Underscoring the Relationship Between Education for Women and National Development in Nigeria

Abstract

This study assessed the impact of education for women at all levels (primary, secondary, and tertiary) on economic development and child welfare in Nigeria. The choice of these two domains was to give a thorough assessment economic impact of women’s education. The Augmented Dickey-Fuller (ADF) was used to test for the stationarity of variables. The Autoregressive Distributed Lag (ARDL) bound test with the aid of data obtained from World Bank indicators was employed for the analysis. The analysis revealed that the more women with secondary and tertiary education, the better the economic growth and child welfare in Nigeria. Hence, the paper concluded that the government should make and implement appropriate policies to boost women’s education enrolment at all levels.

Introduction

The welfare of any country is largely determined by its economic development structure and this has resulted in voluminous literatures on the determining factor of economic growth. Of the various social, economic, and political factors identified to be the major determinant of growth in an economy, human capital development (education) is identified as a fundamental factor (Barro, 2003; Barro & Lee, 2013). Human capital development fosters technological innovation through cognitive skills and enhances labor earnings and productivity, which stimulates economic growth and development (Aghion et al., 2009). Hence, there is a need for all nations to fortify their citizens educationally regardless of gender for sustainable growth and development.

Gender parity in all phases of life has been a contending topic in Africa since Beijing Declaration in 1995. Yet, the declaration of the period 2010 to 2020 “African Women’s decade” by UN revealed that gender balance and empowerment of women in Africa is way too slow in the region when compared to the western economies (FEMNET, 2016). Currently, international development organizations such as United Nations Women (UN women), Association for Women’s Rights in Development and Womankind Worldwide are pressuring the government to incorporate education policies which accentuate gender balance. This is because recent economic development literatures associate economic growth and poverty reduction with increased female education enrolment.

In Sub-Saharan Africa (SSA), women are exposed to less economic, health, and education opportunities when compared to their men counterparts. According to African Development Bank group (2020), roughly about 87% of human development opportunities available to men are available to women. The limited human development opportunities available to women limit their ability to earn high paying-jobs; start and sustain lucrative businesses; and attain political roles. This costs the region about 95 billion US dollars every year (United Nations Development Programme [UNDP], 2016) and impedes growth.

As a result, the Nigerian Education National Policy stated that education accessibility is an essential basic right for all Nigerian irrespective of disability, gender, or religion (African Sisters Education News, 2020). In the past, Nigerian society perceives the role of women to be limited to the household where she takes the responsibilities of a wife, housekeeper, and a mother. Women who fend for themselves or participate actively in economic activities considered for men are often times seen as odd. According to Adamu (1999), this cultural orientation creates an enabling environment for women to be marginalized and denied access to education.

Recently, Nigerian women are beginning to break barriers and significant proportions of them now enrol in schools and engage in economic activities. This could be because of some policies introduced to encourage women education. Some of these policies include—the Compulsory, Free Universal Basic Education Act in 2004, the 2006 National Policy on Gender in Basic Education, the Education Reform Act in 2007, the 2017 National Policy on Inclusive Education, and the UNICEF’s Girls’ Education Project (GPE3) introduced at all states.

Regardless of the introduction of these policies, gender gap still exists in Nigeria. This could be as a result of improper implementation and poor monitoring of the implementation of these policies. The high cost of education could also contribute to this setback since most poor and northern families enrol just male children in education and subject women to early marriage and poverty. It is also argued in Nigeria, that more educated women often challenge their husbands and leave their responsibilities at home in the hands of help while participating in economic activities. As a result, some men prefer to marry less educated women. This has further discouraged some poor families from educating their female children. Hence, this paper intends to underscore the relationship between women education and national development by highlighting the impact of women education on economic development, labor force participation rate, and child welfare.

It is expected that the findings of this paper will encourage families to educate their women since women education does not just benefit the nation but also benefit the individual homes of women. This study will be analyzed using Autoregressive Distributed Lag (ARDL) bound test with the aid of time series data obtained from World Bank development indicator. The broad objective is to underscore the relationship between different levels of education for women and Nigerian economic growth for relevant policy recommendations.

