusion Regional variations can influence household financial decisions, with urban residents having broader access to financial services than those in rural areas, where bank branch density is low and political institutions may be weaker The General Statistics Office of Vietnam (2017) reports that about 65 percent of the population lives in the countryside, underscoring how urban–rural differences can affect the overall level of financial inclusion and should be addressed in the analysis In addition, studies by Allen et al (2012) and Ampudia & Ehrmann (2017) identify marital status as a determinant of financial inclusion, but the impact of these factors is not quantified in this paper due to data limitations.
Another caveat is that the study does not capture all aspects of financial inclusion By defining financial inclusion as the usage of financial services, it overlooks other dimensions such as outreach and the quality of services Moreover, the analysis concentrates only on the use of deposit and credit products, while excluding other financial offerings like insurance and payments.
As indicators for measuring financial inclusion expand, future research can address current limitations and deliver a more accurate, holistic view of financial inclusion In Vietnam, mobile banking and e-wallets are rapidly expanding and gaining popularity, with predictions they will gradually replace traditional banking This ongoing digital shift makes mobile finance a prominent area for further investigation and analysis in the field of financial inclusion.
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