Convergence of Female Entrepreneurship in Selected Asian Countries
NUR SHAFIENA SIDIK, DAYANG AFFIZAH AWANG MARIKAN
Faculty of Economics and Business,
Universiti Malaysia Sarawak,
Jalan Datuk Mohammad Musa, 94300 Kota Samarahan, Sarawak,
MALAYSIA
Abstract: The aggregate convergence analysis of female entrepreneurship in selected Asian countries resulted
in divergence. The club convergence analysis, however, identified China, Indonesia, and India as outliers. The
result is in line with what the Global Entrepreneurship Monitor (GEM) has found, which is that women are
more likely to start their own businesses in developing countries. The remaining countries are categorized into
three distinct clubs. The finding shows that the countries with different levels of income and different indices of
gender gaps are clustered together. This suggests that female entrepreneurship is not necessarily affected by the
level of income and gender gaps. Apart from that, the formation of the convergence clubs may suggest female
entrepreneurship as one of the key drivers of globalization.
Key-Words: Female entrepreneurship, convergence analysis, Asian
Received: July 25, 2022. Revised: February 5, 2023. Accepted: February 26, 2023. Published: March 16, 2023.
1 Introduction
Global Entrepreneurship Monitor (GEM) estimates
that 274 million women are active in start-ups, and
139 million own existing firms, [1]. Female
entrepreneurs are "women who have ventured into
new economic perspectives despite preconceptions,
criticism, and societal constraints, [2]. Despite this,
women's labor engagement and self-employment
have risen rapidly in the previous two decades, [1].
According to [4], Southeast Asia has 61.3 million
female entrepreneurs, which represents 9.8% of the
population, [3]. Female entrepreneurship boosts
gender equality through employment creation, [4].
Apart from that, gender equality might contribute
$12 trillion to the global economy by 2025, [5],
[13]. Women, men, girls, and boys have equal
rights, resources, opportunities, and protection,
according to UNICEF, [6]. In addition, one of the
Sustainable Development Goals (SDG) aims to
decrease gender gaps and empower women and girls
worldwide, [7]. Promoting female entrepreneurship
would produce employment to empower women and
promote gender equality, according to the ILO, [8].
Many attributes and factors can affect female
entrepreneurship in a country. Numerous works of
literature show that female entrepreneurship is
mainly affected by a country's economic well-being
and income, [15]. However, as the feminist
movement has been on the rise recently, some
literature attributes gender disparity to affecting
female entrepreneurship [5].
In addition, GEM reported that female
entrepreneurship might decline when an economy
reaches a developed status [1]. However, recently,
female entrepreneurship has grown considerably,
notably in Asia, [1], [20]. In these countries, women
starting their businesses is the way to gain economic
power and equality; for families' well-being, the
fight against poverty, and long-term economic
growth, [31].
Creating an environment where women can be
entrepreneurs and reach their full potential can
benefit households, communities, and economies. In
addition, long-term growth and convergence may
affect a country's well-being, [14], [16].
Hence, the objective of this study is to observe
if the nation's income level or gender disparity
would lead to the convergence of female
entrepreneurship in the long run. The convergence
study and its finding are crucial to facilitating
policymaking in the economic bloc involving these
countries.
1.1 Gender Equality, Level of Income and
Female Entrepreneurship
Fourteen Asian nations are chosen for their
diversity, gender equality index, incomes, and
female entrepreneurship.
Past studies show female entrepreneurship
significantly affects gender equality, [9], [10], [11].
Nonetheless, not all economies reflected this theory.
For example, female entrepreneurship is not thriving
in industrialized nations despite greater chances and
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fewer financial restraints, according to the Global
Entrepreneurship Monitor 2020–2021, [1].
Table 1. Selected Asian Countries
Countries
Gender Gap
Index Scores
(0 to 1)
Level of
Income
Female
employers (%
female
employment)
Philippines
0.784
Lower
middle
2.16
Laos
0.750
Lower
middle
0.22
Bangladesh
0.719
Lower
middle
0.63
Thailand
0.710
Upper
middle
1.42
Vietnam
0.701
Lower
middle
1.14
Indonesia
0.688
Lower
middle
1.89
South
Korea
0.687
High
3.43
Cambodia
0.684
Lower
middle
0.08
China
0.682
Upper
middle
1.11
Malaysia
0.676
Upper
middle
1.66
Armenia
0.673
Upper
middle
0.32
Jordan
0.638
Upper
middle
0.72
India
0.625
Lower
middle
0.56
Oman
0.608
High
0.65
Source: The Gender Gap Report 2021; World Bank
Open Data 2019
Note: Gender gap index closer to 1 indicates a
narrower gender gap. The position of the countries
is arranged from the highest to the lowest.
