nazar dahmardeh; Marzieh Esfandiari; zohreh eskandaripour
Abstract
Introduction
Achieving economic growth along with improving the distribution of income is always one of the main goals of economic development. In this regard, policy makers are the tools and policies that enhance the growth and distribution of income in a coherent way. On the other hand, it is expected ...
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Introduction
Achieving economic growth along with improving the distribution of income is always one of the main goals of economic development. In this regard, policy makers are the tools and policies that enhance the growth and distribution of income in a coherent way. On the other hand, it is expected that the insurance industry will be able to provide simultaneous access to economic growth and distribution of income, taking into account the function of risk distribution and its compensation, as well as its role in financial development. To test this hypothesis, here has used of the AutoRegressive Distributed Lag (ARDL) approach during the period 1975-2016.
The results showed that the development of the insurance industry could provide simultaneous access to economic growth and income distribution in the short run. But in the long run, it will only lead to economic growth. However, in the long run, it could be reliant on human and physical capital for simultaneous access to economic growth and the distribution of income. Also, based on the error correction model, 88.2% and 68.1% of the non-equilibrium related to the non-oil per capita gross domestic product and Gini coefficient are adjusted in each period, respectively.
Theoretical framework
In The second half of the 20th century onwards, especially since the 1970s, following widening the income gap between the poor and the rich as well as the development in public awareness, it has been emphasized on increasing the quality of life (Mehregan & Salarian, 2008:13). In general, classical and neoclassical economists believe that an uneven distribution of income can have a positive effect on the growth process, while others such as Mirdal and Sen believe that economic growth entails an improvement in income distribution and in fact considers the reduction of inequality necessary (Khodadad Kashi & Heidari, 2008: 153). However, if economic growth and improvement in income distribution are considered two essential components of economic development, there are three strategies for development (Sharifzadegan, 2007: 23-24):
A) Growth then Redistribution (GTR): Accordingly, with economic growth and the creation of vast economic capacities and enlarging the size of the economy, the conditions for employment is automatically provided for all social and income groups, thereby achieving a balanced income distribution.
B) Redilribution then Growth (RTG): In this strategy, comprehensive resources are mainly spent on proper distribution of income, and investment on economic growth and attention to it comes at a lower level, and practically undermines the social capacity of the community. Many experiences and studies show that in the long run, this policy will not achieve a balanced distribution of income or economic growth.
C) Growth with Redistribution (GWR): This strategy emphasizes that income redistribution cannot work without relying on a booming economy. In this strategy, executive policies should be able to work both for economic growth and for income distribution. The development of the insurance industry with the aim of fostering economic growth and improving income distribution can also be considered as one of the policies of this strategy.
Methodology
In this section, the following two econometric models are considered to examine the effects of the development of the insurance industry on economic growth and income distribution:
(1)
(2)
In the empirical studies, the variable level of income is present in the income distribution model, but the present study assumes that the level of income of individuals affected by physical wealth (physical capital or CAPL) and human wealth and capital (skill, expertise, and education level or HCAP). Accordingly, the LHCAP and LCAPL variables are used in the income distribution model instead of the natural logarithm of the income level. The research models for the period 1975-2016 will be estimated using the ARDL method.
Result and discussion
Because dynamic short-run interactions between variables are not considered in OLS method, the use of this method in estimating the long-run relationship does not necessarily yield unbiased estimation. Therefore, it seems reasonable that in such cases those models be considered that have short-term dynamics and thus make the model coefficients more accurately estimated. The ARDL method is a dynamic model that allows to estimate the long-run coefficients of the model with appropriate accuracy in addition to the cointegration test between variables (Nofersti, 2008). The main advantage of using the ARDL method is that regardless of whether the research variables have unit root in levels or some become stationary by one time differentiation, a long-run cointegration relationship between the variables can be obtained.
