Revenue Journal: Management and Entrepreneurship, vol.3(1), pp. 01–06, 2025
Received 05 May 202 /published 20 June 2024
https://doi.org/10.61650/rjme.v3i1.8344

Poverty and Gender Inequality on GRDP
per Capita in Indonesia: A Path Analysis
Diana Berliyani, Marselina, Arivina Ratih Yulihar Taher
Lampung University, Indonesia
Corresponding author: dianaberlian5@gmail.com

Keywords
Poverty, Gender
Inequality, GRDP Per
Capita, Path Analysis.

ABSTRAK This study investigates the impact of poverty and the Gender Inequality Index (GII)
on Indonesia’s Gross Regional Domestic Product (GRDP) per capita across 34 provinces using
secondary data from the Central Bureau of Statistics (BPS) for the years 2021–2023.
Employing path analysis, the study examines both the direct and indirect effects of poverty and
gender inequality on regional economic performance. The findings reveal that the poverty
rate significantly negatively affects GRDP per capita, with a path coefficient of -0.305,
indicating that higher poverty levels are associated with lower economic output per person.
Furthermore, the GII significantly affects poverty (coefficient = 0.244), suggesting that
increased gender inequality contributes to worsening poverty. Additionally, GII directly
negatively affects GRDP per capita (coefficient = -0.269), implying that regions with greater
gender disparities tend to have lower economic performance. The study also confirms an
indirect effect, where gender inequality exacerbates poverty, depressing economic
productivity and growth. These findings underscore the importance of integrating gender
equality into economic development strategies. To achieve inclusive and sustainable economic
growth, policies must reduce gender-based barriers in education, employment, and
healthcare, empowering women to contribute fully to economic development and poverty
reduction.
© The Author(s) 2025

Introduction
Poverty is multifaceted in various economic,
social, political, and environmental domains
(Danaan, 2018). United Nations data shows
that 70% of people live below the poverty line
(Jurnal Perempuan, 2005). One of the most
devastating problems affecting economies
globally is poverty, which is particularly
widespread and severe in low- and middleincome countries (LMICs) (OECD, 2021).
Although many efforts have been made to
address the various dimensions of poverty,
most LMICs still face significant challenges in
poverty alleviation, as poverty is an everchanging, complex issue that is unique to each

country (Wang & Wang, 2016).
Poverty remains a persistent issue in several
developing countries, including Indonesia.
The National Long-Term Development Plan
and the Sustainable Development Goals
(SDGs) Agenda prioritize poverty reduction in
their development programs (Arifin, 2020).

0,52
15

0,5

13 11,22
10,8610,64
9,82 9,41 9,78 10,14 9,54 9,36
11
9,03

0,48

9

0,44

7

0,42

0,46

0,499

0,488
0,472

0,465 0,459

0,447

2018 2019 2020 2021 2022 2023

5

2015201620172018201920202021202220232024
Kemiskinan (%)

Figure 1.1 Indonesia Poverty Data
Source: Processed BPS Data
The graph above illustrates the trend of
poverty rates in Indonesia from 2015 to 2024,
showing an overall decline from 11.22% to
9.03%. The highest poverty rate occurred in
2015 at 11.22%, while the lowest was
recorded in 2024 at 9.03%. Multiple factors
influence poverty conditions, as poverty is a
multidimensional issue. One contributing
factor to poverty is household income. This
aligns with the concept that an increase in
average income per person (per capita
income) is often used to indicate economic
well-being and poverty reduction (Sukirno,
2010; Saraswati & Cahyono, 2014). According
to Todaro (2000), low per capita income and
a wide disparity in income distribution are
two major poverty indicators.
In addition, based on the theory of the
feminization of poverty, gender inequality
contributes to higher poverty levels (Sanjay,
2018). Therefore, gender equality is crucial in
combating poverty (Tavares & Martins, 2020).
An analysis by Bread for the World Institute
indicates that gender-based discrimination
makes women more vulnerable to poverty
and hunger (Staff Bread for the World, 2016).
Gender inequality refers to the unequal or
unfair treatment of one gender—typically
women—across various domains such as
education, the economy, politics, and social
life. López-Marmolejo and Rodríguez
Caballero (2023) argue that enhancing gender
equality policies can improve women's
participation in the labor market and, in turn,
stimulate economic activity.