1.2 Solow Growth Model and Convergence
The Solow Growth Model analyzes changes in an
economy's output over time as a result of population
growth, savings, and technological improvement,
[17]. Solow Growth Model assumptions:
(1) Population is growing, (g). N' = N(1+g) relates
the existing population (N) to the future
population (N').
(2) All consumers save s of their wages and spend
the rest. C = (1-s) Y relates consumption to
production (represented by Y).
(3) All firms use a profit manufacturing approach.
Y=aF links production, capital, and labor (K, L).
Solow Growth Model expects scale returns
(CRS).
(4) Capitalization equation K'= K(1-d) + I connect
K, K', d, and capital investment (represented by
I). The convergence hypothesis explains the
shift from early to complete industrialization.
Convergence occurs when developing countries
"catch up" to wealthy nations. However, political, or
social concerns, such as a lack of resources, prevent
certain nations from converging. In similar
conditions, the hypothesis permits less developed
countries to grow faster than developed ones.
This research attempts to compute the convergence
of female entrepreneurship in Asian nations with
varied economic levels and gender equality.
1.3 Problem Statement
Empowering women in the global economy and
reducing gender disparities are vital to achieving
SDG 2030: gender equality. However, Asia is a
traditional, conservative region. Most traditions see
men as superior to women. For women, business is
like swimming against a tsunami.
The Asian nations included in this research are
grouped by GDP per capita and World Bank data,
[12]. South Korea and Oman are high-income
countries. Meanwhile, Armenia, China, Jordan,
Malaysia, and Thailand are upper-middle-income
countries. Bangladesh, Cambodia, India, Indonesia,
Laos, and the Philippines are lower-middle-income
countries. The Philippines and Laos have different
gender discrepancies despite both being in the
lower-middle-income level. High-income countries
like Oman rank last in gender equality and have
huge gender inequalities. High income does not
necessarily mean small gender disparities, [19].
The Global Gender Gap Report, [19], found that
the pandemic made it take longer to reach gender
equality, from 99.5 to 135.6 years. From the data,
SDG-Gender Equality will not be achieved any time
soon. The COVID-19 pandemic has harmed the
economy and widened gender inequality.
Concurrently, reducing gender disparities would
help women quickly revive the economy after the
epidemic, according to the UN, [18]. Gender
equality and women's empowerment would improve
job opportunities, increase purchasing power, and
facilitate economic activity.
Global Entrepreneurship Monitor (GEM) data
show that the most female entrepreneurs are in
developing countries in Asia and Africa, while the
number of female entrepreneurs in developed
countries is decreasing, [19]. The findings by GEM
contradict the Female Entrepreneurship Index,
which found that high-income western nations had
the most female entrepreneurs, [21]. The Female
Entrepreneurship Index revealed that developed
nations have excellent conditions to support female
entrepreneurship, despite female overall
entrepreneurship activity being lower in wealthy
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countries than in medium- and low-income
countries, [20], [21].
The contradiction of female entrepreneurship’s
factors makes it harder for the economists to
understand the factor that affect growth of female
entrepreneurship in the long run. Thus, this research
aims to examine the connection between gender-
related factors and female entrepreneurship and to
analyze the impact of gender-related factors on
various levels of female entrepreneurship.
Aside from that, the novelty of this research lies in
its effort to decipher the female entrepreneurship
convergence clubs amongst Asian nations by
applying data on the number of female employers.
Despite the fact that the body of knowledge has a
plethora of material on convergence clubs, the
aforementioned field of research remains
unexplored. In addition, by creating convergence
clubs between various economies, broader
hypotheses on gender inequality and varying income
levels can be drawn from this research.