Research Objectives

• (1) Determining the significant impact of different education levels for women on sustainable economic growth;

• (2) Ascertaining the contribution of different education levels to child welfare.

Literature Review

Studies of economic growth and women education produced contradictory results as can be seen in the following literatures. Omowumi (2019) observed that Nigeria seems to be achieving its growth target but the growth is an imbalance. The analysis revealed that the growth is inclusive of the rich and exclusive of the poor, especially women and young adults. The qualitative research carried out by Enyioko (2021), revealed factors such as religion, poor education among parents/guardians poverty as the major challenges limiting female education in Nigeria. The researcher proposed that the government should make adequate provisions to alleviate these hindrances and encourage female education in Nigeria. The findings of Okorie (2017) support this. He evaluated the existing literature on the challenges encountered in gender equality in educational system of Nigeria and found that religion, family structure, and culture were identified as determinants of Girl Child Education in Nigeria. They also observed that there is an improvement in women education enrolment at various levels. Hence, the paper argues that if the current trend is maintained, women will likely attain equal education status with men.

Aimua (2021) also supported these observations. Using qualitative analysis, it was identified that cultural belief, poor education, religion, and low income are the challenges that limit women participation in Nigerian economic activities. He concluded that concluded that, women education enrolment at all levels is still low and that gender disparity In education accessibility is still widening. Onyeachu and Maduewesi (2020) examined the implication of women education to national development in Nigeria and observed that parents’ attitude, fear of educated women’s attitude, and poverty are the constraints that limit women education.

In an investigation carried out by Saâd and Assoumou Ella (2019), it was revealed that increase of gender equality index at all levels of education increases GDP per capita. The research examined if gender inequality in education affects GDP per capita of the Central African Economic and Monetary Community (CEMAC) countries. The work of Agénor et al., (2015) supports this finding. The authors observed the higher the gender equality in education, the better the human capital available for the future generation. Using an overlapping generation models of endogenous growth, the authors showed that higher gender equality in education improves human capacities in the next generation. It was further observed that higher education equality for women leads to a better investment in children.

The works of Licumba et al., (2015), Seguino and Were (2013), Klasen and Lamanna (2008), and Klasen (2002) further revealed the effect of gender equality in education in an economy. They observed that increase in gender equality in education impacts positively on economic growth in Africa. The study found that inequality in gender education affects economic growth directly by decreasing the average level of human capital and indirectly through its investment and population growth impact.

Baba and Anumaka (2019) investigated the significance of women education on the socio economic development in Yobe state, Nigeria. The research revealed that women Education have a very significant and positive relationship with their household income level, standard of living, and economic development. Klasen and Pieters (2015) and Thévenon, and Del-Pero (2015) using longitudinal cross-country data covering 30 countries on education (the Barro-Lee dataset) and growth (update of OECD data), it points out that increase in women’s education enrolment positively and significantly impacts on output per capita growth unlike men educational enrolment. Klasen and Pieters (2015) also observed that women education significantly increase the social development of a nation in a areas such as decrease in maternal mortality rates, and fertility and infant mortality rates. The work of King and Winthrop (2015) supports this finding. Their study indicates that increase in female education leads to an increase in women earning and their children’s standard of living because women invest greater proportion of their income in their families than men do.

Contrarily, In an Isiaka (2019) investigated the effect of gender education on African economic growth. They employed the GMM methods, the pooled, fixed, and random effects methodology and it revealed that male enrolment in education and stock of human capital stock have a greater contribution to economic growth than female enrolment in education and human capital stock. Hence, there is need for African government to provide standard education for men and women so as to increase their stock of capital for better contribution to economic growth. Eritobor (2017) employed qualitative research methodology to examine if higher education level enhances women’s empowerment and foster economic development in Nigeria. A total of 26 Nigerian women were interviewed and it was revealed that higher level of education for women neither empower them nor enhance national economic development in Nigeria. The study further stated that this could be because women are still subdued under men regardless of their education attainment. The study further revealed that culture and religion deepens patriarchy practice in the country and this prevents educated women from contributing significantly on economic activities. Asongu and Odhiambo (2021) used the generalized method of moments and data from 42 countries to examine how gender inclusive education is to promote inclusive economic participation in sub-Saharan Africa. It was observed that gender equality in tertiary education promotes economic inclusion unconditionally, while gender inequality in tertiary education affects gender economic inclusion negatively.

