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Introduction: 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.
Method:  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.
Discussion: 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

Keywords: workforce productivity, education level, Ginni coefficient, Dynamic panel model

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