2 Literature Review
Entrepreneurs play a vital role in driving economic
growth and are characterized as risk-takers who are
always seeking to innovate, [22]. The term "feminist
economics" was not used for the first time until the
early 1990s. However, research into how men and
women are mistreated began much earlier and took
many different forms. Feminist research done by
experts on the history of economic thought shows
that people were worried about the economic status
of women as early as the 18th century, [23].
Feminist economics tries to understand how men
and women are treated differently in the economy. It
does not just add gender as another "variable" to be
analyzed. It is insufficient to divide data by gender.
Instead, it makes gender a vital category that can be
used to look at the patriarchal parts of the economy
and economic theory. From this point of view, [24],
mentioned "acknowledgment, redistribution, and
representation" frameworks show that gender is a
vital part of how the global economy works.
The inclusion of women in higher education and
the workforce has been critical. Feminist economics
focuses on finance and instilling an entrepreneurial
spirit among women to facilitate women's economic
autonomy, [25].
However, a meditation on gender inequalities
must be linked to an intersectional perspective since
the theoretical and political elements of gender are
also related to numerous axes of social inequality.
These intersections aid in understanding how
individuals who have uneven access to and control
over resources and power are unequally impacted by
their economic situations and respond to them using
various skills. Taking this into account, it is evident
that legislation and policies can never be "neutral"
regarding gender, [25]. Their design will
undoubtedly have an impact, whether favorable or
adverse, on women's disadvantage or men's
privilege.
On the other hand, the study in [26] mentioned
that the concept of social feminism helps in
producing several different options, ambitions, and
viewpoints. The men see the family and business as
two different distinct domains, but the women find
the family and business as a single economic entity.
According to [27], this conflict and duality provide
women with some aspects of specific strategies that
might not be as effective according to the aspects
embraced by men. Though the previous research
paper showed the importance of equality differences
between female and male entrepreneurs, there are
still wide gaps between them.
2.1 Push and Pull Factors of
Entrepreneurship
Push and pull factors are imperative in
understanding the enthusiasm behind female
entrepreneurship and empowerment. "Push factors"
are applicable the women who are usually pressed
under the circumstances and are forced to be self-
dependent. For instance, women in rural areas
usually tend to become entrepreneurs to make ends
meet. Moreover, the study in [28] explained that
women with push motivation are usually affected by
external conditions. Various external conditions
would cause a woman to become an entrepreneur.
Sometimes, time flexibility and unemployment
problems help them support their sandwich
families.
The term "sandwich families" and generation
can be referred to as the circumstances. In these
situations, couples must take care of their aging
parents, including their children. The woman is
sandwiched between two generations and two-
family responsibilities in this scenario, [29].
Intergenerational poverty has led to this
circumstance, which is when a low-income family
stays poor for more than two generations. This great
responsibility makes them have a higher cost of
living in any country. Due to the pressure, the
women of any family tend towards self-employment
to take care of the families more efficiently. These
circumstances are more prevalent nowadays; it is a
global phenomenon, [32]. Additionally,
discrimination in the workplace also pushes women
in the direction of entrepreneurship. For instance,
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women working in the corporate often hit the 'glass
ceiling' and cannot reach upper executive positions
compared to their male counterparts, [30].
Women entrepreneurs who are pulled in their
journey are generally self-driven in the way of
exploring their skills for getting immense growth.
Women who get pull motivations typically have
sophisticated education levels. This type of
entrepreneur is termed ‘affluent entrepreneur’ as
they are probably wealthy and privileged enough to
have this kind of motivation. The women are aware
of the risks in their business ventures, and they
require capital for their business ventures.
Entrepreneurs, particularly women entrepreneurs,
with enormous dedication and a lack of resources,
assist them in creating their own identities and
tolerating the financial risks of massive growth and
expansion in international business. According to
[31], those with "pull factors" would express several
incentives for being an entrepreneur. Among the
incentives is freedom from regimented work, being
out of routine, and not being stuck at a tedious job.
Higher education levels and increased female
representation in business or politics are "pull
factors" for women entrepreneurs, [32].
Typically, most entrepreneurs also fall under the
"self-employed" category. Nevertheless, there are
some distinctive differences between self-
employment and entrepreneurship. For example,
self-employed people may have the same push and
pull factors. However, rather than creating a legacy
to pass on, self-employed entrepreneurs focus on
generating steady income and are not keen on
financial risks. Hence, women in developing
countries; are usually motivated by "push factors"
and mostly fall under self-employed entrepreneurs
due to their need to make ends meet.
