Syntax Literate: Jurnal Ilmiah Indonesia p�ISSN: 2541-0849 e-ISSN: 2548-1398
Vol. 7, No. 11, November 2022
THE EFFECT OF E-MONEY, HUMAN
DEVELOPMENT INDEX (HDI), AND INTERNET PENETRATION ON ECONOMIC GROWTH IN
INDONESIA
Ullaya Shifa Darmawan, Meiryani
Fakultas
Akuntansi, Universitas Bina
Nusantara, Jakarta, Indonesia
Email: [email protected],
[email protected]
Abstract
This study is to provide insight to the public and
government in understanding whether E-money, Human Development Index (HDI), and
internet penetration significantly impact or not the economic growth in
Indonesia in order to prevent a decline in economic value in Indonesia. This
study uses quantitative methods with secondary data obtained from the official
websites of Statistics Indonesia, known in Indonesia as BPS (or Badan Pusat Statistik, the Central Bureau of Statistics), Bank
Indonesia (BI), and Indonesian Internet Service Providers Association (APJII).
The data obtained and analyzed using multiple linear regression test the
significant value or effect. As a result of this research found that e-money,
the Human Development Index (IPM), and internet penetration have a significant
effect on the stability of economic growth. This shows that if e-money, HDI,
and internet penetration increase, economic growth will also increase.
Keywords: Economic growth; Human Development Index
(HDI); E-money; internet penetration; Indonesia; Gross Regional Domestic
Product.
Introduction
The state of the economy in
Indonesia in 2020 has an unstable condition, according to data from the Central
Bureau of Statistics (BPS). Economic growth contraction decreased to -2.07
percent. Based on news from Kompas.com, the minister of finance, Sri Mulyani Indrawati, corrected that
economic growth during 2020 would decline to -1.7 percent (Kompas.com 2020).
Efforts can be made to improve economic performance so that a recession does
not occur by making innovations like the digital economy (Bank Indonesia 2020).
In the era of Industry 4.0, Indonesia was encouraged to innovate in digital
technology. Minister of Communications and Informatics Johnny G Plate stated
that internet penetration in Indonesia reached 73.7 percent of the total
population of 202.6 million. It can be said that Indonesia is one of the
countries that are fast in adopting digitalization to increase economic growth.
Bank Indonesia estimates that
Economic growth will reach 4,7�5.5 percent in 2022 from 3.2 � 4.0 percent in
2021. Innovations made to create immunity from Covid-19 in the short term and
through policy stimulus. The Governor of BI, Perry Warjiyo,
conveyed the statement in 2021. Advances in technology affect economic growth.
An example of technological advances is the emergence of electronic money, also
known as E-Money. Minister of Trade explained that the potential of the digital
economy is still wide open. The digital economy currently contributes 4 percent
to the economic growth in 2020. For the potential of the digital economy to be
optimal, it is necessary to develop a new wave of technology so that more
people use the internet and make payments using E-Money (Erlanitasari
2020, 145-56). Indonesia, a digitally oriented society, can be seen from the
number of internet users. According to data from Bank Indonesia, E-Money is a
product that has the potential to increase financial inclusion, which in turn
will affect economic growth.
Research conducted by Elistia (2018) concludes that human development has a
causal relationship with economic growth, that they influence each other and
are related. According to Farid (2019), the number of internet users
significantly affects economic growth. Nizar and Sholeh
(2021) stated that the increasing number of internet users and quality human
resources shows that E-Money, human development, and internet users
significantly influence the growth economy. Another study by Hodrab, Maitah, and Smutka (2016) concluded that the internet positively
affects economic growth. Payments using non-cash have helped increase economic
activity in Nigeria, a developing country that shows that all E-Money products
positively impact economic growth (Omodero 2021,
40-53).
The novelty in this study with
previous studies is the differences in types and total numbers of variables and
periods in the sample. The previous study used five provinces in Indonesia as
the object for 2010-2016. Currently, the author uses all 34 provinces in
Indonesia for the period 2017-2021. The author chose the period 2017-2021 to see
if there were any new results from previous research. Based on the background
described, this research has the following problem formulation: Does E-Money
affect economic growth? Does the Human Development Index (HDI) influence
economic growth? Does Internet Penetration have any effect on economic growth?
