A Qualitative Analysis Of Teenage Pregnancy In Mauritius

OSSREA-Mauritius Chapter
Journal
of
Social Policies
and
Development
Special Issue Vol. 2
“Gender in Economic, Social and Political Arenas”
November 2013

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Liaison Officer for Mauritius Chapter
Nicolas Ragodoo
FSSH,
University of Mauritius
Réduit
n.ragodoo@uom.ac.mu
Tel: +230 403 76 84
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ISSN 1694-1977

EDITORS:
OSSREA - Mauritius Chapter
N.Ragodoo (Liaison Officer)
FSSH, University of Mauritius
n.ragodoo@uom.ac.mu
Dr D. Padachi (Chapter Secretary/Finance)
SAFE, University of Technology
kpadachi@utm.intnet.mu
R. Suntoo (PR/Membership Drive Officer)
FSSH, University of Mauritius
r.suntoo@uom.ac.mu
MEMBERS OF THE SCIENTIFIC COMMITTEE:
Prof S. Sivaraju, Dean of the Tata Institute of Social Sciences
Prof Roger Southall, Department of Sociology, University of the Witwatersrand
Prof Micheal Neocosmos, Department of Sociology, University of South Africa
Prof Sheila Bunwaree, Faculty of Social-Studies and Humanities, University of Mauritius
Prof Sanjeev Sobhee, Faculty of Social-Studies and Humanities, University of Mauritius
Assoc Prof H. Chittoo, School of Business, Management and Law, University of Technology,
Mauritius
Assoc Prof R. Durbarry, Head of Department Marketing, Tourism and Hospitality, University
of Bedfordshire
Assoc Prof Jay S Matadeen, Faculty of Law and Management, University of Mauritius

Editorial
Nicolas Ragodoo, Dambeegan Padachi & Rajen Suntoo
Background
In 2012, Mauritius ranked 70th out of 186 countries according to the Gender Inequality Index of
the UN. In spite of the significant efforts made through amendments in the legislations and
educational campaigns, wide disparities still prevail in terms of opportunities available for men
and women. Indeed, even if in terms of academic performance girls clearly outperform boys,
this is not replicated in the work environment, where women are still very much under-
represented in the decision-taking spheres. With regards to the rates of unemployment, it stood
at 11.9% for women, compared to 5.4% for men. Also, despite labour laws and remuneration
orders, women still suffer from discrimination in terms of salary in some areas. Even if the
number of women occupying high positions in government services, the number of seats
occupied by women in the national assembly, and the number of women entrepreneurs are all
on the rise, there is still a long way to reach gender equality in these spheres. On the other hand,
cases of gender-based violence have been high and rising, and the feminization of poverty is
gaining ground.
Objectives
The primary aim of this Special Issue is to review the trend followed by Mauritius in terms of
gender policies during the recent decades, analytically classifying them, developing typologies
of varieties and their prevalence, and evaluating their impact on the development path followed
by the country. The effectiveness of measures taken in the gender field, lessons learnt, as well as
the gaps and the challenges ahead in the economic, social and political spheres are investigated.
Topics covered
The Journal covers a comprehensive set of topics related to gender in economic, social and
political arenas such as gender equality and economic development, education, growth and
gender, women entrepreneurship, the feminization of the teaching profession, the emancipation
and vulnerability of women, teenage pregnancy, and sexual violence.
The Editorial committee would like to thank OSSREA Headquarters for its continuous support
for the achievement of this project. Special thanks to the members of the Scientific Committee,
and congratulations all those who have their papers in this present edition.

CONTENTS
Education and growth: Does Gender matter? .................................................................................................................1-7
Boopen Seetanah
Gender equality and economic development in Mauritius: a win-win situation? ................................................... 8-21
Verena Tandrayen-Ragoobur and Harshana Kasseeah
Revisiting the role of Women centers in the empowerment of women in Mauritius..............................................22-29
Ibrahim Koodoruth
Gender equality in education and economic growth: the case of Mauritius ............................................................30-39
Sheereen Fauzel
A feminist analysis of sexual violence in Mauritius.....................................................................................................40-49
N. Fokeerbux and Horatio Caine
The nexus between Sustainable Development Practices and women entrepreneurs: The case of Mauritius ......50-56
Meghna Raghoobar
The Feminization of the teaching profession in Mauritius..........................................................................................57-62
Vidyaluxmi Mayaram
Emancipation and Vulnerability of Women................................................................................................................. 63-68
Yashinee Bholah
The impact of the 2009 Transitional Unemployment Benefit on the welfare of women
in the EPZ sector in Mauritius: a qualitative study......................................................................................................69-74
Asrani Gopaul
A Qualitative Analysis of Teenage Pregnancy in the Mauritian Society.................................................................. 75-84
Anishta Bhoodoo
Is there really a place for Gender Equality in the Economic, Social and Political Arenas?.................................... 85-89
Mahendrenath Motah

OSSREA Journal of Social Policies and Development
1
EDUCATION AND GROWTH:
DOES GENDER MATTER?
Boopen Seetanah,
Department of Finance and Accounting, University of Mauritius, Republic of Mauritius
ABSTRACT
This research analyses the relationship between gender
separate education and economic growth for the case of
Mauritius, one of the fasting nation in Africa. Using a
multivariate dynamic estimation technique to account
for dynamic and feedback effects in the education-
growth link for the period 1960-2010, it is established
that that both female and male education are important
ingredients in explaining growth. They are interestingly
shown to have nearly the same productivity level.
Further analysis suggests that bi-causality exist between
female/male education and economic growth. Indirect
effects via capital stock accumulation, a proxy for
investment, are also reported.
Key words: Human Capital, Education, Economic
Growth.
1.0 INTRODUCTION
A sizeable important amount of empirical work on the
economic importance of gender-neutral education exists.
Overall there is a consensus that education attainment
has a positive and significant effect on economic growth
thus confirming the theoretical predictions. However
studies on the gender separate education effects on
economic progress have largely ignored until recently
even, then there exists a great deal of contradictory
evidences. For instance Barro and Lee (1994) find that
growth is positively related to male education and
negatively related to female education. Caselli, Esquivel
and Lefort (1996), however, find the opposite, while
Birdsall, Ross and Sabot (1997) report no significant
difference between the genders.
Moreover, most of the scarce evidences have been based
on cross country and panel data analysis and focused on
developed countries cases. Studies on country specific
cases using rigorous time series analysis, especially for
developing countries, have been particularly lacking.
More importantly, to our knowledge, no study has been
performed for the case of small island developing states
and we should take into account the fact that empirical
findings from developed countries’ cases are not directly
applicable and relevant to island states given their
vulnerability and special characteristics. Furthermore
previous studies have largely ignored Moreover, it is
only lately that scholars have been implicitly dealing
with the issue of reverse causality and dynamics in the
education and economic growth link
The aim of this paper is to thus to address the above
issues in the investigation the empirical link between
gender separate education and economic progress for the
case of the small island developing state of Mauritius. It
allows for dynamic and feedback effects in the
education-growth link by using a multivariate dynamic
estimation technique, namely a difference vector
autoregressive framework for the period 1960-2010
The structure of this paper is as follows
Section II discusses very briefly the theoretical
underpinnings of the link between education and
economic growth and the relevant empirical literature.
Section III describes the preferred modeling function
used and elaborates on the data collection. It also
investigates the empirical link between gender separate
education and economic growth for the case of
Mauritius. Section IV concludes.
2.0 LITERATURE REVIEW
Before focusing on the gender-separate theoretical
literature, it is first necessary to briefly review the role
of aggregate human capital in economic growth. In fact
more and better education is a prerequisite for rapid
economic development around the world. Education
stimulates economic growth and improves people’s lives
through many channels i) by increasing the efficiency
and thus increasing an individual’s earning potential of
the labor force, ii) by fostering democracy (Barro, 1998)
and thus creating better conditions for good governance,
iii) by improving health and reducing fertility and iv) by
enhancing equality (Aghion, Caroli and García-
Peñalosa, 1999), and so on. In fact education produces a
“ripple effect” throughout the economy by way of a
series of positive externalities. Michaelowa (2000)
provides a comprehensive overview of theoretical
underpinnings about the link between education and
economic growth and this is shown in adapted figure 1
below. It summarises the education-growth theoretical
arguments namely that i) educated persons as well as of