3 Research Methodology
3.1 Data Description
Since entrepreneurship research began, self-
employment data has been used as a cross-country
comparison, [32]. However, due to linguistic
barriers and differing definitions of entrepreneurship
across countries, World Bank self-employment data
is used as a proxy for entrepreneurship, [32].
Entrepreneurship and self-employment are
interchangeable, [33]. Previous research on
entrepreneurial definitions reveals how scholars
vary. The article in [34] defines an entrepreneur as
a self-employed person who hires workers.
Entrepreneurship creates jobs, which helps the
economy grow, and using random opportunities to
start a business makes value, [35], [36].
3.2 Non-linear Factor Model
The model may be divided into series and country-
specific portions. Factor analysis may help explain
large-scale variation.
(1)
The is an observable series that consists of an
array of log female total entrepreneurship activity
per country, i, [i = 1……N and at the time, t =
1…...N)]. Usually, is detached due to
unobservable components of systematic and
transitory. At this point, there are no parametric
assumptions for and in Equation 1. Thus, the
framework may consist of either non-linear, linear,
non-stationary, or stationary data. Alas, both and
may as well contain distinctive and common
elements, [38].
Nevertheless, Equation (1) will need time-
independent factor loadings and covariance
stationary distinctive elements if an additional non-
linear structure is not imposed. Hence, Phillips and
Sul specifications are used because they consider
the loading coefficients’ time variation, [37]. By
using Philips and Sul method, the common
stochastic trend component can be factorized, [28].
It is suggested to do the specification as follows:
(2)
Equation (2) specifies the two time-varying
components; the common and the distinctive
by disintegrating. This means the factor
would be the proxy for the measurement of distance
and the unit-specific element. The error term
element is dispersed and then serves as the
idiosyncratic element, which is evolving. On the
other hand, is the alternate element is used to
represent the common trend component in the panel.
It is thought to have varying deterministic and
stochastic trends that would in turn, influence the
transitory element , as t is approaching infinity,
∞.
As per [28] the strategy does not rely on the defined
hypotheses, about the stochasticity of non-stationary
or trend stationarity. Apart from that, the individual
transition behavior of certain cross-sectional data
can be extracted by focusing on time-varying
loadings, . The terms can be defined as follows:
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(3)
The is fixed across i and refers to iid (0, 1).
The here implied distinctive scale parameters,
while L (t) acts as a sluggish varying function. The
parameter indicates the rate at which the
parameters will disintegrate to zero. This can be
presumed as . ∞ and ∞. In
the nutshell, Equation (3) developed by Philip and
Sul warrants that converges for .
3.3 Transition Path
As the approximation of the time-varying factor
loadings is the main concern in the strategy
suggested by [38], the approximation would capture
the information on the transitional manners of
certain panel units, [38].
(4)
In Equation (4), the relative transition parameters is
determining the loading coefficient, . This is due
to the panel average
does not equivalent
to zero. The variable will elucidate the transition
path of unit at the time. This being said, is
signaling the comparative take-off of the economy
with a widespread growth path, . Thus, any
divergence of being echoed by the transition path
Besides that, as the corresponding transition
path converges to zero and ∞, the panels unit
converges and all factor loading will be
estimated to a fixed . This feature can be instilled
for testing out all the null hypotheses of
convergence to the countries into the convergence
clubs.
Nevertheless, this model would render the
representation of Equation (1) unsuitable. This is
due to the key time series of macroeconomic
applications recurrently having business cycle
elements. Thus, by integrating the business cycle
effect, Equation (1) would be protracted as follows:
∞
(5)
Philip and Sul suggested employing the Hodrick-
Prescott filter to smooth and coordinate the
mechanism of segregating the business cycle, [37].
The cross-sectional averages are visualized as:
(6)
Using Equation (6), the generated approximation of
the transition path is written as:
(7)
Within small samples, the panel average of
is asymptotical. This can be used to
perform various time series analyses such as income
per capita.
3.4 Log-t Regression
Using the varying factor statement from Equation
(2), an exceptional convergence test and clustering
algorithm are analyzed by depending on a clear-cut
time series regression. The null and alternative
hypothesis are set up as follow:
Firstly, after determining the transition path
estimation, the cross-sectional variance ratio
is
computed, where;
(8)
The transition path is illustrated as follows:
∞
(9)
A denotes a positive constant and sluggish varying
function is shown by and
the speed for convergence is determined by a.
