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1
Effects of human capital development on bank deposits
Nikhil Srivastava1, Prof. David Tripe2, Dr. Mui Kuen Yuen3
Abstract
This paper investigates the effects of human capital development on bank deposits
using 2SLS and dynamic panel methods (two-step difference and system GMM) in a cross-
country setup. We use human development index (HDI), development of the public healthcare
system, and the education level of the country to measure the human development level of the
country. The results show a positive relationship between HDI and bank deposits. This result
is more prominent in high income and financially included countries. We also find that a better
healthcare system increases the income level of households, which translates into an increase
in bank deposits mainly in high income and financially included countries. We employ two
dependent variables: deposit to GDP ratio and value of total deposit (USD). The impact of HDI
and healthcare expenditure on total bank deposits of the country is higher than bank deposits
to GDP ratio. This suggests that improvement in HDI and healthcare increases the income of
households and a proportion of that increased income goes into the banking system. We further
examine the importance of education on bank deposits and find a positive impact on bank
deposits.
* We are grateful to Prof. Faruk Bali, Prof. Martin Young, Prof. Srikanta Chatterjee, and Prof. Martin Berka from
Massey University for invaluable feedback that improved the paper quality substantially. We are also thankful to A/Prof. Ivan, Dr Tram Vu, A/Prof. Shyamal Chowdhury, A/Prof. Andrea Menclova, Professor Ruhul Salim and
A/Prof. Debdulal Mallick, and A/Prof. Sunder Ramaswamy. We are also grateful to the participants of research
symposium in the IFMR, Krea University, seminar participants at Massey University and the feedback from the
participants of New Zealand Association of Economists, Victoria University of Wellington, New Zealand. 1 PhD Student at School of Economics and Finance, Massey University, New Zealand 2 Professor at School of Economics and Finance, Massey University, New Zealand 3 Senior Tutor at School of Economics and Finance, Massey University, New Zealand
2
1. Introduction
Bank as an engine of the financial system play a pivotal role in economic development
(Galbis, 1977). Although some economists argue that financial markets’ role is more prominent
in economic development (Rajan & Zingales, 1998; Scharfstein, 1988), the role of banks
cannot be ignored (Arestis, Demetriades, & Luintel, 2001; Beck & Levine, 2004). The
importance of a stable banking system has been observed in the global financial crisis (GFC).
This crisis led into the great recession and caused an estimated USD 14 trillion loss of wealth
for US households (Porter, 2014). One of the primary reasons for the banking crisis was over
reliance on wholesale funding. Therefore, after the GFC, the Basel committee advocated
increasing the proportion of bank deposits to improve stability, with this being codified in Basel
III. Historically, deposits has been considered one of the most stable sources of funding (King,
2013), and it has thus become important for banks to be able to identify the factors which can
influence bank deposits.
Previous research identifies many factors which can influence bank deposits such as
interest rates (Diebold & Sharpe, 1990), brand value of banks (Dick, 2007; Zephirin, 1994),
and financial inclusion (Cull, Demirgüç-Kunt, & Lyman, 2012; Han & Melecky, 2017). Recent
literature has highlighted the importance of financial inclusion in attracting deposits
(Fungáčová, Hasan, & Weill, 2019; Han & Melecky, 2017). The research suggests that one of
the primary reasons for less financial inclusion is less human capital development (Allen,
Demirguc-Kunt, Klapper, & Peria, 2016; Arora, 2012; Atkinson & Messy, 2013). The impact
of human capital development on bank deposits have not been explored, and this is a primary
focus of this study.
Human capital development not only includes the education level in the country but also
people’s health. The current pandemic (COVID-19) has made us realize that how important it
is to have a good healthcare system, income, savings, and education level in a country to
3
combat any uncertain events (Ahmed, Ahmed, Pissarides, & Stiglitz, 2020). Even though Italy
was top ranked countries in terms of healthcare, it still failed in providing healthcare facilities
to its citizens (Pearson & Triglione, 2020). Similarly, India, due to poor healthcare
infrastructure, struggled to provide the healthcare services to the people. Indian government
has also faced difficulty in reaching out to people to inform them about the pandemic due to
the country’s low literacy rate. Therefore, governments around the globe are realizing that they
need to work on the development of human capital (Walsh & Resch, 2020; WBG, 2020). Better
education, health, and income improve the quality of lifestyle and productivity, which
eventually determine economic growth (Jimenez, Nguyen, & Patrinos, 2012). This also
determines the people’s saving behavior and use of the financial system (Lusardi, 2008). We
use the human development index developed by the UNDP to measure countries’ human
capital development levels, supplemented by country data on healthcare expenditure and
education index to investigate the impact of the same on bank deposits.
The aim of this paper is to explore the influence of human capital development on a
country’s bank deposits. To accomplish this aim, we conduct regression analysis using 2SLS
and GMM methods on panel data for 107 countries, including high-income and low-income
countries. This study has several policy implications. One is that the development of a
healthcare system can improve work-life endurance, lifespan, and cognitive abilities of
households, which, in turn, increases the income level, thereby increasing bank deposits.
Governments of low-income countries should be cautious in promoting public and private
contributions for the healthcare system. These contributions reduce households’ disposable
income, decreasing their usage of the banking system. The results show that education
increases the cognitive abilities and skillsets that are used along with the good health to improve
the income status of households. Education facilitates households understanding of financial
4
products. They use deposit products for transaction and saving purposes thus increasing bank
deposits.
This paper contributes to the human development, health economics literature studying
the effects of health shocks on income and savings of households (Genoni, 2012; Wagstaff,
2007). It extends the work of Jappelli, Pistaferri, and Weber (2007) who studied the impact of
the quality of healthcare systems on income inequality in Italy. Their study was at the district
level and found that districts with lower quality healthcare systems had increased income
inequality, and that precautionary savings tended to increase in those districts. The paper also
touched upon the Solow growth model (Gyimah-Brempong & Wilson, 2004) by examining the
impact of the healthcare system on the financial system, the banking system in particular. This
study explores the relationship between household incomes and the healthcare system and
further contributes to the human capital and financial development literature by studying the
effect of education on bank deposits in a cross-country set up.
The main findings of the research are as follows: human development encourages
households to use the banking system, thereby increasing bank deposits that will increase credit
creation and support economic growth. Public expenditure on improving the healthcare system
improves the income level of households, which translates into bank deposits. This result is
consistent across regions and the incomes levels of the countries studied. However, the impact
of the healthcare system is more prominent in high income and financially included countries.
The results also show that education plays a key role in increasing usage of the banking system,
although we do not find statistically significant results in subgroup analysis. Moreover, good
governance in countries encourages households to use the banking system, especially in
countries with better regulatory qualities and less corruption.
5
The rest of the paper is organized as follows: section 2 presents the existing literature
related to health shocks, education, and savings. Section 3 present data collection, econometric
methodologies and primary investigations. Section 4 shows presents the discussion and
analysis of the findings. Section 5 presents the additional analysis with other relevant
macroeconomic variables and section 6 concludes.
2. Existing literature
Health expenditure is one of the biggest causes of bankruptcy in the United States (US). It
is estimated that around 530,000 families file for bankruptcy every year in the US due to heavy
medical expenditures (Konish, 2019). According to Miller, Hu, Kaestner, Mazumder, and
Wong (2019) nearly 20 percent of the US population reported medical debt in their credit
report. Medical expenditures arise due to sudden health shocks in the family, and in most cases
households are not prepared financially for such events (Fisher & Montalto, 2011). To combat
health shocks and uncertain medical expenditures, households save money in good times
(Deaton, 1989; Jappelli et al., 2007), which is called precautionary savings. Precautionary
savings depend on the income level of the household. A high-income earner can save relatively
more than a middle-income earner. However, households with lower income may not have
enough funds to even meet regular expenditure, making it difficult for such people to save for
rainy days.
Households use many ways to meet their health expenses such as withdrawing savings,
insurance, availing themselves of credit facilities, and selling assets. Wagstaff (2007) found
that poor households in Vietnam rely on dissaving and informal credit to cover medical
expenditures. Wagstaff and Lindelow (2010) obtained similar results in Laos. On the other
hand, Genoni (2012) reported an insignificant relationship between health shocks and dissaving
in Indonesia. However, according to the survey conducted by Lusardi, Schneider, and Tufano
(2011), 62 percent of households in the United States prefer using savings accounts (including
6
retirement investments and investments with a penalty withdrawal facility) to cover unexpected
expenditures. Similarly, in a recent study in India, Pradhan and Mukherjee (2018) reported a
positive relationship between dissaving and health shocks.
