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Expended Abstract
Introduction
Sexual discrimination is not defined as the equalization of facilities and opportunities among men and women. One of the dimensions of gender inequality can be expressed in terms of the inequality of employment opportunities, types of jobs, and the level of payment for equal work between men and women.By looking at the results of the workforce surveys of 1395, it can be said that about 11 percent of the active population of the country is unemployed and the unemployment rate among women is higher than that of men. Iran’s ranking in the gender inequality index was 118 in 2015, with the participation rate of 16.2% for women and 72.2%. for men
Theoretical Framework
There is some evidence that the relationship between women›s employment and GDP as a criterion for economic development is in the form of a non-uniform curve. In such a way that the impact of economic development on gender equality in employment follows the effects of wages from the U-shaped model.
Some economists have found a vague link between education and women›s participation through the outcome of the both effects of income and replacement.
Democracy in countries is recognized as a factor in promoting the status of women. While in conservative and authoritarian regimes, the presence of the patriarchal system, the reproduction of the traditional role of women in society, the low level of empowerment of women and their dependence on the state as vulnerable segments of society are still common.
International trade and direct foreign investment (FDI) can create job opportunities for women.
Political globalization with a role in organizations and international conventions is a key variable in promoting gender equality.
Economic growth, both as a factor in increasing the independent income of women, is an increase in their scope for the participation and accumulation of human capital, and as a factor in undermining gender equality.
Government spending as a macroeconomic variable such as economic growth can have different effects on women›s employment in the labor force market.
Domestic investment is one of the components of overall demand and influences on gender equality. This variable is the main source of employment, creating wealth and innovation.
Demographic variables can be regarded as other factors affecting gender equality in employment.
An increase in the level of ICT infrastructure improves gender equality in the level of work activity.
The presence of oil and the resource curse  has led to a reduction in women’s labor force participation in the Middle East compared to other parts of the world. If the growth of a country is dependent on oil and mineral resources, with the oil sector booming, commodity trade will decline, and women will be more likely to face wage cuts as more women work in the commercial sector.
There is a vague connection between the unemployment rate and the employment of women.
It can be said that in Islamic countries, active participation of women has been accepted, and they can keep their roles as women and mothers at the same time taking care of their children, and this matter seems to «have no real opposition to women›s professional practice».
Methods and Results
Based on theoretical foundations, gender equality is largely a function of the following factors:
GE= f (GDP, Eqprimeduc, Eqseceduc, Domec, Open, FDI, Polglob, Ecgth, Govexp, Inv, Urban, Popg, Fempopshare, Internet, Mobile, Ythunemp, Natural Resources, Christian, Muslim(
As can be seen, based on theoretical foundations, the Bayesian data panel model is used.
Table 2: The Significance Test
Condition    Cases    Successes    Probability
model2.moslem > 0    20000    19796    0.9898
model2.oil < 0    20000    14846    0.7423
model2.christian > 0    20000    19861    0.99305
model2._youth_unemployment_rate > 0    20000    18768    0.9384
model2.population_growth < 0    20000    20000    1
model2._female_population < 0    20000    18913    0.94565
model2.Urban_population < 0    20000    18740    0.937
model2.domestic_investment < 0    20000    19926    0.9963
model2.goverment_expenditure < 0    20000    19160    0.958
model2.economic_growth > 0    20000    16547    0.82735
model2.Political_globalization_index < 0    20000    11401    0.57005
model2.FDI < 0    20000    19859    0.99295
model2.Trade_open > 0    20000    10205    0.51025
model2.Democracy < 0    20000    16362    0.8181
model2.GDP_per_capita < 0    20000    11217    0.56085
model2.internet > 0    20000    11734    0.5867
model2.mobile < 0    20000    18595    0.92975
model2.Gendereq_in_secondary_edu > 0    20000    18859    0.94295
model2.Gendereq_in_primary_educa < 0    20000    16862    0.8431

Table 3
The Results of the Convergence Test for the Markov Chain Monte Carlo for Each of the Estimated Parameters.
    