EDUCATION AND GROWTH: DOES GENDER MATTER?
2
those who indirectly learn from them benefit from
increased earnings and this can be interpreted as a
reflection of productivity gains. If the population
reaches a higher educational attainment, economic
productivity should be fostered and thus leading to
higher growth. Moreover, the wage differential reflects
the higher value of human capital which, being an input
factor in the national production
Figure 1 : The economic return to education
Source : Michaelowa, K., (2000)
function, contributes to an increased national output. ii)
Education positively influences another dimension of
human capital with similar consequences for increased
productivity and growth through its impact on health.
iii) Education also leads to reduced birth rates through
its impact on reduced population growth. From a
statistical point of view this increases national income
and growth are considered on a per capita basis. In
addition it is clear that the number of childbirths affects
women’s physical ability to work and their productivity
and finally iv) education has often been argued to induce
more persons to participate in the labor force. This
might in turn lead to a reallocation of the population
towards economically more productive activities and
ultimately having an impact on growth.
2.1 Empirical Evidences
Classical work from Jorgenson, Gallop and Fraumeni
(1987) has reported a positive contribution of education
and human capital to economic growth. Maddison
(1991) while estimating similar impact also positive
reported output elasticity with respect to quality of
labour force with elasticities between 0.1 and 0.5.
Mankiw, Romer, and Weil (1992) provide one of the
best known and most influential contributions to the
empirical growth literature, particularly on the growth
effects of human capital. They reported an output
elasticity of education of 0.3. The results are in line with
those by Barro (1991, 1993) and Levine and Renelt
(1992). Young (1995) also confirmed (elasticity of 0.1)
the above for the case of East Asia economies. A survey
review by Englander and Gurney (1994) based mostly
on studies from G7 suggests that the growth of human
capital typically accounts for a tenth to a fifth of growth
in total output. Similar surveys from Griliches (1997)
reported that this could have accounted for perhaps a
third of the productivity residual in the US over the
post-war period. More recently Gemmell (1996),
Klenow and Rodriquez (1997), Temple (1998) and Hall
and Jones (1999) reported output elasticities from
education of between 0.1 to 0.3. Accounting for
feedback issues, Teixeira and Fortuna (2003) studied the
human capital effects on economic growth of Portugal
using (Vector Autiregressive)VAR and cointegration
analysis and obtained a long-run estimate for human
capital elasticity of 0.42. Pina and St. Aubyn (2004)
subsequently confirmed the results using similar
techniques. The scarce evidences from developing
economies also yield positive returns of education in
general (see Psacharopoulos, 1994; Glewwe, 1996;
Andreosso-O’Callaghan, 2002 and Baldacci, Clements,
Gupta and Cui, 2004)
However, several well-known studies have also found
the correlation between human capital and growth to be
surprisingly weak (for instance Benhabib and Spiegel,
1994; Islam, 1995; Barro and Sala-i-Martin, 1995;
Caselli, Esquivel and Lefort, 1996; Pritchett 1997;
Durham, 1999; Bils and Klenow, 2000; Temple, 2001).
A summary of evidences would concur with Temple
(2001) who noted that ‘the empirical evidence that
education matters for growth is surprisingly mixed.’
2.2 Gender separate human capital growth
literature1
The theoretical growth and welfare benefits of female
education have mostly generated by microeconomics.
Greater female education has often been found to lead to
lower fertility rates (see Blau, 1986; Ketkar, 1978; Cain
and Weininger, 1973) which in turn result in lower rates
of infant mortality and longer life expectancies (e.g.,
Blau, 1986; Behrman and Deolalikar, 1988 and Benefo
and Schultz, 1996). There is also evidence of the inter-
generational effects of maternal education on children’s
education, health and welfare (e.g., Bach et al., 1985;
Blau, 1986; Schultz, 1988; Behrman and Deolalikar,
1988; Feinstein and Symons, 1999; Behrman et al.,
1999).
1
Lorgelly (2000) provides the most comprehensive survey
review of the literature.

3
While there are large amount of evidences on the
aggregate human capital-growth hypothesis, the
contribution of gender separate education to growth at
macroeconomic level has scantly been dealt with.
Benavot (1989) was the first to realise this and notes
that “models of the impact of education on economic
development largely ignore the issue of gender”
(Benavot, 1989, p.14). The author investigated the
impact of gender differences in education on
development for a sample of 96 countries and found that
both female and male primary enrolment rates had a
positive and significant effect on growth, with
secondary enrolments having little effects. Similar
results were found with different sub samples,
particularly with respect to less developed countries.
Interestingly the authors found that the parameter
associated with females (.0064) was higher than that
associated with the primary education of males (.0056),
suggesting that the education of females is more
important than that of males. So “educational expansion
among school-age girls at the primary level has a
considerably stronger effect on long-term economic
prosperity than does educational expansion among
school-age boys” (Benavot, 1989, p.27). Psacharopoulos
(1994) also found that the rate of return to female
education is positive and marginally higher than that to
male education.
The most often cited research remains that of Barro and
Lee (1994) who extended the earlier work of Barro
(1991) by widening the measure of human capital to
include both health and education and further divide
education into separate female and male. Using
Seemingly Unrelated Regression Equations (SURE)
technique applied to cross-country data for two time
periods (1965-1975 and 1975-1985), Barro and Lee
(1994) found that while growth is positively related to
male education, it was negatively related to female
education This “puzzling finding” was explained by the
fact that “a high spread between male and female
secondary attainment is a good measure of
backwardness; hence, less female attainment signifies
more backwardness and accordingly higher growth
potential through the convergence mechanism.” (Barro
and Lee, 1994). Barro and Sala-i-Marting (1995), Barro
(1996) and Lorgelly and Owen (1999) found similar
results. However, Barro (1997, 1998, 1999) using
revised data for his panel data concluded that the return
of female education to growth is essentially zero in fact.
On the other hand, using dynamic GMM panel
estimation for 97 countries for the years 1960 to 1985,
Caselli, Esquivel and Lefort (1998) surprisingly found
the reversal of the signs on the female and male
education variable from those reported by Barro and Lee
(1994). The authors argued that the changes in their
results are due to the impact of using the GMM. Forbes
(1998) subsequently supported these results. As Forbes
argued “this pattern of signs may not support traditional
human capital theory, these coefficients … are similar to
those found in other growth models estimated using the
same technique [namely Caselli, Esquivel and Lefort]”
(Forbes, 1998, p.13).
Empirical Analysis from Birdsall, Ross and Sabot
(1997), in their “Barro-style” regression, reported “that
increasing primary school enrolments for girls is just as
effective in stimulating growth as increasing primary
enrolments for boys”.
Other studies while investigating the varying importance
of male and female education focused on the gender
gaps in human capital and whether this gap hindered
economic growth and productivity. Among the first
studies was that of Hill and King (1993, 1995) who
found support for increases in female education and
decreases in the gender gap (measured as the ratio of
female to male enrolments) resulting in increases in
social well-being. The authors found that that failure to
improve female education to at least the same average
level as that of males may act as a brake on
development. Sadeghi (1995) also confirmed the above
results.
Recent studies from Klasen (1999) found that the initial
gender gap and the expansion of the female-male ratio
both have a significantly positive impact on economic
growth. Klasen also found a significant role for gender
gaps in education indirectly hindering economic growth
through its impact on investment and population growth.
A summary of empirical evidences shows that while a
relatively large amount of work exists in the aggregate
education- growth debate, although with mixed results,
gender separate studies have not however not received
due attention. Moreover the existing ones tend to focus
on developed country cases and on cross sectional and
panel data sets with little or no time series studies found
for the case of developing, especially small island
developing states, and taking account dynamic feedback
issues that may occur.
3.0 METHODOLOGY AND ANALYSIS
3.1 Dynamic Feedback
Endogeniety is an important issue, often overlooked by
existing works. In fact there may be the presence of bi-
causality in the sense that it not only education that
drives growth but that educational attainment are also
driven by government policy and income level of the
country. It seems plausible that as output and tax
revenues increase, governments might allocate more
resources to education thus increasing its standards,
attainment and quality (See Mincer, 1996 and Bils and

