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
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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,
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.
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.
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
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.
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.