CC BY
2
УДК 519.868
Zherebyatyeva N.D. master student of 1st year IFF group 1-2m
Scientific adviser: Tregub I. V., Ph.D. in Technics, Sc.D. in Economics,
Professor
Financial University under the Government of the RF
Russia, Moscow Жеребятьева Н.Д. студент, магистратура 1 курс, МФФ группа 1-2м Научный руководитель: Трегуб И.В., к.тн., д.э.н., профессор Финансовый университет при Правительстве РФ
Россия, Москва ANALYSIS OF POSSIBLE AVAILABILITY OF THE "DUTCH DISEASE" IN ECONOMY OF IRAN
Abstract. This article is devoted to the analysis of the possible presence of "Dutch disease" effect in the Iranian economy through analysis of correlation between gross domestic product and selected parameters. The goal of the model was to reveal correlation between selected parameters in order to investigate to what extent changes in oil production, oil prices and volume of export and import affects GDP. However, results of the first model showed that there is a need to rearrange indicators in order to see "clear picture" and make conclusions concerning such phenomena as "Dutch disease" in Iran.
Key words: Dutch disease, oil production, Iran, Iran economy, econometrics model, oil export.
In order to analyze model 4 exogenous (independent) variables were taken: real oil prices, relation of USD/IRR, total exports and total imports; and 2 endogenous (dependent) variables - GDP and General government revenue. Model has following specification:
Yt = C(l) + C(2) * X1t + C(3) * X2t + C(4) * X3t + et Xlt = C(5) + C(6) * Yt + С(7) * X4t + C(8) * X5t + Et Where:
• Yt - GDP at market prices (current US$ billions);
• Xl eral government revenue (current US$ billions);
• X2t - Real oil prices (USD per barrel); ial exchange rate (IRR per US$, period average);
• X4t -Exports of goods and services (current US$ billions); rts of goods and services (current US$ billions).
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Data collected for the period from 1997 to 2015 from the open and reliable statistical resources. 1
Estimation was made using Two-Stage Least Squares method and following results were obtained: System: UNTITLED
Estimation Method: Two-Stage Least Squares
Date: 02/16/17 Time: 10:19
Sample: 1997 2015
Included observations: 19
Total system (balanced) observations 38
Co efficient S td. Error t-Statistic Prob.
C( 1) -23.67697 28.91083 -0.818965 0.4193
C(2) 3.288337 1.087492 3.023780 0.0051
C(3) 1.522979 1.070179 1.423106 0.1650
C(4) 0.004834 0.002181 2.216207 0.0344
C(5) 15.14811 16.55602 0.914961 0.3675
C(6) -0.874649 1.037574 -0.842975 0.4059
C(7) 2.553758 2.355801 1.084029 0.2870
C(8) 1.845610 2.362658 0.781158 0.4408
Determinant residual covariance 1016183.
Equation: Y=C(1)+C(2)*X1+ C(3)*X2+C(4)*X3 Instruments: X2 X3 X4 X5 C Observations: 19 R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat
0.916446 Mean dependent var 296.3264
0.899735 S.D. dependent var 171.5238
54.31246 Sum squared resid 44247.64 0.752381
Equation: X1=C(5)+C(6)*Y+C(7)*X4+C(8)*X5 Y=C(1)+C(2)*X1+ C(3)*X2+C(4)*X3@X2 X3 X4 X5
X1=C(5)+C(6)*Y+C(7)*X4+C(8)*X5+C(9)*X6@X2 X3 X4 X5 Instruments: X2 X3 X4 X5 C
Observations: 19
R-squared 0.228414 Mean dependent var 56.87841
Adjusted R-squared 0.074097 S.D. dependent var 31.03656
S.E. of regression 29.86457 Sum squared resid 13378.39
Durbin-Watson stat 0.841801
As we it s een from the table value of the multiple coefficient of determination R2=0,92 shows that 92 % of total deviation of GDP is explained by the variation of
1 The Official site of the World Bank, Iranian statistical data profile http://data.worldbank.org (Accessed at
14.02.2017)_
general government revenue, oil prices and in relation of USD/IRR. Such a high value of the R2 is quite good it is close to 1 (maximumR2 = 1). However, if we look at the second equation of the model we can see that the value of R2 is only 0,22. This means that selected factors influence the given model non-significantly.2 DW test was shows that there exist autocorrelation between indicators.
In order to understand why this model showed poor results for the country of OPEC, that exports oil products amounted more than 80% from the total volumel [1], it was decided to analyze the chosen indicators' dynamic more detailed. It can be seen from the table given below that the dynamic of Iranian currency experienced significant falls. After analyzing different articles and news on this topic, it became clear that there exist important political issues that influence the exchange rate of Iran. The peak of sanctions against Iran has begun in 2002 because he has issued "nuclear program" for extraction of uranium in large numbers. Such policy of Iran wasn't pleasant to the USA and Europe as it was visible that Iran extracts uranium not in the peace purposes. Respectively, sanctions against Iran that amplified every year have been imposed. 3
Table 1. Iranian currency (IRR) exchange rate fluctuation during the year 2002
Year Exchange Rate of IRR X6
1997 1753,39 0
1998 1752,33 0
1999 1753,40 0
2000 1764,90 0
2001 1754,03 0
2002 6907,04 1
2003 8193,89 1
2004 8613,99 1
2005 8963,96 1
2006 9170,94 1
2007 9281,15 1
2008 9428,53 1
2009 9864,30 1
2010 10254,18 1
2011 10616,31 1
2012 12175,55 1
2013 18414,45 1
2014 25941,66 1
2015 29011,49 1
2 Трегуб И.В., Хацуков к.л. Проверка применимости модели для прогнозирования экономических показателей // Экономика и социум. 2014. № 4-4 (13). С. 1345-1349.