4
Klenow, 2000 on the two way causality issue).
Moreover better education may also have a signaling
effect and attract more inwards and foreign direct
investment which in turn increases output level. The
issue of causality and feedback effects is thus important
to the analysis of education – growth link.
To incorporate the above issue, the analysis uses
dynamic econometrics techniques, namely a Vector
Autoregressive Model (VAR), following recent studies
in the field (see Erk and Ate, 1999; Teixeira and
Fortuna, 2003; and Pina and St. Aubyn, 2004).
3.2 The Economic and Econometric Model
Our economic model is derived from Griliches (1997),
who wrote that “the main, and possibly only, approach
to testing the productivity of schooling directly is to
include it as a separate variable in an estimated
production function”. Other authors (Barro, 1991;
Mankiw, Romer and Weil, 1992; Levine and Renelt,
1992 and also from more recent works from De la
Fuente, 2003 and Pina and St. Aubyn, 2004) also
regressed standard measures of economic development
on measures of human capital (decoupled between male
and female), controlling for the other variables found in
an aggregate production function.
A Cobb-Douglas production function is thus specified
whereby human capital, which is segregated into male
and female human capital, enters as additional and
separate inputs into the following extended Cobb
Douglas production function
4
3
2
1
)
(
)
(
)
(
)
( 



t
t
t
t
t
t HF
HM
L
K
A
Y  (1)
where Y is the country’s national output and is
measured by GDP at constant prices, K is the country’s
capital stock which has been constructed using the
Perpetual Inventory Method (PIM) as recommended by
the OECD (2001a), L is the amount of people in
employment (a proxy of labour), HM and HF are the
secondary enrolment ratio of males and females
respectively. The latter are measures employed to proxy
for the quality of human capital and have been widely
used by Barro, 1991; Levine and Renelt, 1992;
Englander and Gurney, 1994 and Barro and Sala-i-
Martin, 1995 among others. They are the only consistent
and available measure available over the period of study
(1960-2010). The use of interpolations was kept to a
strict minimum.
Data for the dependent variable and for the construction
of the capital stock was obtained from the Penn World
Table (6.1) whereas employment and male and female
secondary enrolment ratio figures were available from
the country’s Central Statistical Office’s biannual digest
of Statistics for employment and education respectively.
Taking logs on both sides in equation 1 and denoting the
lowercase variables as the natural log of the respective
uppercase variable results in the following:





 




 hf
hm
l
k
y 4
3
2
1
0 (2)
where β0 is the constant term, β1 , β2 , β3 and β4 represent
the elasticity of output relative to capital, labour, male
and female education respectively.
We proceed to investigate the univariate time series
properties of the data series, particularly with respect to
the degree to which they are integrated. The study
employes both augmented Dickey-Fuller (ADF) (1979)
and Phillips-Perron (PP) (1988) unit-roots tests and the
results revealed that our data series are I(1). Proceeding
to a test of co integration using the Johansen
Methodology, it is observed that the hypothesis of
cointegration is rejected and thus no long term
relationship exists. In the absence of cointegration (but
I(1)) data) , a Differenced Vector Autoregressive
(DVAR) model is used to capture the short-run
dynamics and to model and compare the contribution of
male and female education attainment on growth of the
growth rate of the different variables. This is consistent
with the standard procedure in the literature (See Pereira
and de Frutos, 1999 and Pereira and Sagales, 2003).
3.3 The Difference VAR Model
A VAR model in a generalised form is given by
t
p
t
p
t
t
t Z
Z
Z
Z 
 






 

 ....
2
2
1
1 t=1….t
where Zt is a vector of endogenous variables (n
variables),  is a constant, p is the order of the VAR,
 is the matrix of coefficients, and t
 is an error
term.
In this study, the VAR consist of four endogeneous
variables (n = 5), Zt =[ y, k, l, hm,hf ] and a constant
term . So Zt is a 5 x 1 vector and the variables are as
previously defined and are in logarithmic terms.
The general form of the difference VAR is thus,
t
p
t
p
t
t
t Z
Z
Z
Z 



 







 

 ....
2
2
1
1
t=1….t
Where Δ is the first difference operator and ω are the
parameters.
The order of the VAR was chosen by minimising the
final prediction error due to SBC which suggested a
VAR specification 1. A constant was also included. The

5
results of the OLS estimation of the unrestricted VAR
are presented in table 1.
The above equations all pass the Lagrange multiplier
residual serial correlation test. Focusing on the first
column, it is observed that both male and female
education attainment have been positively affecting
output level with respective output elasticity of 0.18 and
0.16. In the first case it would appear that a 10%
increase in the male secondary enrolment ratio might
have led to a 1.8% increase in the country’s GDP
whereas female a 10% increase in female secondary
enrolment ratio is expected to contribute to around
1.6%. Interestingly it is observed that there is no much
difference in the contribution of each gender type
education and indicates that female workers are as
productive as compare to male. The results are
consistent with the findings of Birdsall, Ross and Sabot
(1997) who found similar results for the case of 108
developed and developing countries over the period
1960-1985 using OLS estimations.
The economy’s level of capital stock is reported to have
been the most important ingredient of growth (output
elasticity of 0.71) and the proxy labour has an elasticity
coefficient of 0.13 and is slightly on the lower side of
what was expected. Further analysis from the second
column of the table suggests that both male and female
education (with a slightly higher contribution) helps in
enhancing investment level in the country. Thus this
indicates the presence of some indirect effects of
education on growth as well.
There is evidence of important feedback effect from the
economy’s output level to both female and male
education as witnessed by the positive and significant
coefficient of yt-1 in the last two columns of table 1
(where male and female education are the dependent
variable respectively). This confirms the bi-causal link
between these variables. No reverse causation is
observed for the case of education-private investment
link though. Moreover one can argue that both genders
education mutually drives each other for the betterment
of the country’s investment and output as revealed by
the positive and significant coefficient of female and
male education variables respectively.
3.4 OLS Estimates of the Unrestricted Regression in First Difference
Table 4 : OLS results of the unrestricted regression in difference.
Δy Δk Δl Δhm Δhf
Δyt-1 -0.63**
(-3.27)
0.17*
(1.98)
0.24
(0.66)
0.13*
(1.69)
0.05*
(1.87)
Δkt-1 0.71***
(2.92)
0.65***
(4.5)
0.16*
(1.77)
-0.014
(-1.34)
0.13
(1.37)
Δlt-1 0.13***
(2.91)
0.034
(1.12)
1.48***
(3.84)
0.283
(1.48)
0.328
(1.45)
Δhmt-1 0.18**
(2.16)
0.06*
(1.87)
0.08
(-0.68)
0.88***
(3.52)
0.17*
(1.69)
Δhft-1 0.158*
(1.99)
0.19*
(1.88)
-0.0244
(-0.27)
0.166*
(1.89)
11.22***
(5.64)
Constant -1.36
(-0.99)
0.58
(0.37)
1.48
(1.18)
3.55**
(-2.57)
1.67
(1.23)
R2 0.61 0.73 0.86 0.88 0.87
DW 1.98 1.87 2.4 1.93 1.99
*significant at 10%, ** significant at 5%, ***significant at 1%

