Arthatama

Journal of Business Management and Accounting
ISSN: 2655-786x (p), 2774-5678 (e)
Vol. 6, No. 1 (2022), pp. 12-25

Empirical Relationship between Gender Equality and
Socio-economic Developments: An Error Correction
Model
Uswatun Khasanah1, Hadi Sasana1
Universitas Tidar, Magelang, Indonesia
Corresponding author: khasanah2606@gmail.com
1

Abstract
Gender equality is to obtain equal opportunities without gender differences and
equality in their rights to feel the results of development. The low gender will
trigger injustice, where one gender will be harmed. This form of gender injustice
often occurs through stereotyping, labeling, subordination, marginalization, and
double burdens, which usually will harm women. This study aims to determine
the active contribution of women to the world economy and to see how big the
role of women in improving the economy from the political realm is carried out
using the ECM-EG approach and sample time series data from 1990-2018 in
Indonesia. The results showed positive effects of education, health, and
representation in the political field on gender equality. The findings theoretically
suggested further research on gender in employment runs by the fast-slow pattern
of the legal process of The Law of Diminishing Return (TLDR), which is very
influential by the quality of human resources and technological advances as well
as the employment opportunities needed by women.

Keywords
Gender Equality, Socio-economic Developments, Women Empowerment, Error Correction
Model, Indonesia

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Uswatun Khasanah, Hadi Sasana
Empirical Relationship between Gender Equality and Socio-economic Developments: An Error Correction
Model

1.

Introduction

The population is a factor that is quite influential on the economic growth of a
country and is a driving force for a country’s economy. High population growth
should be balanced with the quality of the population and an adequate number of
jobs so that the increasing population will become development capital and not a
burden on the economy. Adika & Rahmawati (2021) showed that the increase in
population not followed by the expansion of employment opportunities would result
in high unemployment and a decrease in population productivity.

Source: World Bank processed
Figure 1: Number and Percentage of Female Population in Indonesia

The number of populations every year must experience a sufficient increase so
that women continue to receive special attention, especially in the development of
health levels, as well as increase the same as men (Boserup et al., 2013). The same
level of men and women in education and health will increase productivity,
encouraging economic growth (Ranis et al., 2000). Gender inequality is often
associated with biological characteristics that women often experience. Women are
described as feminine humans who make the impression of being gentle, while men
are stronger; this encourages the creation of gender subordination (Falk & Hermle,
2018). Furthermore, it will encourage the creation of different workloads based on
the type and put women in burden positions, namely more and longer burdens
(Iversen et al., 2020; Beer, 2009). As development subjects, men and women have
the same societal roles, positions, and responsibilities.
Meanwhile, inequality and gender inequality cause development to not reach
its maximum potential (Sari, 2021). Therefore, this study looks at the relationship
between gender and economic growth in Indonesia, given that women’s role is
currently needed in its development. The topic of the discussion focuses on seeing
women’s active contribution to the economy and how big the role of women is in
improving the economy from the political realm.

2.

Literature Review

Gender is a cultural concept that separates roles, behavior, mentality, and
sentimental personality between men and women (Archer, 1989; Neculaesei, 2015).
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Vol. 6, No. 1 (2022), pp. 12-25

Global Gender Gap Report 2018 found that Indonesia’s gender equality level in
income is 96, employment is 118, life expectancy is 88, elementary school graduates
are 122, and the number of women who enter parliament is 87 (World Economic
Forum, 2018). It found a culture and community mindset related to women’s
position and a fairly high difference between the achievement of benefits and
development outcomes for women over men (World Economic Forum, 2018).
In the era of globalization, which has high economic growth, women are active
in various aspects of life, especially the economy. Gender equality gives birth to
solutions to increase a country’s economic growth (Momsen, 2009). Today, the
Millennium Development Goals (MDGs) have set gender as a quality goal, where
women can improve a creation as a result of producing and giving birth so that the
role of women in various aspects of life, especially the economy, cannot be
underestimated (Webb et al., 2017).
Increasing the role of women in having a vital meaning in giving birth to equal
development partnerships between men and women in various fields of life.
Women’s contribution to increasing development and economic resilience is very
important, and their low contribution to the world of work will trigger sub-optimal
economic growth (Loh & Dahesihsari, 2013). The existence of Woman20 and G20
is very good and useful in increasing women’s empowerment so that they can become
a driving force for the economy towards a more advanced economy (Rimmer, 2017).
In addition to participation and contribution to economic activities, women’s role
in politics currently has a high intensity (Robinson & Bessell, 2002; Davies, 2005).
It is evidenced by the increasing number of women who are owned as members of
parliament; even now, 30% of women’s representation in parliament is not just a
formality but a representation of women who should be given more attention
(Purwanti, 2015; Prihatini, 2019).