Households’ saving decisions depend on health insurance and the healthcare system of
the country. If a country has comprehensive public health insurance, it reduces the financial
damage arising due to health shocks, thereby reducing households’ demand for precautionary
savings (Cheung & Padieu, 2015; Hsu, 2013; Starr-McCluer, 1996). The same results are
obtained via using government healthcare system (De Freitas & Martins, 2014; Jappelli et al.,
2007). The reduction in precautionary savings increases surplus funds. A robust healthcare
system provides improved medical facilities, which increases households’ capability and life
span (Fanti & Gori, 2011). This increased life span and capability improve households’ income,
which they manage through a banking system either for consumption or for savings. This paper
investigates the effects of health shocks on bank deposits at the macro level.
In the last few decades, the importance of financial education on households’ financial
decision-making have been widely explored (Bernheim, Garrett, & Maki, 2001; Cole,
Sampson, & Zia, 2011; Lusardi & Mitchelli, 2007). Lusardi and Mitchelli (2007) reported that
financial illiteracy is one of the main reasons for lack of retirement savings. Furthermore,
according to the Lusardi et al. (2011) households’ financial fragility survey in the United States,
less educated households were more severely prone to financial difficulties. The study shows
that financial literacy enhances households’ skill sets in the optimal allocation of funds in high
yield assets (Lusardi & Mitchell, 2011). Van Rooij, Lusardi, and Alessie (2011) reported that
around 23.8 percent of households hold stocks in the Netherlands. This percentage of stock
ownership increases with education and financial literacy. Hence, education enables
households to understand and analyze financial products and use them according to their needs
and desires.
7
To operate a bank account, one needs to be educated enough to at least read and write.
Although bank employees generally help people who face difficulties due to their limited
literacy in filling in forms for the deposits and withdrawals, they feel embarrassed and thus
avoid such situations. Education gives confidence to households to operate a bank account.
Hogarth, Anguelov, and Lee (2005) stated that amongst unbanked households, the proportion
of less educated people were high. Demirguc-Kunt and Klapper (2012) showed education is
one of the important factors in using the banking system. They found that people with higher
education in developing and emerging economies are two times more likely to have formal
accounts than the people with only primary education. According to the Cole et al. (2011)
survey in Indonesia, the second most cited reason for people being unbanked is lack of
knowledge of using a bank account. Hence, financial education helps households to understand
sophisticated financial products and increases the usage of such products (Calvet, Campbell,
& Sodini, 2007; Collins, 2013; Hilgert, Hogarth, & Beverly, 2003).
Several studies show that financial literacy is based on the cognitive abilities of
households (Hogarth et al., 2005). Hence, they used education as a proxy for the financial
literacy. Most researchers reported a strong positive relationship between education and
cognitive abilities. Sekita (2011) stated that people with higher education are more likely to be
financially literate. Data related to financial literacy is not available for the selected countries;
therefore, the education index (UNDP) has been employed as a proxy for the cognitive abilities
of households in this paper.
2.1 Research objective
The objective of this paper is to identify the effect of human capital development on
bank deposits at country level.
1. How does the development of human capital affects bank deposits at country level?
8
H0 : Human capital development increases bank deposits.
H1 : Human capital development decreases the use of banking system for savings, hence
decreases bank deposits.
3. Data collection and methodology
We have collected data for bank deposit to GDP, inflation, bank stability (Z score) and
per-capita income covering the period 2002 to 2016 from the World Bank Database. We could
not cover the recent period due to limited data availability. Data on political stability, regulatory
quality, voice and accountability, control for corruption, and government effectiveness indexes
are also collected from the World Bank Database. The data related to government expenditure
on healthcare system to GDP ratio and public and private compulsory contribution to health
care expenditure to GDP ratio have been obtained from the World Health Organization (WHO).
In human capital development literature, enrollment in primary school, secondary
school, and government expenditure on education are used for the measurement of education
level of the country (Baldacci, Clements, Gupta, & Cui, 2008; Loening, 2005; Ranis, Stewart,
& Ramirez, 2000). However, we use human development index (HDI) and education index
developed by UNDP has been used in this paper to measure the education level of the country.
One of the main reasons for not using traditional variables is limited data availability. The
education index developed by UNDP is constructed using the mean and expected years of
schooling (UNDP, 2018).
Table 1 Variables’ name, notations, and their expected signs
Variables’
Name
Abbreviation Measure Expectation Literature
Dependent
Variable
Deposit to GDP DGDP Log of Total Deposit to
GDP
Deposit Value Deposit Log of total Deposits
Explanatory
Variables
9
Health
Public and
private
compulsory
contribution to
healthcare
financing
scheme to GDP
(%)
PPCCGDP Log of public and
private compulsory
contribution to
healthcare financing to
GDP
Positive Cheung and Padieu
(2015); Hsu (2013);
Starr-McCluer (1996);
Wagstaff and Van
Doorslaer (1992)
Government
Expenditure to
GDP
GEGDP Log of Government
expenditure to GDP
Guruswamy,
Mazumdar, and
Mazumdar (2008);
Gupta, Verhoeven, and
Tiongson (2002)
Education Index EI Education index
designed by UNDP
Positive Ghosh (2006); Iqbal
and Daly (2014);
Gürlük (2009)
Financial
System
Stability of Firm Bank Z Z score captures the
probability of default of
a country’s banking
system.
Positive Berger, Klapper, and
Turk-Ariss (2009);
Goetz (2018); Hakenes
and Schliephake
(2019); Fu, Lin, and
Molyneux (2014)
Macroeconomic
factors
GDP growth
rate
GDPG Real GDP growth rate Positive Bacha (1990); Bikker
and Metzemakers
(2005)
Inflation Inflation Country level consumer
price inflation
Positive Bourke (1989) Barth,
Lin, Ma, Seade, and
Song (2013)
HA Higher age Percentage of
population over and
above 65 years
Positive Bonsang and Costa-
Font (2020); Craig and
Dinger (2013)
Trade openness TO Trade Openness is sum
of export and import
divided by GDP of the
country
Negative Menyah, Nazlioglu,
and Wolde-Rufael
(2014); Ulaşan (2015);
Yanikkaya (2003)
The World
governance
indicators
WGI The world governance
indicators measure the
political stability,
regulatory quality,
voice and
accountability, rule of
law, and control for
corruption in the
country.
Positive Köhler (2015); Ahamed
and Mallick (2019);
Ashraf (2017); Beck,
Demirgüç-Kunt, and
Levine (2006)
Data were initially collected for 146 countries, but this was later reduced to 107
countries due to the limitations in data availability. The data set of 107 countries covers 38
high-income, 69 low-income countries. The countries are also distributed by region, covering
10
East Asia and Pacific (EAP, 11), Europe and Central Asia (ECA, 37), Latin American and
Caribbean (LAC, 20), the Middle East and North Africa (MENA, 11), North America (NA, 1),
South Asia (SA, 5), and Sub-Saharan Africa (SSA, 22).
In the financial development literature, the deposit to GDP ratio is generally used as a
proxy for the usage of the banking system at country level, and it has thus been used as the
dependent variable in this study. Along with the deposit to GDP ratio, the total value of deposits
has also been used as dependent variable to identify the change in total deposit base of the
country. We use log value of deposit to GDP ratio (Jokivuolle, Pesola, & Viren, 2015), deposit
value (Kraft & Galac, 2007), and health expenses (Bech, Christiansen, Khoman, Lauridsen, &
Weale, 2011; Hartwig, 2008).
Table 2 Summary statistics
Variables Name Observations Mean Std.
Dev.
Min Max
Total Deposit (in billions) 1,591 1597.97 406.21 0.17 13742.09
Deposit to GDP ratio 1,591 50.08 44.13 4.91 472.05
Human Development Index (HDI) 1,588 0.70 0.15 0.26 0.95
Education Index (EDI) 1,588 0.64 0.17 0.12 0.95
Public and private compulsory contribution to
health care financing scheme to GDP (%)
1,591 3.69 2.11 0.34 13.97
Government expenditure in healthcare to GDP
(%)
1,564 31.90 11.25 9.48 65.10
Inflation 1,590 4.79 4.91 -4.48 55.41
Trade openness 1,591 89.75 54.99 20.69 441.60
GDP growth rate 1,590 3.96 3.77 -14.81 34.47
Military Expenditure 1,534 1.93 1.57 0.00 13.33
Government Effectiveness 1,591 0.17 0.89 -2.08 2.44
Political Stability 1,591 -0.04 0.87 -2.81 1.76
Rule of Law 1,591 0.11 0.91 -1.79 2.10
Control for Corruption 1,591 0.06 0.96 -1.72 2.47
Regulatory Quality 1,591 0.25 0.81 -1.35 2.26
Voice and Accountability 1,591 0.13 0.87 -1.91 1.80
3.1 Methodology
It is important to identify a robust econometric methodology to find out the effects of
healthcare system and education on bank deposits. In economic development literature,
11
healthcare and education are considered endogenous variables (Gilleskie & Harrison, 1998).