Cooray and Mallick (2011) observed that human capital when separated by gender impacts differently on economic growth. Male human capital had a positive significant effect on growth, while female human capital does not significantly affect growth when the openness variables are considered. The finding of Zaman (2010) differed from this. He investigated the causal relationship between female education enrolment rates and economic growth in Pakistan by employing co-integration and Granger causality test. The study reveals that a unidirectional causality relationship between GDP and female enrolment in of Pakistan.

El Alaoui (2016) employed the “general” panel model and a “gender” panel model to examine if women’s education affects the economic growth in Morocco, Algeria, Tunisia, and Egypt. It revealed that educating women (in tertiary education), institutional capital, and women’s participation in the labor force have a positive effect on economic growth. While, primary and secondary school education enrolment of women negatively affect economic growth. Esen and Seren (2021) studied the impact of gender inequality in education and employment on economic performance in Turkey using Johansen cointegration tests and ordinary least squares estimation methods. The study revealed that there is a long-term relationship between inequality in education and economic performance. It was also observed that better equality in both gender education and employment have a strong and significant impact on real gross domestic product (GDP) per capita in the long term.

Following the contradictory observations and limited studies on women education and Nigerian economic growth, this study deemed it necessary to investigate the relationship between women education and national development in Nigeria by carefully analysing the influence of women education (all levels of education) on economic growth and child welfare.

Methodology

The paper is footed on “human capital theory” and the extended form of “Robert Solow’s neoclassical growth theory” following Cooray & Mallick (2011). Human capital theory implies that training and education imparts the needed skills and knowledge of workers by increasing workers’ productivity and increasing growth in the economy (Becker, 1975). Becker (1964) suggests that investments in human capital (education/training) affect the employment and income pattern and the distribution of an economy and this determines the level of a country’s development. The theory portrays education as an investment since it yields returns; it is designed for nations with great number of residence because of the large availability of human resources. Hence, the reason for adapting the theory for studies in Nigeria. The idea is to test if all levels of education (primary, secondary, and tertiary) for women significantly impact on national development and the type impact (positive or negative) this have.

Solow’s neoclassical growth theory, on the other hand, suggests that for steady economic growth rate to be achieved, a combination of labor (human capital development), capital, and technology is needed. The growth theory states that while capital accumulation and usage within an economy is important for economic growth, capital, and labor relationship determines a country’s productivity. Technology on the other hand is said to augment labor productivity. The model is expressed as: Y = AF (K, L)

Where, Y = output (Economic Growth),

K = stock of capital,

L = labor, and

A = the technology which is exogenously determined.

Hence, the models that will be used for objectives one and two are generated as follows; the linear form of the models is stated as:

$$\mathrm{RGDP}=\mathrm{F}\left(\mathrm{CF},\mathrm{PEF},\mathrm{SEF},\mathrm{TEF},\mathrm{GEH}\right)$$

(1a)

$$\mathrm{CW}=\mathrm{F}\left(\mathrm{CF},\mathrm{PEF},\mathrm{SEF},\mathrm{TEF},\mathrm{GEH}\right)$$

(1b)

Econometrically the model is stated as;

$$\mathrm{RGDP}={ɑ}_0+{ɑ}_1\mathrm{CF}+{ɑ}_2\mathrm{PEF}+{ɑ}_3\mathrm{SEF}+{ɑ}_4\mathrm{TEF}+{ɑ}_5\mathrm{GEH}+{\mu }_{\mathrm{t}}$$

(2a)

$$\mathrm{CW}={ɑ}_0+{ɑ}_1\mathrm{CF}+{ɑ}_2\mathrm{PEF}+{ɑ}_3\mathrm{SEF}+{ɑ}_4\mathrm{TEF}+{ɑ}_5\mathrm{GEH}+{\mu }_{\mathrm{t}}$$

(2b)

Where

Real Gross Domestic Product (RGDP)—used for Economic Growth and Development;