3.

Research Methods

3.1. Research Design and Sampling
The population and sample in this study are time series data for the period 19902018 from GDP per capita (current US$), Life expectancy ratio to age 65, Female to
Male (%), School enrollment rate, primary, female to male (%), Employability,
female to male (%), Proportion of seats held in national parliaments, Female to
male(%) in Indonesia.
This quantitative research uses secondary data from the publications of
statistical institutions, namely the Central Statistics Agency and the World Bank,
with the time series type for the period 1990-2018 in Indonesia. The variables used
in this study are four independent variables that measure gender equality in health,
education, employment, and political participation, while the dependent variable is
economic growth.

3.2. Operational Definitions

1) The dependent variable is measured by the natural logarithm of GDP per
capita (current US$).

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Uswatun Khasanah, Hadi Sasana
Empirical Relationship between Gender Equality and Socio-economic Developments: An Error Correction
Model

2) The independent variable, namely gender inequality or inequality between
men and women, is measured using the following indicators:
a. Gender equality in the level of health as measured by Life expectancy
ratio to age 65, Female to Male (%).
b. Gender equality in women’s education level as measured by the value
of School enrollment rate, primary, female to male (%).
c. Gender equality in the level of women’s work participation as measured
by the value of Employability, female to male (%).
d. Gender equality in the level of political participation as measured by
the value of the proportion of seats held in national parliaments,
Female to male (%)

3.3. Analysis Method
The testing used is firstly using the classical assumption test. The normality test
was carried out to see the spread of the data on the tested variables, whether normally
distributed or not; the model indicated by normality made the data or variables not
normally distributed (Adika & Rahmawati, 2021). The normality test of the data in
this study can be done using the Jarque-Berra (J-B) approach if prob. Jarque-Berra >
5% significance level, then the data has passed the normality test. Furthermore,
autocorrelation is a condition where a variable that depends on a certain period is
correlated with a variable in another period. This test only occurs on time series data.
Autocorrelation will cause the variance to be lower and the estimate to be biased, so
the model is inefficient and not BLUE (Adika & Rahmawati, 2021). Autocorrelation
also makes the value of R2 higher than expected, so the hypothesis using t-statistics
and F-statistics will be misleading.
In addition, multicollinearity indicates a correlation between independent
variables so that the results of the regression processing produce a large standard
error value, the coefficient of determination (R2) remains high, and the F-statistic
test is significant even though many variables are not significant. Multicollinearity
can be seen from the VIF value greater than 10, where the higher the VIF value
indicates greater collinearity (Adika & Rahmawati, 2021).
This study uses an Error Correction Model (ECM) approach that can overcome
the blunt regression model that occurs when there is no correlation between ordinary
variables, but the resulting regression coefficient is feasible, and the coefficient of
determination is high (Agus, 2013; Li et al., 2013). Systematically the basic model
used in this study is as follows:
Y = f (R_STA, R_SEP, R_EMPLY, R_PSHNP…………………………………. (1)
GDP_CAP = GDP per capita (current US$)
R_STA = Life expectancy ratio to age 65, Female to Male (%)
R_SEP= School enrollment rate, preprimary, female (%)
R_EMPLY = Employability, female to male (%)
R_PSHNP = proportion of seats held in national parliaments, female to male (%)

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Vol. 6, No. 1 (2022), pp. 12-25

Furthermore, the equation is transformed into an econometric model to
simplify the analysis. The following econometric models used are:
Log GDP_CAP t = β0 + β1R_STAt + β2 R_SEP t + β3 R_EMPLY t + β4 R_PSHNP t +
et …… (2)
Log GDP_CAP = GDP per capita (current US$) period t
β0 = Constant
β1, β2, β3, β4 = coefficient in the long run
t = period (1990-2018)
e = error
To get a short-term estimate, the model used is:
DLog GDP_CAP t = β0 + Dβ1R_STAt + Dβ2 R_SEP t + Dβ3 R_EMPLY t + Dβ4
R_PSHNP
+
ECT(-1)
+
t
et……………………………………………………………………………………………. (3)
ECT= error correction terms
D= Stationarity Level 1st (first difference)
In the ECM model, the ECT coefficient (-1) must range from -1 to 0 to
determine whether the ECM model is valid. If valid, continue with long-term and
short-term estimates as well as classical assumption tests and statistical tests to get the
BLUE (Best Linear Unbiased) estimation regression results.