Therefore, these variables are considered endogenous in this study too. Furthermore, we
conduct an endogeneity test and find evidence for the endogeneity of these variables. We do
not believe that this endogeneity is due to reverse causality between human capital (healthcare
and education) and bank deposits. It is because of omitted variables which influence human
capital development but not bank deposits. Literature suggest that to improve the quality of
human capital and education level in the country, it is important to have good government
policy, transparency, and better implementation of government policies (Baland, Moene, &
Robinson, 2010; Boeninger, 1991; Campante, Do, & Guimaraes, 2013; Jain, 2001). The
government effectiveness index measures the quality of public, civil service, policy
formulation, implementation, and the government’s commitment to improve the governance
system in the country. The governance system in the country plays a key role in the economic
growth. Thus, we use this as an instrument variable for HDI and EDI. The other main variable
of interest is government expenditure on healthcare system and public and private contribution
to healthcare. These variables also have omitted variable bias. To address this issue, we use
military expenditure to GDP ratio4 as an instrument variable for the healthcare expenditures.
Literature suggests that depositors monitor banks (Diamond & Rajan, 2001) and
penalize them by asking for higher interest rates on deposits or withdrawing funds from them
(Egan, Hortaçsu, & Matvos, 2017). Thus, bank stability has reverse causality issue. To address
the issue of reverse causality, we employ the lagged value of bank Z score. The Hausman test
confirms that the fixed effects method would be suitable for this study. The heteroscedasticity
test results favor using the heteroscedastic model. We do not find multicollinearity in the
regressor variables through variance inflation factor (VIF) test. Bank deposits carry a lagged
4 As a part of budget allocation to different sectors, government first ensure the security of their borders. Hence,
rationing for military expenditure, reduces the proportion of budget for healthcare system (Deger, 1985;
Langlotz & Potrafke, 2019).
12
effect, which means that the deposits of period (t) depends on the deposits of period (t-1). The
panel fixed effect OLS model gives biased results in such situations. Clustering the standard
errors at group level addresses the issue of autocorrelation (Demirguc‐Kunt, Detragiache, &
Merrouche, 2013; Nichols & Schaffer, 2007).
To address the issue of autocorrelation Arellano and Bond (1991) have proposed a two-
step difference GMM estimator. In the first step, they assume that the errors are homoscedastic
and estimate the residuals by using the first difference of the variables to eliminate the firm
specific factors. The model uses the lagged level of variables as instruments. In the second step,
the residuals are used to estimate the weighting matrix that makes the estimator asymptotically
efficient and robust when the dataset is heteroscedastic. However, this model was later
criticized by Blundell and Bond (2000) when instruments are weakly correlated with the first
difference equation. They proposed the extended system GMM method that uses both level
and first-differenced variables as instruments for each other to reduce the bias and provide
better estimation even in a smaller dataset. The Windmeijer (2005) correction has also
employed to make the two-step system GMM estimation more robust. Even though the system
GMM is an advanced technique, it has certain limitations such as using too many instruments.
To avoid this situation, we use the collapse function to make the set of instruments smaller.
The Hansen tests have been performed to check for the over-identification of instruments
(Roodman, 2009). We also present the results of the two-step difference GMM estimator.
We apply the model on a full dataset of 107 countries to identify the effect of health
and education on bank deposits. Then, the model is replicated for the subgroups analysis based
on the income level. The empirical model has the following form.
Υ𝑐𝑡 = 𝛽0 + 𝜃𝑐 + Υ𝑐𝑡−1 +∑𝛽𝑔
𝒢
𝑔=1
𝒳𝑐𝑡𝑔+∑𝛽𝑒
𝐸
𝑒=1
𝒳𝑐𝑡𝑒 + 𝜇𝑐 + ℰ𝑐𝑡 ……(1)
13
Where Υ𝑐𝑡 is the dependent variables: ratio of bank deposits to GDP ratio and total
deposits at a time "t" and of country "c". Υ𝑐𝑡−1 is a lag of dependent variables of one year. 𝜃𝑐 -
country fixed effects and 𝜇𝑐 presents the time effects. 𝒳𝑐𝑡𝑔
consists of the banking industry
factors such as financial stability of the firm. 𝒳𝑐𝑡𝑒 indicates the vector of macroeconomic factors
including the health expenses and the education index. ℰ- denotes disturbance or error term.
4. Discussion and analysis
This section discusses the main results of the study and presents the results of sensitivity
analysis. The sensitivity analysis is conducted using (i) economic development level, (ii)
financial inclusion level, and (iii) including different control variables.
We use the human development index (HDI) as our explanatory variable and the log of
deposit to GDP (DGDP) ratio as our dependent variable. Pooled OLS regressions with and
without control variables are shown in columns 1 and 2 respectively of Table 3. Columns (3-
4) show the results obtained through panel fixed effects and instrumental variable (2SLS)
methods. Then we apply two-step difference and system GMM, results are shown in columns
5-6 of Table 3.
Column 1 of Table 3 shows a positive relationship between HDI and DGDP. The
coefficient for HDI is 2.957 which is statistically significant at 1 percent. The result is obtained
through pooled OLS and without any control. Thereafter, we apply control variables such as
bank Z score, higher age population, trade openness, GDP growth rate, and inflation in the
pooled OLS regression. The relationship is still positive and statistically significant at 1
percent. Moreover, due to panel dataset, we apply panel fixed effects method controlling for
country and time fixed effects. The results show a positive coefficient 2.64, statistically
significant at 1 percent.
14
The economic and financial development literature suggests a bi-directional
relationship with human capital development. Hence, we apply instrumental variable method
to address this issue. Finding an instrument is a really difficult task. However, we use
government effectiveness as an instrument for the human capital development5. The Stock-
Wright test confirms the validity of the instrument. Column 4 of Table 3 shows a higher
coefficient of 11.8 for the dependent variable DGDP. This suggest that a one-point change in
HDI will change the DGDP of 11.8 percent. We further apply two-step difference and system
GMM to address the autocorrelation issue in the dataset. We do not find a significant
relationship between HDI and DGDP. However, the GMM results have over-identification
issues. Therefore, we are cautious in making any inference from these results.
Table 3 Human capital development: bank deposits
The table presents the results for 107 countries for the period of 2002-2016. Dependent variable deposit to GDP
ratio and inflation is in natural log form. The robust standard errors are in parenthesis for columns 3-6. The sign
***, **, and * present the statistical significance at 1%, 5% and 10% level respectively. The GMM methods used
collapse function after using lag of endogenous variable i.e. HDI between 1 and 3.
OLS OLS Fixed
Effects
2SLS Difference
GMM
System
GMM
(1) (2) (3) (4) (5) (6)
DGDP DGDP DGDP DGDP DGDP DGDP
DGDP-1
0.937***
(0.066)
0.886***
(0.051)
HDI 2.957***
(0.089)
1.828***
(0.136)
2.640***
(0.827)
11.774**
(4.689)
0.639
(0.679)
0.239
(0.293)
BankZ-1
0.020***
(0.002)
-0.008***
(0.003)
-0.009***
(0.003)
-0.008**
(0.003)
-0.007***
(0.002)
Inflation
-0.014***
(0.003)
-0.000
(0.001)
-0.001
(0.002)
-0.001
(0.001)
-0.002
(0.002)
Higher Age
0.017***
(0.004)
-0.051***
(0.016)
-0.012
(0.025)
0.003
(0.007)
-0.003
(0.005)
Trade Openness
0.002***
(0.000)
0.001*
(0.001)
0.002**
(0.001)
0.001***
(0.000)
0.000
(0.000)
GDPG
-0.028***
(0.003)
-0.010***
(0.002)
-0.015***
(0.003)
-0.008***
(0.001)
-0.006***
(0.002)
F 1095.19 291.232 19.949 13.087
r2 0.408 0.543 0.588 0.370
N 1588 1480 1480 1480 1374 1480
ar2 -3.312 -3.391
Country Effect No No Yes Yes Yes Yes
5 Government effectiveness measures the quality of public, civil service, policy formulation, implementation,
and the government’s commitment to improve the governance system in the country. The governance system in
the country plays a key role in the economic growth (Baland et al., 2010; Boeninger, 1991; Campante et al.,
2013; Jain, 2001).