Figure 1.2. Gender Inequality Index.
Source: Processed BPS Data
The data above indicates that the Gender
Inequality Index (GII) improved from 0.499 in
2018 to 0.447 in 2023, reflecting a significant
reduction in gender disparities across various
dimensions such as health, education, and
economic participation. The GII is measured
on a scale from 0 to 1, where 0 represents
perfect gender equality and 1 indicates
maximum gender inequality.
Based on the background described above, it
is evident that high poverty rates limit
people’s purchasing power and their
participation in economic activities, thereby
hindering Gross Regional Domestic Product
(GRDP) growth. On the other hand, a high
level of gender inequality, as reflected by a
higher GII score, reduces women's
contributions to economic development, both
as members of the workforce and as
entrepreneurs.
Therefore,
this
study
examines the direct and indirect effects of
poverty and the Gender Inequality Index on
GRDP per capita across the 34 provinces in
Indonesia.
Literature Review
2.1 Poverty
Poverty is when individuals cannot meet basic
needs such as adequate housing, food,
clothing, health care, and education (Utami &
Siregar, 2021). Poverty includes primary
aspects such as lacking assets, sociopolitical
organization, knowledge, and skills. In
addition, it also encompasses secondary
elements, including the lack of social
networks, financial resources, and access to
information (Arsyad, 2015).
The Central Bureau of Statistics (BPS, 2004)
defines poverty as the inability to meet a
minimum standard of basic needs, including
food and non-food necessities. The poverty
line is defined as the value of food
expenditures per person required to meet
basic nutritional needs of 2,100 kilocalories
2

per day, plus monthly non-food expenditures
(Damayanti, 2013).
2.2 Gender Inequality
Gender inequality can be defined as
differences in attitudes or actions based on
specific gender roles that restrict women from
thoroughly enjoying the outcomes and
participating in development. Benjamin’s
(2007) study on poor Black women in South
Africa found that women's social class affects
their own and their children’s poverty status.
These women are particularly vulnerable to
exploitation under patriarchy, which
pressures them to perform unpaid domestic
labor, and capitalism, which treats them as a
cheap labor force.
Morrison et al. (2005) argue that aside from
economic growth, patriarchy is a significant
determinant of gender-based poverty,
because in male-dominated societies, women
are less likely to benefit from national
economic development. The impact of gender
inequality extends beyond roles and activities
to other fundamental socioeconomic aspects
for women and girls. It affects their
reproductive health, empowerment, and
participation in the labor market (Mwiti &
Goulding, 2018; Willie & Kershaw, 2019). A
recent study by Alabuja et al. (2023) also
provides evidence from Nigeria that gender
inequality leads to persistent poverty among
Nigerian women.
2.3 GRDP per Capita
The net value of goods and services produced
through economic activity over a specific
period is known as Gross Regional Domestic
Product (GRDP) (Parwata et al., 2016). GRDP
per capita is defined as the net value of final
goods and services produced by various
economic activities in a region within a
specific period, and is used as one of the
indicators to measure the success of regional
economic development (Sasana, 2006). This
aligns with Sukirno (2010), who notes that the
increase in average income (per capita
income) often indicates economic welfare and
poverty reduction.
2.4 Gender Inequality and Poverty
Historically, poverty has been defined based
on the idea that certain groups experience a
lack of income (Bazan et al., 2011). However,
this definition has evolved, as poverty is now
studied from a broader perspective, linking it