3 Expert Online Journal, Iranian Economy, June, 2014, Available at: http://expert.ru/2014/02/6/v-ozhidanii-peremen/ (Accessed at 16.02.2017)_
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That is why the extra variable X6 was included in the model in order to smooth down this hard fluctuation of exchange rate. The following results was obtained:
System: UNTITLED
Estimation Method: Two-Stage Least Squares Sample: 1997 2015 Included observations: 19
Total system (balanced) observations 38
Coefficient Std. Error t-Statistic Prob.
C( 1) -1 5.74411 2 6.92958 -0.584640 0.5633
C(2) 2.685356 0.993287 2.703506 0.0114
C(3) 2.057049 0.982381 2.093943 0.0451
C(4) 0.004455 0.002039 2.184474 0.0372
C(5) 13.53520 7.311163 1.851307 0.0743
C(6) -0.328602 0.199220 -1.649449 0.1098
C(7) 1.332526 0.501873 2.655106 0.0127
C(8) 0.895851 0.707177 1.266799 0.2153
C(9) -16.56023 16.33418 -1.013839 0.3190
Determinant residual covariance 262012.6
Equation: Y=C(1)+C(2)*X1+ C(3)*X2+C(4)*X3 Instruments: X2 X3 X4 X5 X6 C Observations: 19 R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat
0.926601 Mean dependent var 296.3264
0.911921 S.D. dependent var 171.5238
50.90513 Sum squared resid 38869.98 0.882670
Equation: X1=C(5)+C(6)*Y+C(7)*X4+C(8)*X5+C(9)*X6 Instruments: X2 X3 X4 X5 X6 C Observations: 19
R-squared 0.858510 Mean dependent var 56.87841
Adjusted R-squared 0.818084 S.D. dependent var 31.03656
S.E. of regression 13.23760 Sum squared resid 2453.277
Durbin-Watson stat 1.306596
As we it seen from the table value of the multiple coefficient of determination in the second equation is equal to R2 =0,85 compare to the value of 0,22 received in the first model. This means that selected factors influence the given model
significantly, and the downturns in the exchange rate can be smoothed out in order to analyze dependence of Iranian Economy on oil production in ore objective way, i.e. without the effect of sanctions imposed on country4. DW test was shows that there exist not significant autocorrelation between indicators.
Conclusion
Nowadays, research on the "Dutch disease" problem become especially relevant due to interesting economic trends arising on the world market. Iran as a huge oil-exporter needs a lot of attention in terms of high volatility. For oil-producing country it is a real struggle to face the "Dutch disease" phenomena in conditions of world economic crises. Especially, if we take into the account the fact that external political climate for Iran cannot be named stable. It was mentioned previously, that despite the fact that the sanction was imposed, and Iranian currency experienced high volatility, this country still remains on of the leading position of oil-producing countries. That is exactly why more attention should be paid on economic policies regarding oil-production as well as export of oil.
After two models' analysis, based on economic data for the period 19972015, we are able to make following conclusions: 2nd model used, could possibly be used to estimate influence of used factors on gross domestic product, how changes are determined by general government revenue, oil prices and net exports but without accounting on the exchange rate, due to instable political situation and sanctions imposed on Iran.
References
1.The Official site of the World Bank, Iranian statistical data profile http://data.worldbank.org (Accessed at 14.02.2017).
2.Трегуб И.В., Хацуков к.л. Проверка применимости модели для прогнозирования экономических показателей // Экономика и социум. 2014. №2 4-4 (13). С. 1345-1349.
3.Трегуб А.В., Трегуб И.В. Методика прогнозирования показателей стохастических экономических систем //Вестник Московского государственного университета леса - Лесной вестник. - 2008. - №2 (59). - С. 144-152.
4.Oil and the Future of Iran: A Blessing or a Curse? Future of Iran Economy September, 2013 Available at: http://li.com/docs/default-source/future-of-iran/the-future-of-iran-(economy)-oil-and-the-future-of-iran-a-blessing-or-a-curse-pdf.pdf?sfvrsn=2 (Accessed at 16.02.2017).
5.Expert Online Journal, Iranian Economy, June, 2014, Available at: http://expert.ru/2014/02/6/v-ozhidanii-peremen/ (Accessed at 16.02.2017).
4 Expert Online Journal, Iranian Economy, June, 2014, Available at: http://expert.ru/2014/02/6/v-ozhidanii-peremen/ (Accessed at 16.02.2017)_
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