تحلیل اثر برابری جنسیتی آموزشی بر الگوهای مهاجرت زنان

Analyzing the effect of educational gender equality on women's migration patterns

Zahra Nasrollahi , Leili Sadat Tabaei Ardakani



Abstract



While economic theories suggest that education, as a human capital that is effective in achieving higher returns, has a positive effect on migration, empirical findings are contradictory. This complexity arises from multiple mechanisms: on the one hand, gender moderates this relationship due to social norms and labor market structure, and on the other hand, education indirectly increases the likelihood of migration through the expansion of social capital and support networks. As a result, a detailed understanding of this phenomenon requires going beyond linear analyses and examining the interaction between economic, social, and contextual variables. In this study, the effect of gender equality in the country's education system on the rate of internal (provincial) migration of women is examined using data from the Population and Housing Censuses of 2006, 2011, and 2016.

The results show that the variables of human development index, information and communication technology development index, real GDP per capita and foreign investment have a positive and significant effect, and the variables of Gini coefficient and gender equality index in the education system have a negative and significant effect on the proportion of women's migration in each province.

The findings show that higher education has a positive effect on the probability of women's migration and acts as a key tool for achieving economic and social independence. However, this process is not linear and uniform, and education can be both a factor for empowerment and a tool for reproducing gender inequalities in new contexts.

Keywords: Internal migration, women's migration, gender equality in the education system

JEL classification: I24, J24, J16, J61

Extended Abstract

Introduction:

This article directly examines this phenomenon and raises the question of how gender equality in education affects women's decision to migrate between provinces. The main objective of this study is to quantitatively analyze the relationship between indicators of educational equality (such as the ratio of girls to boys in educational institutions) and the net migration flow of women in the provinces of the country. Unlike studies that focus solely on individual education levels, this study emphasizes a structural and contextual indicator, namely "equality" at the provincial level. This approach allows us to understand whether a province's success in women's educational empowerment makes it a migrant-first or a migrant-receiving region. The findings of this study can provide valuable information for policymakers in the fields of higher education, regional planning, and employment to formulate coordinated policies to exploit women's human capital in the same provinces of origin and prevent unbalanced development.

Method:

In this study, due to the limited time series data (3 years), the use of panel data is the best and most efficient method to use. This study, based on the theoretical foundations and research of Zhao et al. (2025) and Nazari and Seidan (2016), has designed the model (1).

Model (1)

〖RMIGwoman〗_it=α+β_1 〖GELE〗_it+β_2 〖HDI〗_it+β_3 〖CINI〗_it+β_4 〖IDI〗_it+β_5 〖Gdprel〗_it+β_6 〖fdi〗_it+u_it

where RMIGwoman is the ratio of female immigrants entering to leaving each province, GELE is the gender equality index in the education system. TOPSIS method was used to create gender equality indices in education. This method has a special place in the literature of multivariate statistics due to the possibility of weighting indicators. It should be noted that the population of girls and boys of that age group is considered to create each of the indicators.

Ratio of literate girls to boys (in the age group of 6 years and above)

Ratio of girls to boys in primary school (6 to 11 years old)

Ratio of girls to boys in secondary school (12 to 17 years old)

Ratio of girls to boys in higher education (18 to 40 years old)

And the variables HDI, GINI, IDI, GDPrel, FDI represent the human development index, adjusted Gini coefficient, information and communication technology development index, real gross domestic product per capita and foreign direct investment, respectively.

Results :

The results of the generalized least squares (FGLS) model are reflected in Table 1.

Table 1. Model estimation results (1)

Coefficient P-VALUE Symbol Variable name

-0/507 0/005 GELE Gender Equality Index in Education

3/207 0/000 HDI Human Development Index

-1/340 0/000 GINI Gini coefficient

0/021 0/013 IDI Information and Communication Technology Development Index

0/0012 0/076 Gdprel GDP per capita

0/006 0/037 fdi Foreign direct investment

0/632 0/045 cons cons

Source: Research findings

Conclusion:

According to the estimation of model (1), an increase in the Gender Equality in Education Index (GELE) reduces the proportion of female immigrants from each region.

One of the most important drivers for women to enter the labor market is their level of education. Also, educated women are more dynamic in their decision to migrate and have a greater desire to find or advance a job (Esmaili and Mahmoudian, 2016; Tanha et al., 2014; Moshfegh and Khazaei, 2015).

Improving the Human Development Index (HDI) leads to a positive balance of female immigrants and an increase in the proportion of female immigrants in each province. Spatial inequality in development between different regions is one of the important and key platforms in the formation of internal migration. The cities of the country with different levels of development contribute to unbalanced population movements and displacements.

The Gini coefficient has a significant negative effect on female migration, meaning that as the coefficient increases and income distribution becomes more unequal, well-educated women tend to move to places where income is more fairly distributed.

Women, who traditionally face a gender wage gap, face a lack of job opportunities in unequal economies. These barriers discourage potential female migrants from moving to such destinations because the cost of living may be higher than the potential income. Areas with greater economic inequality and unequal income distribution may have lower public services (e.g., health care, childcare) and be less attractive to women seeking stability (Zhang and Yu, 2024).

The Information and Communication Technology Development Index has a positive effect on the female migration ratio.