to social welfare and giving it a more
comprehensive dimension (Ponce, 2013). One
of the root causes of poverty is gender
inequality. Blau (2003) explains that
countries with greater gender equality tend to
experience lower levels of economic
inequality. In addition, Jayachandran (2015)
notes that the factors contributing to
inequality are more commonly found in
developing countries.
Poverty, along with gender inequality, is
among the most prevalent social issues
globally and affects virtually every nation
(Dormekpor, 2015). In a study by Lawanson
and Umar (2019), it is stated that gender and
poverty negatively impact economic growth.
2.5 Poverty and GRDP per Capita
One key indicator of the welfare level of a
region's population is GRDP per capita.
Income enables people to meet their basic
living needs. Regional income can be
measured through per capita income (Todaro
& Smith, 2006). Per capita income refers to
the average income of the population in a
region during a specific period, calculated by
dividing total income by the total population
(Sukirno, 2019).
When people’s incomes decline, it becomes
challenging to meet their basic needs (Wahyu
Azizah et al., 2018). The higher the GRDP per
capita of a region, the greater the potential
revenue sources for that region, due to the
increase in public income (Simanjuntak,
2001). This implies that as GRDP per capita
increases, the area's population becomes
more prosperous. In other words, the number
of people living in poverty will decrease.
Research Methodology
Using a quantitative research approach, this
study measures and analyzes both the direct
and indirect effects of independent
variables—namely, wages and average years
of schooling—on the intervening variable, the
female labor force participation rate, and the
dependent variable, economic growth. The
processed data are used to explain the study
findings. Panel data from the Central Bureau
of Statistics (BPS) covering 34 provinces in
Indonesia from 2021 to 2023 are employed as
secondary data for this research.
This study applies path analysis to determine
the effect of independent variables on the
3

dependent variable, both directly and
indirectly. This path analysis aims to identify
the magnitude of the influence exerted by the
independent variables on the dependent
variable, either directly or through the
intervening variable. The study is conducted
using SmartPLS (Partial Least Squares)
version 4.
The first structural model used in this
research is:
PDRBP = β₁ Poverty + β₂ GII + e ........(1)
The second structural model used in this
research is:
GIIᵢₜ = β Povertyᵢₜ + e ........(2)
Results and Discussion
4.1 Hypothesis Testing Results
Path
Relationship
Poverty →
GRDP per
Capita
GII →
Poverty
GII → GRDP
per Capita

Original
Sample (O)

Sample Mean Standard
(M)
Deviation

T-Statistics P-Value

-0.305

-0.304

0.060

5.077

0.000

0.244

0.245

0.078

3.137

0.002

-0.269

-0.263

0.111

2.425

0.015

The results of the study indicate that poverty
has a significant negative effect on GRDP per
capita, with a path coefficient of -0.305,
suggesting that the higher the poverty level in
a region, the lower its GRDP per capita. These
findings are consistent with previous studies,
such as those by Alhudhori (2017) and
Zuhdiyaty & Kaluge (2017), which found that
GRDP has a negative and insignificant effect
on poverty. From an economic theory
perspective, poverty limits individual
productivity and the ability to contribute
optimally to the economy.
With a path coefficient of 0.244, the Gender
Inequality Index (GII) also significantly
positively affects poverty. This indicates that
higher gender inequality correlates with
higher poverty levels. Gender inequality—
reflected in women’s limited access to
education, employment, and healthcare—
reduces their ability to contribute to
household income and overall economic
growth, thereby exacerbating poverty. This
finding is consistent with previous research
emphasizing the impact of gender inequality
on household economic resilience (Klasen &
Lamanna, 2009).
Furthermore, gender inequality directly
negatively affects GRDP per capita, with a path

coefficient of -0.269. This influence suggests
that gender inequality restricts women's
economic participation, reducing per capita
economic output. Additionally, the indirect
effect of gender inequality on GRDP per capita
through poverty demonstrates a mediating
role: gender inequality contributes to
increased poverty, leading to lower GRDP per
capita.
4.2 Coefficient of Determination
The analysis results show that the Gender
Inequality Index (X2) contributes only 6% to
the variation in poverty (X1), with an adjusted
path coefficient of 5%. Although this
contribution is relatively small, the earlier
path coefficient indicates that the effect of X2
on X1 is statistically significant. This suggests
that, while gender inequality is an essential
factor influencing poverty, its impact is
partial. The existing literature has shown that
gender inequality affects poverty through
various mechanisms, such as limiting
women’s access to decent employment,
education, and healthcare (Klasen & Lamanna,
2009). However, labor market conditions,
income distribution, and the existence of
social safety nets are among other
contributing factors to poverty.
Meanwhile, the model for GRDP per capita (Y)
shows that the variables Poverty (X1) and
Gender Inequality Index (X2) together explain
20.5% of the variation in GRDP per capita,
with an adjusted R-squared of 18.9%. This
indicates that, although these two variables
contribute
to
economic
growth,
approximately 79.5% of the variation remains
unexplained. This finding aligns with
economic growth theory, which emphasizes
that socioeconomic factors are not the only
determinants of GRDP per capita; other
contributing factors include investment,
trade, infrastructure, political stability, and
technological innovation (Barro, 1991).
4.4 Indirect Effect

