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The Phillips curve usually has been estimated in a linear framework which implies a stable constant relationship between inflation and unemployment. Some of the studies claim that the slope of the Phillips curve is a function of macroeconomic conditions and also the relationship is asymmetric. This article deals with a smooth transition regression model for relationship between inflation and unemployment for Iran, during the period of 1971 -2007. Smooth transition regression model is a non linear time series regression model which could be considered as developed form of regime switching regression model. Results show that there is a negative and nonlinear relationship between inflation and unemployment in short-term. Regarding this result it's highly important for policy makers to be able to make a relationship between these two variables

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One of the empirical relationships that have been used to study the dynamics of labor market is the Beveridge curve -the scatter plot of unemployment rates versus vacancy rates- that is used to summarize the state of that market. The starting point for deriving the Beveridge curve is a matching function between unemployed workers and vacant jobs. In this research, provincial data are combined to estimate the Beveridge curve and the matching function of Iran. The matching function is estimated using provincial data for the period 1993-2008 and for estimating the Beveridge curve, provincial data for period 2005-2008 are used. The outcomes imply that the number of unemployed workers and vacant jobs has a positive and significant relationship with successful job matches. The elasticity of matching function for the unemployed and job vacancies equals 0.24 and 0.79, respectively. The Beveridge curve depicts a convex and negative relationship between unemployment rate and vacancy rate, although the relationship is not strong.

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The purpose of this study is to evaluate the impact of trade openness and economic globalization on employment. This study employs the Bounds test method and Autoregressive Distributed Lag(ARDL) model for Iranian economy during 1979-2009. Comparing with the other empirical studies, this study in addition to traditional index of trade liberalization as trade openness has been applied the new and more comprehensive economic globalization index as one dimension of the new KOF globalization index. This index includes the actual flows of trade such as trade, foreign direct investment and portfolio investment, and restrictions such as trade barriers and tariffs on actual flows. Also, the other control variables effective in employment such as GDP per capita, industrialization and government size has been considered. The results show a negative relationship between trade openness and employment but they show that the impact of economic globalization on employment is positive. Thus, it seems that the new economic globalization (KOF index) which is a broader comprehensive index is a better proxy of globalization.

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Women as half of the workforce in society can be an effective lever to promote economic and social development goals. In recent years, participation of women in development activities has increased. But the participation of women has been associated with discrimination against them. In recent years with increasing participation of women in the labor market, the income gap between men and women in the labor market is one of the most important discrimination they are facing with it. Understanding the factors affecting women's employment and the impact of these factors on the wage level and understanding the factors affecting the gender gap, can be very useful in reducing discrimination and achieving sustainable desirable development.
With attention, in this study, we tried to analyze influence employment, productivity, education and educating of men and women on decrease gender gap in wages paid to an econometric model. Theoretical basis of this model is based on Blinder- Oaxaca gender gap (1974) and in the period 1370 to 1390. The experimental results with model coefficients using panel data and with using Stata and Eveiws software is obtained and then analyzed.
Based on the results, however, productivity and employment increase wages of men and women but it Increase the gender income gap. Education increases wages of men and women but it reduces the gender income gap. Educating reduces wages of women and gender income gap too.