One of the most important indicators for evaluating any economic policy is to examine its distributive effects, so governments try to be aware of the redistribution of revenue and the possible effects of such policies in a quantitative and tangible manner before implementing the policy. Therefore, in the present study, for the first time in the Iranian economy, with quarterly data and generalized quantum method, the distributive effects of credit policies on income inequality in the provinces of the country were investigated. Based on this, the results showed that in the primary quantile (0.05), credit to the trade and services sector had a positive and significant effect on income inequality and also credits to agriculture, housing, industry and mining had a negative and significant effect on income inequality in the provinces. In the Middle Quantile (Quantile 0.5), lending to the agricultural and commercial sectors had a negative effect, and lending to the housing, services, industry, and mining sectors had a positive effect on income inequality. Also in the upper quantum (Quantile 0.95), facilities granted to the agricultural, commercial, industrial and mining sectors had a negative effect, as well as loans to the housing and services sectors had a positive effect on inequality in the provinces of Iran. Regarding the effect of human capital and economic growth on income inequality, the results showed that in the provinces of the country, increasing economic growth and human capital due to creating more opportunities to increase income, has reduced income inequality. According to the research results, in all quantiles, the increase in the effect of inflation and capital stock has caused more income inequality in the provinces.

BACKGROUND AND OBJECTIVES: This study is investigated on endogenous variables inclusive of green growth by developing the concept of inclusive green growth in Indonesia. The objective of the current study was to describe the conditions of inclusive green development in each province in Indonesia, which is due to the unavailability of data describing the conditions of inclusive green development. METHODS: This study used time series data from 2011-2019, and cross section data of 34 provinces, which were analyzed using panel data regression research methods. The novelty of this study is the use of environmental quality variables to replace environmental degradation in calculating the composite variable of inclusive green growth. The determinants of inclusive green growth used in this study were inclusive human development, regional independence, infrastructure, crime, industrialization and natural disasters. FINDINGS: The important study findings were inclusive human development, regional financial performance, infrastructure and natural disasters have a significant positive effect on inclusive green growth in Indonesia. On the other hand, industrialization had a significant negative effect on inclusive green growth in Indonesia, while crime did not have a significant effect on inclusive green growth in Indonesia. Simultaneously, inclusive human development, regional independence, infrastructure, crime, industrialization and natural disasters had a significant impact on inclusive green growth in Indonesia. CONCLUSION: The second hypothesis in this study proved to be accepted. Meanwhile, the first hypothesis is not entirely accepted. Therefore, it is highly recommended for the provincial government in Indonesia to intervene on the variables of inclusive human development, regional financial performance, infrastructure, industrialization and natural disasters to increase inclusive green growth. Meanwhile, for future researchers, it is recommended to find other variables that contribute in achieving inclusive green growth.

Based on transaction cost theory, active management leads to a reduction in transaction costs and thus reduction in the distance between the bids and ask gap. On the contrary, based on the adverse selection hypothesis, when a group of shareholders has the advantage of information over another, there is an information asymmetric which in turn will reduce liquidity. This study is primarily aimed at testing the mentioned theories. Using a panel smooth transition regression model, as a new econometric technique, we examined the data to explore the asymmetric impact of blockholders ownership on liquidity in the 148 firms for the period 2009 to 2018 from TSE. Our empirical results strongly rejected the null hypothesis of linearity, and the test on inexistence of nonlinearity indicated a model with one transition function and two threshold parameters. The first regime (levels of blockholders ownership below 36. 1 percent) showed that liquidity increases with institutional ownership while the trend was reversed in the second regime (levels of blockholders ownership above 36. 1 percent). Blockholders are inherently good or bad for the capital market because their impact varies from diet to regimen, and it seems to be dependent on the percentage of ownership of blockholders and corporate characteristics.

To investigate the spatial error correlation in panel regression models, various statistical hypothesizes and testings have been proposed. This paper, within introduction to spatial panel data regression model, existence of spatial error correlation and random effects is investigated by a joint Lagrange Multiplier test, which simultaneously tests their existence. For this purpose, joint Lagrange Multiplier test statistic and its asymptotic distribution is introduced. A simulation study is performed for considering the size and power of the test this test for joint hypothesizes. Then the application of this test is shown with investigating spatial errors correlation and random effects in data of agricultural product exports of ECO member states. Finally, discussion and conclusion are given.

In many fields such as econometrics, psychology, social sciences, medical sciences, engineering, etc., we face with multicollinearity among the explanatory variables and the existence of outliers in data. In such situations, the ordinary least-squares estimator leads to an inaccurate estimate. The robust methods are used to handle the outliers. Also, to overcome multicollinearity ridge estimators are suggested. On the other hand, when the error terms are heteroscedastic or correlated, the generalized least squares method is used. In this paper, a fast algorithm for computation of the feasible generalized least trimmed squares ridge estimator in a semiparametric regression model is proposed and then, the performance of the proposed estimators is examined through a Monte Carlo simulation study and a real data set.

One of the key factors of countries’ planning in order to reach a stable long-term economic growth is existence of a proper financial market. Based on economic literature, efficient financial markets can positively affected economic growth of a nation. Furthermore, investigation of effects of insurance which under a favorable financial market can make a direct and indirect influences on economic growth is mainly crucial for scholars. The main purpose of this study is to find out the effects of insurance and financial Structure on economic growth of 40 selected countries using a panel data approach over the period of 2000-2012. The results revealed that financial structure in some cases has significant effect, while in a number of cases, it shows insignificant impact. Also, the financial development index has a negligible negative effect on the economic growth of advanced countries and in the same way, this variable in MENA countries has a negative impact on economic growth. Moreover, this variable has various coefficients in several groups of countries. Furthermore, the findings depicted a positive significant effect of insurance on economic growth in advanced economies, while it negatively affects the economic growth in American nations.

Trade and climate change are some of the important factors affecting income inequality. With the globalization and trade interrelationships between countries, income inequality has been reduced to some extent. On the other hand, climate change as an important and current issue in the world has had great effects on economic growth and income inequality of countries. The purpose of this study is to analyze the relationship between trade and climate change and income inequality. To examine the asymmetric effects of trade and climate change on income inequality in 60 countries, the quantile panel regression model is used by 2010 to 2020 data. In addition, the Climate Change Performance Index for climate change and the Trade Openness Index for trade has been used. The results show that climate change over the years has increased income inequality in selected countries. Also, the results show that the third to seventh quantiles of income have been most affected by climate change. On the other hand, trade between countries has reduced the inequality of selected countries in different income quantiles.