4.

Results

Based on the results of data collection from 1982 to 2020, as shown in Table 1,
the results showed that for the variables LOG GDP_CAP, R_STA, R_SEP,
R_EMPLY, R_PSHNP sequentially, the average data is 3.169415, 1.102414,
0.977524, 0.374521, and 0.158255 with a minimum value of 2.666469, 1.09,
0.948687, 0.252955, 0.086957 and the maximum values are 3.59038, 1.11,
1.039127, 0.56686 and 0.247216. All variables with 29 observations passed the
Jarque-Bera data normality test because they had a probability of 0.05.
Testing the stationarity of the data is very important. It should be done because
the validity and stability of the data will be things that need to be considered if the
data is not stationary. The data will be stationary if the variance value is constant and
does not fluctuate systematically during the observation time, which can be known
by performing the Augmented Dickey-Fuller (ADF) test. In this ECM model, all
variables should be stationary at the same integration test level and not stationary at
the same level (Adika & Rahmawati, 2021). However, if the data is not stationary, it
will encourage false regression (Adika & Rahmawati, 2021). The results are shown
in Table 2.

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Uswatun Khasanah, Hadi Sasana
Empirical Relationship between Gender Equality and Socio-economic Developments: An Error Correction
Model

Table 1: Descriptive Statistics
LOG_GDP R_STA
R_SEP
R_EMPLY R_PSHNP
CAP
Mean
3.169415
1.102414 0.977524 0.374521
0.158255
Median
3.060797
1.1
0.973454 0.388646
0.142857
Maximum
3.59038
1.11
1.039127 0.56686
0.247216
Minimum
2.666469
1.09
0.948687 0.252955
0.086957
Std. Dev.
0.304455
0.007863 0.01994
0.069252
0.051779
Skewness
0.143691
-0.444366 1.062211 0.113697
0.329949
Kurtosis
1.553776
1.798964 4.520827 3.656507
1.818981
Jarque-Bera
2.627101
2.697402 8.24818
0.583273
2.211578
Prob.
0.268864
0.259577 0.016178 0.74704
0.33095
Sum
91.91303
31.97
28.34821 10.8611
4.589385
Sum Sq. Dev. 2.595402
0.001731 0.011133 0.134282
0.075071
Observations 29
29
29
29
29
Source: World Bank, Central Bureau of Statistics processed with eviews10
Table 2: Data Stationarity Test (First)
Intermediate ADF test results D(UNTITLED)
Series
Prob.
Lag Max Lag Obs
D(LOG_GDP_CAP) 0.0014 0
5
27
D(R_STA)
0.0021 0
5
27
D(R_SEP)
0.005
4
5
23
D(R_EMPLY)
0.0003 1
5
26
D(R_PSHNP)
0.0015 0
5
27
Source: World Bank, Central Bureau of Statistics processed with eviews10

The unit root test, as shown in Table 2, found that the data variables
LogGDP_CAP, R_STA, R_SEP, R_EMPLY, and R_PSHNP are not stationary at the
level. Because the test results at the level are not stationary, the testing process is
continued at the first difference level. The test results at the first difference level show
that the LogGDP_CAP, R_STA, R_SEP, R_EMPLY, and R_PSHNP data are
stationary. Thus, the hypothesis was accepted.
Next, the cointegration test was carried out to know the residual value of the
model used in the study. Before testing the independent and dependent variables’
cointegration, both have the same degree of integration (Adika & Rahmawati, 2021).
The regression model with cointegration illustrates that there has been a balance and
the independent and dependent variables have an influence in the long run. Ways
to determine cointegration in the regression model can use by the Johansen test,
bound test, and the Engel-Granger Cointegration Regression Durbin-Watson
(CRDW) test (Adika & Rahmawati, 2021). The Engel-Granger test approach was
used, with the result being shown in Table 3.

