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Reducing gender-based
unemployment in India: the
impact of social inclusion and
foreign funds inflows
Imran Khan
Department of Humanities and Social Sciences,
Birla Institute of Technology and Science, Dubai, United Arab Emirates, and
Darshita Fulara Gunwant
Department of Management Studies, Birla Institute of Technology and Science,
Dubai, United Arab Emirates
Abstract
Purpose –The purpose of this paper is to empirically analyze the impact of social inclusion factors and
foreign fund inflows on reducing gender-based unemployment in India.
Design/methodology/approach –A time series data set for the period of 1991–2021 has been considered,
and an autoregressive distributed lag methodology has been applied to measure the short- and long-run impact
of social inclusion and foreign fund inflows on reducing gender-based unemployment in India.
Findings –According to the study’sfindings, both social inclusion and foreign fund inflows are critical
factors for reducing male unemployment. However, in the case of female unemployment, only social inclusion
factors play an important role, whereas foreign fund inflows have no role in it.
Originality/value –Analyzing the factors that affect gender-based unemployment has always been a grey
area in literature. There are very few studies that capture gender-based unemployment in India, making this
study a novice contribution. Second, it examines the relationship between foreign fund inflows, social
inclusion and unemployment, which is another novel area of investigation. Finally, this study provides
comprehensive and distinct results for both male and female unemployment that can help policymakers
devise gender-based unemployment policies.
Keywords Unemployment, Social inclusion, Foreign direct investment (FDI), Remittance, India,
Sustainable development
Paper type Research paper
1. Introduction
Seeking employment has always been a high-priority requirement for individuals of
different age groups and genders. Whether there is a college graduate, an executive, a
JEL classification –E24, F21, Q01
Funding declaration: The authors did not receive any funding for the research, writing or publication
of this article.
Conflict of interest: The authors declare no potential conflicts of interest regarding this research
publication.
Data availability statement: The data sets used and/or analyzed during the current investigation
are available upon reasonable request from the corresponding author.
Gender-based
unemployment
Received28 July 2023
Revised 18 December2023
Accepted24 February 2024
Indian Growth and Development
Review
© Emerald Publishing Limited
1753-8254
DOI 10.1108/IGDR-07-2023-0103
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1753-8254.htm
professional, an unskilled labor, a highly educated person or an uneducated person, all of
them need employment. Out of many, there are primarily three benefits to being employed.
First, being employed provides a major source of income for the survival of an individual as
well as their families. Second, employed people can actively contribute to the success of an
economy. Third, employment brings a feeling of self-satisfaction to further grow and
achieve new heights in their respective careers.
However, finding employment has always been a tough task. Governments and economic
policymakers struggle to find ways that can help generate employment. Statistically, by the
end of 2020, there were 220 million people globally unemployed (United Nations Statistics
Division, 2023); similarly, it has been reported that 6.2% of the global labor workforce was
unemployed in the year 2021 (World Bank, 2023a), and it is expected that employment
growth will indicate a pessimistic sign for the year 2023, with the unemployment rate
expected to stand at 5.8% of the total global workforce (ILO, 2023).
Examining the formidable task of furnishing gainful employment opportunities to its
workforce, India stands out as a nation grappling with persistent unemployment challenges
over the course of several decades. Hindered by rapid population expansion, India has
encountered significant hurdles in effectively accommodating its burgeoning labor force within
the workforce. Even by taking stringent policy measures, out of the total labor force, the total
unemployed workforce was 5.59% in 1991, but it has reduced only to a level of 5.27% by the
year ending 2019 (World Bank, 2023a). This decline shows a meagre change of 5.72% over a
period of 28 years. While male and female unemployment existed at a level of 5.37% and
6.27%, respectively, in 1991, it had changed to a level of 5.36% and 4.93% by the year ending
2019 (World Bank, 2023e,2023d). Because these were disappointing statistics, the emergence of
the COVID-19 pandemic has further enhanced the global unemployment level. During the year
2020, approximately 255 million jobs were lost globally, which was four times higher than job
losses compared with the economic crisis of 2008 (United Nations, 2021a). This effect was also
noticed in India, as by the year ending 2020, total unemployment had risen to a level of 7.99%,
whereas male and female unemployment rose to a level of 8.61% and 5.50%, respectively
(World Bank, 2023b). Although these figures clearly represented the bleak picture of the Indian
unemployment rate, the recent report of the Centre for Monitoring Indian Economy has
highlighted that by the year ending 2022, the unemployment level had rose further up to a level
of 8.30%, which was higher in comparison to the past 16 months. This equates to 37.4 million
active unemployed people who are seeking employment (CMIE, 2022).
These statistical figures clearly indicate that unemployment is a bigger challenge, noting
that India is one of those countries that is committed to achieving United Nations
Sustainable Development Goals (SDGs) (United Nations, 2021b). The achievement of SDG
8.5 of reducing unemployment seems to be a tough task, as it has been mentioned by the
United Nations that the unemployment challenge still remains with India (United Nations
Statistics Division, 2023). Hence, the question arises: what are those factors that can help
India reduce its unemployment level?