Conclusion
Achieving high economic growth coupled with improved income distribution has always been a major concern for policymakers in developing countries. In this regard, based on their historical experiences and those of other countries as well as the theoretical and empirical studies, countries prefer the strategy of Growth with Redistribution over GTR strategy or vice versa. On the other hand, insurance is expected to provide simultaneous access to economic growth and income distribution, given its functional role in risk distribution and compensation as well as its role in financial development. In the present study, this hypothesis was tested for the Iranian economy over the period 1975-2016 using the ARDL method.
The results of estimating income distribution model as ARDL (1, 1, 2, 3, 3, 1) showed that insurance penetration factor variables had a negative effect on Gini coefficient immediately and with a one-year lag. Oil revenues also have an impact on the Gini coefficient similar to that of the insurance industry, with its effect initially positive and negative with a one-year lag. But human capital with a two- and three-year lag and physical capital with a three-year lag have a negative effect on the Gini coefficient. Also, the results of estimation of economic growth model as ARDL (2,1,2,2,0) showed that development of insurance industry has positive and immediate effect on economic growth. The variables of human and physical capital have a positive significant lagged and non-lagged effect, and business openness variables have a positive and significant effect on economic growth after one-year lag.
hamid Yari; arezo yari
Abstract
In any market, knowing the best method of measuring risk can be very useful for investors and policymakers. In this regard, twenty of best seller corporations of Tehran Stock Exchange (TSE) were studied using monthly historical data (from April 2004 to March 2011). Several variations of the capital asset ...
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In any market, knowing the best method of measuring risk can be very useful for investors and policymakers. In this regard, twenty of best seller corporations of Tehran Stock Exchange (TSE) were studied using monthly historical data (from April 2004 to March 2011). Several variations of the capital asset pricing model (CAPM), such as Lower Partial Moment-Capital Asset Pricing Model (LPM-CAPM), Asymmetric Response Model (ARM), and traditional CAPM were empirically tested. The results show that the traditional Capital Asset Pricing Model is the best model at the present period.
JEL Classification: G12, G32
Theoretical Framework
A plethora of empirical tests of the CAPM that implicitly assume the mean-variance based preference of the investors have been performed. However, statistical tractability of mean-variance analysis based on multivariate normality is a more important consideration in the development of the theory than the explicit recognition of investor preferences. Beginning from about the last quarter of the twentieth century, alternative theories based on different perceptions of systematic risk have challenged the dominance of the mean-variance notion of risk-return relationship. The most prominent of these is the asset pricing theory which recognizes risk as the deviation below a target rate of return. Downside risk measures and the associated asset pricing models are motivated by economic and statistical considerations; investor psychology is consistent with asymmetric treatment of the variations in the returns and empirical return distributions appear to be non-normal. Bawa and Lindenberg (1977) developed an asset pricing model, which we refer to as the mean-lower partial moment (MLPM) model, based on downside risk. In the MLPM model, risk is defined as the deviation below the risk-free rate. For normal and student-t-distributions of returns, the MLPM model reduces to the conventional CAPM. In the MLPM model, the downside beta simply replaces the CAPM beta. Bawa and Lindenberg (1977) argue that their model explains the data at least as well as the CAPM does. Harlow and Rao (1989) developed an asset pricing model in the downside framework that is more general in that the risk is defined as the deviation below an arbitrary target rate.
Methodology
Twenty of best seller corporations of Tehran Stock Exchange (TSE) were studied using monthly historical data (from April 2004 to March 2011). Several variations of the capital asset pricing model (CAPM), such as Lower Partial Moment-Capital Asset Pricing Model (LPM-CAPM), Asymmetric Response Model (ARM) and traditional CAPM, were empirically tested.
Results and Discussion
The results show that, traditional Capital Asset Pricing Model, is better than Lower Partial Moment-Capital Asset Pricing Model (LPM-CAPM) and Asymmetric Response Model (ARM) in our selected period (from April 2004 to March 2011).
Conclusions and Suggestions
The results show that despite some empirical tests in recent years, it seems that by using long samples, traditional CAPM model could be a reliable test. Finally, authors suggest that to achieve more reliable results in CAPM models, researchers have to consider environmental conditions to use the best model.