15
Year Effect No No Yes Yes Yes Yes
First-GEFF 21.19
The same relationship is investigated using total deposits of the country as a dependent
variable. We apply first pooled OLS regression and find a positive relationship between HDI
and total deposits of the country, as shown in column 1 of Table 4. We further apply pooled
OLS with control variables as mentioned earlier. We find strong positive impact of HDI on
total deposit base of the country. Column 3 of Table 4 shows a positive coefficient of 6.90 for
total deposits of the country and statistically significant at 1 percent. The 2SLS method shows
a positive and statistically significant coefficient of 20.52 for HDI. Similarly, we find positive
relationship between HDI and total bank deposits of the country using two-step system GMM
method. Like Table 2, we find overidentification issues in the GMM results. Therefore, we are
cautious in making any inference from these results. The 2SLS method does not have
overidentification and it also addresses the endogeneity issue of the dataset. Therefore, we rely
on the 2SLS method for further analysis. In a nutshell, the results suggest that HDI and bank
deposit have positive relationship.
The HDI measures the economic, education, and life expectancy in the country. The
positive relationship between HDI and bank deposit suggest that human development is
necessary element for the development of banking sector (Hatemi-J & Shamsuddin, 2016;
Outreville, 1999).
Table 4 Human capital (healthcare and education) development: bank deposits
The table presents the results for 107 countries for the period of 2002-2016. Dependent variable deposit to GDP
ratio and inflation is in natural log form. The robust standard errors are in parenthesis for columns 3-6. The sign
***, **, and * present the statistical significance at 1%, 5% and 10% level respectively.
OLS OLS Fixed Effects 2SLS Difference
GMM
System GMM
(1) (2) (3) (4) (5) (6)
Deposit Deposit Deposit Deposit Deposit Deposit
Deposit-1
0.920***
(0.086)
0.867***
(0.048)
HDI 10.435***
(0.289)
10.600***
(0.466)
6.902***
(1.072)
20.517***
(5.843)
1.130
(0.949)
1.101**
(0.540)
BankZ-1
0.034***
(0.005)
-0.007***
(0.003)
-0.009**
(0.004)
-0.006**
(0.003)
-0.003
(0.003)
Inflation -0.028*** -0.001 -0.002 -0.001 -0.006**
16
(0.009) (0.001) (0.002) (0.001) (0.002)
Higher
Age
0.025**
(0.013)
-0.094***
(0.019)
-0.035
(0.031)
0.001
(0.008)
0.004
(0.005)
Trade
Openness
-0.012***
(0.001)
-0.000
(0.001)
0.001
(0.001)
0.001***
(0.000)
-0.001
(0.001)
GDPG
-0.010
(0.012)
-0.008***
(0.002)
-0.014***
(0.004)
0.001
(0.001)
0.002
(0.002)
F 1303.44 275.499 44.390 27.312
r2 0.451 0.529 0.822 0.662
N 1588 1480 1480 1480 1374 1480
ar2 -3.207 -2.764
Country
Effect
No No Yes Yes Yes Yes
Year
Effect
No No Yes Yes Yes Yes
First-
GEFF- F
Test
21.19
We employ the bank Z score to control for the difference in bank stability in the country.
To address reverse causality between the bank Z score and dependent variables, we use the lag
of the one-year Z score as a control variable. Columns 2 of Table 3 and 4 shows positive
relationship between bank Z score and dependent variables, deposit to GDP ratio and total
deposit of the country. However, applying fixed effects turns this variable negative. Columns
3 and 4 of Table 3 show negative coefficients 0.007 and 0.009 and statistically significant at 1
percent. Similarly, fixed effects and 2SLS methods show negative relationship between bank
stability and total deposit of the country. The control variable inflation does not show a
significant relationship on deposit to GDP ratio, except with pooled OLS which shows a
negative relationship with the dependent variable (see Table 3). Similarly, Table 4 shows an
insignificant relationship between inflation and total value of deposit, except columns 2 and 6.
However, column 2 presents the results of pooled OLS and column 6 presents system GMM
result, which has over-identification issue. Therefore, we cannot make any inference from these
results.
Literature suggests that proportion of higher age population affects the deposit behavior
of households (Craig & Dinger, 2013). Hence, we control for the proportion of higher age
population in the regression model. Columns 2 of Table 3 and 4 show positive relationship
17
between higher age population and dependent variables i.e. DGDP and Deposit. On the other
hand, this relationship turns negative using fixed effect methods. No other regression shows a
significant relationship between higher age population and dependent variables. Hence, we are
cautious in making inference from these results.
Trade openness has been employed as a control variable to measure the impact of
openness in the economy. Table 3 shows a positive impact of trade openness on deposit to GDP
ratio using pooled OLS, fixed effects, 2SLS and difference GMM. The pooled OLS, fixed
effects, and 2SLS show coefficients of 0.002, 0.001, and 0.002 with the deposit to GDP ratio,
as shown in columns 2, 3, and 4 of Table 3. On the other hand, column 2 of Table 4 shows a
negative relationship between trade openness and total deposits using pooled OLS regression.
However, this result turns positive and statistically significant at 1 percent using two step
difference GMM method. Although no other regression results show significant relationship
between trade openness and total value of deposit, we infer that trade openness has a positive
impact on a country’s deposits.
Lastly, the growth rate of economy has been used as a control variable in the regression
model. All regression results of Table 3 show a negative relationship between GDP growth rate
and deposit to GDP ratio. According to column 4 of Table 3, the coefficient of GDP growth
rate is -0.0014 and statistically significant at 1 percent. Similarly, we find a negative
relationship between GDP growth rate and total deposit of the country using panel fixed effect
and 2SLS methods, as shown in columns 3 and 4 of Table 4. The negative relationship suggests
that as the economy grow, households use other financial instruments such as equity, bond, and
mutual funds for higher yield, which reduces the total deposits of the country.
The HDI mainly considers three components, education, health, and income of
individuals to form the human development index. We further investigated the impact of
18
healthcare system and education level on bank deposits. The government expenditure, public
and private contribution to healthcare system of the country and education index have been
used to measure the impact of the healthcare system and education on the deposit of the
country. The results are presented in Table 5. Columns 1-5 present the results for DGDP ratio
and columns 6-10 are for total value of deposit. First, government healthcare expenditures and
public and private contribution to healthcare system are used separately for both dependent
variables, as shown in columns 1, 2, 6, and 7 of Table 5. Columns 3 and 8 presents the impact
of education index on the dependent variables. Further, we apply GGEGDP and PPCCGDP
along with the education index to find the impact of both variables on the dependent variables,
deposit to GDP ratio and total deposit of the country, as shown in columns 4, 5, 9, and 10.
Columns 1 and 2 of Table 5 show positive coefficients for GGEGDP and PPCCGDP at
0.84 and 0.92, statistically significant at 1 percent. This suggest that 1 percent increase in the
expenditure to improve healthcare increases the DGDP ratio of the country by 1 percent. The
same results are obtained using total deposits of the country as a dependent variable (see
columns 6 and 7 of Table 5). We apply an education index to measure the impact of education
level of the country on bank deposits. Columns 3 and 8 show a positive relationship between
education index and bank deposit using both dependent variables, DGDP and total deposit of
the country. The coefficients of EDI are 15.67 and 26.26 for the dependent variables, DGDP
and deposit of the country respectively. We further used both the healthcare expenditure and
education index in the regression model. The relationship between healthcare system
(GGEGDP and PPCCGDP) and bank deposit turns insignificant while using education index
as a control variable for both dependent variables, DGDP and deposit of the country. However,
we still infer that improvement in the healthcare system and education level in the country
influences the bank deposits as shown in columns 1, 2, 3, 6, 7, and 8 of Table 5.
19
The bank Z score shows a negative relationship with DGDP. The coefficients are in the
range of -0.015 to -0.006, as shown in columns 1-5 of Table 5. Columns 7 and 8 also show
negative relationship between deposit and bank Z score. Thus, we can conclude that there is a
negative relationship between bank Z score and deposit of the country, consistent with the main
findings. Similar to main findings, we do not find a significant relationship between DGDP,
deposits of the country, and inflation (see Table 5). The other control variable higher age
population shows negative impact on bank deposit. This result is consistent with the findings
of main results. The coefficients of higher age population are in the range of -0.09 and -0.042
for DGDP and -0.0993 to -0.144 for total deposit as dependent variable.