This inquiry yields a bifurcated response. First, augmenting the fiscal resources within
an economy is imperative. The infusion of additional capital enhances the purchasing
capacity of the populace, thereby fostering heightened demand, increased production and a
concomitant necessity for expanded employment opportunities, thus mitigating
unemployment. Second, it is crucial to acknowledge that the mere availability of financial
resources may not serve as a panacea for this predicament. Hence, other variables, including
but not limited to social inclusion, necessitate commensurate consideration to address the
issue comprehensively. As per the report from Oxfam, gender-based discrimination is the
reason for the 98% employment gap between men and women. Similarly, a report has also
IGDR
shown that there is a wide difference between the pay scales of men and women (Oxfam
India, 2022). Highlighting the fact that women represent 27% of the total participant labor
force (World Bank, 2023c), if such a large proportion of the workforce is discriminated
against, then it will eventually affect the overall unemployment rate of the country. Taking
this blazing argument into account, it is required to find out those factors that can help
reduce the unemployment rate.
First, as far as fund inflow is concerned, two such sources of funds that have gained
worldwide attention are remittance inflows and foreign direct investment. These financial
sources are particularly important because, by the end of 2022, India became the first
country to receive $100bn in remittances, which account for approximately 3.4% of its gross
domestic product (GDP) (World Bank, 2022). While foreign direct investments had also
achieved their highest ever level by attaining $83.6bn during the financial year 2021–2022,
which is equivalent to 2.94% of the Indian GDP (Ministry of Commerce and Industry, 2022),
in combination, these two sources represent more than 5% of GDP and, in financial terms,
contribute more than $184bn. One perspective posits that remittance inflows primarily serve
the purpose of familial assistance, resulting in augmented household income, facilitation of
family members’education and the acquisition of skills conducive to employment
opportunities. Conversely, foreign direct investments are earmarked for fostering the
establishment of industries and the infusion of capital into advanced technologies.
Second, from the standpoint of social inclusion, school enrollment, gender parity and the
proportion of seats held by women in national parliaments are the two such factors that
deeply highlight the importance of social inclusion in reducing unemployment. In India,
during the year 1991, school enrollment gender parity was at a level of 0.77%, but it has
improved and will reach a level of 1.009% by the year 2021 (World Bank, 2023b). Similarly,
women’s representation in the Indian parliament was 3.6% in 1991; that has also improved
and reached a level of 14.44% by the year ending 2021 (World Bank, 2023b). Hence, it shows
that India is improvingitself in terms of social inclusion.
However, these statistics left us with a few questions. First, although foreign fund
inflows have increased exponentially over the past few years, do they play any role in
reducing unemployment in India? Second, India has also progressed in its social inclusion
indicators, but do these social inclusion factors help India reduce its unemployment?
Although there have been some studies done in the past to find out the factors that can help
reduce unemployment, the majority of them focused on Organisation of Islamic Cooperation
countries (Ali et al., 2022), Egypt (Abouelfarag and Qutb, 2020), South Asia (Shabbir et al.,
2020), Nigeria (Fawole and Ozkan, 2019) and Indonesia (Amrial et al., 2019). Furthermore,
some of the studies focused on the recent COVID-19 pandemic and highlighted its impact on
unemployment (Blustein et al., 2020;Couch et al.,2020). But still, there exists a wide gap in
the literature to consider those factors that can help reduce gender-based unemployment in
India.
Hence, the authors have found three major reasons that motivated them to perform this
study. First, comparative statistics clearly indicated that unemployment is a bigger
challenge for India, and until now it has achieved very little success in it. Second, the
COVID-19 pandemic has aggravated this issue, and Indian policymakers are currently
looking for a probable solution for unemployment. Third, as a participant in achieving the
sustainable development goal, Indian policymakers are searching for probable ways to
reduce unemployment so that they can achieve the target set by the United Nations.
Furthermore, the current study also contributes to the existing literature in at least three
different aspects. First, at present, there is no study available in the literature that has
focused on reducing gender-based unemployment in the context of India. Hence, it will be a
Gender-based
unemployment
novel contribution. Second, it will be the first study to capture the impact of foreign fund
inflows and social inclusion on gender-based unemployment. Third, the study will provide
exhaustive, separate results for both male and female unemployment. Therefore, scholars
and policymakers can independently leverage the study’s outcomes when formulating
policies aimed at reducing unemployment. Thus, the research question of the study is as
follows: Does foreign fund inflows and social inclusion help India reduce male
unemployment? Does foreign fund inflows and social inclusion help India reduce female
unemployment?
The further sections of the study are as follows: In Section 2, the theoretical support and
review of literature related to unemployment are mentioned. In Section 3, the research
design is given in detail. In Section 4, the results and discussion are mentioned. In Section 5,
the conclusion, recommendation and policy implication are mentioned. Finally, in Section 6,
the limitations of the research and the future scope of work are given.
