Unemployment rate is an indicator of socio-economic situation to allocate effectively the funds and conducting employment plans or policies we need a reliable estimate of employment and unemployment characteristic in different areas of country. The national statistical offices face to the problem that the sample size of current notional surveys are not planned to provide reliable direct estimates for such small areas and the increase in size that is necessary to cover all these areas adequately is not affordable. Thus we need to use the small area estimation techniques to make reliable estimate for these areas. In model-assisted approach that relate response variable to some auxiliary variables by a model, it is possible to improve precision of the small area estimators by borrowing strength from auxiliary variables. In this article logistic regression model and multinomial logit model with random area effect are introduced for estimate unemployment rate in ostans. Next in a simulation study by using 1389 labor force survey data we compare efficiency of model-based and design-based small area.