The “Dutch disease” in Algeria

Algeria, officially the People's Democratic Republic of Algeria, is a sovereign state in North Africa on the Mediterranean coast. Hydrocarbons have long been the backbone of the economy, accounting for roughly 60% of budget revenues, 30% of GDP, and over 95% of export earnings. Algeria has the 10th-largest reserves of natural gas in the world and is the sixth-largest gas exporter. It ranks 16th in oil reserves. Hydrocarbon exports have enabled Algeria to maintain macroeconomic stability and amass large foreign currency reserves and a large budget stabilization fund available for tapping. In addition, Algeria's external debt is extremely low at about 2% of GDP. However, as many big oil-producing countries, Algeria has certain problems developing industries not related with oil industry because of heavy regulation and an emphasis on state-driven growth.1 Here come “Dutch disease” phenomena, explaining the relationship between resource production “boom” and significant decline in other spheres, especially in manufacturing.

Many scientists have been analyzed the influence of oil prices fluctuations on Gross domestic product of Algeria and Dutch disease phenomena. Tunisian scientists R. Jbir and S. Zouari-Ghorbel in their article Oil Price and Dutch Disease: The Case of Algeria, published in Energy Sources journal in 2011 proved the fact that Algeria was affected by Dutch disease through the oil price shock and its correlation with the exchange rate.2

In this article we examined the economic model, which describes, how GDP is affected by General government revenue, exports (taking into account, that in Algeria export mainly represented by Hydrocarbon sector i.e. oil, gas and refined products, and their relation with the exchange rate, in terms of Dutch disease.

In order to analyze model 4 exogenous (independent) variables were taken: real oil prices, relation of USD/DZD, total exports and total imports; and 2 endogenous (dependent) variables – GDP and General government revenue. Model has following specification:

Y = C(1) + C(2) * X1t + C(3) * X2t + C(4) * X3t + Et .X1t = C(5) + C(6) * Yt + C(7) * X4t + C(8) * X5t + Et

{

Where Y_t - GDP at market prices (constant 2010 bln US$), X1_t -General government revenue bln US$, X2_t - Real oil prices (USD per barrel), X3_t - Official exchange rate (DZD per US$, period average), X4_t-Exports of goods and services (constant 2010 bln US$), X5_t-Imports of goods and services (constant 2010 bln US$). Data collected for the period from 1997 to 2014 from the open and reliable resources.

Estimation was made using Two-Stage Least Squares method and following results were obtained:

	Coefficient	Std. Error	t-Statistic	Prob.
C(1)	135.5945	42.88607	3.161736	0.0038
C(2)	0.084056	0.029785	2.822046	0.0087
C(3)	-3.859731	1.605750	-2.403693	0.0231
C(4)	-0.724734	0.676566	-1.071195	0.2932
C(5)	-7810.180	1556.172	-5.018841	0.0000
C(6)	232.1329	53.58001	4.332453	0.0002
C(7)	-188.7766	61.41876	-3.073598	0.0047
C(8)	-275.2532	92.10389	-2.988507	0.0058

Determinant residual covariance           16435207

Equation: Y=C(1)+C(2)*X1+ C(3)*X2+C(4)*X3 Instruments: X2 X3 X4 X5 C

Observations: 18

R-squared

Adjusted R-squared

S.E. of regression Durbin-Watson stat

0.884669 Mean dependent var

0.859955 S.D. dependent var

10.01809 Sum squared resid

2.929614

139.8959

26.77019

1405.070

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

Instruments: X2 X3 X4 X5 C

Observations: 18

R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat

0.934472 Mean dependent var          3277.372 0.920431 S.D. dependent var            1928.923 544.1107 Sum squared resid            4144790. 1.744285

As we it seen from the graph value of the multiple coefficient of determination R 2 =0,93 shows that 93 % of total deviation of GDP is explained by the variation of general government revenue, oil prices and in relation of USD/DZD. Such a high value of the R 2 is quite good it is close to 1 (maximumR² = 1). This means that selected factors influence the given model significantly.³ DW test was passed successfully and this means that no autocorrelation detected, F-test was passed as well.

Conclusion

Nowadays, research on the “Dutch disease” problem become especially relevant due to interesting economic trends arising on the world market. Algeria as a huge oil-exporter needs a lot of attention in terms of high volatility. For oilproducing country it is a real struggle to face the “Dutch disease” phenomena in conditions of world economic crises. It was mentioned previously, that any market volatility could provoke economic misbalances.

After model’ analysis, based on economic data for the period 1997-2014, we are able to make following conclusions: 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 as well as exchange rate, in terms of “Dutch disease”. Algerian economy relies heavily on hydrocarbon sector and depends on exports volume. Of course the way out of that situation is not easy to find at all.