Internationalization of Futures Markets: Lessons from ChinaForeign access to China’s commodity...

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1 Effects of human capital development on bank deposits Nikhil Srivastava 1 , Prof. David Tripe 2 , Dr. Mui Kuen Yuen 3 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

Transcript of Internationalization of Futures Markets: Lessons from ChinaForeign access to China’s commodity...

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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

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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

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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

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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.

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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

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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.

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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?

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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

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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

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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,

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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).

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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)

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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.

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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).

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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**

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(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

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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

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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.

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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.

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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

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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

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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.

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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

.

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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.

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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

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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.

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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,

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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

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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,

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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

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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.

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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

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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

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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

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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

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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

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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

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