6
Impulse response analysis has also been used to
investigate the effect of a one percent point shock in the
rate of growth of the secondary enrolment ratio, both
female and male independently, on the other variables of
the model. The analysis confirms that both types of
education have a positive effect on the country’s level of
output and that this is effect tends to die out after some
25 years. The female/male education-investment link
and reverse causation are also confirmed, thus
consolidating the previous results.
4.0 SUMMARY OF RESULTS
Using a difference Vector Autoregressive model, the
paper investigated the dynamic relationship between
gender separate education and the economic
performance for the case of the small island developing
state of Mauritius for the period 1960-2010. Results
from the analysis suggest that both female and male
education, as proxied by their respective secondary
enrolment ratio, are important ingredient in explaining
growth. Moreover they are reported to have nearly the
same productivity level. Further analysis suggests that
bi-causality exist between female/male education and
economic growth. Indirect effects via capital stock
accumulation, a proxy for investment, are also reported.
The analysis confirms the positive theoretical and
empirical link between education, particularly gender
separated education, and output level and further on
provides new evidences from a small island developing
state (SIDS) using recent a dynamic framework.
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GENDER EQUALITY AND ECONOMIC DEVELOPMENT IN MAURITIUS: A WIN-WIN SITUATION?
8
GENDER EQUALITY AND ECONOMIC DEVELOPMENT
IN MAURITIUS: A WIN-WIN SITUATION?
Verena Tandrayen-Ragoobur and Harshana Kasseeah
Department of Economics and Statistics, University of Mauritius
v.tandrayen@uom.ac.mu; h.kasseeah@uom.ac.mu
ABSTRACT
The paper investigates the causal link between gender
equality and economic development. Time series data
for the Mauritian economy from 1975 to 2011 is used
to analyse the gender equality in education and
economic development nexus. Our results reveal that,
the promotion of gender equality in education offers a
win-win solution for economic development. Short run
causality exists between all education levels (primary,
secondary and tertiary) and GDP per capita while long
run causality prevails only between tertiary education
and economic development. The paper concludes with
a discussion of economic policies that can promote a
win-win outcome with greater gender equality and
economic development.
Keywords: Gender Equality, Economic Development,
Mauritius
1. INTRODUCTION
Gender equality is first and foremost a human right.
Women empowerment has been recognised as an
indispensable tool for advancing development and
reducing poverty. In addition, promoting gender
equality and empowering women accumulation is one
of the eight Millennium Development Goals (MDGs)
set by the United Nations and it is on the public policy
agenda of almost every country of the world. Goal 3
of the MDGs involves the elimination of gender
disparity in primary and secondary enrolment
preferably by 2005 and at all education levels by 2015.
Gender equality is a development goal in its own right
and has instrumental value for the long-term growth
prospects of countries (Klasen, 2002; World Bank,
2001). The success with which developing countries
can integrate more skilled female workers into the
labour force determines in part their level of
competitiveness in the global economy. Hence, linking
gender equality and economic development, the axiom
“a rising tide lifts all boats” holds some truth (World
Bank, 2012). Several decades of rising global income
have contributed to an “unprecedented narrowing of
gender gaps” in the realms of education, health, and
labour opportunities. Economic development is a
necessary component of greater gender equality but
gender equality is just as important to achieve
economic growth. Since, the links between gender
equality and development work out both ways, each
direction of this relationship matters for policy making
(World Bank, 2012).
Though, important improvements towards reaching
this goal have so far been achieved, for instance a
remarkable rise in girl’s school enrolment world-wide
over the last decade, the situation of women remains
largely unsatisfactory. Women still face enormous
obstacles, particularly in developing countries. Fewer
girls attend primary and secondary school than their
male counterparts; occupationally, women are still
largely denied access to the formal labour market, do
not have equal opportunities to qualify for higher
employment and are consequently less likely to occupy
administrative or managerial positions, and lag
significantly behind in terms of career development
and earnings increases (UNDP, 2005). Hence, women
still turn to characteristically “female” (and, often,
lower-paying) sectors such as communications
services and retail; women outnumber men as victims
of domestic violence; and governing bodies around the
world are by-and-large male-dominated.
There is considerable evidence showing that an
appropriate leverage of the talents of women and men,
a better diversification of women and men in
occupations and an enhanced balance in decision
making processes contribute positively to development
(Maseno and Kilonzo, 2011). Closing gender gaps is
therefore not only an issue of equality of rights, but
also an issue of economic concern (ILO, 2010).
Because gender inequality is unlikely to fix itself, there
is a broad consensus that a wide range of gender-based
policies are needed (Duflo, 2012). Studies based on
cross-country differences, while informative, have
proved to be of limited use for policy design because
they often do not identify the causal link from gender
equality to growth (Bandiera and Natraj, 2013).
In Mauritius, the role of women has been changing
rapidly over the years. In 2012, Mauritius reached 74
percent of the objectives of the Protocol of the
Southern African Development Community (SADC).

9
For the Gender Inequality Index, Mauritius received a
score of 0.353, placing the country at 63 out of 146
countries. Though progress has been made, there are
still concerns in governance, women participation in
higher decision making processes and domestic
violence. Though Mauritius is seen as an outlier in the
African region in terms of economic development,
gender inequality is well present in the small economy.
The objective of the paper is to investigate the causal
link between gender equality and economic
development for the small island economy. The
purpose of this study is to highlight the impact of
gender equality in education, at various levels, on
economic development in Mauritius. The paper
further assesses the role of openness, foreign
investment and domestic investment in affecting the
process of economic development in the long run. Our
methodology rests on time series analysis for the
Mauritian economy from 1975 to 2011 where gender
equality is measured by the Gender Parity Index (GPI)
at primary, secondary and tertiary levels.
This paper is organised in five sections. Section 2
reviews the theoretical literature and empirical work
on gender equality and economic development.
Section 3 discusses gender equality in Mauritius and
the country’s global position in terms of three main
gender equality indices. Section 4 discusses the
methodology used while section 5 presents the
findings. We finally conclude in section 6.
2. LITERATURE REVIEW
Gender inequality is the unequal treatment or
perceptions of individuals based on their gender. It
arises from differences in socially constructed gender
roles as well as biologically through chromosomes,
brain structure, and hormonal differences (Wood,
2005). Gender inequalities may reflect discrimination
against women, but they could also be the outcome of
society’s preferences towards gender roles (Cuberes
and Teignier, 2011). In the latter case the resulting
allocation of resources may not be inefficient from a
welfare point of view. In the former case, reductions in
gender inequality may potentially increase welfare and
efficiency. It is thus important to explain the origins or
effects of gender inequality. Another conceptual
important point that is often missed in the literature is
the distinction between inequality of opportunities vs.
inequality of outcomes. Gender inequality in wages
refers to the latter, while gender gaps in schooling,
labor force participation, occupational choice, political
and / or economic rights are examples of inequality of
opportunities (Cuberes and Teignier, 2011).
There are a wide range of theories on development that
have tried to advocate for equal participation of
women. Thus, the concept of engendering
development implies engaging men and women
equally in the production process, geared towards
enhancing equality in development. There are three
main theories developed to enhance participation of
women in the development process namely Women in
Development (WID), Women and Development
(WAD), and finally Gender and Development (GAD).
Each of these approaches is based on different
understandings of and assumptions about the
development process, the role of women and men in
this process, and thus the design of relevant policies in
relation to these linkages.
From the conventional economic theories, economic
growth development is propelled largely by physical
capital accumulation (Todaro and Smith, 2006).
Development brings the benefits of industrialisation:
higher living standards, wages and education levels,
among others (Rathgeber, 1989). With the emphasis
on capital accumulation in the context of aggregate
models of growth, there is little to no consideration to
women as a distinctive group. Development theory
was an almost exclusively male enterprise (Elson,
1991), until Boserup (1970) argues that women have
been marginalised and existing practices of
development treat women differently from men
(Benería, 2001a).
Thus, the “Women into Development” (WID)
processes, promote policies that draw women into
modernisation, for instance by increasing female
labour force participation in industrialising sectors via
targeted education and training (Benería, 2001a). The
WID approach rationalises equality between women
and men on efficiency grounds, where women are an
untapped resource whose inclusion in the modern
economy will benefit growth and development (Moser,
1993). However, the WID focuses only on the
integration of women into ongoing development
initiatives and ignores the sources and nature of
women's subordination and oppression. In addition, the
extent to which women are already overburdened with
tasks and responsibilities is disregarded. It also fails to
account for women's reproductive roles.
Thus, women need development is replaced by
development needs women. Social justice and equity
arguments are complemented with arguments of
economic efficiency. The marginalisation and isolation
of women from the mainstream of the development
process is seen as economically inefficient and
hampering economic growth. “Women and
Development” (WAD) thus focuses more on the fact
that women are not marginalised from development,