Gross Capital Formation (CF)—used as a proxy for stock of capital;

Primary Education Enrolment for Females (PEF)—“Percentage of students in primary education who are female”;

Secondary Education Enrolment for Females (SEF)—“Percentage of students in secondary education who are female”;

Tertiary Education Enrolment for Females (TEF)—“Percentage of students in tertiary education who are female”;

Child Welfare (CW)—Proxy for “Prevalence of Anemia Among children Under 5”;

Government Expenditure on Health (GEH)—used as a control variable;

ɑ₀—constant parameters;

ɑ₁ to ɑ₅—the coefficients of the explanatory variables; and

μ_t—stochastic or error term.

ARDL model which uses the bound test was used to measure the extent to which women education affects economic growth and child welfare and to test for a long-run and short-run relationship among relevant variables. The tabulated F-values provided by Pesaran et al. (2001) is compared with the calculated F-test statistics. The ARDL models are known for having lags of the dependent variable, as well as lags (and possibly the current value) of the explanatory variables. Rewriting equations (1b and 2b) in an Autoregressive Distributed Lag (ARDL) model form:

$$\begin{array}{l}\Delta {\mathrm{RGDP}}_{\mathrm{t}}={\lambda }_0+{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\lambda }_{1\mathrm{t}}\Delta {\mathrm{RGDP}}_{\mathrm{t}-\mathrm{i}}+{\displaystyle \sum }_{\mathrm{i}=0}^{\mathrm{n}}{\lambda }_{2\mathrm{t}}\Delta {\mathrm{CF}}_{\mathrm{t}-\mathrm{i}}\\ +{\displaystyle \sum }_{\mathrm{i}=0}^{\mathrm{n}}{\lambda }_{3\mathrm{t}}\Delta {\mathrm{PEF}}_{\mathrm{t}-\mathrm{i}}+{\displaystyle \sum }_{\mathrm{i}=0}^{\mathrm{n}}{\lambda }_{4\mathrm{t}}\Delta {\mathrm{SEF}}_{\mathrm{t}-\mathrm{i}}\\ +{\displaystyle \sum }_{\mathrm{i}=0}^{\mathrm{n}}{\lambda }_{5\mathrm{t}}\Delta {\mathrm{TEF}}_{\mathrm{t}-\mathrm{i}}+{\displaystyle \sum }_{\mathrm{i}=0}^{\mathrm{n}}{\lambda }_{6\mathrm{t}}\Delta {\mathrm{GEH}}_{\mathrm{t}-\mathrm{i}}\\ +{\lambda }_7{\mathrm{RGDP}}_{\mathrm{t}-1}+{\lambda }_8{\mathrm{CF}}_{\mathrm{t}-1}+{\lambda }_9{\mathrm{PEF}}_{\mathrm{t}-1}\\ +{\lambda }_{10}{\mathrm{SEF}}_{\mathrm{t}-1}+{\lambda }_{11}{\mathrm{TEF}}_{\mathrm{t}-1}+{\lambda }_{12}{\mathrm{GEH}}_{\mathrm{t}-1}+{\epsilon }_t\end{array}$$

(3a)

$$\begin{array}{l}\Delta {\mathrm{CW}}_{\mathrm{t}}={\delta }_0+{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\delta }_{1\mathrm{t}}\Delta {\mathrm{CW}}_{\mathrm{t}-\mathrm{i}}\\ +{\displaystyle \sum }_{\mathrm{i}=0}^{\mathrm{n}}{\delta }_{2\mathrm{t}}\Delta {\mathrm{PEF}}_{\mathrm{t}-\mathrm{i}}+{\displaystyle \sum }_{\mathrm{i}=0}^{\mathrm{n}}{\delta }_{3\mathrm{t}}\Delta {\mathrm{SEF}}_{\mathrm{t}-\mathrm{i}}\\ +{\displaystyle \sum }_{\mathrm{i}=0}^{\mathrm{n}}{\delta }_{4\mathrm{t}}\Delta {\mathrm{TEF}}_{\mathrm{t}-\mathrm{i}}+{\displaystyle \sum }_{\mathrm{i}=0}^{\mathrm{n}}{\delta }_{5\mathrm{t}}\Delta {\mathrm{GEH}}_{\mathrm{t}-\mathrm{i}}\\ +{\delta }_6{\mathrm{CW}}_{\mathrm{t}-1}+{\delta }_7{\mathrm{CF}}_{\mathrm{t}-1}+{\delta }_8{\mathrm{PEF}}_{\mathrm{t}-1}\\ +{\delta }_9{\mathrm{SEF}}_{\mathrm{t}-1}+{\delta }_{10}{\mathrm{TEF}}_{\mathrm{t}-1}+{\delta }_{11}{\mathrm{GEH}}_{\mathrm{t}-1}+{\epsilon }_t\end{array}$$