X2 ->
X1 -> Y

Original
sample (O)

Sample
mean
(M)

Standard
deviation

-0.074

-0.072

0.023

T
statis
tics
3.28
4

P
values
0.001

The results above indicate an indirect effect of
the Gender Inequality Index (GII) on GRDP per
capita through poverty. The coefficient of 4

0.074 suggests that the GII (X2) hurts GRDP
per capita (Y), but this effect occurs through
poverty (X1) as a mediating variable. In other
words, an increase in gender inequality
exacerbates poverty, reducing the GRDP per
capita of a region.
Thus, although gender inequality directly
affects GRDP per capita, its indirect impact via
poverty is more pronounced. The T-statistic
value of 3.284 indicates that this effect is
highly significant, with a P-value of 0.001,
meaning that the indirect influence of gender
inequality on GRDP per capita through
poverty is statistically substantial and
strongly supported within this model.
Conclusion
This study reveals that poverty and gender
inequality significantly impact GRDP per
capita in Indonesia. The analysis shows that
an increase in poverty levels directly reduces
GRDP per capita, reflecting the limited
economic productivity of impoverished
communities. On the other hand, gender
inequality plays a crucial role in influencing
both poverty and GRDP per capita, where high
levels of inequality exacerbate poverty and
restrict women's contributions to economic
growth.
The indirect effect of gender inequality
through poverty highlights that gender
disparity can harm the economy as a whole.
Therefore, development policies must
prioritize reducing gender inequality to
enhance
women's
active
economic
participation, which will help alleviate
poverty and promote GRDP per capita growth.
Reducing gender inequality and improving
societal welfare are expected to foster more
inclusive and sustainable economic growth.
References
Abbas. 2011. Gender Dalam Sastra.
Makasar:Universitas Hassanudin.
Alabuja, F., Anthony, L., & Ebuk. Iba, E. (2023).
Socio-economics characteristics, income
inequality, and poverty status of female
headed cassava (Manihot esculenta
Crantz) farming households in federal
capital
territory,
igeria. International Journal of Agriculture
Environment and Food Sciences, 7(1), 29–
40.
Alhudhori, (2017). Pengaruh Ipm, Pdrb Dan

Jumlah Pengangguran Terhadap Penduduk
Miskin
Di Provinsi Jambi. Ekonomis:
Journal of Economics and Business, 1(1),
113.
https://doi.org/10.33087/ekonomis.v1i1.
12
Altiok, Tayfur dan Melamed Benjamin. 2007.
Simulation Modeling and Analysis with
Arena. Amsterdam: Academic Press
Arifin, J. (2020). Budaya kemiskinan dalam
penanggulangan kemiskinan di Indonesia.
Sosio Informa,
6(2),
114–132.
https://doi.org/10.33007/inf.v6i2.2372
Arivia, Gadis. 2005. Jurnal Perempuan. Jakarta
: Yayasan Jurnal Perempuan.
Arsyad,
Lincolin.
2015.
Ekonomi
Pembangunan Edisi Kelima. Yogyakarta:
UPP STIM YKPN.
Barro, R. J. (1991). Economic growth in a cross
section of countries. The Quarterly Journal
of
Economics,
106(2),
407–443.
https://doi.org/10.2307/2937943
Bazán O.A., Quintero S.M.L., Hernández E.A.L.
Evolución del concepto de pobreza y el
enfoque multidimensional para suestudio.
Quivera.2011;13(1):2072019. http://ww
w.redalyc.org/pdf/401/40118420013.pdf
Blau, F. D., & Kahn, L. M. (2003).
Understanding international differences in
the gender pay gap. Journal of Labor
Economics,21(1),106-144.
https://doi.org/10.1086/344125
Danaan, V. V. (2018). Analysing poverty in
Nigeria through theoretical lenses. Journal
of Sustainable Development, 11(1), 20–31.
https://doi.org/10.5539/jsd.v11n1p20
Jayachandran, S. (2015). The roots of gender
inequality in developing countries. Annual
Review of Economics, 7(1), 63-88.
https://doi.org/10.1146/annureveconomics- 080614-115404
Klasen, S., & Lamanna, F. (2009). The impact
of gender inequality in education and
employment on economic growth: New
evidence for a panel of countries. Feminist
Economics,
15(3),
91–132.
https://doi.org/10.1080/1354570090285
3603
López-Marmolejo, A. & Rodríguez-Caballero,
C. Vladimir. (2023). Assessing the effect of
gender-related legal reforms on female
labour participation and GRDP per capita in
the Central American region. Regional
Statistics, Vol. 13. No. 3. 2023: 391–411.
Morrison, A., Raju, D., & Sinha, N. (2005).
Gender equality, poverty, and economic
5