10
but rather the unequal terms under which they are
incorporated in the development process (Elson,
1991). Like WID, however, the WAD concentrates on
the productive sector, at the expense of the
reproductive side of women's work and lives. It fails to
undertake a full-scale analysis of the relationship
between patriarchy, differing modes of production and
women's subordination and oppression.
Based on these shortcomings, the development process
itself needs engendering and a more dynamic “Gender
and Development” (GAD) approach was adopted,
where the emphasis is widened to include women’s
rights, women’s role as active participants and agents
in development, and their role as actors with a specific
agenda for development. They shift from
understanding women’s problems as based on their sex
to understanding them as based on gender (Moser,
1993). GAD links the social relations of production
with the social relations of reproduction that is
exploring why and how women and men are assigned
to different roles and responsibilities in society and
how these dynamics are reflected in social, economic,
and political theories and institutions, and thus these
relationships affect development policy effectiveness.
Women are cast not as passive recipients of
development aid, but rather active agents of change
whose empowerment is a central goal of development
policy (Moser, 1993; Rathgeber, 1989).
The literature investigating the growth-inequality link
is very broad (Deininger and Squire, 1996; Chen and
Ravaillon 1997; Easterly, 1999 and Dollar and Kraay,
2002). The theoretical literature on the relationship
between growth and inequality originates from the
Kuznets hypothesis (Kuznets, 1955), which suggests
that the distribution of income will deteriorate over the
initial stages of development as an economy
transforms over the initial stages of development. This
reasoning can be applied to gender inequality and
development, because as a nation develops, thriving
industries may either be male or female dominated, for
example heavy industry and textiles, respectively. As
these gender-dominated industries grow, wages rise
and leading to a rise in inequality. With further
development, there will be increased micro-diversity
and less gender segregation in the workplace which
will then lead to declining inequality.
Further, Sen (1980) identifies four types of
entitlements in a market-based economy: production-
based entitlements; own-labour entitlements; trade-
based entitlement; and inheritance and transfer
entitlements. He argues that freedom is the principle
means and end of development, and the focus should
be shifted from those with low-income to those lacking
development of human capabilities (Streeten, 2000).
With high levels of gender inequality, women’s
freedoms and capabilities are hindered. Tisdell et al.
(2003) apply Sen’s entitlement theory to women, and
show that women’s status depends on these
entitlements, endowments and bargaining power.
Gender inequality, is thus explained by a lack of these
entitlements (Tisdell et al., 2003). Sen’s capability
approach (1990) has been used in various analyses of
contemporary development challenges. It offers a
capability-based perspective to equity in development
(Fukuda-Parr, 2003).
The macroeconomics literature on gender inequality
and development has grown rapidly since the early
1990’s. Klasen (1999) analyses the direct and indirect
mechanisms through which greater gender inequality
in the use of human resource in an economy impact
growth. The first pathway works directly through
labour markets; it relates to the productivity of labour
and the extent to which economies are making optimal
use of their human resources. If ability and talents are
assumed to be evenly distributed by gender, then the
failure to educate and make use of women’s abilities
and talents as those of men represents a major market
distortion. This is called an artificial restriction on the
pool of talent available in the country hence, lowering
the average productivity of its human capital (the
‘selection-distortion effect’).
Galor and Weil (1996) present a model that studies the
causes and effects of gender gaps on wages and labour
force participation. In their model, economic growth
generates a positive feedback loop by reducing
fertility, which leads to a demographic transition and
faster output growth. Men are endowed with more
physical strength than women but both sexes have the
same endowment of mental input. The latter is
assumed to be more complementary to physical capital
than physical strength. As a result, the increase in
capital intensity that accompanies economic growth
raises the relative wage of women, given that women
have a comparative advantage in the mental labour
input. Assuming that the income effect associated with
this higher wage is lower than the substitution effect,
as in Becker (1981), this rise in women's relative wage
lowers the fertility rate since it induces women to
switch from childrearing and to participate in the
labour market. Finally, higher wages and lower
population growth lead to higher levels of capital per
worker, and hence faster output growth. Therefore, this
model offers a rationale for why gender inequality may
have a negative impact on economic growth.
Lagerlof (2003) argues that gender equality in
education has a positive impact on economic growth
because of its effects on fertility and on the human
capital of children. He proposes a model in which
families play a coordination game against each other
when deciding the human capital level of their

11
offspring. Despite the fact that the two sexes are
modeled as being symmetric in terms of talent, gender
discrimination arises as a Nash equilibrium. If
everybody expects families to behave in a
discriminatory manner by educating their sons more
than their daughters, it is optimal for a family to do so,
since daughters will then marry more educated men
who will earn more. As economies re-coordinate
towards a more “gender-equal” equilibrium, women’s
human capital increases and their time becomes more
expensive, which then leads families to substitute
quality for quantity in children. This eventually leads
to a higher stock of human capital and hence faster
economic growth.
A second pathway sets out by Klasen (1999) relates to
family relations. It examines the positive externalities
generated by greater gender equality on household
decisions relating to human capital determinants of
growth. Women’s access to education and economic
opportunities is more likely to lead to greater
investments in the human capital of their children.
Gender equality allows the mother to have a better
participation in household decision making. This leads
to an improvement in the child’s well being,
educational attainment and health. Thus, an
improvement in decision making leads to an
improvement in the productivity of the next generation
of workers, an intergenerational transmission of
earnings capability which will create better future
growth prospects.
Higher levels of female education and labour force
participation have also been found to be a major factor
in bringing about fertility decline, which in turn
reduces the dependency burden in the economy and
increases the supply of savings. Hence, increases in
female earnings can stimulate short term growth via
higher consumption and promote long term growth
through higher savings. Many of these effects operate
through the increased bargaining power associated
with women’s education and employment and the
associated increase in their ability to exercise control
over their own fertility as well as influence
investments in their children. Figure 1 below presents
a framework on the various channels through which
gender equality can impact current and future
economic growth. For instance, gender inequality in
households, markets and society will stimulate current
and long-term economic growth.
FIGURE1: A FRAMEWORK FOR
UNDERSTANDING THE LINKS BETWEEN
GENDER EQUALITY AND GROWTH
Source: Morrison, Dhushyanth and Sinha (2007)
Therefore both direct and indirect effect of gender
inequality can be expected to have significant impact
on growth. Gender inequality deserves to be included
in an empirical growth model.
Becker and Lewis (1973) and Becker (1981) argue that
the income elasticity of the number of children that
families choose to have is greater than the income
elasticity of the education level received by each child.
Therefore, there must be an income threshold above
which fertility in a given country declines and this
generates an increase in investment in each child.
According to this analysis, rising income is the main
trigger of the demographic transition. The main
implication of this model is that the associated lower
fertility facilitates the incorporation of women into the
labour market, hence reducing the gender gap in labour
force participation.
Greenwood et al. (2005) argue that technological
progress leads to the introduction of labour-saving
consumer durables such as washing machines, vacuum
Women have
better access to
markets
Increased women’s labour
force participation,
productivity and earnings.
Improved
children’s well-
being
Women have better
education and health
Mother’s
greater
control over
decision
making in
household
Current poverty
reduction and
economic growth
Future poverty
reduction and
economic
Income /
consumption
expenditure
Differential
savings Better health and
educational
attainment and
greater productivity
Increased gender equality in households, markets and
society (Equality in rights, resources and voice)

12
cleaners or refrigerators, which make it possible for
women to start working in the market rather than being
confined to home production. In their model,
households derive positive utility from the
consumption of non-market goods, which are produced
using household capital and labour. Technological
change in capital goods reduces the relative price of
these household durables and encourages their
adoption. The use of more productive appliances then
frees up time formerly devoted to housework and
allows women to increase their participation in the
labour market.
Furthermore, it can be said that under-investment in
girls means that society is not allocating its resources
efficiently. It is possible that inefficiencies that have
been created by this gender inequality are sizeable
enough to decrease productivity to a low level that will
harm growth rates.
Although there are several measures of gender
inequality, the most commonly used measure of
gender inequality is regarding access to schooling/
education. It has been found that gender inequalities
in education tend to be greatest in poor countries and
worst among the poor (World Bank, 2001). Another
measure of gender inequality is wage inequality
(Seguino, 2000). Gender differential wage rates can to
a large extent be explained by the fact that women tend
to be crowded into lower paying jobs.
The Gender inequality index considers five indicators
grouped in three dimensions, consisting of: maternal
mortality, adolescent fertility, parliamentary
representation, educational attainment and labour force
participation. Hence, there are different dimensions to
gender equality such as access and achievement in
education, improvement in health, indexes of legal and
economic equality of women in society and marriage
and measures of women’s empowerment (Dollar and
Gatti, 1999).
Dollar and Gatti (1999) find that one of the strongest
empirical regularities is that measures of gender
equality are positively related to per capita incomes.
Inequality seems to be perpetuated by underlying
structural, institutional, political, social and economic
factors. Barro and Lee (1994) kicked off a heated
debate when they identified a positive relationship
between gender inequality and economic growth.
Bandiera and Natraj (2013) find that the balance of
evidence suggests that gender disparities in
educational attainment and economic outcomes are
negatively related to development and growth. This
finding is of limited use to policy unless it can be
proved that reduced inequality is likely to be
endogenous to growth because it directly affects
gender disparities.
Most studies of the 1990s estimate the relationship
between gender inequality in education and growth
rather than the level of income. A number of
researchers have attempted to investigate the
relationship between gender inequality in schooling
and economic growth (Hill and King, 1993, Knowles
et al, 2002). Easterly (1999) shows that a negative
cross-country correlation exists between income and
gender inequality in education but there is no
correlation within countries. Several researchers have
examined the impact of inequality in education and
growth and development. These include studies by
Hill and King (1995), Klassen (1999) and Knowles et
al (2002).
Hill and King (1993, 1995) explicitly introduce a
gender gap into a cross-section output regression.
Income per capita is regressed on the gender gap,
female secondary school enrolment and a set of control
variables. The results obtained suggest that the level
of female education has a significant positive effect on
the level of GNP and that larger gender disparities are
associated with lower levels of GNP.
Klasen (1999) finds that there is considerable impact
of gender inequality on economic growth; in
particular, gender inequality in education lowers the
average quality of human capital which then has an
impact on investment and population growth. It is
pointed out that gender inequality in education also
prevents progress in reducing fertility and child
mortality rates which compromises progress and well-
being in developing countries. Knowles et al (2002)
estimate a neoclassical growth model that includes
female and male education as separate explanatory
variables. The paper stresses on the fact that educating
girls has a catalytic effect on every dimension of
economic development, including higher productivity
and faster economic growth. In many developing
countries of Africa and South Asia, males enjoy
considerably higher levels of schooling than females.
Empirical results suggest that female education has a
statistically significant positive effect on labour
productivity. Similarly, Psacharopoulos (1994) finds
that the rate of return to female education is positive
and marginally higher than that of male education.
Others, for instance, Lagerlӧf (1999) examine the
impact of education on fertility and economic growth.
It is found that initial gender inequality in education
can lead to a self-perpetuating equilibrium of
continued gender inequality in education with the
consequences of high fertility and low economic
growth.
Some researchers have reported both a direct and an
indirect effect regarding gender inequality and growth
(Baliamoune-Lutz, 2007). The direct effect is that

