University of Tabriz Applied Theories of Economics 2423-6586 7 1 2020 05 21 Analyzing the Impact of Corruption on Employment in Iran and Selected Islamic Countries: A Panel Cointegration with Cross-Sectional Dependency Approach Analyzing the Impact of Corruption on Employment in Iran and Selected Islamic Countries: A Panel Cointegration with Cross-Sectional Dependency Approach 265 288 10721 10.22034/ecoj.2020.10721 FA Siamak Shokouhifard Department of Economics, Tabriz Branch, Islamic Azad University, Tabriz, Iran Roya Aleemran Department of economics-Economics&managment faculty-Tabriz azad islamic university-tabriz-Iran Nader Mehregan دانشگاه بوعلی همدان Farzad Rahimzadeh Assistant Professor, Department of Economic, Accounting and Tourist Management, Faculty of Literature and Humanities, University of Guilan, Rasht, Iran Journal Article 2019 11 10 The employment rate is among the key indicators of the labor market and is subject to various economic components. One of the most important factors affecting employment is the corruption variable. Therefore, in this study, the effect of different indicators of corruption on employment level in Iran and Selected Islamic Countries is studied and three separate models are estimated in the period 2000- 2018. After estimating the models using panel data method, the cross-sectional dependency test was performed on the estimated models and at the 5% significance level, the existence of cross-sectional dependency was accepted. Therefore, for obtain reliable results, the Cup-FM Method was used to estimate the model. The results of model estimation showed that in all three models, corruption index had a negative effect on employee-to-population ratio and this effect was statistically significant at the 5% level of significance. The effect of banks' lending facilities on the ratio of employee-to-population ratio was positive in all three models but not significant at the 5% level. Capital stock and real wage had a positive and negative effect on employee-to-population ratio, respectively, and this effect was statistically significant at the 5% level. Trade openness also had a positive effect on the employee-to-population ratio, and only in the first model was this effect statistically significant. According to the estimated results, the effect of inflation on employment level was negative and this effect (except in the second model) was statistically significant. The employment rate is among the key indicators of the labor market and is subject to various economic components. One of the most important factors affecting employment is the corruption variable. Therefore, in this study, the effect of different indicators of corruption on employment level in Iran and Selected Islamic Countries is studied and three separate models are estimated in the period 2000- 2018. After estimating the models using panel data method, the cross-sectional dependency test was performed on the estimated models and at the 5% significance level, the existence of cross-sectional dependency was accepted. Therefore, for obtain reliable results, the Cup-FM Method was used to estimate the model. The results of model estimation showed that in all three models, corruption index had a negative effect on employee-to-population ratio and this effect was statistically significant at the 5% level of significance. The effect of banks' lending facilities on the ratio of employee-to-population ratio was positive in all three models but not significant at the 5% level. Capital stock and real wage had a positive and negative effect on employee-to-population ratio, respectively, and this effect was statistically significant at the 5% level. Trade openness also had a positive effect on the employee-to-population ratio, and only in the first model was this effect statistically significant. According to the estimated results, the effect of inflation on employment level was negative and this effect (except in the second model) was statistically significant. https://ecoj.tabrizu.ac.ir/article_10721_915f9fcdba1d16de79d55bff9c2c8303.pdf