The contribution of this research expects that the government can maintain the
stability of economic growth by considering specific indicators or factors. The
results of this study will be related to the digital economy, which will be
needed to maintain and increase economic growth in the industrial 4.0 era. This
research expects help to provide information or knowledge to the public or
readers as a reference in future studies in economics or accounting.
The development of modern
technology characterizes the theory of endogenous growth. This theory arises
because of the knowledge externality that expects companies to be more
productive in economic growth (Maharani & Isnowati
2014). This theory believes that human capital in the form of technological
growth influences growth (Romer 1986). Arguments according to Aghion and Howitt
(1998), there are many reasons that technological progress can influence
economic growth. So, as in the explanation of the endogenous theory, it is
hoped that human development and technological progress will help in economic
growth in Indonesia. In the theory of endogenous growth, the role of humans
supports economic growth in the long term (Sunusi et
al. 2014). Therefore, endogenous theory can explain the Human Development Index
(HDI) variable. Endogenous theory can also explain the variables of E-Money and
internet penetration because the theory discusses technological developments.
The framework of this study is as follows:
E-Money (X1) Human Development Index
(X2) Economic Growth (Y) Figure 1 Framework Internet Penetration (X3)
Technology development keeps
advancing and growing in today's industry, including the payment industry,
which has experienced some rapid changes. The growth of computers and the
expansion of access to the internet network are making it possible to build an
efficient payment service system. The payment system is related to transferring
funds from one party to another and includes various components, such as
payment methods and clearing (Bank Indonesia 2008).
Electronic money, an example
of a non-cash payment, is also known as E-Money. In 2016, Suseco
explained that E-Money showed better features in terms of speed and efficiency
in transactions compared to credit and debit cards. E-Money was created as an
innovation in the field of payment instruments, and it can be said that E-Money
in Indonesia has been around for quite some time. One of the reasons why
E-Money is popular and widely used by the public is that it is easy to use. The
benefits of E-Money are that it can be used anywhere and anytime, the scanning
process is fast hence there are no hassles of transactions. Using E-Money is
safer than cash as there are always records of transactions.�
Research from Omodero (2021) shows that electronic money products have a
significant positive impact on economic growth, and payments with electronic
money have increased economic activity. Research conducted by Sitompul (2020) also shows that electronic money has a
positive and significant long-term effect on economic growth. Still, there is
no short-term effect between electronic money and economic growth in Indonesia.
This it can be seen that electronic money (E-Money) can encourage and influence
economic growth. Based on the results of previous studies, the hypotheses of
this study are:
Human development was first
mentioned in 1990. Human development has criteria for developing skills and
abilities of a person to set one's destiny to an income level, and it
significantly impacts discussions about how best to improve the quality of life
(Appiah 2019, 101-109). Human development was initially characterized as an
"individual decision-making process" that allows people to live long
and healthy lives, obtain information, and approach the assets needed for
traditional lifestyles (Hopkins 1991). The World View of Human Development
created by the United Nations Development Program (UNDP) in 1990 treats human
development as a demonstration of development that wants to expand alternatives
that can be achieved through empowering people. The Human Development Index
(HDI) is a complex index that measures the average success of a region or
country in achieving several indicators (Central Bureau of Statistics 2016).
The Human Development Index
(HDI) is an indicator of economic growth in a country, as indicated by the
Gross Domestic Product (GDP) per capita value. Economic development is an
increase in the Human Development Index (HDI). It is a composite indicator
covering three areas of human development that are widely considered. The
indicators are as follows: (1) Health sector: longevity. (2) Education and
knowledge. (3) Economic sector: Decent standard of living (United Nations
Development).
Damanik et al. (2021) research show that the Human Development Index
significantly influences economic growth. Still, partially the Human
Development Index does not have a significant effect on economic growth. The
results of research conducted by Nawawi et al. (2021) show that the higher the
human development index, the higher the economic growth rate, which means that
there is a positive and significant influence on economic growth. Thus, it can
be seen that the Human Development Index's value can encourage and influence
economic growth. Based on the results of previous studies, the hypotheses of
this study are:
Internet penetration is
represented by the percentage of the population using the internet. The
internet creates new industries and expands opportunities with the ability to
drive innovation, spread knowledge, empower consumers, build networks, and
regulate social interactions around the world (Chu 2013). As the digital market
continues to grow, internet users also increase. The direct impact of ICT can
be measured as a percentage of the Gross Regional Domestic Product (GRDP). The
importance of Information and Communication Technology (ICT) for economic and
social development has become a dramatically unique position since the rapid
growth of technology, and its market began in the mid-1990s (Hodrab et al. 2016, 765-775).