(3b)

From equation (2a and 2b), the summation signs together with the terms determine the short-run relationship while the others correspond to the relationships of the long-run among the variables. ∆ and n are the first difference operator and the lag lengths of the model, respectively, while λ₀ and δ₀ are the constant term and ɛ_t the error term, respectively.

The Wald F statistics test (bound test) was estimated next to obtain the co-integration among the variables. If a long-run relationship (co-integration) exists, then an estimate of a long-run model (level model) will be done. Pesaran et al. (2001) provided the tabulated F-values used for comparison with the F-test statistics. The F-values are the upper bounds and lower bounds which depend on the number of variables.

Unit root is employed to examine time series stationarity by looking at the pattern and trends in the data to examine if the variables exhibit constant mean, variance, and covariance over the time series. The test reveals the order of integration of the unit series being examined. The Augmented Dickey-Fuller (ADF) test would be utilized to avoid spurious result problems encountered when dealing with time series data. The hypothesis is stated thus;

H_0: unit root exists

H₁: unit root does not exist

Time series annual data for the year 1985 to 2019 were employed for this analysis. The World Bank Development Indicator is the source of all data utilized. The estimation was carried out using E-views version 9.0 Econometric Software.

Presentation and Discussion of Results

The result of the ADF Unit Root diagnostic test result is summarized in Table 1.

Table 1.

ADF Unit Root Diagnostic Test Result.

Variables	ADF statistics	5% critical value	5% critical value
RGDP	−4.906356	−2.954021	1(1)
FLFPR	−4.529517	−2.954021	1(1)
CW	−8.085555	−2.954021	1(1)
CF	−11.83781	−2.957110	1(1)
PEF	−3.238687	−2.951125	1(0)
SEF	−7.918362	−2.954021	1(1)
TEF	−7.292934	−2.954021	1(1)
GEH	−3.076867	−2.95112	1(0)

Source. Authors’ compilation.

ADF Unit Root Diagnostic Test Result

Once the absolute Phillips-Perron test statistic is greater than MacKinnon critical value at 5% level of significance, the variable is stationary.

From the results above, Primary Education for Female (PEF) and Government Expenditure on Health (GEH) have their ADF statistics greater than their 5% Critical Value at level form. While, all other variables have their ADF statistics greater than their 5% Critical Value at their first difference. This suggests that all variables are stationary at their first difference except Primary Education for Female (PEF) and Government Expenditure on Health (GEH) which is stationary at level.

Results of the ARDL Bounds Test

Since all variables are stationary at their first difference and level form, the condition for adopting ARDL model is certified. Hence, the bounds test was carried out to examine if a long-run equilibrium relationship exists. The ARDL bound test results can be seen in Table 2a and 2b. Since Secondary school certificate is the least recognized certificate in Nigeria, it was used for the bounds test in table 2a for model 2a.

Table 2a.

Results of ARDL Bounds Test for equation (3a).

F-statistics	I(0) Bound (lower bound) 5% level	I(1) Bound (upper bound) 5% level
4.913147	2.62	3.79

Source. Authors’ estimation.

Table 2b.

Results of ARDL Bounds Test for equation (3b).

F-statistics	I(0) Bound (lower bound) 5% level	I(1) Bound (upper bound) 5% level
3.842423	2.62	3.79

Source. Authors’ estimation.