Moving to other macroeconomic control variables, we find a positive relationship
between trade openness and DGDP (see columns 2 and 3 of Table 5). However, the results turn
insignificant when use healthcare expenditure and education index as independent variable. No
other regression results show statistically significant relationship. This relationship is broadly
consistent with the main results for both dependent variables, DGDP and deposits. Lastly, we
use GDP growth rate, which show a negative relationship with DGDP, as shown in columns
(1-3) of Table 5. Except column 8 of Table 5, no other regression result shows a significant
relationship between GDP growth rate and deposit. However, due to the large number of
regression results showing a negative relationship between GDP growth rate and a country’s
deposits, we believe that there is a negative relationship as shown in main findings.
20
Table 5 All countries with healthcare expenditure and education variables
The table presents the results for 107 countries for the period of 2002-2016 using health expenses and EDI as proxy for human capital development. The healthcare system
expenditures, inflation, and dependent variables, deposit to GDP ratio and total deposits are in log form. Healthcare expenditure and EDI are used as endogenous variables. We
employ military expenditure and government effectiveness as instruments for healthcare expenditures and EDI respectively.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
DGDP DGDP DGDP DGDP DGDP Deposit Deposit Deposit Deposit Deposit
GGEGDP 0.837***
(0.257)
-0.174
(1.040)
0.861***
(0.276)
-1.122
(1.858)
PPCCGDP
0.924***
(0.347)
-0.181
(1.611)
0.946**
(0.414)
-1.640
(3.394)
EDI
15.067*
(8.669)
16.238
(13.535)
16.238
(19.548)
26.256*
(13.745)
31.841
(23.455)
38.007
(41.536)
BankZ-1 -0.006**
(0.003)
-0.008***
(0.003)
-0.014**
(0.006)
-0.015*
(0.009)
-0.014*
(0.009)
-0.004
(0.003)
-0.007*
(0.004)
-0.017*
(0.009)
-0.022
(0.016)
-0.021
(0.019)
Inflation -0.001
(0.001)
0.003
(0.003)
-0.006
(0.004)
-0.007
(0.006)
-0.009
(0.016)
-0.001
(0.002)
0.003
(0.004)
-0.011
(0.007)
-0.013
(0.010)
-0.024
(0.034)
Higher age -0.042**
(0.018)
-0.087***
(0.021)
-0.065*
(0.037)
-0.069
(0.045)
-0.059
(0.062)
-0.103***
(0.023)
-0.149***
(0.026)
-0.128**
(0.059)
-0.156**
(0.077)
-0.083
(0.134)
Trade
Openness
0.001
(0.001)
0.003***
(0.001)
0.003*
(0.002)
0.003
(0.002)
0.003
(0.002)
-0.001
(0.001)
0.001
(0.001)
0.003
(0.003)
0.004
(0.004)
0.001
(0.004)
GDPG -0.005**
(0.002)
-0.006***
(0.002)
-0.019**
(0.007)
-0.021
(0.015)
-0.020
(0.018)
-0.000
(0.002)
-0.002
(0.002)
-0.021*
(0.012)
-0.031
(0.026)
-0.034
(0.039)
F 30.017 14.215 5.378 3.881 4.321 44.907 24.403 6.342 3.672 2.719
r2 0.567 0.243 -0.921 -1.247 -1.252 0.788 0.669 -0.737 -1.605 -3.039
N 1405 1431 1480 1405 1431 1405 1431 1480 1405 1431
First- F test
Military Exp
58.56 4.79 30.50 6.13 58.56 4.79 30.50 6.13
GEFF 4.13 2.38 2.53 4.13 2.38 2.53
Standard errors in parentheses
Standard errors are clustered at country level * p < 0.10, ** p < 0.05, *** p < 0.01
21
4.1 Income level
Literature suggests that the banking systems behave differently according to countries’
economic development level (Demirguc-Kunt & Levine, 2008; Dietrich & Wanzenried, 2014;
Gupta, Tressel, & Detragiache, 2005; Hoggarth, Reis, & Saporta, 2002). Therefore, we divided
the dataset into two subgroups viz. high-income countries and low-income countries. This
classification is based on the World Bank report. Table 6 and Table 7 presents the results of
high-income countries and low-income countries. Column 1 and 7 present the results of HDI
in both Tables 6 and Table 7. Columns 2-6 and 8-12 follow the same pattern of presentation as
in Table 5.
We do not find a statistically significant relationship between HDI and DGDP. However,
column 7 of Table 6 shows a positive and statistically significant relationship with total deposit
of the country. The coefficient of HDI for total deposit is 13.92 and statistically significant at
1 percent. We find a positive elasticity of GGEGDP and PPCCGDP using both dependent
variables, DGDP and total deposits of the country, as shown in columns 2, 3, 8, and 9 of Table
6. The coefficients of the explanatory variables, GGEGDP and PPCCGDP for the dependent
variable total deposits are more than the DGDP. However, this relationship turns insignificant
when apply education index as a control variable. This result is consistent with the main
findings.
We do not find a significant relationship between education index and DGDP and total
deposit. However, when we apply GGEGDP, it turns positive. The coefficients are 6.18 and
12.51 and statistically significant at 10 percent. The results are significant at 10 percent. Bank
Z score does not show a significant relationship in high income countries. Inflation has been
used as a control variable in all regressions. Contrary to main findings, we find a positive
relationship between inflation and deposit, when we apply HDI, GGEGDP, and PPCCGDP as
an explanatory variable (see column 7, 8, and 9 of Table 6). The other regression results for are
22
insignificant. The positive relationship suggests that in high income countries, as cost of living
increases households save money in banks to combat inflation that increases the total deposit.
23
Table 6 High income countries with HDI, healthcare expenditure, and education variables
The table presents the results for 38 countries for the period of 2002-2016 using HDI, health expenses, and EDI for human capital development for high income countries.
The healthcare system expenditures, inflation, and dependent variables, deposit to GDP ratio and total deposits are in log form. Healthcare expenditure and EDI are used
endogenous variables. We employ military expenditure and government effectiveness are used as instruments for healthcare expenditures and EDI respectively.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
DGDP DGDP DGDP DGDP DGDP DGDP Deposit Deposit Deposit Deposit Deposit Deposit
HDI 2.816
(4.243)
13.916***
(4.288)
GGEGDP
1.508***
(0.369)
0.240
(0.649)
1.602***
(0.333)
-0.964
(1.266)
PPCCGDP
1.299***
(0.306)
0.562
(1.103)
1.370***
(0.249)
-2.040
(4.333)
EDI
4.629
(6.904)
6.178*
(3.275)
4.211
(5.912)
22.875
(40.939)
12.502*
(7.390)
19.494
(26.724)
BankZ-1 -0.003
(0.003)
0.004
(0.003)
0.002
(0.004)
-0.004
(0.004)
-0.004
(0.005)
-0.002
(0.006)
-0.004
(0.003)
0.005
(0.005)
0.003
(0.005)
-0.010
(0.020)
-0.010
(0.010)
-0.014
(0.024)
Inflation 0.005
(0.005)
0.010
(0.006)
0.004
(0.007)
0.002
(0.008)
-0.001
(0.008)
-0.001
(0.009)
0.013**
(0.006)
0.020***
(0.007)
0.014**
(0.006)
0.000
(0.026)
-0.002
(0.013)
-0.007
(0.024)
Higher age -0.013
(0.020)
0.018
(0.024)
-0.016
(0.022)
-0.007
(0.022)
-0.000
(0.028)
-0.008
(0.024)
-0.053***
(0.020)
-0.020
(0.033)
-0.058**
(0.028)
-0.026
(0.093)
-0.057
(0.058)
-0.020
(0.084)
Trade
Openness
0.000
(0.001)
0.000
(0.001)
0.003**
(0.001)
0.001
(0.002)
0.001
(0.001)
0.002
(0.002)
0.000
(0.001)
-0.001
(0.001)
0.002**
(0.001)
0.004
(0.009)
0.001
(0.002)
-0.002
(0.006)
GDPG -0.013***
(0.002)
0.005
(0.005)
-0.002
(0.003)
-0.014***
(0.005)
-0.012
(0.009)
-0.010
(0.012)
-0.011***
(0.003)
0.012***
(0.005)
0.005
(0.004)
-0.020
(0.027)
-0.023
(0.018)
-0.032
(0.047)
F 40.297 28.526 18.608 28.063 23.122 41.995 46.922 33.617 28.810 3.787 7.815 2.762
r2 0.497 0.389 0.259 0.358 0.228 0.442 0.731 0.578 0.624 -1.738 -0.020 -1.843
N 531 512 517 531 512 517 531 512 517 531 512 517
First
Military
Exp
77.96 28.42 62.90 15.27 77.96 28.42 62.90 15.27
GEFF 7.49 0.26 17.56 17.48 7.49 0.26 17.56 17.48
Standard errors in parentheses
Standard errors are clustered at country level * p < 0.10, ** p < 0.05, *** p < 0.01
.
