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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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Estimating the size of shadow economy is of special importance in setting macroeconomic variables and fiscal policies. In recent years, the fuzzy inference sets have been used for measuring shadow economy. In this paper, we present eight new fuzzy indicators for modeling and estimating the size of shadow economy. Thus, according to Lucas definition, we divide the shadow economy into four sectors and define two indicators for each sector. After three fuzzy inference phases, we measure the size of shadow economy. Our results indicate that the effect of production household on Iran’s shadow economy size is decreasing; and irregular, informal and illegal sectors impact size of shadow economy. In addition, the size of Iran’s shadow economy is estimated around 13 percent of GDP, on average, over 1970- 2007.  

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This paper uses the non-linear auto-regressive distributed lags (NARDL) model to examine the short- and long -run effects of the negative and positive shocks of the shadow economy on financial development in Iran over the period 1974 - 2015. For this purpose, the ratio of liquidity to gross domestic product is used as an indicator of financial development. The shadow economy includes all market-based production activities that are deliberately hidden from government officials due to the escape or avoidance of payments, such as taxes and social security contributions. In this study, the MIMIC (multiple indicators and multiple causes) calculations by Piraee and Rajaee (2015) are used for estimating the shadow economy. The results show that the effects of positive and negative shocks of shadow economy on financial development are asymmetric in the short- and long-run. This asymmetry means that in the short- and long-run, the negative shock to shadow economy is more effective than its positive shock. Therefore, in order to maintain the current level of financial development, the government can monitor size of shadow economy through strict control of illegal activities in the short-run, identify the illegal activities, and reduce them in the long run‎.