From Table 2a, the 5% upper bounds critical value (3.79) is less than the calculated F-statistics value (4.913147) of economic growth (RGDP). This result depicts that co-integration (long-run relationship) exist between the independent variables in model 2a since.

From Table 2b, the result depicts that co-integration (long-run relationship) exist between the independent variables in model 2b since the 5% upper bounds critical value (3.79) is less than the calculated F-statistics value (3.842423) of Child welfare (CW).

This implies that the ARDL model can be established to determine the long-run and short run slope coefficient for the two models.

ARDL Model Results

The Akaike information criterion (AIC) guided the choice of selecting lag lengths and lag lengths 3, 2, 2, 2, 1, and 0 were selected for RGDP, GCF, PER, SEF, TEF, and GEH, respectively. From the ARDL results portrayed in Table 3a, the study found that the first and second previous years of RGDP had a positive significant long run effect on RGDP. Thus, indicating that in the long run, a unit increase in RGDP in the first and second previous years brings about on the average 0.6% and 0.1% increase in the current Nigeria’s RGDP. The third previous year of RGDP had a negative significant long run effect on the current RGDP. This implies that in the long run, a unit increase in RGDP at the third previous year brings about on the average 0.4% decrease in the current FLFPR in Nigeria.

Table 3a.

Long-Run Analysis of ARDL Estimates for equation (3a).

Variables	Coefficient	SE	t-value	p-Value
RGDP(−1)	.564170	0.042593	13.24549	.0057
RGDP(−2)	.103461	0.035070	2.950173	.0983
RGDP(−3)	−0.373837	0.039409	−9.486142	.0109
GCF	5.108499	0.646401	7.902992	.0156
GCF(−1)	−.814564	0.468432	−1.738915	.2242
GCF(−2)	7.384443	0.519606	14.21163	.0049
PEF	−3.329666	2.063793	−1.613282	.1205
PEF(−1)	−4.713166	2.407495	−1.957705	.1894
PEF(−2)	−24.71385	3.673444	−6.727706	.0214
SEF	61.84826	7.325894	8.442419	.0137
SEF(−1)	20.91302	2.212628	9.451668	.0110
SEF(−2)	9.852207	3.125239	3.152465	.0876
TER	8.48190	1.792481	4.731932	.0419
TER(−1)	7.46860	3.419161	2.184337	.0116
GEH	17.20909	7.691019	2.237557	.1547

Source. Authors’ estimation.

Table 3b.

Long-Run Analysis of ARDL Estimates for equation (3b).

Variables	Coefficient	SE	t-value	p-Value
CW(−1)	.645217	0.104812	6.155954	.0000
PEF	−.219383	0.051542	−4.256418	.0003
SEF	.152656	0.056927	2.681594	.0128
TER	.795354	0.574130	1.385322	.1782
TFR(−1)	−.168144	0.642344	−0.261766	.7956
TFR(−1)	−.976190	0.577087	−1.691583	.1032
GEH	.594754	0.212566	2.797969	.0098

Source. Authors’ estimation.

It was further observed that the current and second previous year of Gross Capital Formation (GCF) had a positive and significant long run effect on RGDP, implying that a unit rise in GCF in the current and second year brings about on the average 5.1% and 7.38% rise in the growth and development of Nigerian economy respectively. However, the first lag of GCF had a negative insignificant effect on RGDP. These results can be seen in Table 3a.

Furthermore, the study found that the second previous year of primary school enrolment for females (PEF) had a negative significant long run effect on RGDP. This implies that an additional year of female primary school education brings about on the average 24.7% decrease to Nigeria RGDP. The negative outcome is similar to that of Dauda (2013). This does not conform to a priori expectation, ordinarily the more women enrol in primary education, the more they are expected to contribute positively to RGDP. However, the negative effect could be as a result of government expenditure on primary education which its enrolment is dominated by minors. The current and first previous years are statistically insignificant.

It was also observed that secondary school education enrolment (SEF) for females had a positive significant effect on RGDP. This indicates that an additional year of female secondary school education brings about on the average 61.8% increase in Nigeria RGDP. This result is similar to the findings of Okojie (1995), Anyanwu et al. (1998). The first and second lags of this variable had no significant impact on RGDP.