24
Turning to other control variables, we find an insignificant relationship between higher
age population and DGDP. On the contrary, columns 7 and 9 of Table 6 show a negative
relationship between higher age population and total deposit. This result is consistent with the
findings of main regressions. Trade openness is showing a positive relationship with DGDP
(see columns 3 and 9 of Table 6). This relationship is consistent with the main findings. Further,
we apply GDP growth rate as a control variable and find a negative relationship between GDP
growth rate and bank deposits using both dependent variables, deposit to GDP ratio and total
deposits, except using GGEGDP as an explanatory variable and total deposit as a dependent
variable (see column 8 of Table 6). Due to contradictory relationship between GDP growth and
total deposit, we are cautious in making an inference from this result.
The results for low-income countries show a positive relationship between HDI and
bank deposits using both dependent variables, DGDP and total deposit of the country.
Coefficients of HDI are 19.30 and 27.8 for deposit to GDP ratio and total deposit of the country
(see columns 1 and 5 of Table 7). This result is consistent with the main findings. We also find
that the impact of HDI is higher for low-income countries than the high-income countries. This
is because of the poor existing human capital level in low-income countries, even a smaller
change in human development has a larger impact.
25
Table 7 Low-income countries with healthcare expenditure and education variables
The table presents the results for 69 countries for the period of 2002-2016 using HDI, health expenses, and EDI for human capital development for low-income
countries. The healthcare system expenditures, inflation, and dependent variables, deposit to GDP ratio and total deposits are in log form. Healthcare expenditure
and EDI are used endogenous variables. We employ military expenditure and government effectiveness are used as instruments for healthcare expenditures and
EDI respectively.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
DGDP DGDP DGDP DGDP DGDP DGDP Deposit Deposit Deposit Deposit Deposit Deposit
HDI 19.298*
(11.231)
27.834**
(13.506)
GGEGDP
0.580**
(0.248)
0.663
(0.571)
0.607**
(0.298)
0.734
(0.889)
PPCCGD
P
0.754*
(0.453)
0.824
(0.737)
0.790
(0.600)
0.912
(1.135)
EDI
25.184
(18.621)
19.577
(18.598)
12.404
(14.523)
36.322
(24.798)
29.990
(24.310)
21.433
(21.324)
BankZ-1 -0.012*
(0.006)
-0.009**
(0.004)
-0.012***
(0.004)
-0.021*
(0.012)
-0.018
(0.011)
-0.018**
(0.008)
-0.011
(0.007)
-0.007*
(0.004)
-0.010**
(0.004)
-0.024
(0.017)
-0.021
(0.016)
-0.020
(0.013)
Inflation -0.003
(0.003)
-0.003
(0.002)
0.002
(0.004)
-0.010
(0.009)
-0.010
(0.008)
-0.002
(0.010)
-0.005
(0.004)
-0.003**
(0.002)
0.001
(0.005)
-0.015
(0.011)
-0.014
(0.011)
-0.006
(0.014)
Higher age -0.067
(0.043)
-0.053**
(0.026)
-0.096**
(0.037)
-0.308
(0.205)
-0.247
(0.205)
-0.219
(0.135)
-0.094*
(0.052)
-0.077***
(0.028)
-0.123***
(0.043)
-0.441
(0.273)
-0.373
(0.268)
-0.335*
(0.196)
Trade
Openness
0.002
(0.002)
0.002**
(0.001)
0.003***
(0.001)
-0.001
(0.004)
-0.001
(0.004)
0.002
(0.004)
0.000
(0.003)
0.001
(0.001)
0.003*
(0.001)
-0.003
(0.006)
-0.003
(0.006)
-0.000
(0.006)
GDPG -0.017***
(0.005)
-0.008***
(0.002)
-0.009***
(0.003)
-0.023*
(0.013)
-0.020
(0.013)
-0.016
(0.010)
-0.017***
(0.006)
-0.005***
(0.002)
-0.006**
(0.003)
-0.026
(0.018)
-0.024
(0.017)
-0.018
(0.015)
F 6.048 21.033 12.228 2.590 4.325 6.670 13.507 57.956 24.787 3.922 5.907 7.870
r2 -0.033 0.650 0.387 -2.737 -1.583 -0.528 0.503 0.851 0.743 -1.339 -0.740 -0.067
N 949 893 914 949 893 914 949 893 914 949 893 914
First-
Military
Exp
6.38 21.43 1.58 13.89 4.31 6.38 21.43 1.58 13.89 4.31
GEFF 2.72 1.00 1.27 2.72 1.00 1.27
Standard errors in parentheses, Standard errors are clustered at country level * p < 0.10, ** p < 0.05, *** p < 0.01
26
Table 7 also shows the results of healthcare expenditure and education index on bank
deposits. Column 2 and 8 show the coefficients of GGEGPD are 0.58 and 0.61 for DGDP and
total deposit of the country. Similarly, PPCCGDP show the coefficients of 0.75 for DGDP.
However, this relationship is significant at 10 percent and turns insignificant when we use total
deposits as a dependent variable, as shown in column 9 of Table 7. This suggests that in the
low-countries, government should focus on public expenditure for improving the healthcare
facility, which eventually increases bank deposits. This result is consistent with the main
findings.
The other regression results for PPCCGDP and education index are insignificant. We
find the impact of healthcare system in high income countries is higher than the low-income
countries. It may be because of the poor infrastructure of healthcare system in low -income
countries. Like the main findings, bank Z score shows negative relationship with both the
dependent variables, DGDP and deposits. Other control variable results are broadly consistent
with main findings.
4.2 Financial inclusion
The effects of the education and healthcare systems will be higher in countries which
have higher financial inclusion (Arora, 2012). The dataset is therefore divided into two
subgroups, high and less financially included countries. To identify the high and less financially
included countries, we have taken the median value of the percentage of account holders for
the selected countries of year 2014. The country which has higher percentage of account
holders6 than the median value is considered high financially included country and remaining
are considered less financially included. Due to limited data availability on the percentage of
account owners over the age of 15, the total number of countries for this study is reduced to
6 Account holders whose age is more than 15 years.
27
102, out of which 55 are high financially included and the remainder as less financially
included.
The relationship between the HDI and dependent variables viz. DGDP and deposits in
both subgroups of countries is positive and statistically significant. The impact of government
expenditure on healthcare system positively contributes to the bank deposits. This result is
consistent with the main findings and high-income countries. Although PPCCGDP show a
positive relationship with bank deposit in high financially included countries, the result for less
financially included countries is insignificant. on bank deposits is higher in high financially
included countries. We do not find a significant relationship between EDI and bank deposits in
both subgroups. The results of other control variables are broadly consistent with the high, low
-income countries.
5. Additional Analysis
The economic development literature mentions that factors such as political stability
and quality of governance play an important role in economic growth (Barth et al., 2013;
Fratzscher, König, & Lambert, 2016; Neanidis & Papadopoulou, 2013). The variables political
stability, voice and accountability, regulatory quality, and control for corruption have been
employed in the main model. Moreover, literature suggest that health and education are crucial
for economic growth. Therefore, we also employed the interaction of GGEGDP, PPCCGDP
and EDI in the regression models. We use government effectiveness as endogenous variable
for HDI and military expenditure and government effectiveness for the interaction of
GGEGDP, PPCCGDP and EDI. To save space, we only discuss the findings of regressions
using the governance indicators. These results are available upon request.
The relationship between HDI and bank deposits have been explored using all the
country governance indicators such as rule of law, control for corruption, regulatory quality,
28
political stability, and voice and accountability as control variables. Moreover, the interaction
of GGEGDP and EDI show a positive relationship with bank deposits. Similarly, the interaction
of PPCCGDP and EDI show a positive elasticity for bank deposits. All, but except voice and
accountability, the governance indicators show a positive relationship with bank deposits.
Voice and accountability do not show a statistically significant relationship with bank deposits.
The other control variables relationship is broadly aligned with the main findings.