13
lower female education lowers the average level of
human capital and thus has a negative impact on
growth. The indirect effect is that gender inequality an
effect on population growth and investment and thus
produces an indirect impact on growth. In many
developing countries female education produces social
gains by reducing fertility and infant mortality and
improving family and child health, increasing life
expectancy and increasing the quantity and quality of
children’s educational attainment. Baliamoune-Lutz
(2007) further suggests that if lower wage is a result of
lower educational levels, then the relationship between
gender inequality and education and growth may turn
out to be positive. Using data on Sub-Saharan African
and Arab countries, Baliamoune-Lutz (2007) find that
lower female education leads to lower growth. The
two measures of inequality used are the ratio of girls to
boys in primary and secondary enrolment and the ratio
of young (15-24 year old) female to male literacy rates
as these 2 indicators are specifically associated with
MDG3.
Pervaiz et al. (2011) analyse the impact of gender
inequality on economic growth of Pakistan. An annual
time series data for the period of 1972-2009 was used
and their results show that labour force growth,
investment and trade openness have statistically
significant and positive impact whereas gender
inequality has a significant and negative effect on
economic growth of Pakistan. Further, Agénor and
Canuto (2013) analyse the long-run impact of policies
aimed at fostering gender equality on economic growth
in Brazil. They used a gender-based, three-period
overlapping generation model that accounts for
women’s time allocation between market work, child
rearing, human capital accumulation, and home
production. Their findings show that fostering gender
equality, in terms of women’s time allocation and
bargaining power may have a substantial positive
impact on long-run growth in Brazil.
However, both the theoretical and empirical literatures
are divided regarding the relationship between growth
and inequality. Some studies find that inequality leads
to faster growth (Forbes 2000) while other studies find
that inequality leads to lower growth (Alesina and
Rodrick 1994, Alesina and Perotti 1996). Similarly,
some studies are also divided regarding the direction
of causality, that is, if causality runs from inequality to
growth and vice versa. But, most studies have
investigated the relationship between gender inequality
in education and growth rather than the level of
income. It is important to understand the values, social
norms and other mechanisms involved in the
relationship between gender equality and development.
3. GENDER EQUALITY IN MAURITIUS
3.1 Gender Policies in Mauritius
Gender equality is both a core concern and an essential
part of human development. Women are often
discriminated against in health, education and the
labour market, which restricts their freedoms. In the
case of Mauritius, the government has been focusing
on rebalancing growth, boosting productivity,
consolidating social development and social justice
and promoting environmental protection. Real gross
domestic product (GDP) grew by 4 per cent in 2011,
up from 3.1 per cent in 2009 but lower than the 5.5 per
cent in 2008. Despite challenges at home and abroad,
the government has maintained a steady growth path.
The standard of living in Mauritius is currently among
the highest in the African region with a present real
Gross National Income per capita of around USD
13,400. The Gini Coefficient stood presently at 0.39
and the poverty rate is a low 8 percent. Our main
social indicators have been performing well over the
last decade, with increased life expectancy and a fall in
infant mortality.
Moreover, Mauritius leads Sub-Saharan Africa in
economic freedom and is ranked 12th
worldwide,
according to the 2010 Index of Economic Freedom
(Heritage Foundation, 2011). For the third consecutive
year, the World Bank's 2011 Doing Business report
ranks Mauritius first among African economies (20th
worldwide, out of the 183 economies) in terms of
overall ease of doing business.
As far as gender equality and women’s empowerment
are concerned, Mauritius undertook a comprehensive
review of laws and adopted important legislative
measures to protect and promote women’s rights,
namely the approval of the Equal Opportunities Act in
2009. Mauritius has ratified several important
international human rights agreements (Tandrayen-
Ragoobur and Gokulsing, 2013). It acceded to the
Convention on the Elimination of all forms of
Discrimination against Women (CEDAW) in July
1984 and ratified the Convention in 1985. The
Optional Protocol to the CEDAW, the Optional
Protocol on the CRC on the Involvement of Children
in Armed Conflicts and the Optional Protocol on the
CRC on the sale of children, child prostitution and
child pornography has been signed in November 2001.
It has also signed the SADC Declaration on Gender
and Development in 1997 and in September 1998, it
signed the Addendum to the Declaration on the
Prevention and Eradication of Violence against
Women and Children. Mauritius is a party to the
Beijing Platform of Action and has also identified
gender-based violence as one of the critical priority
areas at the Fourth World Conference on Women in

14
Beijing. Mauritius has also signed the Protocol to the
African Charter on Human and People’s Rights on the
Rights of Women in Africa.
3.2 Gender Equality Indicators and Mauritius
Global Gender Gap Index
The Global Gender Gap Index which is a framework
used to capture the magnitude and scope of gender
based disparities, examines the gap in four
fundamental categories namely economic participation
and opportunity; educational attainment; health and
survival and lastly political empowerment. The score
of the overall gender gap index for Mauritius has
increased slightly over the years from 0.63 in 2006 to
0.65 in 2012, implying an improvement towards
equality in education, economic participation, health
and political attainment (Tandrayen-Ragoobur and
Gokulsing, 2013). However the scores for economic
participation and political attainment remain very low,
being respectively 0.55 and 0.095. In addition,
Mauritius slips down three spots relative to its
performance last year partially due to a decrease in
women in ministerial positions (Global Gender Gap
Report, 2012). This is shown in Table 1 below.
TABLE 1: GENDER GAP INDEX FOR
MAURITIUS, 2006-2011
Gender
Gap
Index
Overall Economic
Participation
Educationa
l
Attainment
Health &
Survival
Political
Attainment
R S R S R S R S R S
2006 88 0.6
3
95 0.4
8
65 0.9
8
1 0.9
8
73 0.09
2007 85 0.6
5
100 0.5
5
75 0.9
8
1 0.9
8
82 0.09
2008 95 0.6
5
103 0.5
3
77 0.9
9
1 0.9
8
90 0.09
2009 96 0.6
5
109 0.5
5
80 0.9
9
1 0.9
8
92 0.09
2010 95 0.6
5
103 0.5
5
76 0.9
9
1 0.9
8
91 0.09
2011 95 0.6
5
105 0.5
5
74 0.9
9
1 0.9
8
86 0.09
where R is Rank and S is Score
Source: Global Gender Gap Report, 2011
Gender Inequality Index (GII)
The extent of discrimination can also be measured
through the Gender Inequality Index (GII), which
captures the loss of achievement due to gender
inequality in three dimensions: reproductive health,
empowerment and labour market participation. The
higher the GII value; the greater the discrimination.
Based on 2012 data for 148 countries, the GII shows
large variations across countries, ranging from 0.05 (in
Netherlands) to 0.75 (in Yemen), with an average of
0.46. High gender disparities persist in South Asia
(0.57), Sub-Saharan Africa (0.58) and the Arab States
(0.56). Though many countries in Sub-Saharan Africa
show improvement in their GII value between 2000
and 2012, they still perform worse than countries in
other regions, mainly because of higher maternal
mortality ratios and adolescent fertility rates and huge
gaps in educational attainment. One of the most
disturbing trends concerns the sex ratio at birth, which
is deteriorating in some fast-growing countries. The
natural ratio for children ages 0–4 is 1.05 (or 105 boys
to 100 girls). In some countries, sex-selective abortion
and infanticide are artificially altering the demographic
landscape, leading to a shortage of girls and women
(Human Development Report, 2013).
According to the latest figure published in the 2013
UN Human Development Report, Mauritius ranked
70th
out of 146 countries with a GII value of 0.38.
Netherlands ranked first with a value of 0.05 and
Yemen last with a value of 0.75. This is shown in
Table 2 below.
TABLE 2: GENDER INEQUALITY INDEX (GII),
2012
Country Rank Value
Netherlands 1 0.045
Sweden 2 0.055
France 9 0.083
Singapore 13 0.101
United Kingdom 34 0.205
China 35 0.213
Mauritius 70 0.377
Rwanda 76 0.414
South Africa 90 0.462
Ghana 121 0.565
Kenya 130 0.608
India 132 0.610
Source: Human Development Report 2013
Gender Parity Index (GPI)
The Gender Parity Index (GPI) is another measure and
it is essentially a socioeconomic index assessing the
relative access to education of males and females. It is
calculated as the quotient of the number of females by
the number of males enrolled in a given stage of
education (primary, secondary, etc.). It is the ratio of
girls to in primary, secondary and tertiary education
that is the ratio of the number of female students
enrolled at primary, secondary and tertiary levels of
education to the number of male students in each level.
A GPI of 1 indicates parity between the sexes; a GPI
that varies between 0 and 1 typically means a disparity
in favour of males; whereas a GPI greater than 1
indicates a disparity in favour of females. It is used to