The results of research
conducted by Tchamyou et al. (2019) show that
internet penetration effectively influences economic growth. The results of a
study conducted by Sani (2019) show that the internet is vital in increasing
economic growth in the short term. This it can be seen that the value of
internet penetration can encourage and influence economic growth. Based on the
results of previous studies, the hypotheses of this study are:
Methodology
The type of research method used is quantitative research using
secondary data. Secondary data is data and information obtained from
pre-existing sources, and secondary data can also be obtained from previous research results.
The collection is done by downloading from the official websites of Statistics Indonesia, known in
Indonesia as BPS (or Badan Pusat Statistik, the Central Bureau of Statistics) www.bps.go .id, Bank Indonesia (BI) www.bi.go.id, Indonesian
Internet Service Providers Association (APJII). The data taken is from the years 2017 to 2021. This
study's data analysis uses descriptive statistics, and the result provides a simple summary of the
sample and its measures (Mishra et al. 2019,
67). The selection of sample criteria is as follows:
Table 1 Sample Criteria
|
Sample Criteria |
Quantity � |
|
Provinces
in Indonesia from 2017 to 2021. |
170 |
|
Provinces
that do not have complete information regarding research variables. |
(68) |
|
Total
|
102 |
This study also uses a classical assumption test in the form of a
normality test aiming to see whether the regression sample, independent and
dependent variables have a normal distribution
or not. The
normality test is an essential step in determining the measures
of central tendency and statistical methods for data analysis (Mishra et al. 2019, 67). There are three types of classical assumptions tests in the study, namely the normality test,
heteroscedasticity test, and multicollinearity test.
The heteroscedasticity test is carried out by using
the White test method. If the displayed value
is 0.05, there are no heteroscedasticity indications. The multicollinearity can
be observed by examining the correlation matrix. If the correlation matrix is
less than 0.8, it can be stated that there is no sign of multicollinearity. The
test is performed with the help of an application program named STATA. Another analysis used is a multiple linear regression test using fixed effect methods. Here is the formula (Farid 2019):
Description:
GRDP ���������� = Economic growth
EM ���������������� = E-Money
HDI ��������������� = Human
Development Index ����������
ICT ���������������� = Internet
Penetration�
a �������������������� = Constant
e �������������������� = Error term� �
The Coefficient of determination is a test used to estimate the extent
to which the model shows variations in the independent variables. The value of
the Coefficient of determination is between the values of zero (0) and one (1). The value of the Coefficient of determination that leans to the number
one means the ability of the independent variables to put almost all the
information needed to estimate the dependent variable. The simultaneous significance test (f test) evaluates
whether all independent variables have a simultaneous effect on the dependent
variable. A parameter significance test (t-test) reveals how much influence
each independent variable has in explaining the variation in the dependent
variable (Ghozali
2018).
Results and Discussion
Data Description �
The variable used in this
study is Gross Regional Domestic Product (GDRP) as the dependent variable as
the proxy of economic growth. In addition, the independent variables are
E-Money, Human Development Index (HDI), and penetration internet.
Table 2 Descriptive
Statistics Test Results
|
Variable |
Obs |
Mean |
Std. Dev. |
Min |
Max |
|
GRDP1 |
102 |
11.98885 |
1.138346 |
10.18857 |
14.4341 |
|
EM |
102 |
24650.45 |
51221.82 |
2 |
282983 |
|
HDI |
102 |
71.16078 |
3.881203 |
60.44 |
81.11 |
|
ICT |
102 |
64.82676 |
16.79046 |
21.7 |
91.4 |
Source: Data Processing STATA, 2022
Table 2 displays the results
of the descriptive statistics. There are 102 total data used in this study. The
variable maximum value (GDP) of Gross Regional Domestic Product (GRDP) is
14,434, and the minimum value is 10,188. The average result is 11,988, with a standard
deviation of 1,138. This variable's standard deviation value is less than the
average value. This result implies that the distribution of GRDP data is
equally distributed. E-Money (EM) has a minimum value of 2 and a maximum value
of 282983. The obtained average value is 24650, with a standard deviation of
51221. This variable's standard deviation value is more prominent than its
average value. This finding suggests that the distribution of EM data is
unequal. The Human Development Index (HDI) has a minimum of 60.44 and a maximum
of 81.11. The obtained average value is 71.16, with a standard deviation of
3.88. This variable's standard deviation value is less than the average value.
This result implies that the distribution of HDI data is equally distributed.