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Vol. 6, No. 1 (2022), pp. 12-25

The test results in Table 3 regarding the stationary residue show that the
stationary residue is at the level. It found the existence of integration between
variables as evidenced by the residual probability value of ADF of 0.0038, which is
smaller than 5% alpha. These results showed that there is cointegration between
variables and found that there has been a balance between these variables in the long
run. Therefore, the hypothesis was accepted.
Table 3: Residual Stationary Testing Results (ECT) at Level
t-Statistic
Prob.
Augmented Dickey-Fuller test statistic -3.785885 0.0079
Test critical values:
1% level
-3.689194
5% level
-2.971853
10% level
-2.625121
Source: World Bank, Central Bureau of Statistics processed with eviews10

Further analysis is to examine ECM-EG Model Selection. In the ECM-EG test,
the residual coefficient of ECT(-1) must range from -1 to 0 and is significant, so the
selection of estimates indicates that the ECM model used is valid with the following
formula used:
DLogGDP_CAP t = β0 + Dβ1R_STAt + Dβ2 R_SEP t + Dβ3 R_EMPLY t + Dβ4
R_PSHNP t + ECT(-1) + e t …………………………………………………………
(4)
Empirical results, as shown in Table 4, showed that the residual ECT coefficientvalue (-1) ranges from -1 to 0, which is -0.340377, and has a significant probability
value, which is seen from the probability value of 0.0587, which is smaller than 10%
alpha, so the model ECM-EG is valid.
Table 4: ECT Coefficient Test Results
Variable Coefficient Std. Error t-Statistic Prob.
ECT(-1) -0.273232
0.137037 -1.993859 0.0587
Source: World Bank, Central Bureau of Statistics processed with eviews10
Table 5: Short-Term and Long-Term Regression
Short Term
Long Term
Variable
Coefficient
Prob.
Variable
Coefficient
Prob.
C
0.025115
0.1256 C
-16.34559
0.0086
D(R_STA)
8.049802
0.1265 (R_STA)
16.74825
0.0031
D(R_SEP)
0.869084
0.4425 (R_SEP)
0.625658
0.6491
D(R_EMPLY) 0.390455
0.1455 (R_EMPLY) -0.210226
0.6511
D(R_PSHNP) 0.546275
0.4945 (R_PSHNP) 3.277258
0.0002
ECT(-1)
-0.273232
0.0587
Source: World Bank, Central Bureau of Statistics processed with eviews10

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Uswatun Khasanah, Hadi Sasana
Empirical Relationship between Gender Equality and Socio-economic Developments: An Error Correction
Model

Moreover, estimates in the short and long term were conducted to examine
ECM-EG results. The results of the regression processing, as shown in Table 5, show
that both in the short and long term, the variables R_STA, R_SEP, and R_PSHNP
have a positive effect on GDP_CAP, while the variable R_EMPLY has a positive
effect in the short term and negative in the long term.
The results of econometric equations, in the long run, are shown as follows:
̂ t= -16.34559+ 16.74825 DR_STAt - 0.625658 DR_SEPt - 0.210226
ln𝐺𝐷𝑃_𝐶𝐴𝑃
DR_EMPLYt + 3.277258 DR_PSHNPt
Meanwhile, the results of econometric equations in the short run are as follows:
̂ t = 0.025115 + 8.049802R_STAt ln𝐺𝐷𝑃_𝐶𝐴𝑃
0.390455R_EMPLYt + 0.546275R_PSHNPt

0.869084

R_SEPt

+

In the short term, no variables have a significant effect, while in the long term,
only the variables R_STA and R_PSHNP have a significant effect. Based on the
results, every 1 percent increase in R_STA in the long term will increase economic
growth by 16.74825 percent. And every 1 percent increase in R_PSHNP in the long
term will increase economic growth by 3.277258 percent. At the same time, other
variables have no significant effect.
In addition, the classical assumption test aims to see whether the estimation
results meet the classical assumptions or not. It should be done because it relates to
the conditions of the best linear unbiased estimator (Figure 2).

Source: World Bank, Central Bureau of Statistics processed with eviews10
Figure 2: Data Normality Test Results

Based on Figure 2, the results showed that the data is normally distributed; the
prob. value evidence this. Jarque-Berra (J-B) of 0.067090 is more than the 5% alpha
significance level. This study conducted an autocorrelation test using the BreuschGodfrey test (Table 6).
Table 6 found that the data is homoscedastic. It is evidenced by the results of
Breusch-Pagan-Godfrey, which show the value of Obs* R-squared 4.516762, which
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is greater than the 5% alpha significance. And when viewed from the value of prob.
Chi-Square has an alpha significance level of more than 5%, so the data passes and
is safe from heteroscedasticity problems. The test results, as shown in Table 7,
indicate that the data is safe from multicollinearity conditions. It is evidenced by the
VIF value, which is less than 10.
Table 6: Heteroscedasticity Test Results
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic
1.106903
Prob. F(4,24)
0.3762
Obs*R-squared
4.516762
Prob. Chi-Square(4) 0.3406
Scaled explained SS 3.420119
Prob. Chi-Square(4) 0.4901
Source: World Bank, Central Bureau of Statistics processed with eviews10
Table 7: Multicollinearity Test Results
Variable