Subsequently, the limit distribution and the power
properties are subjected to this unwanted sample
fraction, at some step, the t becomes (rT), where it
represents the integer part. Thus, the choice of r is a
crucial component. As per [28], the suggested r to
be used is 0.3, [29]. Henceforth, the log-t test is
performed as regards:
(10)
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According to Equation (10),
symbolizes the ratio
of the cross-sectional variation at the beginning of
the sample. For instance, ( means t=1) across
the corresponding variation at time in which
. In addition, the ratio is to calculate the
distance between the panel and the common limit
and and .
Finally, by employing the t-statistics, provided
that , the null hypothesis of convergence
will be rejected. When the is positive or equal to
zero, it is determined panel convergence. On the
other hand, the null hypothesis is rejected when is
negative and significant.
3.5 Convergence Clubs and Cluster
Algorithm
By referring to the previous literature on empirical
convergence, it is possible to have numerous
equilibrium points. Moreover, rejecting the null
hypothesis would not exclude the probability of club
convergence.
Plus, it is not obligatory for each unit in the
panel to only follow its own path independently
after the elimination of.
Apart from that, the regression of the t-test
can be
taken into account of the underlying algorithm to
clustering and club convergence.
The algorithm to achieve club convergence is as
follows:
Step 1: Ordering
The panel units of the last observation are
arranged and ordered accordingly.
Step 2: Formation of a Core Group
The highest individual in the panel is the first k
elected to make the subgroup for
approximately Then, the t-statistics for
the convergence is computed. Subsequently, the
core group size is determined according to the
maximum with .
Step 3: Club membership
After the core groups are formed, the residual
members are added individually. If the t-statistics
are exceeding the critical value, the new country is
included in the convergence club. This process is
repeated until there is no country left in the panel
dataset.
Step 4: Recursion and stopping
Any countries that are not selected in the
convergence club in Step 3 are assembled into an
alternative group. And then, the log-t regression is
run for the alternative group. Subsequently, these
will form another cluster if it converges. Step 1 to
step 3 is repeated to form several sub-clusters, on
the side of the spectrum, if no core group can be
found, it is said that the countries are divergences.
Fig. 1: Club convergence and clustering algorithm
4 Findings and Discussion
This section reports the finding for convergence and
club convergence of female entrepreneurship in
selected countries in Asia.
4.1 Aggregate Convergence
Fourteen nations are analyzed using the log-t test for
panel convergence finding. Table 2 shows the panel
convergence approach findings for Armenia,
Bangladesh, Cambodia, China, India, Indonesia,
Jordan, South Korea, Laos, Malaysia, Oman,
Philippines, Thailand, and Vietnam. Philip and Sul
used empirical log-t regression on time series data,
excluding the first r percent (0.3), [37]. As Philip
and Sul's technique uses a nonlinear factor model,
unit root testing is unnecessary for convergence
analysis, [37].
Table 2. Aggregate convergence analysis
Countries
t-statistics
Remark
All selected
Asian
countries
-0.6240
-40.67*
Divergence
Note: Asterisks (*) denote rejection of null hypothesis
at 5% and critical value of more than -1.65
The result shows the t – statistic is at -40.67, which
is less than -1.65, and the null hypothesis of
convergence is rejected at the 5% significant level.
It indicates that the overall selection of Asian
countries is heterogeneous regarding female
entrepreneurship. In addition, the coefficient of -
Step 1:
Ordering
Step 2:
Formation of
core group
Step 3:
Club
membership
Step 4:
Recurssion and
stopping
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0.6240 demonstrates that the divergence occurs at a
sluggish rate. Nevertheless, this does not imply that
there is no convergence in the sub-group of the
selected Asian countries. Philip and Sul also
mentioned that the cointegration test is not ample
for further convergence tests despite the highly
related cointegration and convergence, [37].
4.2 Relative Transition Path
Cross-sectional variation is required to investigate
the overall convergence of transition parameters,
which is specifically designed to measure the
loading coefficient concerning the panel average
at This method is suitable for the analysis of
convergence and to the measurement of the
transition path.