Internet Penetration (ICT) has a minimum value of 21.7 and a maximum value of
91.4. The obtained average value is 64.82, with a standard deviation of 16.79.
This variable's standard deviation value is less than the average value. This
result implies that ICT data is equally distributed.
Classical Assumption Test
Normality Test
This normality test is
used to show whether the regression model is a normal distribution or not. A
good regression model is a form of a regression model with a normal
distribution or close to normal. The decision-making in the normality test is by looking at the probabilities if the value is
more than 0.05, indicating that the residual is normally distributed. Based on
the table below, the results show that the data used in this study is normally
distributed.
Table 3 Shapiro-Wilk Normality Test Results
|
Variable |
Obs |
W |
V |
z |
Prob>z |
|
res |
102 |
0.98027 |
1.650 |
1.120 |
0.13133 |
Source: Data Processing STATA, 2022
Multicollinearity Test
Based on the table below, multicollinearity can be determined by looking at the correlation matrix
value. The results on table 4 show that the correlation matrix is less than
0.8, and the results shown in table 5 show that the VIF value is less than ten
and the 1/VIF value is less than 1, indicating no sign of multicollinearity.
Table 4 Multicollinearity Test Corelation Matrix
|
|
GRDP1 |
EM |
HDI |
ICT |
|
GRDP1 |
1.0000 |
|
|
|
|
EM |
0.6745 |
1.0000 |
|
|
|
HDI |
0.4463 |
0.4811 |
1.0000 |
|
|
ICT |
0.1654 |
0.2549 |
0.2724 |
1.0000 |
Source: Data Processing STATA, 2022
Table 5 Multicollinearity
Test VIF
|
Variable |
VIF |
1/VIF |
|
HDI |
1.34 |
0.744530 |
|
EM |
1.33 |
0.751959 |
|
ICT |
1.10 |
0.905855 |
|
Mean VIF |
1.26 |
����������������������������������� Source:
Data Processing STATA, 2022
Heteroscedasticity Test
Based on the table below, the White-test heteroscedasticity
shows that prob>chi2 is 0.2798, indicating no sign of heteroscedasticity in
the data using STATA.
Table 6 Heteroscedasticity Test
Result
����������������������� White's test for
����������������������������������� Against
|
chi2(9)������� = |
10.94 |
|
Prob > chi2 = |
0.2798 |
Source: Data Processing STATA, 2022
Regression Analysis Test
The table below shows the outcomes of the regression analysis
using STATA.
Table 7 Regression Analysis Results
|
Fixed-effects (within) regression |
Number of obs |
= |
102 |
|
Group variable: PROV |
Number of groups |
= |
34 |
|
R-sq: |
Obs per group: |
||
|
within = 0.1388 |
min |
= |
3 |
|
between = 0.1647 |
avg |
= |
3.0 |
|
overall = 0.1645 |
max |
= |
3 |
|
F(3,65) |
= |
3.49 |
|
|
corr(u_i, Xb) = 0.2466 |
Prob > F |
= |
0.0205 |
|
GRDP1 |
Coef. |
Std. Err. |
t |
P>t |
[95% Conf. |
Interval] |
|
EM |
-3.36e07 |
2.38e07 |
-1.41 |
0.162 |
-8.11e07 |
1.38e07 |
|
HDI |
.0525208 |
.0207739 |
2.53 |
0.014 |
.0110324 |
.0940091 |
|
ICT |
.0002134 |
.0002307 |
0.92 |
0.358 |
-.0002474 |
.0006741 |
|
_cons |
8.24588 |
1.471841 |
5.60 |
0.000 |
5.30641 |
11.18535 |
F test that all u_i=0: F (33, 65) = 1959.30����������������� ����������������������������������������������� ����������������������� Prob
> F = 0.0000
�
Source:
Data Processing STATA, 2022
a.