Coefficient Uncentered Centered
Variance
VIF
VIF
C
32.57662
47989.25
NA
(R_STA)
26.01527
46577.59
2.287569
(R_SEP)
1.843931
2596.644
1.042795
(R_EMPLY) 0.210755
44.9855
1.437596
(R_PSHNP) 0.582171
23.69844
2.220045
Source: World Bank, Central Bureau of Statistics processed with eviews10

Next, to examine the coefficient of determination (R2), Table 8 found that the
test results in the long term and short term obtained R-Squared values of 0.81796
and 0.322457, respectively. The economic growth variable is influenced by gender
equality in health, education, job opportunities, and empowerment. Politics in the
long term is 81.8%, while other variables influence 19.8%. Moreover, the effect is
32.2% in the short term, while the remaining 68.8% is influenced by other variables
that are not estimated in this study.
Table 8: Results of the Coefficient of Determination
Long Term
Short Term
R-squared 0.81796 R-squared 0.322457
Source: World Bank, Central Bureau of Statistics processed with eviews10
Table 9: F Statistical Test Results
Long Term
Short Term
F-statistic
26.95981 F-statistic
2.094056
F-Table
2.78
F-Table
2.78
Prob(F-statistic) 0.000
Prob(F-statistic) 0.104574
Source: World Bank, Central Bureau of Statistics processed with eviews10

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Uswatun Khasanah, Hadi Sasana
Empirical Relationship between Gender Equality and Socio-economic Developments: An Error Correction
Model

The F statistical test is used to see whether all variables significantly affect the
dependent variable if taken collectively. Table 9 showed that the long-term F-arith. is
26.95981, the short-term is 2.094056, and the F-table is 2.78. Thus, the hypothesis
stating that the level of gender equality in the fields of health, education,
employment, and empowerment affected Indonesia’s economic growth from 19902018 in the long term was accepted. However, the results cannot empirically prove
the hypothesis in the short term.
Lastly, the test examines partial tests (t-test) in the short-term and long-term.
Table 10 found that in the short term, the hypothesis is rejected. However, the
hypothesis was accepted in examining the effect in the long run. It means that in the
long term, there is a significant positive influence between gender equality in the
health sector and Indonesia’s economic growth from 1990-2018. In the influence of
women’s education level on economic growth, based on the t-table value and
compared with the t-count-value, it found no significant effect in the long and short
term of gender equality at the education level on Indonesia’s economic growth from
1990-2018. In terms of the influence of women’s labor participation rate on
economic growth, the results showed that in the short and long term, there is no
significant positive effect of gender equality in employment on the level of the
Indonesian economy for the period 1990-2018. Lastly, in the influence of women’s
political participation rate on economic growth, the results showed that there is no
significant positive effect in the short term, while in the long term, there is a
significant positive effect between gender equality in political empowerment at the
level of the Indonesian economy for the period 1990-2018.
Table 10: The results of t-arithmetics
Variable R_STA
Short Term Long Term
Value t-arith.
1.588237
3.283638
Table t-value
1.708
1.708
Variable R_SEP
Short Term Long Term
Value t-arith.
0.782067
0.460749
Table t-value
1.708
1.708
Variable R_EMPLY Short Term Long Term
Value t-arith.
1.509157
-0.457928
Table t-value
1.708
1.708
Variable R_PSHNP Short Term Long Term
Value t-arith.
0.694699
4.295217
Table t-value
1.708
1.708
Source: World Bank, Central Bureau of Statistics processed with eviews10

5.