15
capture the efforts of countries to eliminate gender
disparities in primary and secondary education and
emphasises the plight of girls in unequal access in third
world countries.
In Mauritius, there exists no gender disparity in
education. There were around 97 girls for every 100
boys at primary level during the period 2000 to 2010,
seemingly in favour of boys. However, given that
there are fewer girls than boys of primary school age
(sex ratio is around 97), there is no gender disparity at
primary level. At secondary level, the ratio is in
favour of girls in spite of more boys of secondary
school age being more numerous than girls in the
population (see Table 3 below).
TABLE 3 – RATIO OF GIRLS TO BOYS BY
EDUCATION LEVEL 1
, REPUBLIC OF
MAURITIUS 2000 - 2010
Education
Level
2000 2005 2010
Primary 96.8 97.0 96.8
Secondary 101.3 103.2 105.4
Source: Ministry of Education and Human Resources,
2011
The Gender Parity Index in both primary and
secondary level enrolment has been increasing over the
years for the small island economy. For primary
enrolment, the figure has been rather stable at 1
showing parity between the sexes. The GPI even
exceeded 1 between 1990 and 1998; with a higher ratio
of girls than boys attending secondary schools. With
respect to the GPI for tertiary education, the index has
been on the rise for the last 3 decades, with a low ratio
of 0.6 in 1990 to 1.3 in 2011 (see Figures 2 and 3
below).
1
Number of girls per 100 boys
FIGURE 2: GENDER PARITY INDEX IN
PRIMARY AND SECONDARY LEVEL
ENROLMENT IN MAURITIUS FROM 1975 -2011
Source: World Development Indicators, 2012
FIGURE 3: GENDER PARITY INDEX IN
TERTIARY LEVEL ENROLMENT IN
MAURITIUS FROM 1975 -2011
Source: World Development Indicators, 2012
.8
5
.9
.9
5
1
1
.0
5
1970 1980 1990 2000 2010
Year
GPIprim GPIsec
0
.5
1
1
.5
G
P
IT
ertia
ry
1970 1980 1990 2000 2010
Year

16
4. METHODOLOGY
Solow’s (1957) neo classical model provides the
essential foundations for growth estimation; however,
it ignored the role of human capital in the
determination of economic growth. Mankiw et al.
(1992) thus incorporate human capital in growth
models. Similarly, various models have been used to
incorporate the impact of gender biasedness in
economic growth (Kalsen, 1999). His model is based
on simultaneous equations; and this model can only be
applied for time series estimation, if all the variables
are stationary at level. To overcome such issues
Seguino (2000) developed a single equation model that
establishes the foundation for our empirical model.
Drawing from above, we use the following
specification for estimating the effects of gender
equality on economic development of the Mauritian
economy from 1975-2011:
t
t
1
t
1
t
1
t
3
t
2
t
1
0
t
y
GPItertiar
ln
ondary
sec
GPI
ln
GPIprimary
ln
gdfcfgdp
ln
fdigdp
ln
gdp
exp
ln
gdppc
ln
















(1)
where lngdppc is log of per capita Gross Domestic
Product which is the measure of economic
development. In addition, lnexpgdp is log of exports
as a share of GDP which measures the level of trade
openness of the economy and lngdfcfgdp is log of
Gross Domestic Fixed Capital Formation as a share of
GDP which captures domestic investment in the
country. The other control variable is lnfdigdp
denoting log of foreign direct investment as a share of
GDP to examine the impact of foreign investment on
economic development. t is the error term. Exports,
FDI and GDFCF are all growth determinants and are
expected to be positively related with GDP per capita.
Gender equality is depicted by the Gender Parity Index
(GPI) at primary, secondary and tertiary levels. GPI in
equation (1) is the ratio of the number of females by
the number of males enrolled in a given stage of
education where GPIprimary is at the primary level,
GPIsecondary is at secondary level and lastly
GPItertiary is the ratio of female to males enrolled in
tertiary education. Gender equality in education is
simply that boys and girls experience the same
advantages or disadvantages in attending school,
receiving teaching methods, curricula, and academic
orientation, and producing equal learning
achievements and subsequent life opportunities
(UNESCO, 2003).
Several studies have documented the positive
externalities of educating women and the important
inter-generational effects of female education.
Educated women contribute to the welfare of the next
generation by reducing infant mortality, lowering
fertility, and improving the nutritional status of
children (Klasen 1994; Smith and Haddad, 1999;
World Bank 2007a, 2007b; Lagerlöf, 2003).
Applying regression on time series data may generate
spurious results (Granger and Newbold, 1974; Philips,
1986) due to the possibility of non-stationarity data.
Hence checking the stationarity of data is a
prerequisite for applying co-integration test. As a
result, the Augmented Dickey-Fuller (ADF) test
(Dickey and Fuller, 1979, 1981) and the Phillips-
Perron test (Phillips and Perron, 1988) were applied.
Once the variables are stationary of the same order, the
second step is to check for co-integration or long run
co-integrating relationship among the variables. The
Johansen Co-integration Test (Johansen 1988;
Johansen and Juselius, 1990), which uses maximum
likelihood testing process is applied, to know the
number of co-integration vectors in the Vector Auto-
Regressive (VAR) setting.
In fact, the static single equation framework often fails
to take into account the presence of dynamic feedback
among relevant variables. Accordingly, we opted to
use a VAR approach to study the relationship between
gender equality and economic development. Such an
approach does not impose an a priori restriction on the
dynamic relations among the different variables. It
resembles simultaneous-equation modeling in that
several endogenous variables are considered together.
The common form of VAR is as given below:
t
k
t
k
1
t
t
t Z
.........
Z
Z 


 



 

(2)
where Zt is an (nx1) vector of k variables having
integrated order of 1 that is I(1),  is a (nx1) vector of
intercepts, t,..... t-k, are parameters and t is a
normally distributed residual term. The common VAR
based model in equation (2) may also take the form of
the Vector Error Correction Model (VECM) as
follows:
t
1
t
i
t
1
k
1
i
i
t Z
Z
Z 



 



 




(3)

17
where Zt is an (nx1) vector of k variables,  is a (nx1)
vector of intercepts, t is is an (nx1) vector of residuals.
Further,  is the difference operator and and  are
coefficient matrices.  is also known as the impact
matrix as it explains the long term equilibrium
relationship of the variables, while  explains the short
run effect. The VECM linking short term and long
term causality between gender equality and economic
development is set as follows:
t
1
t
n
1
j
j
t
6
n
1
j
j
t
5
n
1
j
j
t
4
n
1
j
j
t
3
n
1
j
j
t
2
n
1
j
j
t
1
0
t
ECT
y
GPItertiar
ln
ondary
sec
GPI
ln
GPIprimary
ln
gdfcfgdp
ln
fdigdp
ln
gdp
exp
ln
gdppc
ln












