Fig. 2: Female entrepreneurship transition path
Figure 2 shows the cross-sectional variance of 14
Asian nations from 2000 to 2019 in female
entrepreneurship. Again, HP smoothing is used to
extract cyclical components from raw time-series
data. China has the most significant transition path,
whereas Armenia has the lowest. Even though
Armenia has the least number of women running
their businesses, the number keeps increasing.
Thailand and Cambodia exhibit moderate declines
from 2009 to 2019.
4.3 Club Convergence
Philip and Sul think that the clustering algorithm
works by choosing the base country based on its
highest rank and using a log t-test to make a core
group, [37]. First, the rank of the base country is
established based on data from the last year of the
dataset. In this study, South Korea is chosen as the
core country because, compared to the other
countries, it had the most female entrepreneurs in
2019. Then, the remaining countries are added
separately to the core group to test the convergence
hypothesis.
The process is repeated until the value of the t-
statistic is less than -1.65, which is the critical value.
It indicates that the convergence club is formed.
Nonetheless, the procedure is repeated until all
countries are in their respective clubs. It is also
important to note that some countries may not
belong to any group. These outliers are the non –
convergent countries or divergent countries.
Table 3. Club convergence analysis
Rank
Country
t-statistics
Remark
1
China
-2.9126
Divergence
2
Indonesia
3
India
4
Philippines
4.3313*
Convergence
5
S. Korea
6
Vietnam
7
Thailand
4.9912*
Convergence
8
Bangladesh
9
Malaysia
10
Laos
8.3669*
Convergence
11
Oman
12
Cambodia
13
Jordan
14
Armenia
Note: Asterisk (*) denotes rejection of null hypothesis at
5% and critical value of more than -1.65 and indicating
convergence.
As a rule of thumb, the countries’ rank orders are
determined by the value of female entrepreneurship
in the last year of the data, [28]. The country with
the highest number of female entrepreneurs in the
previous year of the data will be ranked first in the
analysis. The countries are ranked in descending
order, from the highest value to the lowest value.
China had the highest number of female
entrepreneurs in 2019, placing first in the ranking.
Hence, China is the base and core group for the log-
t test. The following country in the list, Indonesia, is
added to the group, and the t-statistics are -2.9126,
less than -1.65, and statistically significant. Its
shows that China and Indonesia are diverging from
each other. According to [37], if the log-t test shows
a statistically significant result and the countries
diverge, the country added to the core group
becomes a new base. As such, Indonesia became a
new base, and India was added in order to perform
another log-t test. The tabulated result shows that
the t-statistics of Indonesia and India are less than
the critical value, indicating divergence.
As China, Indonesia, and India log-t tests
resulted in divergence, and they are the outliers in
this study. The outliers do not converge with each
other and with any clubs. Furthermore, India
became a new base country, and the Philippines was
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added to the log-t test analysis. The result shows a
divergence as -2.9126 is less than the critical value
of -1.65. Nevertheless, as the Philippines became
the new base country, the first club was formed with
South Korea and Vietnam.
As Thailand is added to the first club, the log-t
test resulted in divergence as -2.5196 is less than the
critical value of -1.65. Moreover, Thailand,
Malaysia and Bangladesh then formed the second
club. As Laos was added to the second club, the log-
t test resulted in divergence. Laos then started the
third club with Oman, Cambodia, Jordan, and
Oman, as the log-t test is greater than the critical
value.
The visual representation of the club’s
convergence is as follows:
Fig. 3: Female entrepreneurship club convergence
The Philippines, South Korea, and Vietnam make
up Club 1, Thailand, Malaysia, and Bangladesh
make up Club 2, and Laos, Cambodia, Oman,
Jordan, and Armenia make up Club 3. China,
Indonesia, and India were outliers since they did not
converge with any other countries and diverged
from each other.
In the core group, China is the upper-middle-
income country, but Indonesia and India are the
lower-middle-income countries. It fits with the
Global Entrepreneurship Monitor reports, which say
that women are more likely to start their businesses
in upper- and lower-middle-income countries
because of needs and a lack of jobs.