2. Theoretical background
Education is the transfer of information, skills, beliefs, values and conventions within a
society that symbolize the ideals of any country. Several researchers, like Costescu (2013),
have connected education and employment to emphasize the importance of both. It is well
known that cognitive development in a child’sfirst five years is crucial for future learning.
More developing countries are promoting early childhood education, particularly for
children from the most marginalized communities. Specifically, investing in human capital
involves investing in people, which is vital for any nation’s future. It asserts that those with
higher levels of human capital, such as formal education, vocational skills and job-related
training, have a greater likelihood of finding work, which further enhances their social
inclusion status. To create human capital relevant to employment, vocational and
professional training, in addition to occupational orientation programs, must be enforced at
all levels of schooling Refrigeri and Aleandri (2013). Moreover, a wealth of evidence
supports the assertion that increased education for female children contributes significantly
to noteworthy advancements in social, economic and health domains. This, in turn, has the
potential to mitigate gender-based unemployment over time. Hence, in line with this concept,
Schultz’s (1961) Human Capital Theory envisioned that schooling and training are means to
help individuals make smart decisions in a changing world. The acquisition of knowledge
and education, coupled with practical experience, culminates in adept decision-making
capabilities and enhancements in prospects for gainful employment. According to this
hypothesis, education, the acquisition of skills and participation in training programs all
play an important role in lowering gender-based unemployment and increasing social
inclusion.
Moving to the second strand of the connection, Alalawneh and Nessa (2020) discovered
that foreign fund inflows are important drivers for boosting economic development and
minimizing unemployment. Foreign investments contribute to the augmentation of state tax
revenues, consequently fostering heightened government expenditure and localized
investments. This phenomenon engenders the generation of fresh employment prospects,
ensures the stability of seasonal labor markets and facilitates the establishment of labor-
intensive initiatives featuring contemporary technologies. The outcome is the emergence
and diversification of novel employment opportunities. Also, one of the secondary effects of
foreign direct investment is that it helps stop the loss of talent, skills and capital by keeping
workers and capital in the home country to work with the investor instead of leaving the
economy. The influx of foreign funds in the form of remittances increases the purchasing
power of consumers, enables them to meet their financial obligations and facilitates
IGDR
educational opportunities, business ventures and professional advancement. Besides, remittances
have an encouraging effect on overall output growth through their direct utilization for
consumption and investment (Noushad et al.,2020;Khan and Gunwant, 2023a). Furthermore,
according to Azizi (2018), remittances increase school enrollment, school completion percentage
and private school enrollment. It additionally boosts education investment in girls more than in
boys, resulting in higher quality education and, ultimately, a rise in gender-based employment.
The structural transformation theory given by Lewis (1954) is consistent with the
abovementioned concept linking foreign fund inflows and unemployment. This model
emphasizes structural changes that reduce unemployment and increase social inclusion. It
suggests moving from low-productivity sectors like agriculture to high-productivity ones
like industry. Foreign money, technology and market access may aid structural
transformation. These funds may help families pay for food, shelter and education. Thus,
beneficiaries’well-being improves, reducing unemployment’s detrimental effects.
2.1 Literature review
Because theoretically, it has been proven that foreign fund inflows are important for
reducing unemployment, it has become an area of interest for researchers as an alternative
source of income. In the past, it has been found in the literature that foreign fund inflows can
improve the economic condition of a country. In this context, Khan (2023) found that positive
shocks in remittance inflows to India helped enhance its economic growth. Furthermore, it
has also been found that the resiliency of foreign fund inflows is important for a nation’s
economic development and acts as a shock absorber during economic downturns (Khan and
Akhtar, 2022;Khan and Gunwant, 2023b). Furthermore, it has also been found in past
studies that foreign fund inflows help reduce unemployment.
Mazher et al. (2020) found out in a study of Pakistan by taking a data set over a period of
1972–2014 and applying autoregressive distributed lag (ARDL) methodology that both
remittance inflows and foreign direct investment (FDI) are significant factors in reducing
unemployment in the long run. Similarly, in the case of Saudi Arabia, Alkofahi (2020)
considered a data set over a period of 2005–2018 and applied ordinary least squares techniques.
His findings also confirmed that FDI inflows have a significant effect on reducing
unemployment. In the case of South Asia, Nguyen (2022) has taken a data set over a period of
1998–2017 and applied the VAR technique. The findings of the study confirmed that there is
unidirectional causality running from FDI to unemployment and suggested that the
government should promote macroeconomic policies to enhance FDI. In the case of
South Africa, it was found that FDI negatively affects the unemployment rate, and the study
suggested that the government should promote friendly FDI policies in labor-intensive sectors
(Mkombe et al.,2020).InthecaseofEastAfricancountries,Woldetensaye et al. (2022) have
taken a panel data set for a period of 1996–2021 and confirmed that FDI inflows negatively
affect the unemployment rate in the region, suggesting that the public sector should promote
policies that attract foreign fund inflows. In the case of the USA, Simionescu and Simionescu
(2017) applied the vector error correction model (VECM) technique to analyze the long- and
short-term relationship between FDI and unemployment. It has been found that FDI inflows
significantly affect unemployment in the long run, whereas in the short run, no significant
relationship was found. Tsaurai (2020) has analyzed the nexus between remittance inflows,
inequality and unemployment using a data set for the period of 2003–2016 for the emerging
economies of the world and indicated that remittance inflows help reduce unemployment
across emerging economies. In another study of emerging nations, Siddiqa (2021) took a data
set for the period 2000–2019 and indicated that remittances help reduce unemployment,
suggesting that economies should focus on attracting remittance inflows. Furthermore, in
Gender-based
unemployment
Malaysia, Irpan et al. (2016) have taken a data set from the period 1980–2012 and applied the
ARDL methodology. The findings indicated that FDI inflows significantly help reduce
unemployment. In the case of Balkan countries, Kurtovic et al. (2015) applied the VECM
technique and found that FDI inflows help reduce unemployment.
