What kind of women’ education that affects economic growth?

This communication tries to examine what kind of women’ education affects more economic growth. To illustrate this aim, four countries cases have been presented: Morocco, Algeria, Tunisia, and Egypt. The motive behind choosing them was because these countries have many common characteristics (religious and cultural norms and values). Using panel model over the period 2000-2012, the results show that women’ education, particularly, tertiary education, women’ labour force participation and institutional capital affect positively economic growth. On the contrary, the primary and secondary school enrolment are negatively linked to the economic growth. This paper concludes that women’ tertiary education is a master-key to improve economic growth and development accompanied by a healthy and good quality of institutional capital and by eliminating all forms of gender discrimination.


Introduction
In the early 60s, economists have given a big importance to the concept of hu man capital principally with the writing of Becker (1962), Schultz (1961Schultz ( , 1962   Due to important political and social development realized by MATE albeit there are limited, the roles of women in these societies were enlarged. Improvement of women' contribution in the political activity, increasing their participation in the labour force, and reinforcement the role of the law and justice are the principal sources of these political and social developments. Consequently, the improvement in women' situations are changed many aspects of the society. Using data from WDI (2015) over the period 1960-2013, four economic and social aspects of change in MATE can be identified : (1) the fertility rate is on its downward trend; (2) the average literacy rate of female is lower than that of male; (3) the average labour force participation of female in MATE is lower than that of male, and that of other regions of the World; (4) mortality rates of neonatal, baby and infant in MATE are lower than those of the World. In following, we explain these changes and theirs effect on the economic growth.
 First change on MATE is that the fertility rate is on its downward trend from 7 children to around 3 children, Figure 3. The same trend is recorded in the World, South Africa and South Asia where the fertility rate is at around 3 children. But, this rate remains higher in SSA at around 5 children. Several factors could be having an effect on diminution of the fertility rate as amelioration in the economic growth, intensive participation of women in labour force 1 and increase in the 'literacy rate'. This rate is measured, in this study, by four indicators: 'primary completion rate', 'primary school enrolment', 'secondary school enrolment', and 'tertiary school enrolment'. Figure 4 draws a statistical relationship that links a simple regression of (1) fertility rate (noted TFR) and real GDP per capita (noted g y ); (2) fertility rate and women' participation in labour force (noted LF); and (3) fertility rate and primary completion rate of female (noted HCP-F). Primary completion rate of female (HCP-F) and fertility rate (TFR) are negatively linked, Figure 4(b). The reasoning behind this is quite simple. It states that the fertility rate is driven by female education levels, such as women' education can be accompanied by a corresponding decrease in the fertility rate. Verification of the statistical relationship that links on a simple regression of the fertility rate and other measures of education ('primary school enrolment of female', 'secondary school enrolment of female', and 'tertiary school enrolment of female') reveals similar results. Therefore, there is a negative relationship that links fertility rate and different measures of education. Excluding Egypt, female participation in labour force (LF) and fertility rate (TFR) are negatively linked, i.e. each 1% increase in female participation can reduce by between 0.24% and 0.30% the fertility rate, Figure 4(c). In addition, growth rate of the real GDP per capita (g y ) and fertility rate (TFR) are negatively linked, Figure 4(a). If the growth rate of the real GDP per capita increases by 1%, then the rate of fertility decreases by between 0.05% and 0.24%.
 Second change on MATE is that the literacy rate is on its upward trend from around 55% in the beginning 1987 to 70% in 2012. However, an average rate over the period 2006-2013, literacy rate of the female is always lower than that of male. Table 1 gives an idea about average literacy rates of adult people (ages 15 and above) and of young people (ages 15-24). Measuring in terms of gap between males and females' literacy rates, the differential ranges are just 3 ppt in South Africa and 5 ppt in China, but more than 18 ppt in MATE.  Third remarkable change in MATE is the labour force participation of female in MATE showed that a slight increase from 22% in 1990 to 24% in 2012. But, these rates remain lower than that recorded in other regions of the World. Table 2 gives an idea about average labour force participation per gender of adult people (ages 15-64).  Table 2 reveals that the average labour force participation of females is lower than that of males over the period 1990-2009. The highest rate is recorded in China (75%) and the lowest is recorded in Algeria (13.2%) over the period 1990-2012. Measuring in terms of the gap between males and females' participation in labour force, differential ranges are just 5 ppt in South Africa and 12 ppt in China, but more than 54 ppt in MATE.
 Fourth change in MATE is that mortality rates (per 1,000 live births) of neonatal, baby and infant are on their downward trend. The infant mortality in MATE decreased from an average of 167 per 1,000 live births in 1960 to average of 20 infants per 1,000 live births in 2013, against 34 recorded in the World, Figure 5(a). The neonatal mortality rate decreased also from an average of 29 neonatal per 1,000 live births in 1990 to an average of 13 neonatal per 1,000 live births in 2013, against 20 recorded in the World, Figure 5(b). The baby mortality rate declined, also, from an average of 266 babies per 1,000 live births in 1990 to 23 babies per 1,000 live births in 2016, against 46 recorded in the World, Figure 5(c). All these rates recorded in MATE are lower than that recorded in the World. The economic situation for women in MATE records the lowest levels in education and economic activities. There are potentials that have not been used to improve the economic growth of these countries. Several factors continue to affect the access of girls to education and participation in the labour force, such as poverty, gender gap, level of parents' education, infrastructure, religious and cultural norms.

