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Increase in environmental pollutions of fossil fuels calls for policies stimulating clean technology deployment especially in the transportation sector. To evaluate the efficacy of these policies we should explore the preferences of consumers regarding different technologies. In this paper we use a discrete choice approach used in “Canadian Integrated Model system” (SIMS) to identify the behavioral components. This model has a hybrid top-down bottom-up structure. For this purpose we estimate a multi-nominal logit model (MNL) for different automobile technologies and different transportation modes based on preferences revealed by a sample of 250 Tehran citizens. Then, we evaluate the effects of economic, technological and environmental policies on market share of different automobile technologies and air pollution in Tehran. These policies include gasoline price increase, pollution tax, limitations on single passenger cars and limitations on use of gasoline driven cars. According to this analysis, technology based policies can effectively correct the market shares towards clean traffic technologies

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Life insurance as an investment and assurance tool provides a great source of investment financing in different economies. Despite life insurance development in advanced countries and in many developing economies, it could not get its appropriate share in Iranian family’s basket. This paper investigates factors that affect life insurance demand in Iran. So, random sampling used to get required information in three provinces of Iran (Tehran, East Azerbayjan and Mazandaran). Factors are divided into two main groups: Socioeconomics and psychological. Required data were gathered using questionnaire. Results show that life insurance demand has negative relationship with individual expected health condition, premium, expected inflation, degree of risk aversion and income. Bequest, economic optimism, age, employment of partner and reading has positive relation with life insurance demand. Based on the sample, result show that life insurance demand is not affected by advertisements but is affected by others recommendations.

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The subject of microeconomics is the behavior of firms, individuals and government. According to production theory, rational behavior of the firms, leads to profit maximization. So, one of the most important rational questions in production theory, is the determination of a suitable location of the firm. In the last two decades, several theories have been proposed to analyze the factors affecting the location of economic activities. These theories emphasize on many factors that can be summarized in two categories: factors affecting supply and demand. The aim of this article is to determine and analyze factors affecting the location of electronics industry in Iran. In this regard, 25 factors were identified. Whoever, this number of explanatory variables decreases the number of degree of freedom in a Logit and/or Probit model dramatically. So, the principal component analysis was used to decrease the number of LHS variables. Results show that the industrial development and the creation and maintenance of facilities have important effects on the location of electronics industry in different provinces of Iran. Also due to high proportion of the value to the weight of the final products or the high mobility and tradability of electronics industry products, local and surrounding area‘s income have negative impacts on the industry location.

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There are a lot of techniques and methods for prediction of bankruptcy among them “Statistical methods” or econometrics techniques are more popular. As dependent variable in our study is qualitative it is convenient to use qualitative discrete models. Mixed Logit model is one of the powerful and flexible techniques of discrete choices that allow the coefficients to be random with distribution function. Explanatory variables are financial ratios which derived from Zmijewski’s model. The sample data are from Tehran Stock Exchange’s Brokerage Companies during 2001-2008. We selected two random samples, one for estimation and another for prediction power test. Results show that the degree of successfulness of the model is over 90 percent.