In contrast to the findings of the abovementioned studies, Bayar and Sasmaz (2017) have
analyzed the link between FDI and unemployment in 21 emerging economies by taking a
data set over a period of 1994–2014. Their findings indicated that FDI inflows increase
unemployment; hence, it cannot be a factor in reducing unemployment. Similarly, in a study
of developing countries, Sevencan (2023) has taken a data set for a period of 1990–2015 and
applied VECM and fully modified ordinary least squares techniques and found out that in
low-income countries, remittance inflows do not play any role in reducing unemployment.
Djambaska and Lozanoska (2015), in their study of Macedonia, also found the same result,
indicating that FDI inflows have an insignificant effect on reducing unemployment.
By reviewing the past literature on foreign fund inflows and unemployment, mainly two
findings emerge. First, the majority of the study indicates that foreign fund inflows help
reduce unemployment. Second, there is still a wide gap in the literature toanalyze the impact
of foreign fund inflows on unemployment in developing nations. Hence, the question arises:
do foreign fund inflows help reduce unemployment in India?
Conversely, a scarcity of scholarly literature exists to scrutinize the influence of social
inclusion variables on the mitigation of unemployment. In a study for Great Britain, it was
found that post-BREXIT, it was social inclusion that helped reduce unemployment (Sousounis
and Lanot, 2018). Similarly, in a study of Spain, it was found across 17 Spanish communities
that social inclusion helps enhance skills and abilities and supports people in getting
employment (Lechuga et al., 2019). Moreover, an additional European study underscored the
significance of youth social integration as a critical determinant for aligning skill levels with job
prerequisites, ultimately contributing to a decrease in unemployment rates (Knapp, 2021). In
another interesting finding across England, it has been found that with the aging population,
the social inclusion level declines, and this decline positively affects unemployment levels,
suggesting to policymakers that social inclusion levels must be enhanced in order for the
elderly to get employment (Prattley et al., 2020). In a study of seven countries that was based on
13 qualitative data indicators, it was highlighted that broader community levels, social
cohesion and surroundings are the critical factors that work as catalysts for reducing
unemployment. Furthermore, it has also been advised that unemployed people should be
included in social activities to improve cohesiveness (Toit et al., 2018). In another fascinating
study, it was found that unemployment levels are affected differently by diverse social
inclusion factors and the duration up to which that particular social inclusion factor was not
available, suggesting that each social inclusion variable should be taken care of differently to
contribute positively to reducing unemployment (Rözer et al., 2020).
The findings of this brief literature review of social inclusion and unemployment suggest
that social inclusion is critical for reducing unemployment. However, the majority of the
social inclusion study is focused on developed nations, ignoring developing nations.
Furthermore, there is limited literature available that captures the impact of social inclusion
on gender-based unemployment. Hence, the question arises: does gender disparity due to
social inclusion factors affect unemployment in India?
3. Research design
In this research study, time series data for a period of 1991–2021 has been taken to analyze
the impact of foreign fund inflows (remittance inflows and foreign direct investment) and
social inclusion factors (school enrollment, gender parity and proportion of seats held by women
IGDR
in national parliaments) on gender-based unemployment in India. The data has been retrieved
from the World Bank portal (World Bank, 2023b), and EViews12 software has been used to
analyze it. Because it is a gender-based unemployment study, all econometric techniques have
been applied separately to get the results for both male and female unemployment.
