Nima Mohamadnejad; Mohammadhassan Fotros; Mohammadreza Masoumi
Abstract
Introduction
Financial markets development is one of the major factors in economic growth. According to the literature, financial section could affect economic growth in two ways: enhancing resource allocation and hastening technology development. This study pin out the first way, i.e., the resource ...
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Introduction
Financial markets development is one of the major factors in economic growth. According to the literature, financial section could affect economic growth in two ways: enhancing resource allocation and hastening technology development. This study pin out the first way, i.e., the resource allocation. To this end, this study tries to get an optimized credit allocation between oil-related and non-oil sections.
Iran’s agriculture part is one of the areas that can have an important effect on the growth of country’s economy. Concerning this, variables that can increase value added agriculture have been concentrated on and the government is supporting them. One of these policies is granting loanable facilities from specialist banks to the agriculture part, which was in the specialist banks agendum during recent years.
This study divides GDP to oil-GDP and non-oil GDP and uses GDP growth as a proxy to economic growth. After recognition of the importance of bank credit through optimizing credit allocation between oil and non-oil sections, it turns to clarify the issue in the subsection. Results show that bank credits are more efficient in non-oil section and also the agriculture subsection.
Theoretical Framework
Greenwood and Jovanovic (1990) developed a theoretical model to find that the impact of financial development on economic growth is dependent on the transitional cycles in the economy. Austrian-based credit cycle theories (Hayek, 1933, 1935; von Mises, 1912) and capital-based macroeconomics (Cochran, Call, & Glahe, 1999; Garrison, 2001) generally argue that financial development and credit expansion, especially through money creation, may cause overinvestment problems that lead to unsustainable economic growth. The economic growth, especially in small oil basted economies may experience larger fluctuations according to the credit boom explanation of the business cycle (White, 2006). Thus, the relation between financial development and economic growth in small natural resource-based economies is a non-trivial question and yet to be empirically investigated.
Several empirical studies, using macro and industry-level data, have concluded that the development of financial intermediation has a significantly positive effect on economic growth. King and Levine (1993) provided the most comprehensive empirical work where using cross-sectional data from 80 countries. They found a positive relationship between bank credit and economic growth. Efficient allocation of funds through financial institutions leads to economic growth. Other studies including Levine and Zervos (1998), Levine (1998), and Beck and Levine (2003) found similar results. Eschenbach (2004) reviewed the majority of empirical studies and concluded that the direction of causality between financial development and growth varies across countries, regions and even variables employed by these studies.
Methodology
Bayesian model averaging (BMA) is an empirical tool to deal with model uncertainty in various milieus of applied science. In general, BMA is employed when there exists a variety of models which may all be statistically reasonable but the most likely result in different conclusions about the key questions of interest to the researcher. As Raftery (1995, p. 113) noted, in this situation, the standard approach of selecting a single model and basing inference on it underestimates uncertainty about quantities of interest because it ignores uncertainty about model form." Typically, though not always, BMA focuses on which regressors to include in the analysis. The allure of BMA is that one can quickly determine models, or more specifically, sets of explanatory variables, which possess high likelihoods. By averaging across a large set of models, one can determine those variables which are relevant to the data generating process for a given set of priors used in the analysis. Each model (a set of variables) receives a weight and the final estimates are constructed as a weighted average of the parameter estimates from each of the models. BMA includes all of the variables within the analysis, but shrinks the impact of certain variables towards zero through the model weights. These weights are the key feature for estimation via BMA and will depend upon a number of key features of the averaging exercise including the choice of prior specified. These difficulties made us to apply Bayesian Model Selection (BMS) to conquer BMA model problems. BMS uses the Markov Chain Monte Carlo (MCMC) samplers to gather results on the most important part of the posterior distribution.
The MCMC sampler randomly draws a candidate model and then moves to this model if its marginal likelihood is superior to the marginal likelihood of the current model. In this algorithm, the number of times each model is kept will converge to the distribution of posterior model probabilities. There are two different MCMC samplers to look at models within the model space. These two methods differ in the way they propose candidate models. The first method is called the birth-death sampler. In this case, one of the potential regressors is randomly chosen; if the chosen variable is already in the current model Mi, then the candidate model Mj will have the same set of covariates as Mi but drop the chosen variable. If the chosen covariate is not contained in Mi, then the candidate model will contain all the variables from Mi plus the chosen covariate; hence, the appearance (birth) or disappearance (death) of the chosen variable depends on if it already appears in the model. The second approach is called the reversible-jump sampler. This sampler draws a candidate model by the birth-death method with 50% probability and with 50% probability the candidate model randomly drops one covariate with respect to Mi and randomly adds one random variable from the potential covariates that were not included in model Mi.
Results & Discussion
After decomposing true prior to several economic sections, it is turned out that non-oil section of Iran’s economy has the potential to have more growth than oil related section and also from the non-oil sections, that is, the agriculture and industrial sub-sections , the agriculture sub-section was optimizing the credit resources more efficiently than the industrial one. This study also determines 5 models, accompanied by the highest posterior probability, that place in Occam's window and Lucas-Uzawa approach is determined as the most possible growth model to the Iran’s economy.
Conclusion & Suggestions
The main conclusion of this study highlights the agriculture subsection and the non-oil economic section for having better responses to bank credits and showing more growth. Our conclusion was based on a quasi-Bayesian approach because of the lack of the degree of freedom; in adition to the regression that was implemented just for the economy of Iran. Therefore, future studies, to conquer the lack of the degree of freedom, could apply a panel model among resource-based economies and survey the role of financial development, specifically bank credits.