growth. World Bank.
Mwiti, F., & C. Goulding. (2018). “Strategies for
community improvement to tackle poverty
and gender issues: An ethnography of
community
based
organizations
(‘Chamas’) and
women's
interventions in the Nairobi slums.”
European Journal of Operational Research,
268(3), 875–886. https://doi.org/10.
1016/j.ejor.2017.12.009
OECD, Pensions at a Glance 2021 OECD and
G20 INDICATORS, OECD, 2021. Available
on:https://www.oecd.org/publications/o
ecd-pensions-at-aglance19991363.htm.
(Accessed 20 July 2023).
Sanjay, M. (2018). Feminization of poverty
and dimension of women’s agencies. Asian
Journal of Multidisciplinary Studies, 6(2),
93–100.
Saraswati, S. W., & Cahyono, H. (2014).
Pengaruh Tingkat Pendidikan Dan
Kesehatan Terhadap Pdrb Per Kapita Di
Kota Surabaya. Jurnal Ilmiah, 1–11.
Sasana, Hadi.(2006). Analisis Dampak
Desentralisasi
Fiskal
terhadap
Pertumbuhan Ekonomi Kabupaten/Kota
Provinsi Jawa Tengah.
Simanjuntak, Payaman. (2001). Pengantar
Ekonomi Sumber Daya Manusia. Jakarta:
LPFEUI.
Staff Bread for the World. (2016, March 16).
Gender Inequality Worsens Hunger and
Poverty. Bread for the World.
Sukirno‚ Sadono‚ 2010‚ Mikro Ekonomi Teori

Pengantar‚ Jakarta : Rajawali Pers.
Tavares, P., & Matins, S. M. N. (2020, October
16). We can’t end Poverty without Tackling
Gender Inequality. World Bank Blogs
Todaro P. Michael. 2000. Pembangunan
Ekonomi Dunia Ketiga Jilid I, Jakarta
Penerbit : Erlangga
Todaro, M. P., & Smith, S. C. (2020). Economic
Development
(13th
ed.).
Pearson
Education.
Umar, Husein. 2019. “Metode Riset
Manajemen
Perusahaan”,
Jakarta:
Gramedia Pustaka Utama
Wahyu Azizah E, Sudarti, & dan Kusuma, H.
(2018). Pengaruh Pendidikan, Pendapatan
Perkapita Dan Jumlah Penduduk Terhadap
Kemiskinan Di Provinsi Jawa Timur. Jurnal
Ilmu Ekonomi, 2, 167–180.
Willie, T. C., & T. S. Kershaw. (2019). “An
ecological analysis of gender inequality
and intimate partner violence in the United
States.” Preventive Medicine, 118, 257–
263.
https://doi.org/10.1016/j.ypmed.
2018.10.019
Y. Wang, B. Wang, Multidimensional poverty
measure and analysis: a case study from
Hechi City, China, Springer Plus 5 (1)
(2016) 642.
Zuhdiyati, N., & Kaluge, D. (2017). Analisis
Faktor-Faktor
yang
Mempengaruhi
Kemiskinan di
Indonesia
Selama
Lima Tahun Terakhir (Studi Kasus 33
Provinsi). JIBEKA, 27-31.

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