Mean    Spectral
density at 0    MCMC
sd. error    Relative
Numer. Eff.    Inefficiency
factor
                    
moslem    2.17439    12.6057    0.06293    0.010978    91.0885
oil    -0.06541    0.697603    0.014804    0.002264    441.604
christian    2.38052    13.4004    0.064884    0.010752    93.0085
_youth_unemplo+    0.001157    2.16E-06    2.61E-05    0.041358    24.1791
population_gro+    -0.05694    0.000555    0.000417    0.038508    25.9685
_female_popula+    -0.02976    0.004676    0.001212    0.011641    85.9053
Urban_populati+    -0.00198    1.52E-05    6.91E-05    0.017974    55.6368
domestic_inves+    -0.00212    8.86E-07    1.67E-05    0.110971    9.0114
goverment_expe+    -0.00283    5.06E-06    3.99E-05    0.083217    12.0168
economic_growth    0.000808    2.88E-07    9.51E-06    0.405174    2.46807
Political_glob+    -7.63E-05    3.21E-07    1.00E-05    0.091255    10.9583
FDI    -0.00157    9.34E-08    5.42E-06    0.684832    1.46021
Trade_open    3.76E-06    4.49E-07    1.19E-05    0.054602    18.3144
Democracy    -0.00227    1.34E-05    6.50E-05    0.075739    13.2033
GDP_per_capita    -4.13E-07    1.32E-11    6.44E-08    0.094679    10.562
internet    8.76E-05    1.67E-07    7.25E-06    0.162687    6.14677
mobile    -0.0003    7.48E-08    4.85E-06    0.090343    11.0689
Gendereq_in_se+    0.111469    0.017214    0.002325    0.047011    21.2715
Gendereq_in_pr+    -0.02392    0.000206    0.000255    0.434065    2.3038
tau    444.66    278.889    0.295999    0.733929    1.36253
omega    40.8264    348.491    0.33088    0.101962    9.8076
sigma_e    0.047539    8.08E-07    1.59E-05    0.732259    1.36564
sigma_alpha    0.164679    0.002182    0.000828    0.072843    13.7281

Discussion and conclusion
The results show that population growth, domestic investment, Christianity, and direct foreign investment are the most important determinants of gender equality, respectively. As expected, if population growth is to be allocated to women with lower educational, health and other priorities, domestic investment and direct foreign investment due to inefficiencies and corruption in countries in developing countries, they will lead to an increase in gender equality. On the other hand, Christian countries have more gender equality than Islamic countries in the labor force market. Government spending, secondary education, women›s share of the population, youth unemployment rate, urban population share, mobile users, gender equality in elementary education, economic growth and democracy, with a high probability (82-98%), on the gender equality model are effective in developing countries. While other model variables include oil exporting countries, internet users, the index of globalization, GDP, and open trade, they can have an uncertain probability (about 50%) in the pattern. As a result, it can be stated that demographic variables, internal and external capital, religion, and gender equality in education provide the best explanation for the employment gap between men and women in developing countries. Therefore, maintaining a balance in population growth, preventing domestic investment in inefficient projects, creating a suitable platform for women›s higher education, preventing them from directing to less paid jobs, using the Internet to update women›s skills, trading in goods, limiting the dependence on the oil sector as a male component, and ultimately, increasing economic growth in developing countries including Iran could provide suggestions for reducing gender inequality in the labor market.

Ethical Considerations
Funding
In the present study, did not have any sponsors
Authors’ contributions
All authors contributed in designing, running, and writing all parts of the research.
Conflicts of interest
This article does not conflict with other articles of my.
Acknowledgments
This article follows the principles of ethics and research and is endorsed by the Bu-Ali Sina University in this regard

Keywords: Determinants, Gender equality, Iran, the Baysin model averaging, Youth employment

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