The Philippines, South Korea, and Vietnam are
in Club 1 of this study. The Philippines is the base
country with the highest number of female
entrepreneurs in this club. The Philippines is a lower
middle-income country, but a high-income country
such as South Korea will catch up to it in terms of
female entrepreneurship in the long run. Barro and
Sala-i-Martin found that developing countries tend
to grow faster and catch up to developed countries,
but more needs to be done to encourage women to
start their businesses, [39].
Thailand, Bangladesh, and Malaysia are in Club
2 of this study. Thailand is the base country with the
highest number of female entrepreneurs in this club.
Thailand and Malaysia are the upper-middle income
countries, and Bangladesh is the lower-middle
income.
Laos, Oman, Cambodia, Jordan, and Armenia
are in Club 3 of this study. Laos is the base country
with the highest number of female entrepreneurs in
this club. Laos and Cambodia are the lower middle-
income countries, and Jordan and Armenia are the
upper middle-income countries. However, Oman is
the highest-income country in this club. The gender
gap index is below average in all clubs except for
Club 1. However, in Clubs 2 and 3, the countries
with the highest number of female entrepreneurs in
each club, have above-average gender gap indices.
It has been found that countries with similar gaps
between men and women (below or above average)
do not always group.
Table 4. Convergence clubs, gender gaps and
income level
Last T
Order
Country
Club
Gender Gap
Index 2021
(%)
Income level
1
China
Outlier
68.2
Upper-middle income
2
Indonesia
68.8
Lower-middle income
3
India
62.5
Lower-middle income
4
Philippines
1
78.4
Lower-middle income
5
South
Korea
68.7
High-income
6
Vietnam
70.1
Lower-middle income
7
Thailand
2
71.0
Upper-middle income
8
Bangladesh
71.9
Lower-middle income
9
Malaysia
67.6
Upper-middle income
10
Laos
3
75.0
Lower-middle income
11
Oman
60.8
High-income
12
Cambodia
68.4
Lower-middle income
13
Jordan
63.8
Upper-middle income
14
Armenia
67.3
Upper-middle income
Bold indicates that the gender gap index is below the global average
(67.7 percent)
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.62
Nur Shafiena Sidik, Dayang Affizah Awang Marikan
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678
Volume 20, 2023
5 Conclusion
The aggregate convergence analysis of female
entrepreneurship has resulted in divergence.
Armenia, Bangladesh, Cambodia, China, India,
Indonesia, Jordan, Laos, Malaysia, Oman, the
Philippines, South Korea, Thailand, and Vietnam
cannot catch up with women starting their
businesses.
Divergence occurs as the consequence of a variety
of factors that may have an impact on the outcome.
Each country has its framework of investments in
human and physical capital, technological
advancements, market forces, and government
regulations, [36], [39].
The club convergence analysis shows that the
countries with similar income and gender gap index
levels do not cluster together in terms of female
entrepreneurship. Instead, female entrepreneurs are
more evident in upper and lower-middle-income
countries. This notion is similar to the Global
Entrepreneurship Monitor Index findings, where
women in middle- and low-income countries would
turn to entrepreneurship due to necessities and a
lack of job opportunities. It shows that better
conditions for fostering female entrepreneurship
impact it less than the necessities.
Analyzing the level of global economic convergence
has proven to be an empirically sound way of
gauging the impact of globalization. However,
globalization does not automatically imply global
convergence, [40]. During times of growth, club
convergence tends to be suitable for more
prosperous countries, but only sometimes for poorer
countries. India and China may be two possible
exceptions. This is shown to be accurate by the fact
that China and India stood out among the Asian
countries studied. Interestingly, Indonesia is also an
outlier in this study, joining key economic players
like China and India. Even though the growth of
club convergence may be a sign of globalization, it
is essential to tell the difference between
globalization and internationalization, [40].
Acknowledgement:
The authors acknowledge financial supports from
Universiti Malaysia Sarawak. All remaining flaws
are the responsibility of the authors. Dayang Affizah
Awang Marikan* is the corresponding author for
this paper.
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Nur Shafiena Sidik and Dayang Affizah Awang
Marikan are equally responsible for the research
conceptualization, methodology, retrieving data,
formal analysis and writing the original draft.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
This research is supported by Universiti Malaysia
Sarawak.
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DOI: 10.37394/23207.2023.20.62
Nur Shafiena Sidik, Dayang Affizah Awang Marikan
E-ISSN: 2224-2899
681
Volume 20, 2023
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
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