Furthermore, the ARDL model technique has been applied for its superiority over other
econometric techniques. In econometric models, serial correlation, also known as autocorrelation,
refers to the situation where the residual components in a regression model exhibit correlation
with time, hence breaching the premise of independence. The ARDL model combines both lagged
dependent and independent variables, enabling it to effectively capture and account for the
temporal dependencies present in the data. This aids in mitigating the problem of serial
correlation and guarantees more dependable and precise parameter estimates. Omitted variable
bias is a widespread issue in regression analysis, occurring when crucial variables are excluded
from the model, resulting in distorted parameter estimations. ARDL minimizes this risk by
including past values of both the dependent and independent variables, consequently capturing
the dynamic associations that may be overlooked in models which solely consider current
variables. This feature improves the model’s capacity to account for the influence of variables
that were not included, reducing bias and leading to a more thorough comprehension of the
fundamental economic connections (Loizides and Vamvoukas, 2005). Furthermore, ARDL is
adept at addressing endogeneity concerns that occur when explanatory variables are associated
with the error term. It incorporates lagged values of the dependent variable to address
endogeneity and ensure reliable parameter estimates. It is imperative to avoid biased results and
ensure the dependability of the model’sfindings. The linear equation is as follows:
MU ¼fRE;FD;PA;SC
ðÞ (1)
FU ¼fRE;FD;PA;SC
ðÞ (2)
In equation (1), MU represents male unemployment, RE represents remittance inflows, FD
represents foreign direct investments, PA represents women’s representation in parliament
and, Finally, SC represents school enrollment gender parity. In equation (2), FU represents
female unemployment, while all other variables represent the same as mentioned in
equation (1). The empirical model of the study is as follows:
MUt¼
a
0þ
a
1REtþ
a
2FDtþ
a
3PAtþ
a
4SCtþ
«
t(3)
FUt¼
a
0þ
a
1REtþ
a
2FDt þ
a
3PAtþ
a
4SCtþ
«
t(4)
In equations (3) and (4), time trend is represented by t, constant is represented by
a
0,
coefficients are represented by
a
1,
a
2
,
a
3
,
a
4
and error term is represented by
«
t
.
An ARDL model involves steps, starting with checking the stationarity of the variable.
All the variables must be stationary, either at level or at the first difference. It is important
because in empirical research, time series data is collected for a long time, and there is a
possibility that the series might be making a trend with time. Hence, the series should be
made stationary before moving on to the next step. To check the stationarity, the augmented
Dickey–Fuller (ADF) test or Philips–Perron unit root test are commonly applied. The second
step is to find out the appropriate lag length for analysis. The selection of too many lags
may reduce the degree of freedom and can result in multicollinearity. Wooldridge (2003) has
suggested that the lag length should be kept at a maximum of three lags to not lose the
Gender-based
unemployment
degree of freedom. There are many information criteria to select the appropriate lag length
for, e.g. akaike information criterion (AIC), Bayesian information criterion, Schwarz
information criterion, and Hannan Quinn (HQ). The lowest value among all the information
criteria determines the appropriate lag length, and this becomes the foundation for
determining the ARDL model. The ARDL model specifications are as follows:
MUt¼
b
0þX
r
i¼0
b
1iMUtiþX
r1
i¼0
b
2iREtiþX
r2
i¼0
b
3iFDtiþX
r3
i¼0
b
4iPAtiþX
r4
i¼0
b
5iSCti
þ
d
6MUt1þ
d
7REt1þ
d
8FDt1þ
d
9PAt1þ
d
10SCt1þ
«
t(5)
FUt¼
b
0þX
r
i¼0
b
1iFUtiþX
r1
i¼0
b
2iREtiþX
r2
i¼0
b
3iFDtiþX
r3
i¼0
b
4iPAtiþX
r4
i¼0
b
5iSCti
þ
d
6FUt1þ
d
7REt1þ
d
8FDt1þ
d
9PAt1þ
d
10SCt1þ
«
t(6)
In equations 5 and 6, the variable “r”represents the lag length for each of the variables, and the
error term is denoted by
«
t
. The constant is identified as
b
0
. Short-term coefficients are
expressed as
b
1i
to
b
5i
, whereas long-term coefficients are represented by
d
6
to
d
10
.
Furthermore, the joint null hypothesis that a long-run relationship between the variables does
not exist is tested with the alternative hypothesis that there is a long-run relationship between
the variables. This has been done with the help of the ARDL bounds testing approach, which
was given by Pesaran et al. (2001). In the bound testing approach, there are two sets of critical
values. In one set, the critical values are considered when all regressors are I (0), whereas in
another set, the critical values are considered when all variables are I (1). The F-statistics value
is compared with the lower and upper bounds of the critical values. If the F-statistic value is
less than the lower bound critical value, then there is no cointegration between the variables. If
the F-statistics value is higher than the upper bound critical value, then it is a confirmation that
there exists a long-run relationship between the variables. If the long-run relationship between
the variables is confirmed, then the following error correction model is formulated:
MUt¼
b
0þX
r
i¼1
b
1iMUtiþX
r1
i¼0
b
2iREtiþX
r2
i¼0
b
3iFDtiþX
r3
i¼0
b
4iPAti
þX
r4
i¼0
b
5iSCtiþ
l
ECTt1þ
«
t(7)
FUt¼
b
0þX
r
i¼1
b
1iFUtiþX
r1
i¼0
b
2iREtiþX
r2
i¼0
b
3iFDtiþX
r3
i¼0
b
4iPAti
þX
r4
i¼0
b
5iSCtiþ
l
ECTt1þ
«
t(8)
In equations 7 and 8,
l
represents the error correction indicator, which indicates the
correction of error that runs toward equilibrium. It is required that the value of error
correction should be significant and negative. Finally, diagnostic tests have been applied to
IGDR
ensure that the estimation is free from serial correlation, normality and heteroskedasticity.