Just like the work of Okojie (1995), Anyanwu et al. (1998) Tertiary Education Enrolment for Female (TEF) had a positive significant effect on RGDP at all selected years. This conforms to a priori expectation, since the more women enrol in tertiary education, the more positively they contribute to RGDP.

Finally, the study found that Government Expenditure on health impacts positively and significantly on Nigeria economic growth and development in the long run. This observation suggests that the healthier the women, the more they contribute to Nigerian economic growth and development. This signifies that women health is a significant factor determining the level which they contribute to RGDP. The observation is similar with the findings of Anyanwu et al. (1998) and Cooray and Mallick (2011).

For equation (3b), the lag length selection was guided by the Akaike information criterion (AIC), and the lag lengths selected through this criterion are 1, 0, 0, 2, and 0 for CW, PEF, SEF, TEF, and GEH, respectively. From the ARDL result portrayed in Table 3b, the study found that the first previous year of CW had a positive significant long run effect on the current CW. This indicates that in the long run, a unit increase in CW level in the first previous year brings about on the average 0.6% increase in the current CW level in Nigeria.

It was further observed that the current year of Primary School Enrolment for Females (PEF) had a negative and significant long run effect on CW, implying that an additional year of PEF in the current year brings about on the average 0.2% decrease on the CW level of Nigerians.

It was also observed that Secondary School Education Enrolment (SEF) for females had a positive significant effect on CW level. This suggests that an additional year of female Secondary School Education brings about on the average 0.1% increase in Nigeria RGDP.

Tertiary Education Enrolment for Females (TEF) on the other hand was observed to have a positive insignificant effect on the current year of CW level. While a negative insignificant effect was observed on the first and second previous years.

Finally, the study found that Government Expenditure on health impacts positively and significantly on CW levels of Nigerians. This implies that the healthier the women, the better the welfare of Nigerians.

Policy Implications

The positive effect on economic development observed in secondary and tertiary education for women in Nigeria corresponds with Becker’s human capital theory. Conversely, Female primary education is the only level of education with a negative significant effect on economic development. This could be because at the primary education level, women are too young to implement what they acquire in school. However, the time frame of primary education is a short one that will be outweighed by the outcome of secondary and tertiary education. Capital formation and government expenditure on women health were also observed to have a positive effect on the growth of Nigerian economy. Hence, policies encouraging women education at all levels, capital formation, and women health are encouraged for faster growth and development in the economy.

Secondary and tertiary education was observed to have a positive effect on children’s welfare. This indicates that education equips women with more knowledge to birth and nurture children. Although, primary education indicates a negative impact on CW, the impact can be over looked because primary school graduates are minors and would likely not give children proper care. Hence, the outcome of educating women past the primary level should be our major concern.

Limitations of the Study

This study may have some possible limitations as a result of the following:

Lack of secondary data on child welfare was the greatest challenge encountered in the analysis. As a result of this, data on prevalence of Anemia Among children under 5 was used to capture child welfare. Hence, the observations made by other researchers may differ if other variables are used to capture CW.

In addition, the lack of empirical work addressing the relationship between women education and national development in Nigeria made the literature review focus more on other related concepts in Nigeria and other countries. This suggests that the literature review was not wholly focused on the country and variables of interest. As a result, other researchers are encouraged to work on this field using different proxies for child welfare; adopting various models and theoretical footing; and using other sources of data to have a more convincing output.

Recommendation and Conclusion

Since it was observed that women education at secondary and tertiary level positively impact on sustainable growth in Nigeria, this paper proposes that the government should adopt more feasible and proactive policies that accommodate the current realities of the 21st century to encourage more women to acquire education. By doing so, all levels of education will most probably have a positive significant impact on the economy. In addittion, all levels of education for women impact positively on child welfare except primary education that has a negative impact on child welfare. Additional year of tertiary education was also observed to have an insignificant effect on CW. This could be because most tertiary education graduates engage in economic activities and have little time for family.

As a result of these, this paper proposes that while the government can introduce and monitor the implementation of policies that encourage women education and participation in economic activities.