Investigation of Educational Inequalities and Its Effect on the Dynamics of Industrial Workforce Productivity by Using Dynamic Panel Model

In economic literature, using economic growth resources and parameters (work, capital and technology) are important in the process of economic growth and development. When the economy has a higher level of development, application of physical and human resource intensity will gradually reduce and we try to improve the quality level of resources by technical changes and changes in the efficiency of the factors of production. Therefore, by using capital and work factors more efficiently as well as technology, conditions for increasing the total productivity of production factors in economic activities are provided because continuous and higher level of economic growth in the whole economy leads to faster transition of production structure from one stage to another in economic development. In these structural changes, the higher contribution of productivity of all the production factors will lead to better production instead of bad production (using physical resources rather than the quality change of production).. Nowadays, the quality of human capital plays an important role in a coherent and coordinated system to achieve the higher economic growth goals ; so that workforce with higher knowledge will have higher productivity and higher productivity of the workforce is known as driving force of economic growth.
This research seeks to investigate the human capital quality of the workforce in the industrial sector and in the provinces by using the dynamic panel model and workforce education level of the Ginni coefficient index and its effect on workforce productivity during 1379-1392 years. This was a descriptive and an applied research. Theoretical discussions were collected through library research (books and article), mining documents and taking notes. The population of the study included all information and statistics related to the variables of workforce education (human capital) and workforce productivity in the industrial sector of the country, and the sample was also the information and statistics of the industrial sector by province. Data were collected using time series, economic indicators, magazines and published statistics by the Statistical Center of Iran.
Findings: The results of the Ginni coefficient index of the level of education of the workforce showed that in all provinces during the study period the dispersion of the level of education of the workforce has decreased.. Considering the estimation of research model, all explanatory variables of research (Gini coefficient of education level of the workforce, per capita wage, physical capital per capita and technology indicator) had a significant effect on workforce productivity. Dependent variable lag (workforce productivity) had a positive and significance relationship with workforce productivity which, shows that workforce productivity dynamics acts positively . Among studied variables, the variable workforce per capita wage in the industrial sector had the most effect on dependent variable, workforce productivity. The Effect of training distribution or workforce education level distribution in the industrial sector is negative on workforce productivity.. At last, physical capital per capita and technology index had positive and significant effect with workforce productivity.
The importance of workforce in the production process at the macro and micro level is clear, however, despite the emphasis on policies, the performance of workforce productivity in the Iranian economy in recent decades has shown that the potential of the workforce in the production process has not been used. Considering the results of the research, that the per capita wage variable has the greatest impact on labor productivity and that this variable has been introduced as an indicator of the motivation of the workforce in the model, it is suggested to pay particular attention to the motivational factors of the workforce, especially the wage level. Moreover, in order to improve the productivity of the workforce in the industrial sector, the dispersion of workforce education must be reduced