Abstract

The article deals with statistical modeling of the primary commodity production impact on Iran’s economy. Iran produces about 30 primary mineral commodities, however only few them have significant impact on the country’s GDP. Statistical modeling consisted in investigation of the quadratic regression equation, which establishes interrelation between the total (aggregated) commodity value and Iran’s GDP. Results of investigation have demonstrated that: the commodity sector annually ensures from 30 to 50% of GDP. Quadratic equation has an exponential character, and this means that further growth in commodity production will have huge impact on economic growth..

Keywords: (Commodity value, GDP, quadratic regression equation, economic development)

Introduction

Surprisingly, influence of commodities on economy is one of the worst explored problems of the modern economy and economic geology. Only by the end of the recent century a number of publications appeared where authors tried to investigate interrelation between the commodities and the economic development. First of all, it has been proven that commodity prices do not follow the basic economic rule – interrelation between supply and demand (Deaton and Laroque, 1992). On the other hand, a series of publications studied so-called “commodity currencies” (Browne and Cronin, 2007; Chen and Rogoff, 2003; Cashin, Céspedes, and Sahay, 2003, etc.) showing that commodities have a lot of common with different financial instruments, like currencies, bonds, options, etc. rather than with goods. In another series of publications influence of oil and other commodity prices on GDP was illustrated (Tanaka, 2009; McAlien and Komulainen, 2008, etc.). Mr. Rogoff (2005) has used a simple regression equation for studying interdependence between oil output and its prices and has shown a direct influence of the latter on economic indices, namely, of the stock market quotes, etc.

However, the first analysis of direct impact of primary commodity production, consumption and exports on either the country or the world GDP was performed by us. For this purpose, we have elaborated a special approach (Tvalchrelidze, 2011), slightly updated recently (Tvalchrelidze and Silagadze, 2013).

Theoretical Fundamentals and Methodology

According to the classical theory (Sachs and Larrain, 1993), the Gross Domestic Product (GDP) represents a sum of added values. However, we have proven (Tvalchrelidze, 2011) that using the classical hypothesis of commodities (Sabsford, 1994), GDP may be described in commodity terms because the basis of any good is a corresponding commodity:

 , (1)

Where GDP = Gross Domestic Product, P_i = average weighted annual market price of the i^th commodity, S_i = annual volume of the produced commodity, = price of the i^th commodity processed up to the finished product n, F_n = volume of sold n^th product, A_s = added value of all services (governmental, insurance, bank, education, etc.).

As far as in the equation (1) commodities are expressed in economic terms, we have introduced the notion of “Commodity Value”, which, likewise the added value, shall imply volume of annual primary commodity production multiplied by its average annual world price. In this case the “Total Commodity Value” shall imply sum of all investigated commodity values.

For the statistical modeling, first of all, interrelation between GDP and the total commodity value shall be studied using the classical equation of correlation (Freeman, 2005):

  .        (2)

If the correlation factor is significant and strong, interrelation between two variables may be analyzed by the regression method (Levine et al., 2010):

  ,      (3)

Where = residual of equation (4):

  ,   (4)

and coefficient β is determined by last squares method meaning that deviation of squares of points should be minimum. It is reached by an extremum (Levine et al., 2010):

  . (5)

In none-linear cases it is possible to compute the values of coefficients, standard errors and residue . To do so, we need to know mean values of  and , the standard deviation of x, the standard deviation of y, and the correlation between them. Such computation was realized in the SPSS computer system using ANOVA (analysis of variance) technology (Levine et al., 2010).

For statistical analysis the following international sources were used: (1) Annual yearbooks “BP Statistical Review of World Energy” (see, for instance, BP Statistical…, 2017) – for oil and gas production data; (2) Annual yearbooks on world mineral production by the British Geological Survey (see, for instance, Brown et al., 2018) – for mineral commodity production data; (3) World Development Indicators from the World Bank Group data bank (see http://databank.worldbank.org/data/reports.aspx?source=world-development-indicators) – for GDP data by years. And (4) IMF primary commodity prices – for average monthly prices of mineral commodities (see http://www.imf.org/external/np/res/commod/index.aspx).

Iran is annually producing about 30 energy and mineral commodities (Fig. 1) but only nine of them, the aggregated commodity value of which exceed US$ 300 million (Table 1), have significant impact on economic development.

Figure 1: 2016 Energy and Mineral Commodity Production in Iran as a share of World Production

Table 1: Average Weighted Basic Commodity Production in Iran in 1980-2016

Modeling period was chosen 1980-2016: no statistical data in cited issues are available for the year 2017. Using the described approach we have completed a database for modeling (See annex).

Fig. 2 displays correlation between Iran’s GDP and aggregated commodity value. Extremely high figure of the correlation factor allows us to perform statistical modeling according to the methodology described above.

Figure 2: Interrelation between the Iran’s GDP and Total Commodity Value

Results and Discussion

Fig. 3 provides the quadratic regression equation graph, where interrelation between the aggregated commodity value and Iran’s GDP is explored. The graph has an exponential character, and this means that further growth in commodity production will have huge impact on economic growth. For comparison, the same graph of China demonstrates that the commodity sector of the country is already saturated, and no farther accelerated growth of GDP is expected (Silagadze et al., 2016).

Fig. 4 compares real and model GDP of Iran. Extremely high value of the correlation factor is observed, and accuracy of the statistical model is ±5%.

Figure 3: Statistical Model of Iran’s GDP

 Figure 4: Comparison of Real and Model GDP of Iran

Conclusions

1. As in each country, which develops market relations, impact of commodity sector on economic growth of Iran is huge
2. In the new Millennium this impact annually varies from 30 to 50% of GDP
3. Quadratic equation, which describes interdependence between total commodity value and Iran’s GDP, has an exponential character, and this means that further growth in commodity production will have huge impact on economic growth
4. Using the model hereto, the Iranian governmental decision maker, planning production of basic commodities, may predict rate of economic growth, and the accuracy of such assessment will be ± 5%.

Hence, share of mineral versus energy commodities in total commodity sector is inadequately low in Iran. As far as further growth in oil and gas production meets huge geopolitical and infrastructural encumbrances, the basic bets shall be placed on development of mineral commodity production, and mainly on gold and base metals. For doing this:

1. Investments in exploration & mining shall be promoted
2. Mining Act of Iran shall be updated taking into account the best world mining practice
3. Routine procedures of licensing shall be simplified, and
4. Licenses where no business activities were registered during a fiscal year, shall immediately be canceled.

Acknowledgements

Authors are sincerely grateful to Mr. Reza Bahraman from the Iran Mine House for strong support of this investigation.

References

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Annex. Data Base for Statistical Modelling