Serial correlation was tested by applying the Breusch–Godfrey serial correlation Lagrange
multiplier (LM) test; data normality was tested by the Jarque–Bera test; and
heteroskedasticity was tested by applying the Breusch–Pagan test. Furthermore, to confirm
the stability of the model, the cumulative sum (CUSUM) and CUSUM of the square test were
applied. The variable description of this research study is given below in Table 1.
4. Empirical findings
The findings of the unit root test are the firststepofanalysisanditisgiveninTable 2.TheADF
unit root test has been applied in this research study. Model 1, which indicates
male unemployment, highlights that male unemployment (MU) and remittance inflows (RE) were
nonstationary at level, but they became stationary after taking the first difference of the series.
While all other variables, including foreign direct investment (FD), school enrollment, gender
parity (SC) and the proportion of seats held by women in national parliaments (PA), were found to
Table 1.
Description of
variables
Variable
type Variable Factors depicting Symbol Measurement Source
Dependent Male
unemployment
Unemployment MA Unemployment, male (% of male
labor force)
WDI
Dependent Female
unemployment
Unemployment FE Unemployment, female (% of female
labor force)
WDI
Independent Foreign direct
investment
Foreign fund
inflows
FD Foreign direct investment, net
inflows (% of GDP)
WDI
Independent Remittance inflows Foreign fund
inflows
RE Personal remittances, received (% of
GDP)
WDI
Independent Women in
parliament
Social inclusion PA Proportion of seats held by women in
national parliaments (%)
WDI
Independent School enrollment Social inclusion SC School enrollment, primary (gross),
gender parity index (GPI) (%)
WDI
Source: World development indicators (World Bank, 2023b)
Table 2.
Augmented Dickey–
Fuller unit root test
At level At first difference
Variables t-Statistics Result t-Statistics Result
Model 1 (male unemployment)
MU 0.892 Nonstationary 8.5678 Stationary
FD 4.8505 Stationary 5.0736 Stationary
PA 2.7029 Stationary 5.8564 Stationary
RE 0.4307 Nonstationary 5.6425 Stationary
SC 2.2933 Stationary 4.5497 Stationary
Model 2 (Female unemployment)
FU 1.5796 Nonstationary 15.4975 Stationary
FD 4.8505 Stationary 5.0736 Stationary
PA 2.7029 Stationary 5.8564 Stationary
RE 0.4307 Nonstationary 5.6425 Stationary
SC 2.2933 Stationary 4.5497 Stationary
Source: Authors’calculation via Eviews12 software
Gender-based
unemployment
be stationary at both level and at first difference. Similarly, Model 2, which indicates female
unemployment, highlights that female unemployment (FU) and remittance inflows (RE) were
nonstationary at level, but they became stationary after taking the first difference of the series.
While all other variables, including foreign direct investment (FD), school enrollment, gender
parity (SC) and the proportion of seats held by women in national parliaments (PA), were found to
be stationary at both level and at first difference, Because both of these models confirmed that the
variables are of mixed order of stationarity, we may proceed with the next steps of the ARDL
model. Alam (2022) has also applied the ADF unit root test while using the ARDL technique.
The next step is to find out the optimum lag length, which is provided in Table 3. Out of
many information criteria, this research study has opted for the AIC criterion. It has been
found that in both models, the optimum lag length is 3. Hence, a lag length of 3 has been
selected to find out the best-suited ARDL model for further analysis. For Model 1 (male
unemployment), the ARDL model (1, 3, 2, 1, 3) has been selected. While for Model 2 (female
unemployment), the ARDL model (2, 2, 3, 3, 3) has been selected for further analysis.
Upon selection of the ARDL model, the next step is to confirm whether there exists a
long-run relationship between the variables or not. This requirement is fulfilled by the
ARDL bounds testing technique. For Model 1 (male unemployment), the F-stats value was
found to be 19.05, which was higher than the upper bound critical value of 3.97 at the 5%
level of significance. Similarly, for Model 2 (female unemployment), the F-stats value was
found to be 11.55, which was higher than the upper bound critical value of 3.97 at the 5%
level of significance. Because both of these models confirmed that the F-stats value is higher
than the upper bound critical value, it is a confirmation that there exists a long-run
relationship between the selected variables. The details are shown in Table 4.
Table 5 outlines both short- and long-term outcomes. In the case of model 1, which
pertains to male unemployment, the analysis reveals that over the long term, the presence of
foreign direct investments is associated with a mitigated level of unemployment. Precisely, a
1% increase in foreign direct investments is associated with a 0.09% decline in male
unemployment over an extended timeframe. This result goes in line with the previous
findings of Alalawneh and Nessa (2020), in which it was discovered that FDI helps mitigate
male unemployment in the long run for six developing nations. Likewise, remittance inflows
have been identified as a constructive factor in mitigating male unemployment. Specifically,
a long-term analysis reveals that for each 1% augmentation in remittance inflows, there is a
corresponding decrease of 0.46% in male unemployment. These findings also align with the
Table 3.