(4)
The coefficient of the error correction term (ECTt-1)
indicates that there exists a short run relationship
among the time series variables. The sign and value of
that coefficient provides information about the speed
of convergence or divergence of the variables from
their long-run co-integrating equilibrium. A positive
value denotes divergence while a negative value
represents convergence from the long run equilibrium
point. According to Banerjee et al. (1998), a highly
significant error correction term provides evidence of
the existence of a long-run stable equilibrium
relationship. A negative and significant coefficient of
ECTt-1 is favourable for the stability of a long-run
equilibrium.
5. FINDINGS
It is clear from Table 4 below that the null hypothesis
of no unit roots for all the time series are rejected at
their first differences since the ADF test statistic
values are less than the critical values at 1 percent
levels of significance. These results indicate that all
variables are non-stationary at level and become
stationary at first difference. Thus, the variables are
stationary and integrated of same order that is I (1).
TABLE 4: AUGMENTED DICKEY-FULLER
(ADF) TEST FOR UNIT ROOT
At Level At First
Difference
Variables T-
statistics
P-
values
T-
statistics
P-
values
lnGDPPCt 0.332 0.9788 -5.150 0.000
lnexpgdpt -1.801 0.3799 -5.869 0.000
lnfdigdpt -1.951 0.3083 -6.428 0.000
lngdfcfgdpt -2.281 0.1781 -7.089 0.000
lnGPIprimaryt -1.344 0.6090 -5.909 0.000
lnGPIsecondaryt -2.455 0.1267 -4.659 0.000
lnGDItertiaryt -2.721 0.1705 -7.299 0.000
Source: Authors’ Computation
The next step is the determination of the number of
lags. The selection of optimal lag length is required
before applying the Johansen Co-integration Test.
Thus, Schwarz Information Criterion (SIC) suggests
that optimal lag length 3 needs to be selected for
further VAR based analysis. The Johansen Co-
integration Test is then applied to find out the long run
relationship among the variables.
Table 5 below presents the results of Johansen’s Co-
integration Test. Trace test statistic is used to confirm
the number of co-integrating vectors. The null
hypothesis stating that there is no co-integration is
tested against the alternative hypothesis of co-
integration by using Trace test. The trace statistic
either rejects the null hypothesis of no co-integration
among the variables or does not reject the null
hypothesis that there is a co-integration relation
between the variables.
TABLE 5: UNRESTRICTED CO-INTEGRATION
RANK TEST (TRACE)
Maximum Rank Trace Statistics 5 Per Cent
Critical Value
0 232.52 124.24
1 129.19 94.15
2 78.99 68.52
3 41.79* 47.21
4 24.39 29.68
5 8.76 15.41
6 0.29 3.76
In our test, H0: r = 3 is not rejected at the 5 percent
level (41.79 < 47.21). The final number of cointegrated
vectors with three lags is equal to three, which is rank
(.) = 3. We can thus estimate the VECM model as
follows.

18
TABLE 6: SHORT-RUN ESTIMATES
Coefficient
s
T-
Statistics P Value
LD.lnrealgdppc -0.135 -0.590 0.553
L2D.lnrealgdppc -0.153 -0.580 0.565
LD.lnGPIprimary -0.366 -0.230 0.816
L2D.lnGPIprimary 4.191 1.740 0.082*
LD.lnGPIsecondary 0.861 1.840 0.066**
L2D.lnGPIsecondary 0.332 0.800 0.421
LD.lnGPITertiary 0.184 3.140 0.002***
L2D.lnGPITertiary 0.128 2.600 0.009***
LD.lnexpgdp 0.007 0.090 0.931
L2D.lnexpgdp -0.077 -0.570 0.571
LD.lngdfcfgdp 0.308 2.030 0.042**
L2D.lngdfcfgdp 0.151 1.380 0.168
LD.lnfdigdp 0.001 0.080 0.940
L2D.lnfdigdp -0.010 -0.780 0.436
ECT(-1) -0.036 -3.330 0.001***
ECT(-2) -0.857 -0.680 0.495
ECT(-3) -1.040 -1.850 0.064**
Constant 0.042 3.360 0.001***
R-Squared: 0.89
Number of Observations: 32
Table 6 above, presents the short run causality between
the control variables and the dependent variable log of
GDP per capita. It can be observed that GPI at all
levels of education impact positively on GDP per
capita. We then check whether GPI at primary level
education cause economic development and the chi-
square test rejects the linear hypothesis that GPI at
primary level is zero. Hence, we can conclude that
short run causality runs from GPI at primary education
to GDP per capita. The same exercise is carried out
for GPI at secondary and tertiary education levels and
we further confirm short run causality from gender
parity in education at higher levels to economic
development.
From our results, though we observe short run
causality from domestic investment to economic
development, the same does not hold for both exports
and foreign investment to GDP per capita. This can be
explained essentially by the fact that openness is a long
term strategy where the impacts are observed after
some years. Foreign direct investment also brings
benefits to the host country in the long run. In addition
ECT (-1) is negative and significant confirming the
existence of a long run stable relationship and
causality running from the variables in the equation to
GDP per capita.
The long run coefficients of our analysis are reported
in equation (5) below.
lngdppc = 5.772*
+ 0.0711lnGDPprimary+
0.056lnGPIsecondary + 0.247*
lnGPItertiary +
0.541**
lnexpgdp + 0.101**lngdfcfgdp +
0.456**
lnfdigdp
** indicates the significance at 0.10 and **
significance at 0.05 level.
There is thus evidence that gross fixed capital
formation as a proxy for domestic investment has a
positive and significant impact on economic
development. This is in accordance with theory that
investment enhances economic growth; as it is
supported by numerous studies namely Mankiw et al.
(1992) and Lucas (1988). consistent with
expectations, openness measured by exports as a share
of GDP is significant with positive sign. It supports the
findings of Naqvi (2010), Coe and Helpman (1995)
and Lucas (1988). The reason is that greater openness
of an economy to the outside world represents
improved competitiveness and productivity of the
economy that leads towards better economic
performance (Akram et al., 2011).
As for gender equality, our findings support the
conventional wisdom that gender equality has positive
impact on economic development. Gender Parity
Index at primary, secondary and tertiary levels has a
positive relationship with GDP per capita. It can be
inferred that the gender inequality undermines
economic growth of a country. The results further
assert that although gender equality at the primary and
secondary level has a positive relationship with
economic development in the long run, its impact is
not statistically significant.
In Mauritius, schooling is free and compulsory till the
age of 16; hence both girls and boys attend primary
and secondary schools. The reason behind the
insignificance of GPI at primary and secondary levels
in the long run may be that primary and secondary
education have limited role in contributing to
economic development. Similar results have been
noted by Akram et al. (2011) for Pakistan. Primary
and secondary schooling does not increase the skills or
productivity of individuals and often is not a passport
for a job in the Mauritian labour market. Mauritius has
reached a stage of development where there is a
significant need for skilled workers.
Gender equality at primary and secondary education,
however, lays the foundation for equality at higher
levels, so efforts to curtail gender inequality at
schooling are extremely important. In addition, this is
not an argument against gender equality at primary and

19
secondary level, which has an intrinsic value
regardless of its economic consequences (Akram et al.,
2011). In fact, in Mauritius many unemployed people
have only primary and secondary education and there
is increasingly a greater need for technical and
professional education.
CONCLUSION
Access to education is crucial in order to enable the
youth to acquire the knowledge, skills and
competencies that are required for employment, social
inclusion and active citizenship. In this respect, the
Mauritian Government has had a long-term vision by
providing free education since 1976. Education in
public institutions is free at pre-primary, primary and
secondary levels. At the tertiary level, full-time
undergraduate programmes are free at the University
of Mauritius.
Students at all levels also benefit from free transport.
Furthermore, as way of encouraging continuous
education and delaying exit from the formal education
system, schooling is compulsory till the age of 16.
Public expenditure on education is thus the second
highest item of Government’s recurrent budget after
social security. It is estimated at almost Rs10 billion,
that is more than 16 percent of the national budget, for
the year 2012.
The paper reveals that short run causality runs from
gender equality at primary, secondary and tertiary
education levels to economic development in
Mauritius. For long run causality, the coefficient is
significant only for GPI in tertiary education. So, we
can infer that if the gender differentials in education at
all levels are reduced then the small island economy
can achieve a higher standard of living. Reduction in
gender inequality is the only right and speedy way to
attain sustainable economic development.
It is noteworthy that education reforms are currently
on-going. This is necessary to render the education
system more responsive to the changing needs of the
country and keep pace with its economic and social
development. Emphasis is also given to vocational
education and training. Vocational programmes and
apprenticeships, offered mainly by the Mauritius
Institute of Training and Development (MITD), are
meant to train young people in a variety of trades and
impart relevant practical skills that can readily be
applied in the labour market. In addition, “second
chance” programmes enable students who have not
successfully completed primary education to join the
pre-vocational stream for three years giving them the
opportunity to later enroll on vocational or academic
courses.
Much effort is directed towards the promotion of
tertiary education and there are currently over 60
tertiary education institutions in Mauritius.
Government has moreover set an ambitious target for
increasing access to tertiary education and enhancing
the tertiary enrolment ratio from 45.1 percent in 2010
to 75 percent by 2015.
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