The relationship between the healthcare system and bank deposits are consistent with
the main findings when political stability is used as a control variable. Similarly, the education
index also has positive and statistically significant coefficients. However, we do not find a
relationship between political stability and bank deposits. Similar relationships between
healthcare, education and bank deposits are obtained when using voice and accountability as a
control variable. Although a negative relationship between voice and accountability and bank
deposits has been found in three out of eight regressions, we are cautious in interpreting these
results due to the low significance level.
6. Conclusion
To the best of our knowledge, this paper is first to study the effects of human capital
development on bank deposits. The results show that a strengthening in human capital makes
the banking system stable by increasing bank deposits. Moreover, this study also first studying
the impact of impact of healthcare system on bank deposits. However, the impact of the human
capital development and healthcare system vary depending on countries’ economic
development and financial inclusion levels.
The impact of human capital development in low income and less financially included
countries is higher than in high income and financially included countries. On the other hand,
the impact of government expenditure on the healthcare system on bank deposits is more than
29
that of the public and private compulsory contribution to healthcare systems in high income
and financially included countries. It may be because public and private compulsory
contributions to healthcare include the contribution from households, thereby reducing their
disposable income. Hence, it reduces the households’ deposit in banks. Although improvement
in the healthcare system increases bank deposits, the impact of the healthcare system on GDP
of the country is higher than the deposits. Thus, the elasticity of the dependent variable total
deposit of the country is higher than DGDP.
The results show a greater effect of the healthcare system on bank deposits in highly
financial included and high-income countries than the less financial included and low-income
countries. This may be also due to the better governance in the high financial included and the
high-income countries. This relationship is examined by employing the World Governance
Indicators in the study and as expected; the governance indicators showed a positive impact on
bank deposits.
The relationship between education and the usage of the banking system has been
investigated in terms of obtaining loans, access to financial system, and savings. However, the
relationship between education and bank deposits has had limited attention from researchers.
The positive relationship between education and bank deposits shows that education helps
individuals in understanding and using the banking system, thereby increasing bank deposits
mostly in high-income countries.
What are the policy implications of this study? The relationship between human
development and economic growth has been explored (Ranis, 2004). It plays a pivotal role in
the economic growth. The development of human capital can be achieved through providing
the good quality healthcare system and improving the education level in the country. Public
expenditure on the healthcare system aims on one hand at improving the capability and income,
30
and on the other hand reducing household uncertainty as to expenditures. Several studies have
shown the impact of health shocks on income (Deaton, 2003; Pickett & Wilkinson, 2015; Yogo,
2016) and savings behavior (Fan & Zhao, 2009; Rosen & Wu, 2004). The relationship between
public health insurance, the healthcare system and savings has also been explored (Cheung &
Padieu, 2015; Pradhan & Mukherjee, 2018). Furthermore, education influences cognitive
ability. Hence, it helps in improving households’ saving decisions (Cole et al., 2011) and usage
of the banking system (Demirguc-Kunt & Klapper, 2012). However, it is worth exploring how
human capital development affects bank deposits.
To answer this question, we argue that a good healthcare system provides timely health
services to households, which makes them healthy and increases their general capacities. Good
health increases endurance, life span, and cognitive abilities that helps to improve the income
levels of households. It insures households against financial damage that arises due to health
shocks, thereby reducing the need for precautionary savings and increasing surplus funds.
These funds can be used either for consumption or for savings, depending on households’
incomes and life expectancy. However, for both consumption and savings households find
convenience in using the banking system for managing their funds. In addition, bank deposits,
being the first point of contact to financial system for households, increase. Education also
enhances the financial decision-making abilities of households. It facilitates understanding how
to use the banking system, thereby increasing bank deposits.
There are five suggestions from this paper for improving the bank stability by
increasing bank deposits: (i) Government should focus on strengthening human capital by
improving the healthcare system, which increases the income level of households and allows
them to use that increased income either for savings or for consumption, thereby increasing
bank deposits. (ii) Compulsory contributions by households for healthcare in low and middle-
income countries reduce the disposable income of households. They discourage households
31
from saving, thus reducing bank deposits. Thus, it is recommended to use the private
contribution methods cautiously in low and middle-income countries. (iii) Education plays a
key role in accessing the banking system mainly in high-income countries and highly
financially included countries. Hence, it is advisable to develop a policy which increases the
number of schooling years in the country, as this leads to increased use of the banking system.
(iv) Good governance develops the trust of households in the financial system. This in turn
increases the usage of the banking system for savings and transactions. (v) Higher banks
stability incentivizes them to acquire low-cost fund to increase their profitability in high-
income countries and high-financial included countries, causing fragility in the banking system.
Therefore, banks should be vigilant on their funding portfolio even when they have adequate
capital and are stable.
This study could provide stronger results if we were able to use microeconomic level
data from household surveys. It would enable investigation of the relationship between
households’ characteristics and their usage of the banking system for savings. Moreover, banks
generally provide both transaction accounts and non-transaction accounts; and studying the
relevance of human capital development on individual deposit products will enable better
understanding of the usage of the banking system.
32
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37
Appendix 1 Correlation matrix
Deposit DGDP HDI EDI PPCCGDP GGEGDP Inflation Trade
openness GDPG Military
Exp GEFF PS ROL COC RQ Voice
Deposit 1.00
DGDP 0.68 1.00
HDI 0.68 0.64 1.00
EDI 0.56 0.52 0.95 1.00
PPCCGDP 0.45 0.49 0.63 0.63 1.00
GGEGDP 0.42 0.48 0.65 0.65 0.76 1.00
Inflation -0.23 -0.33 -0.28 -0.22 -0.28 -0.24 1.00
Trade
openness
-0.04 0.38 0.30 0.29 0.13 0.18 -0.13 1.00
GDPG -0.23 -0.30 -0.26 -0.24 -0.36 -0.38 0.13 0.03 1.00
Military Exp 0.13 0.00 0.13 0.07 -0.09 0.14 -0.04 -0.07 0.01 1.00
