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One of the main concerns that many countries of the world are encountering, is some economic activities which are usually hidden from official view. Activities such as exchanging stolen goods, drug trafficking, corruption, gambling, smuggling, are among illegal activities, and others like refusing to report the incomes, fringe benefits, and cash discounts for the staff are among the legal activities of shadow economy. A considerable part of economic literature during the past decade focused on the research findings concering the ways of measuring, defining, and determing the extense of shadow economy in the world. Using the “Structural Equation Modeling” and some literature-specified causes and indicators we aim to reach the case study of Iran. Estimation of size and evolution of Iranian shadow economy is analyzed through “Dynamic Multiple Indicators-Multiple Causes” which is one of the most important indirect techniques available. The advantage of this model is evaluation of the simultaneous impacts of all variables on each other, and has fewer restrictions compered to other models. The research findings reveal the increasing trend of shadow economy in Iran and acts as an alarm for policy makers and authorities.

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In this article, after defining a conceptional framework for defining and measuring shadow economy in Iran a close attention is also paid to a more precise definition of shadow economy itself. It is also tried to estimate it's changing process and size during 1975-2007 based on the new definition. Direct and indirect approaches are also briefly discussed to estimate the shadow economy. Then, the strengths and weaknesses of each method are pointed out. So far, almost all of the researches carried out in Iran regarding estimation of shadow economy have mainly focused on structural equation modeling approach using Lisrel. Here in this paper for the first time both structural equation modeling software programs of Amos Graphics and Lisrel are applied to estimate the shadow economy in Iran. A comparison of the process and output of both software packages is also done in this research. Finally, in addition to investigating the direct effects of the causal variables, the interactional effects of them on latent variable of the shadow economy are also analyzed.

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Shadow economy is an important part of economy in almost all countries especially the developing ones. Most of active firms in this part of economy have negative externality on the environment. Considering the importance of sustainable development and growing international pressures to maintain and support the environment more and more attentions have been drawn to the factors affecting and threatening environmental health. The present paper for the first time considers the role of variables like polity index and active population to total population ratio and how they affect the shadow economy. In addition to the main direct effects of these variables on shadow economy the indirect effects of causal variables through interaction with shadow economy are also examined. Since the relationship between shadow economy and air pollution has been somehow disregarded in economic literature to a large extent in Iran and to some extent at international level the present paper for the first time focuses on the relationship between shadow economy and air pollution. The results indicate that on average the ratio of shadow economy to GDP is 12.25% and a 1% increase in the size of the shadow economy raises the water pollution by 0.17%.

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Underground economy is considered as an important economic component among the world countries, and in particular developing countries. It creates a serious consequence in the economy and could lead to a deviation from the correct diagnosis of economy status and as a result prescription of the mistaken policies. Therefore, identifying the size of underground economy and using corrective measures to reduce and control it is the main concern of policymakers. Financial development is among factors affecting the performance of underground economy. It reduces the cost of credit and decreases the incentives of agents in the underground sector. This study aims to investigate how to financial development affect underground sector. To this end, it uses structural equation modeling and multiple indicators – multiple causes (MIMIC) approach. The results suggest that the average ratio of the underground economy to GDP is equal to 20.68% during 1973-2012. For one unit increase in financial development, on average, the size of the underground economy decreases by 0.05 percent.

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The expansion of underground activities and smuggling of goods threats the employment in Iran. This study estimates the volume of smuggling in Iran by using multiple-cause-multiple causes (MIMIC) model and applying maximum likelihood method over the period 1979-2017. Then the effect of smuggling on employment is investigated by using ARDL Bounding test method. The results of smuggling time series show that despite some fluctuations, the smuggling has been generally increasing. The findings of regression model indicate that expansion of smuggling has a negative impact on employment in the short- and long-run.