فصلنامه برنامه ریزی و بودجه
پیاپی 146 (پاییز 1398)

In a rational expectation utility optimization model, the consumption profile is smoothed over time and, under some simplifying assumptions, it equals a constant fraction of total lifetime income. For this result to hold, the consumer must be able to transfer income from one period to another. Otherwise, the consumer is under liquidity constraints. The liquidity constraint affects the allocation of durable and non-durable consumption. We focus on the relationship between the marginal utility of household durable good stock and the marginal utility of non-durable consumption. When capital markets are perfect, the marginal utility of these two variables always bears an equilibrium relationship. But if liquidity constraints are binding, the difference between the lagged value of these two variables can predict the current growth change in non-durable consumption. We estimate the long-run relationship between these two variables using an error correction model and a pseudo-panel data set based on Iranian Household Expenditure and Income Surveys (HEIS). In the error correction model, the faster the error of the long-run relationship corrects, the lower the liquidity constraint. This coefficient is estimated to be statistically significant and is in [-0.22, -0.30] range in different estimates, thus we can reject the permanent-income hypothesis and accept that capital markets are not perfect in Iran.

Emphasis on eliminating deprivation and poverty on the one hand and expanding insurance services on the other, confirms the fact that the concept of insurance has a key role for economic planners and policymakers in achieving the goal of reducing income inequality. The efforts made in the present study are aimed at improving the position of insurance in this field. In this regard, the impact of the insurance penetration coefficient on income inequality in 30 provinces of Iran during 2011-2015 has been calculated using the dynamic panel data in a basic model and two scenarios (based on human development index and insurance penetration coefficient). The results show that inflation has a positive effect on the Gini coefficient and consequently increases income inequality. However, per capita income index and insurance penetration coefficient have a negative effect on the Gini coefficient and lead to a decrease in income inequality.  Furthermore, the effect of this factor on the Gini coefficient in provinces varies depending on the level of human development and insurance penetration coefficient level. So first, there is a significant difference in the extent of the influence of insurance penetration coefficient on the Gini coefficient in two groups of provinces in terms of human development level, so that an increase in the level of human development reinforces the effect of the insurance penetration coefficient on reducing income inequality. Second, there is a significant difference in the magnitude of the impact of insurance penetration coefficient on the Gini coefficient in the two groups of provinces in terms of insurance penetration coefficient level; Thus, by increasing the size of the insurance penetration coefficient, the influence of the insurance penetration coefficient on reducing income inequality is strengthened.

Nowadays, the most important factor affecting production is efficiency, which recovers economic performance, adjusts prices, enhances competitiveness, and improves social welfare and growth sustainability by influencing the production level. This paper aims to estimate the technical efficiency of production in Iranchr('39')s industrial sector by implementing Stochastic Frontier Analysis (SAF); To this end, the Translog Function is used to estimate the production and technical performance of the Iranian industry sector in terms of four-digit International Standard Industrial Classification (ISIC) codes for the period 2010-2018. Accordingly, the panel data technique is applied to estimate the technical efficiency and production function Frontier Production Functions, the estimator of maximum likelihood variables of using value-added variables, labor force, capital, and energy based on four-digit ISIC codes. The results show that the chemical, petroleum, and coal industries ranked first in efficiency; textile manufacturing, clothing industry, wood, and cork industry, manufacture of articles of straw and plaiting materials, and paper and paper products ranked second in efficiency; Coke production and refined petroleum products, chemicals and chemical products, and manufacturing other non-metallic mineral products ranked third in efficiency; and lastly, manufacturing basic metals and fabricated metal products, except machinery and equipment, ranked as fourth.

Due to the increasing demand for natural gas and its benefit of producing less carbon compared to other fossil fuels, pricing the natural gas based on the social expenses is becoming necessary more than ever. Accordingly, presenting models that enable us to find coordination between natural gas pricing and the final cost of carbon reduction is of high importance. This study aims to find the relationship between natural gas pricing, carbon rent, and the degree of pollution caused by natural gas. To this end, the final cost of CO2 reduction for two carriers of natural gas and gas oil in Kerman province has been calculated; Then the measures of social costs based on carbon expenses for natural gas and gas oil in 3 sections of the power plant, industry and houses is conducted. This study presents the evaluation of the final cost of carbon caused by natural gas and gas oil for the three mentioned sectors. The results indicate that the final costs of carbon caused by natural gas are always lower than the final costs of carbon caused by gas oil. This is the major reason as to why the social costs of natural gas are lower than of gas oil in the three sectors. Moreover, the results show that there is not a significant difference between the rates of the final cost of natural gas and the price of natural gas delivery to the end-users in 2013-2016. This is due to the little amount of pollution natural gas produces and the relatively low cost of its pollution. However, the rate of delivery cost to the power plant is 8 percent of social costs and 9 percent of the final costs of natural gas.

The prevailing model in the studies that estimate bus travel time is the linear regression which assumes the limit of the normal distribution for all observations. Besides, survival models can calculate that the probability of an event can change over time. Thus, examining event probabilities that change over time is ideal for risky basic models such as survival ones. Although these kinds of models are used less in the research of bus travel time, in this study Accelerated Failure Time (AFT) survival models and linear regression models are compared in the form of two modeling approaches, link-based, and section-based. As for modeling the Automated Vehicle Location (AVL), data of 32 buses in line number 313 in Tehran (from Sepah Sq. to Enqelab Sq.) is used, including the information for one week for May, August, and November 2015. According to the results, the accuracy of survival models is better than the linear regression model in both modeling approaches. Furthermore, the performance of the linear regression model is unfavorable for both observations of short (less than 100 seconds) and long (more than 900 seconds) travel time. In addition, the particular lane that has been built in the opposite direction in this route reduces the bus travel time by an average of about 15/7 percent.

The present study examines the main and interaction effects of the variables that affect the total factor productivity (TFP) in East and West Asian countries. To this end, this paper provides a short-term and long-term analysis of the factors affecting productivity differences in these countries during the period 1998-2018. According to the results, the main factors that influence the increase in productivity in both the short-term and long-term in East Asia are the share of fossil fuels use, good governance, oil rents, and human capital, respectively; while the most important factors in increasing productivity in West Asia are good governance and trade openness, respectively. Correspondingly, the share of fossil fuels use, human capital, oil rents, foreign direct investment, trade openness, and financial development have been the most important factors in the difference in productivity in East and West Asian countries in both short-term and the long-term. Moreover, good governance has increased the productivity of all factors in West Asian countries (in the short-term), and in East Asian countries (in the long-term). Furthermore, in West Asian countries, in both short-term and long-term, the interaction effect of good governance and oil rents has increased the TFP; also, the interaction effect of human capital and oil rents has reduced the TFP, while the interaction of human capital and foreign direct investment had no effect on the TFP.