Discussion

The analysis found that the experience of gender in learning has increased; this
will have a significant positive impact in the long term. It is because, through school
or education, a person can optimize his potential and have the opportunity to get a
better career. As reported by Central Statistics Agency (2017), in urban areas, the

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percentage of women enrolled in tertiary institutions is 26.86%, while men are only
22.54%; in rural areas, the percentage of women is higher than men, namely 9.62%
while men only 7.36%. Kemenppa (2017) proves that gender experiences in learning
are starting to occur; many women have received education and received higher
education. Gender equality will also be more established if women realize the
meaning of education and self-improvement described in the research of Padang
(2019) and Klasen & Lamanna (2009) suggest that better learning for women will
encourage women to obtain better economic activities.
Driven by the reasons put forward by Harahap & Rejekiningsih (2014), it was
reported that the ratio of the average length of schooling had a significant positive
effect on economic development in Central Java Province. It is because learning
trains humans to master and overcome the complexities of economic development
but also acts as a lever to improve it (Neamtu, 2015). The increase in the ratio
indicates that the learning gap between women and men continues to be low (Arifin,
2020). Inequality in the field of learning in RLS (rentabilitas, likuiditas, solvabilitas) has
had a positive and significant impact on the economic development of East Java
Province from 2009–2018; this situation proves that RLS is increasing (symptoms of
learning are shrinking), approaching the increase in the economy (Rahmawati &
Hidayah, 2020).
In terms of employment, the results of the analysis that has been carried out
show that increasing gender equality in the field of employment will have a positive
impact in the short term and a negative one in the long term. The female labor force
participation rate (TPAK) is a dimension used to estimate the involvement of women
in the employment sector (Padang, 2019). The results of this study are also supported
by research carried out by Qarina & Wahab (2021) with a case study of problems in
South Sulawesi, which found that the inequality ratio of the labor force participation
rate is significant and has a positive influence on economic development. Inequality
in the workforce is still a scourge that needs attention because women’s participation
is still quite low, and there is still an assumption that women are better off doing
housework and raising children (Infarizki et al., 2018).
Gender equality in employment encourages economic development in East Java
Province (Rahmawati & Hidayah, 2020). A study by Padang (2019) found that the
LFPR variable had a positive and insignificant effect on GRDP per capita in
Indonesia from 2011-2017, but the ratio of the labor force participation rate to the
gross regional product does not show a significant effect. It means that any increase
in the labor force participation ratio in an area will not always increase its gross
regional product, especially in the short term.
An increase in the ratio between women and men means a reduction in the gap
between men and women in employment (Arifin, 2020). The gender equality ratio
in employment had a negative and insignificant impact on the economic
development of East Java Province from 2009–2018 due to the unavailability of
employment opportunities that could accommodate both male and female workers
equally (Rahmawati & Hidayah, 2020). So, in the end, it will increase the number of
dependencies and reduce or become a burden on the economy if it is not balanced
with the provision of adequate employment. In addition, if examined further, the
effect of gender equality is strongly influenced by the fast or slow process of The Law

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Uswatun Khasanah, Hadi Sasana
Empirical Relationship between Gender Equality and Socio-economic Developments: An Error Correction
Model

of Diminishing Return (TLDR), which is strongly influenced by the quality of human
resources and technological advances as well as the number of jobs capable of
absorbing male and female workers.
Based on the results of the analysis, it can be explained that the level of gender
equality in the field of political participation and empowerment will have a positive
and significant impact in the long term. It is because the widespread presence of
women in government and leadership can share aspirations for young women to
improve their educational attainment (Beaman et al., 2012). omen as leaders tend to
prioritize investment in public goods, so the position of women is very important in
improving people’s welfare (Chattopadhyay et al., 2010).
The results of this study are supported by Beaman et al. (2012), who reported
that women in the legislative realm could make comprehensive laws and regulations
in defending their people to improve welfare. It is further explained in
Chattopadhyay et al. (2010) research were female heads of state tend to improve the
quality of SDM or the HDI of their population compared to male heads of state.
With the increase in HDI, it will be accompanied by an increase in state income.
Based on Abdurrahman & Tusianti (2021), using multiple linear regression models
and research in 514 districts/cities, the participation of women in parliament,
women as experts or professionals, and women who have careers as entrepreneurs
can increase significantly. HDI in Indonesia, in turn, will have a positive influence
on economic development. So, the participation and contribution of women in the
political sphere will provide positive results for the economy.

6.

Conclusion

Based on the research results, the results showed that the level of gender equality
in health, education, employment, and political participation together affected the
level of economic development in Indonesia from 1990-2018 in the long term.
Where gender equality in the health sector and political participation in the long
term have a positive and significant impact on economic development, which means
that if there is an increase in life expectancy and the level of women’s participation
in parliament, it will further encourage economic development in Indonesia. On the
other hand, gender equality in education and employment has no significant effect
on economic growth in Indonesia, both in the long and short term.

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