Optimal lag selection
Lag LogL LR FPE AIC SC HQ
Model 1 (Male unemployment)
084.4599 NA 0.0004 6.3900 6.6279 6.4627
1 8.6075 146.2488* 0.0000 1.5280 2.955397* 1.9644
2 38.6420 36.4704 0.0000 1.1684 3.7853 1.9684
3 77.3014 33.1366 1.82e-06* 0.192761* 3.9991 1.356384*
Model 2 (Female unemployment)
052.3908 NA 0.0000 4.0993 4.3372 4.1721
1 42.5170 149.1409 0.0000 0.8941 0.5333 0.4577
2 81.5832 47.4375 0.0000 1.8988 0.7180 1.0988
3 129.8444 41.36675* 4.28e-08* 3.560315* 0.245984* 2.396692*
Note: *Indicates the smallest lag length value upto third lag
Source: Authors’calculation via Eviews12 software
IGDR
past findings of Ihedimma and Opara (2022), in which they confirmed that remittance
inflows help reduce male unemployment. Recent studies like Guliyev (2023), have also
observed that technological advancements like artificial intelligence decreases the level of
unemployment. Hence, it can be another important factor that can play a crucial role in
mitigating gender-based unemployment. On the contrary, social inclusion factors, school
enrollment gender parity and women’s representation in parliament were also found to be
significant in reducing male unemployment in the long run. Results indicated that with
every 1% increase in school enrollment gender parity, male unemployment decreases by
16.69%, whereas every 1% increase in women’s representation in parliament helps reduce
male unemployment by 0.46%. These findings further validate the earlier research
conducted by Akinyetun et al. (2021), wherein they substantiated that heightened levels of
unemployment are attributable to social exclusion.
Table 4.
Bound test result
F-stat Value ¼19.05 k¼4
Significance I (0) bound I (1) bound
Model 1 (Male unemployment)
10% 2.68 3.53
5% 3.05 3.97
2.50% 3.4 4.36
1% 3.81 4.92
Model 2 (Female unemployment)
F-stat. Value ¼11.55 k¼4
Significance I (0) bound I (1) bound
10% 2.68 3.53
5% 3.05 3.97
2.50% 3.4 4.36
1% 3.81 4.92
Source: Authors’calculation via Eviews12 software
Table 5.
Long-run and short-
run result
Variable
Long run Short run
Coefficient Std. error t-Statistic Coefficient Std. error t-Statistic
Model 1 (Male unemployment)
FD 0.0920 0.0226 4.0704 0.1456 0.0405 3.5987
PA 0.4615 0.1493 3.0901 0.4315 0.1364 3.1633
RE 0.6599 0.1309 5.0409 0.7551 0.2271 3.3246
SC 16.5995 2.2160 7.4906 2.5724 1.9025 1.3521
Constant 0.5257 0.0995 5.2824 31.5884 5.0695 6.2310
Model 2 (Female unemployment)
FD 0.2510 0.1321 1.9000 0.2549 0.1589 1.6036
PA 0.0896 0.0208 4.3115 0.1403 0.0281 4.9907
RE 0.0245 0.0138 1.7753 0.1047 0.0548 1.9103
SC 1.2055 0.2811 4.2889 0.1163 0.3522 0.3301
Constant 0.0372 0.0077 4.8615 17.7033 2.4630 7.1878
Source: Authors’calculation via Eviews12 software
Gender-based
unemployment
On the contrary, Model 2 (female unemployment) displays a very interesting findingwitha
summary that foreign fund inflows do not help in reducing female unemployment as both the
indicators –remittance inflows and FDI –were found to be insignificant in the long run. This
result is in line with the findings of Umit and Alkan (2017), wherein they revealed that foreign
direct investments affect female employment negatively. While social inclusion factors were found
to be the key factors that reduce female unemployment, the impact of school enrollment gender
parity indicated that for every 1% increase in it, there is a decline in female unemployment of
1.20%. Conversely, the involvement of women in parliamentary activities underscores that for
each incremental rise of 1%, there is a corresponding decrease of 0.08% in long-term female
unemployment. This conclusion is congruent with the findings of Yousefy and Baratali (2011),
who discovered that women who had social inclusion in terms of degrees at higher educational
levels had greater career opportunities. They also discovered that higher education has an
important impact on the employment and advancement of women in their working lives.
4.1 Diagnostic test result
In this study, various diagnostic tests were used to assess the robustness of the estimated ARDL
model. The results are presented in Table 6.TheJarque–Bera test, which examines the normality
of the residuals, yielded p-values of 0.6360 and 0.7103 for Models 1 and 2, respectively. These
results indicate that the null hypothesis, stating that the residuals are normally distributed, cannot
be rejected at the 5% significance level. Furthermore, the Breusch–Godfrey LM test for serial
correlation resulted in p-values of 0.4962 and 0.4659 for Models 1 and 2, respectively. These
findings suggest that there is no evidence of autocorrelation in the estimated model, as the p-values
exceed the 5% significance level. In addition, the Breusch–Pagan Godfrey test for
heteroskedasticity produced p-values of 0.4404 and 0.1057 for Models 1 and 2, respectively.