Table 7 can be
explained by the following equation: hence, it can be stated that a constant
value has a positive value of 8.2458. This positive sign shows the
unidirectional effect between the dependent and independent variables. This
result indicates that if all independent variables such as E-Money, HDI, and
penetration internet are 0 percent and have no changes, the GRDP (economic
growth) value is 8.2458.
b.
The regression
coefficient value for E-Money (X1) is -3.36, which indicates a negative value
with an opposite direction effect between E-Money and GRDP. This result shows
that if the E-Money has increased by 1 percent, on the other hand, the GRDP
will decrease by -3.36. With the assumption that the other variables remain
constant.
c.
The regression coefficient
value for HDI (X2) is 0.0525, which indicates a positive value on the same
direction effect. This result suggests that if HDI increases by 1%, the GRDP
will also increase by 0.0525, assuming that the other variables remain
constant.
d.
The regression coefficient
value for ICT (X3) is 0.0002, which indicates a positive value on the same
direction effect. This result suggests that if ICT increases by 1%, the GRDP
will also increase by 0.0002, assuming that the other variables remain
constant.
Hypothesis Test
1.
Simultaneous
significance test (f test)
If the probability value is less than 0.05 at a significant level of 5%,
it can be seen that all variables simultaneously have a significant effect. It
can be seen in table 7 that the probability value (prob > F) has a value of
0.02, which means that all variables affect economic
growth in Indonesia simultaneously.
2.
Parameter
significance test (t-test)
Based on table 7, the E-Money
variable has a t-value of -1.41 and a significant value of 0.162. The t-test
states that the significance is at five percent if P>|t| more than 0.05,
meaning the E-Money variables do not affect Indonesia's economic growth, and
the first hypothesis is rejected. Variable HDI has a t-value of 2.53 and a
significant value of 0.014, and the t-test is said to be significant at the 5%
level if P>|t| less than 0.05. Therefore, the HDI variable does affect
Indonesia's economic growth, and the second hypothesis is accepted.
Furthermore, the ICT variable has a t-value of 0.92 and a significant value of
0.358, and the t-test is said to be significant at the 5% level if P>|t|
more than 0.05. Therefore, the ICT variable does not affect Indonesia's
economic growth, so the third hypothesis is rejected.
3.
Coefficient of Determination
The results of the Coefficient of determination can be seen from the
R-Squared value. The Coefficient of determination test is used to see how much
the model's ability can simultaneously explain variables' variation. Table 7
shows that the R-Squared value is 0.1388, meaning that all
independent variables can define the dependent variable and have an effect of 13.88%, and 86.12% can be explained and influenced by other variables.
Discussion
1.
Effect of
E-Money on Economic Growth
The E-Money variable has a
t-value of -1.41 and a significant value of 0.162. The t-test states that the
significance is at 5% if P>|t| more than 0.05, meaning the first hypothesis
is rejected. The result shows that the E-Money variable does not influence economic
growth. It implies that if E-Money has increased, economic growth will not be
affected. The outcome of this study is the opposite of the study conducted by Omodero (2021), which stated that E-Money has a significant
positive impact on economic growth. However, the outcome does support research
by Oginni et al. (2013), which researched electronic
payment, which included E-Money. Using the Ordinary Least Square (OLS) method,
the results of their study suggest that E-Money contributed negatively to economic
growth.
Furthermore, Susilawati & Putri (2019) conducted similar research
between E-Money and economic growth. It was stated that the decrease or
increase in E-Money does not affect economic growth, which implies that E-Money
has no significant effect on economic growth in Indonesia. Considering the use
of E-Money causes a shift in public deposits from savings and time deposits to
float, this transfer of funds from banks to non-bank institutions, the use of
E-Money will only encourage the velocity of money, not economic growth in
Indonesia. (Susilawati & Putri 2019, 667-678).
Nonetheless, E-Money can potentially improve welfare and the financial system.
Hence, E-Money can boost economic growth by increasing consumption.����������
2.
Effect of Human
Development Index (HDI) on Economic Growth
The variable HDI has a t-value
of 2.53 and a significant value of 0.014, and the t-test is said to be
significant at the 5% level if P>|t| less than 0.05. Therefore, the HDI
variable does affect Indonesia's economic growth, and the second hypothesis is
accepted. The results are in line with previous researchers, Nawawi et al.