GEFF 0.64 0.68 0.79 0.74 0.69 0.59 -0.36 0.35 -0.25 0.03 1.00
PS 0.23 0.43 0.57 0.56 0.60 0.52 -0.28 0.42 -0.18 -0.15 0.70 1.00
ROL 0.58 0.66 0.75 0.69 0.71 0.61 -0.34 0.34 -0.26 0.02 0.95 0.75 1.00
COC 0.54 0.61 0.70 0.64 0.73 0.57 -0.33 0.32 -0.25 0.03 0.93 0.72 0.95 1.00
RQ 0.57 0.62 0.80 0.75 0.68 0.56 -0.36 0.36 -0.25 -0.02 0.94 0.72 0.93 0.90 1.00
Voice 0.43 0.54 0.61 0.61 0.69 0.57 -0.25 0.18 -0.29 -0.31 0.76 0.65 0.79 0.76 0.79 1.00
38
Appendix 2 List of countries
Row Labels High income Lower middle
income
Upper middle
income
Grand
Total
East Asia & Pacific 4 4 3 11
Europe & Central Asia 24 3 10 37
Latin America &
Caribbean 4 5 11 20
Middle East & North
Africa 5 4 2 11
North America 1 1
South Asia 4 1 5
Sub-Saharan Africa 17 5 22
Grand Total 38 37 32 107
Countries’ Name Period Covered
Albania 2002 2016
Algeria 2002 2016
Armenia 2002 2016
Australia 2002 2016
Austria 2002 2016
Azerbaijan 2002 2016
Bangladesh 2002 2016
Belgium 2002 2016
Benin 2002 2016
Bolivia 2002 2016
Botswana 2002 2016
Brazil 2002 2016
Bulgaria 2002 2016
Burkina Faso 2002 2016
Cambodia 2002 2016
Cameroon 2002 2016
Chile 2002 2016
China 2002 2016
Colombia 2002 2016
Costa Rica 2002 2016
Cote d'Ivoire 2002 2016
Croatia 2002 2016
Czech Republic 2002 2016
Denmark 2002 2016
Djibouti 2010 2010
Dominican Republic 2002 2016
Ecuador 2002 2016
Egypt, Arab Rep. 2002 2016
El Salvador 2002 2016
Estonia 2002 2016
Eswatini 2002 2016
39
Finland 2002 2016
France 2002 2016
Gabon 2002 2016
Georgia 2002 2016
Germany 2002 2016
Ghana 2002 2016
Greece 2002 2016
Guatemala 2002 2016
Guyana 2002 2016
Haiti 2002 2016
Honduras 2002 2016
Hungary 2002 2016
Iceland 2002 2016
India 2002 2016
Ireland 2002 2016
Israel 2002 2016
Italy 2002 2016
Japan 2002 2016
Jordan 2002 2016
Kazakhstan 2002 2016
Kenya 2002 2016
Korea, Rep. 2002 2016
Kuwait 2002 2016
Kyrgyz Republic 2002 2016
Latvia 2002 2016
Lesotho 2002 2016
Lithuania 2002 2016
Luxembourg 2002 2016
Madagascar 2002 2016
Malaysia 2002 2016
Mali 2002 2016
Malta 2002 2016
Mauritius 2002 2016
Mexico 2002 2016
Moldova 2002 2016
Mongolia 2002 2016
Morocco 2002 2016
Namibia 2002 2016
Nepal 2002 2016
Netherlands 2002 2016
Nicaragua 2002 2016
Niger 2002 2016
Nigeria 2002 2016
North Macedonia 2002 2016
Norway 2002 2016
Oman 2002 2016
40
Pakistan 2002 2016
Panama 2002 2016
Paraguay 2002 2016
Peru 2002 2016
Philippines 2002 2016
Poland 2002 2016
Portugal 2002 2016
Romania 2002 2016
Russian Federation 2002 2016
Rwanda 2002 2016
Saudi Arabia 2002 2016
Senegal 2002 2016
Singapore 2002 2016
Slovak Republic 2002 2016
Slovenia 2002 2016
South Africa 2002 2016
Spain 2002 2016
Sri Lanka 2002 2016
Suriname 2002 2016
Tanzania 2002 2016
Thailand 2002 2016
Togo 2002 2016
Trinidad and Tobago 2002 2016
Tunisia 2002 2016
Turkey 2002 2016
Uganda 2002 2016
Ukraine 2002 2016
United States 2002 2016
Uruguay 2002 2016
Vietnam 2002 2016
41
Appendix 3 High Financially Inclusive
The table presents the results for 55 countries for the period of 2002-2016 using HDI, health expenses, and EDI for human capital development for high financially included
economies. The healthcare system expenditures, inflation, and dependent variables, deposit to GDP ratio and total deposits are in log form. Healthcare expenditure and EDI are
used endogenous variables. We employ military expenditure and government effectiveness are used as instruments for healthcare expenditures and EDI respectively.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
DGDP DGDP DGDP DGDP DGDP DGDP Deposit Deposit Deposit Deposit Deposit Deposit
HDI 4.845*
(2.805)
14.568***
(4.251)
GGEGDP
1.116***
(0.371)
0.222
(0.686)
0.970***
(0.355)
-1.085
(1.254)
PPCCGDP
1.092**
(0.426)
0.254
(0.727)
0.954**
(0.392)
-1.139
(1.386)
EDI
6.133
(5.473)
6.723
(4.681)
6.579
(5.151)
18.441
(15.914)
15.456
(10.197)
16.445
(11.387)
BankZ-1 -0.004*
(0.002)
-0.001
(0.003)
-0.007
(0.004)
-0.006**
(0.003)
-0.006
(0.004)
-0.007**
(0.003)
-0.002
(0.002)
0.001
(0.003)
-0.004
(0.004)
-0.008
(0.007)
-0.011
(0.009)
-0.005
(0.005)
Inflation -0.001
(0.002)
-0.000
(0.002)
0.006
(0.005)
-0.003
(0.003)
-0.003
(0.003)
-0.002
(0.007)
-0.002
(0.002)
-0.000
(0.002)
0.004
(0.005)
-0.009
(0.007)
-0.008
(0.005)
-0.015
(0.013)
Higher age -0.013
(0.019)
-0.013
(0.020)
-0.076**
(0.032)
-0.033
(0.024)
-0.030
(0.029)
-0.042
(0.039)
-0.021
(0.032)
-0.068***
(0.026)
-0.123***
(0.032)
-0.080
(0.058)
-0.106*
(0.057)
-0.038
(0.067)
Trade
Openness
0.000
(0.001)
0.000
(0.001)
0.003
(0.002)
0.001
(0.002)
0.002
(0.001)
0.002
(0.002)
0.000
(0.001)
-0.002***
(0.001)
0.000
(0.001)
0.003
(0.005)
0.002
(0.003)
-0.001
(0.002)
GDPG -0.013***
(0.003)
-0.002
(0.004)
-0.004
(0.004)
-0.016***
(0.006)
-0.014
(0.010)
-0.015
(0.010)
-0.011***
(0.003)
0.004
(0.004)
0.003
(0.003)
-0.020
(0.016)
-0.025
(0.018)
-0.025
(0.018)
F 15.634 17.239 11.367 8.414 8.679 8.638 31.453 37.319 27.792 7.475 6.859 5.903
r2 0.543 0.475 0.131 0.256 0.185 0.178 0.751 0.758 0.677 -0.225 0.013 -0.194
N 767.000 760.000 767.000 767.000 760.000 767.000 767.000 760.000 767.000 767.000 760.000 767.000
First
Military
Exp
50.67 21.53 29.62 14.01 50.67 21.53 29.62 14.01
GEFF 12.50 1.28 3.86 3.83 12.50 1.28 3.86 3.83
Standard errors in parentheses
Standard errors are clustered at country level * p < 0.10, ** p < 0.05, *** p < 0.01
42
Appendix 4 Less Financially Inclusive
The table presents the results for 47 countries for the period of 2002-2016 using HDI, health expenses, and EDI for human capital development for less financially included. The healthcare system expenditures, inflation, and dependent variables, deposit to GDP ratio and total deposits are in log form. Healthcare expenditure and EDI are used
endogenous variables. We employ military expenditure and government effectiveness are used as instruments for healthcare expenditures and EDI respectively.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
DGDP DGDP DGDP DGDP DGDP DGDP Deposit Deposit Deposit Deposit Deposit Deposit
HDI 16.963*
(9.796)
24.447**
(11.420)
GGEGDP
0.837***
(0.315)
0.696
(0.777)
0.976***
(0.351)
0.747
(1.223)
PPCCGDP
1.004
(0.645)
0.908
(0.996)
1.164
(0.865)
0.977
(1.547)
EDI
24.985
(19.662)
19.261
(27.364)
10.428
(21.252)
36.008
(25.903)
31.157
(36.896)
20.189
(32.593)
BankZ-1 -0.019***
(0.007)
-0.016***
(0.004)
-0.009
(0.008)
-0.029
(0.020)
-0.027
(0.020)
-0.016
(0.021)
-0.023**
(0.009)
-0.018***
(0.005)
-0.010
(0.011)
-0.038
(0.030)
-0.035
(0.030)
-0.023
(0.032)
Inflation -0.007
(0.005)
-0.004
(0.003)
0.001
(0.005)
-0.010
(0.010)
-0.008
(0.010)
-0.002
(0.011)
-0.007
(0.006)
-0.003
(0.003)
0.002
(0.007)
-0.012
(0.013)
-0.010
(0.014)
-0.003
(0.018)
Higher Age -0.056
(0.051)
-0.046
(0.043)
-0.106*
(0.062)
-0.274
(0.228)
-0.230
(0.297)
-0.191
(0.158)
-0.077
(0.070)
-0.064
(0.046)
-0.135*
(0.074)
-0.390
(0.315)
-0.362
(0.409)
-0.299
(0.249)
Trade
Openness
0.002
(0.002)
0.002*
(0.001)
0.004***
(0.001)
-0.003
(0.007)
-0.003
(0.008)
0.001
(0.007)
0.002
(0.003)
0.002
(0.001)
0.004*
(0.002)
-0.007
(0.011)
-0.006
(0.013)
-0.002
(0.011)
GDPG -0.014***
(0.004)
-0.007**
(0.003)
-0.009**
(0.004)
-0.019
(0.012)
-0.016
(0.014)
-0.013
(0.010)
-0.016***
(0.004)
-0.007***
(0.003)
-0.009**
(0.004)
-0.023
(0.017)
-0.021
(0.020)
-0.017
(0.016)
F 6.658 19.088 11.351 2.394 4.652 8.678 18.610 46.759 21.440 3.664 5.488 9.591
r2 0.263 0.612 0.179 -2.337 -1.253 -0.215 0.633 0.822 0.599 -1.264 -0.855 -0.004
N 644.000 604.000 622.000 644.000 604.000 622.000 644.000 604.000 622.000 644.000 604.000 622.000
First-
Military
Exp
17.49 1.08 12.04 4.04 17.49 17.49 1.08 12.04 4.04
First- GEFF 7.81 2.61 0.70 0.93 7.81 2.61 0.70 0.93
Standard errors in parentheses
Standard errors are clustered at country level * p < 0.10, ** p < 0.05, *** p < 0.01
43