Because these p-values are greater than the 5% significance level, it indicates that the estimated
ARDL model does not exhibit heteroskedasticity. To further evaluate the stability of the estimated
model, CUSUM and cumulative sum of squares (CUSUMSQ) plots of the cumulative sum of
recursive residuals were generated (as depicted in Figures 1 and 2). The plots demonstrate that the
CUSUM and CUSUMSQ values remain within the critical boundaries at the 5% significance level.
As a result, the null hypothesis of parameter coefficient constancy cannot be rejected, suggesting
that the estimated model remains stable throughout the study. Based on these findings, it can be
concluded that the estimated ARDL model used in this investigation is robust, with normally
distributed residuals, no evidence of serial correlation, no heteroskedasticity and stable parameter
coefficients.
Table 6.
Diagnostic test result
Test F-stat. Prob.
Model 1 (Male unemployment)
Jarque–Bera test: normality 0.9052 0.6360
Breusch–Godfrey LM test: serial correlation 0.8599 0.4962
Breusch–Pagan Godfrey test: heteroskedasticity 1.1000 0.4404
Model 2 (female unemployment)
Jarque–Bera test: normality 0.6840 0.7103
Breusch–Godfrey LM test: serial correlation 0.5862 0.4659
Breusch–Pagan Godfrey test: heteroskedasticity 25.7479 0.1057
Source: Authors’calculation via Eviews12 software
IGDR
5. Conclusion and policy recommendation
In summary, this investigation systematically analyzed the influence of social inclusion
variables and the influx of foreign funds on the mitigation of gender-based unemployment
in the context of India. The findings indicate that both social inclusion and foreign fund
inflows are crucial factors for reducing male unemployment. However, for female
unemployment, only social inclusion factors play a significant role, whereas foreign fund
inflows do not contribute to reducing female unemployment. These findings provide
valuable insights for Indian policymakers and stakeholders in formulating strategies to
achieve the sustainable development goal of reducing unemployment. Based on the study’s
findings, the following policy recommendations can be proposed: Policymakers should
prioritize implementing policies that promote gender equality, education, skills training and
women’s empowerment to enhance social inclusion and reduce female unemployment.
Targeted programs, such as vocational training initiatives and gender-sensitive
employment policies, can improve women’s employability and increase their participation in
the labor market. Furthermore, policymakers should actively attract foreign investments in
sectors that create more employment opportunities for men to reduce male unemployment.
Offering incentives and creating a favorable business environment can encourage foreign
investors to invest in sectors aligned with the government’s employment objectives,
leveraging the positive effect of foreign fund inflows on male employment.
Figure 1.
CUSUM and
CUSUMSQ test (male
unemployment)
Figure 2.
CUSUM and
CUSUMSQ test
(female
unemployment)
Gender-based
unemployment
To address female unemployment, policymakers should modify policies to channel a
portion of foreign funds into female-intensive sectors like health care, education and
microenterprises. These targeted investments can create employment opportunities for women
and contribute to achieving gender parity in unemployment rates. In addition, regular
monitoring and evaluation of policies targeting gender-based unemployment is essential for
policymakers to identify effective strategies and make necessary adjustments for maximum
impact. Collecting gender-disaggregated data on employment and conducting continuous
research can provide valuable insights for evidence-based policymaking in this area.
6. Limitation and future scope of study
The study focuses specifically on the impact of social inclusion factors and foreign fund
inflows on gender-based unemployment in India. It does not consider other potential factors
that may influence unemployment rates, such as technological advancements, labor market
dynamics or government policies targeting specific sectors. Furthermore, the study uses a
limited time series data set from 1991 to 2021. While this provides a substantial timeframe
for analysis, there may be limitations in data availability or reliability for certain variables
or time periods, which could affect the accuracy and generalizability of the findings. For
future research, the researchers can explore the impact of other variables, such as
technological advancements, labor market policies, education and skill development
programs, on gender-based unemployment. This broader analysis would provide a more
comprehensive understanding of the factors influencing unemployment rates.
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About the authors
Imran Khan of this research article is a project management professional-certified Operations and
Project Management professional having 14þyears of industry experience across local and
multinational companies with an area of expertise in global remittances, foreign exchange market,
banking operations and financial services. He has a keen interest in research work and his research
interest includes sustainable development, gender diversity, migration studies, remittance behavior
and financial economics.
Darshita Fulara Gunwant of this research article is an International Relations and Marketing
expert having 12þyears of industry experience across local and multinational companies with an
area of expertise in global commodity price, remittances, macroeconomics and marketing techniques.
She has a keen interest in research work and her research interest includes sustainable development,
price volatility, remittance behavior, gender diversity and financial economics. Darshita Fulara
Gunwant is the corresponding author and can be contacted at: df.gunwant@gmail.com
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Gender-based
unemployment