(2021), as the analysis results stated that the higher HDI, the higher the rate
of economic growth, which implies a positive and significant influence on
economic growth. On the contrary, the result of this study does not support the
analysis from Damanik et al. (2021), which indicates
how HDI partially does not have a significant effect on economic growth.
Elistia & Syahzuni (2018) conducted
similar research to study the correlation between HDI and economic growth in
ten ASEAN countries. The results have shown that the correlation is quite
strong between HDI and economic growth and has positively affected each other,
which implies that HDI does indeed have a significant effect on economic
growth. The relationship between HDI and economic growth becomes one of mutual
influence. Hence, increasing levels of human development will lead to increased
opportunities for economic growth and vice versa. Moreover, the result supports
research by Appiah et al. (2019), concluding that human development can
influence economic growth.
Not to mention, the outcomes
show that the higher rate and value of HDI, the higher the rate of economic
growth will going to be. The result of this study can
support research conducted by Salman (2016) which shows that the higher human
development, the better the direct effect on economic growth. Human development
is based on health, knowledge, and standard of living. A higher HDI implies
that better health standards, higher income levels, and higher knowledge will
create opportunities for more economic activities that increase economic
growth. It was suggested to make more resolutions to develop better human
development to stabilize and increase Indonesia's economic growth rate.
3.
Effect of
Penetration Internet on Economic Growth
The ICT variable has a t-value
of 0.92 and a significant value of 0.358, and the t-test is said to be
significant at the 5% level if P>|t| more than 0.05. Therefore, the ICT
variable does not affect Indonesia's economic growth, so the third hypothesis
is rejected. However, the result is in opposition to research conducted by Asongu et al. (2020) which found that internet penetration
dramatically impacts economic growth. On the other hand, the results support
research by Imansyah (2018), which stated that
internet penetration does not affect economic growth in Indonesia. Zhang (2019) has done a similar study to determine whether
information and communication technology (ICT), including internet penetration,
contributes to economic growth. The result shows that internet penetration does
not seem to have contributed to economic growth in Asian countries.
Nonetheless, if provided equally, internet penetration can potentially improve
economic growth as the internet can help with higher online transactions using
e-banking and e-commerce, leading to a better income for the development of the
economy.
Conclusion
This paper has attempted to
investigate the effect of E-Money, HDI, and internet penetration on economic
growth in Indonesia. As an economy becomes central to people's everyday lives,
it is essential to understand how E-Money, HDI, and internet penetration affect
economic growth. This study found that E-Money, HDI, and internet penetration
simultaneously influence economic growth in Indonesia. HDI does have a
significant effect on economic growth in Indonesia, while partially, both
E-Money and internet penetration have no significant impact on economic growth
in Indonesia. The first hypothesis test confirms that E-Money has no
significant effects on economic growth in Indonesia. This result does not
support the study by Omodero (2021), which states
that E-Money has a significant positive impact on economic growth. The result
concludes that E-Money has no capability of influencing economic growth. If the
value of E-Money rises, the economy's growth will fall and vice versa.
The second hypothesis test
shows that HDI significantly affects Indonesia's economic growth. The results
support previous researchers, Nawawi et al. (2021), as they offer the analysis
results that the higher HDI, the higher the rate of economic growth, which
concludes that there is a positive and significant influence on economic
growth. On the other hand, the result of this study does not support the
analysis from Damanik et al. (2021), which indicates
how HDI partially does not have a significant effect on economic growth. The
third hypothesis test also confirms that ICT does not considerably influence
economic growth. The results support research by Imansyah
(2018), which stated that internet penetration does not impact economic growth in
Indonesia. Hence, with the findings of this
study, it is wished for the government to take advantage of the potential of
HDI as they keep increasing and will be more beneficial to economic growth if
adequately taken care of. If approached correctly,
it can increase the potential of providing opportunities for
economic growth.
The research
can be used as input and additional information for the government concerning making decisions and paying attention to
the right time to invest in HDI to maintain the economy
in Indonesia. On the other note, this study expects to gain insight into
economic growth and be a reference for future researchers.
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Copyright holder: Ullaya Shifa Darmawan,
Meiryani (2023) |
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First publication right: Syntax Literate: Jurnal Ilmiah Indonesia |