Dutch disease: the case of Gabon Текст научной статьи по специальности «Экономика и бизнес»

5. Лосева Н.А., Прохоров И.В. Формирование и развитие аудиторской деятельности в России // Аудитор, 2013. № 9.

DUTCH DISEASE: THE CASE OF GABON Gevorkyan L.A.

Gevorkyan Lia Araikovna - Master's Degree student, INTERNATIONAL FINANCE DEPARTMENT, FINANCIAL UNIVERSITY UNDER THE GOVERNMENT OF THE RUSSIAN FEDERATION, MOSCOW

Abstract: in this paper, we attempt to analyze the economy of Gabon for the presence of "Dutch disease". We study the correlation between GDP of Gabon and selected parameters. Model shows, how changes in general government revenue, exports and imports affect GDP. We study main economic indicators of Gabon for the period 1990-2015. Model was estimated using Two-Stage least squares (2SLS) regression analysis in EViews. This study is a practical example of econometric modeling at the real example of the Gabon economy. Given the high volatility of oil prices and key macroeconomic indicators, we analyzed the existence of «Dutch disease». Keywords: GDP, General Government Revenue, Econometrics, Exports, Dutch disease, Economics, Oil prices, Oil-producing country, Gabon.

Introduction

Originally coined in 1977 by The Economist, the term Dutch disease refers to the decline in the manufacturing sector of the Netherlands due to the discovery and exploitation of natural gas deposits in the 1960s. Now it is often used to refer to the detrimental effects of the discovery of any valuable natural resource that causes declines in other sectors of a nation's economy.

The African continent is rich with natural resources like minerals and fossil fuels. That is why production and exports of African countries are highly concentrated in natural resource-based products, but these economies show little evidence of structural change toward high value-added activities outside the natural resource sector. For example, if we turn to the export structure of Gabon, we can see that the top exports are Crude Petroleum ($6.05B), Manganese Ore ($632M), Sawn Wood ($270M), Veneer Sheets ($112M) and Refined Petroleum ($112M). Crude petroleum is about 80% of Gabonese export [5]. (Chart 1)

H Crude Petroleum U Manganese Ore H Sawn Wood

Fig. 1. The structure of Gabonese export, $

I. Two-Stage least squares (2SLS) regression analysis

There are four exogenous variable in the model: real effective exchange rate index (2010 = 100), oil prices, exports of goods and services (constant 2010 US$), imports of goods and services (constant 2010 US$). Also there are two endogenous variables - GDP and total government revenues. The model is the following:

(Yt = C(l) + C(2) * Xlt + C(3) * X2t + C(4) * X3t + et Ult = C(5) + C(6) * Yt + C(7) * X4t + C(8) * X5t + et

- Yt - GDP at market prices (constant 2010 bln US$); ■ X 1 t -General government revenue (bln US$);

- X2 t- Real oil prices (USD per barrel);

- X3 t- Official exchange rate (DZD per US$, period average);

- X4t-Exports of goods and services (constant 2010 bln US$);

- -Imports of goods and services (constant 2010 bln US$).

Data was collected for the period from 1990 to 2015 from the World Bank [4]. Results are the following.

Table 1. Results

Coefficient Std. Error t-Statistic Prob.

C(1) 11.34377 0.230923 49.12351 0.0000

C(2) 0.194947 0.009763 19.96771 0.0000

C(3) 0.010968 0.005365 2.044449 0.0472

C(4) -0.022991 0.001351 -17.02394 0.0000

C(5) -116.3937 28.70251 -4.055174 0.0002

C(6) 17.15230 2.411801 7.111822 0.0000

C(7) 1.756918 2.157032 0.814507 0.4199

C(8) -18.08843 3.670768 -4.927696 0.0000

Determinant residual covariance 2.033209

Equation: Y=C(1)+C(2)*X1+ C(3)*X2+C(4)*X3

Observations: 25

R-squared 0.991683 Mean dependent var 13.33071

Adjusted R-squared 0.990495 S.D. dependent var 1.785741

S.E. of regression 0.174095 Sum squared resid 0.636493

Durbin-Watson stat 1.033040

Equation: X1=C(5)+C(6)*Y+C(7)*X4+C(8)*X5

Observations: 25

R-squared 0.732702 Mean dependent var 67.89311

Adjusted R-squared 0.694516 S.D. dependent var 19.10721

S.E. of regression 10.56067 Sum squared resid 2342.081

Durbin-Watson stat 0.932604

We can see from the results that R-squared (the multiple coefficient of determination) is 0,99. It means 99% of total deviation of GDP can be explained by the variation of general government revenue, oil prices and exchange rate. Durbin-Watson test is passed successfully that means no autocorrelation was detected within the indicators, F-test is passed as well [2]. II. Conclusion

Sub-Saharan Africa is generally considered as the poorest and most corrupt region with backwards economy. We can be sure that Dutch disease is observed in Gabon in 1990-2014. The Gabon's GDP depends on oil exports, oil prices and exchange rate. Some economists say that Dutch disease is not a bad thing. Shouldn't economies focus on what they are most efficient at producing? However, commodity prices fluctuate: most countries need back-up industries. In the case of Gabon, oil sales must be saved in order to prevent further Dutch disease.

References

1. Dutch Disease in Africa: A Case Study of Nigeria and Chad. Jason Gould and Katen N. Kapadia - University of Michigan, 2006. 11 p.

2. Tregub I.V. Mathematical models of economic systems dynamics: Monography. M.: Finance Academy, 2009. 118 p.

3. Natural resource curse in Africa: Dutch Disease and institutional explanations. Richard Mulwa, Jane Mariara, 2016. 26 p.

4. The database of the World Bank. [Electronic resource]. URL: http://data.worldbank.org/ (date of access: 10.02.2017).

5. The Observatory of Economic Complexity. [Electronic resource]. URL: http://atlas.media.mit.edu/en (date of access: 10.02.2017).