Methodology, data and results
This study assumes that the human capital is directly proportional to the educational attainment. Since the effects of education on the growth differ by education levels (primary, secondary and higher). Two types of panel models are estimated, a 'general' panel model and a 'gender' panel model. In the first model, education and labour force participation are introduced regardless to gender. The objective is to test if these two variables affect the economic growth. In the second model, each level of education and labour force participation are divided by gender to measure especially if the women' education affects the economic growth.
The 'general' panel model equation is presented as follows. g it = β 0 + β 1 pe+ β 2 se + β 3 te+ β 4 labor+ β 5 inv_edu it + β 6 ic it + ε i (1) Where, the dependent variable is the growth rate of real GDP per capita (g y ). The independent variables are total of primary education (pe), total of secondary education (se), total of higher education (te), total of labour participation (labor), investment on education or public expenditure on education (inv_edu) 2 , and institutional capital (ic). Term ε is the error term.
The 'gender' panel model equation is presented as follows.
g yit = β 0 + β 1 pefem + β 2 pemale + β 3 sefem + β 4 semale + β 5 tefem + β 6 temale + β 7 laborfem+ β 8 labormale + β 9 inv_edu it + β 10 ic it + ε i (2) Where, the dependent variable is the growth rate of real GDP per capita (g y ). The independent variables are primary education of female (pefem) and of male (pemale), secondary education of female (sefem) and of male (semale), higher education of female (tefem) and of male (temale), labour participation of female (laborfem) and of male (labormale), investment on education or public expenditure on education (inv_edu) and institutional capital (ic). Term ε is the error term. This study uses annual data for variables of growth rate of the real GDP, growth rate of the real GDP per capita, primary, secondary and higher school enrolment, and labour force participation in order to analyse the impacts of women' education on the economic growth. All these data are taken from the World Bank. The variables that measured institutional capital are taken from Worldwide Governance Indicators (WGI) (2014). The countries included are Algeria, Morocco, Egypt and Tunisia. The data are balanced panel and involved from 2000 to 2012 because the variables are not available for all countries and all years. Before applying the classical method to regress equation 1 and equation 2 3 , it is necessary to check the stationarity of the variables. For that, the panel unit root tests are used to check the stationarity of these economic series. Table 3 resumes the results of all tests. However, if these results are not identical we conclude based on most of them. Hausman test is used be sure which model would apply the fixed effect model (FE) or the random effect model (RE). Table 4 displays the result of the 'general' panel model (equation 1) which mentioned that there are three main results. The first result is that the tertiary school enrolment (te) is positively and significantly linked to the economic growth (measured by g and g y ). The second result is that the variables of institutional capital (icr, icg, icva and icl) are positively linked to the economic growth. The third result is, surprisingly, that the primary and secondary school enrolments are negatively linked to the economic growth. The results of 'gender' panel model (equation 2) are given in Table 5. There are five main results. The first result is that the primary school enrolment of males is positively and significantly linked to the economic growth, but that of females is negatively and significantly at 10% linked to the economic growth. The second result is that the secondary school enrolment of males and females are negatively linked, albeit insignificantly, to the economic growth as like in the 'general' panel model. The third result is that the tertiary school enrolment of males and females are positively linked to the economic growth. The fourth result is that the institutional capital (icg, icr, and icva) is positively linked to the economic growth and it is like in the 'general' panel model. The fifth result is that the labour force participation of females is positively linked to the economic growth. In contrast, the labour force participation of males is negatively linked to the economic growth.

Conclusion
After discussing results of the panel models, it is necessary to recognize that MATE countries differ significantly from one to another in the characteristics that influence how women' education may affect the economic growth. The differing political and economic histories and geographical circumstances of these countries have created an array of environments in which women' education institutions operate. Our results show positive impacts of women' education on the economic growth especially tertiary education. Other important result is that the variables of institutional capital play an important role on the economic growth. So, to attain sustainable, rapid and continuous economic growth, it is necessary to encourage especially tertiary education of woman through establishing economic and social strategy able to improve women' situation and, in addition, reducing poverty of the citizens.