Fluctuations of the Real Exchange Rate and the Structure of the Iraqi Economy

Fluctuations of the Real Exchange Rate and the Structure of the Iraqi Economy

Kamaran Qader Yaqub

Sulaimani Polytechnic University

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8100052

Received: 29 September 2024; Accepted: 03 October 2024; Published: 30 October 2024

ABSTRACT

This paper analyzes how a boom in the oil sector affects the relative prices of non-tradable goods compared to tradable goods, the real exchange rate, and the competitiveness of Iraq’s broader economy. As the oil sector booms, Iraq’s currency appreciates, which reduces the competitiveness of the country’s traditional export sectors. This thesis investigates the presence of Dutch Disease in Iraq, an area previously unexplored. It evaluates the impact of growing oil revenues on the non-oil sectors of the economy and provides empirical evidence showing a contraction in the tradable goods sector and an appreciation of the real exchange rate. The findings suggest that Iraq experienced symptoms of Dutch Disease, including real exchange rate appreciation, a shrinking tradable goods sector, and an expanding non-tradable goods sector. Ultimately, the appreciation of the real exchange rate is responsible for shifting Iraq‘s economy from manufacturing and agriculture (tradable sectors) to construction and services (non-tradable sectors).

Keywords: Real Exchange Rate, Tradable and non-tradable goods

INTRODUCTION

The growing expansion of the natural resource sector has brought about a huge and growing literature focusing on the influence of such growth on the rest of the economy. The boom of the exporting natural resource sector might influence other economic sectors either positively or negatively. Some economists have argued that the changes in economic structure would occur due to changes in the real exchange rate. Such adverse effects resulting from a boom are termed the ‘Dutch disease’. This ‘disease’ was identified in the 1970s, after the discovery of huge natural gas reserves in the Netherlands (Corden 1981). The Netherlands’ economy experienced certain sectoral structural changes since this time. These changes were in the form of a reduction in the non-natural resource (gas) traded sector, which later caused de-industrialization and also an enlargement in the services sector. That was caused by an appreciation in the local currency and a modest upsurge in local wages, as well as the increasing cost of the social security system (Rodrik 2007).

The Iraqi oil sector has experienced solid growth in terms of level of production and export values since the early 1970s, accompanied by unstable increases in the Iraqi real exchange rates. However, the experience of other oil-exporting countries proposes that high oil prices (oil boom period) could cause a contraction of the tradable sectors (agriculture and manufacturing sectors) mostly after appreciation RER (Corden and Neary 1982). Sachs et al. (1995) stated that countries abundant in natural resources would witness a slow growth in tradable sectors compared to poorer natural resource countries. The “Dutch disease” refers to a situation where the reversal of positive effects or negative effects of oil booms on countries hampers their economic transformation where they are extracted. Dutch disease theory predicts that part of the boom revenues is spent on the non-tradable goods which bring about an appreciation of the real exchange rate, and which, in turn, draws resources toward the non-tradable sector from the tradable sector. Furthermore, the increased profitability of a non-tradable sector bids up the prices of factors of production, leading to a reduction of the agriculture and manufacturing sector as a result of the reduction in production factors.

In this paper, we shall translate the theoretical framework related to shifting the structure of economy in oil exporting developing counties like Iraq into a quantitative analysis. To facilitate a quantitative assessment of some of the effects of the booming sector (oil sector, in our case) on the Iraqi economy. The purpose of this paper is to empirically estimate the behavioral equations that have been built based on Edwards’s model by using both Ordinary Least Squares (OLS) and Two Stage Least Square (2SLS) regression methods, which are employed using time-series data from 1970-2013. The reason behind using 2SLS is related to problems with “endogeneity problem”.[1] Regarding satisfaction of time series data, before running the regression, it is important to satisfy properties of time series via testing stationarity, co-integration (long-term relationship), multicollinearity and some other tests (Yaqub et al.; 2024).

LITERATURE REVIEW

In the very early stages, Salter (1959) introduced the two economic sectors framework for the macroeconomic model. He introduced a diagram to explain the features of the linkage between price and expenditure effect. To reconcile the balance of payments and the full employment strategy, Salter divided the product market into two main categories: traded goods and non-traded goods. Traded goods consist of importable and exportable goods with perfect substitution. Their prices are exogenously determined. Non-traded goods, on the other hand, are produced and also consumed locally so that their prices are determined by the interaction between domestic demand and domestic supply. Salter also mentioned the three means by which one of them may affect an internal and external disequilibrium. These means can be explained as follows:

• Excess demand:

An increase in income will lead to excess demand for both groups of goods (tradable and non-tradable) and, consequently, trigger an increase in expenditure. Changes in relative prices will appear if the expenditure continues to rise (e.g. as a result of high revenue from the oil sector), leading to an increase in the price of non-traded goods relative to traded goods. However, the magnitude of responses in the supply and demand for both types of goods (tradable and non-tradable) depends upon the degree of elasticity of substitution. On the other hand, excess demand must always lead to a balance of payments deficit; for here, the price and expenditure effects work in the same direction so that there is no question of the one offsetting the other. External and internal balance can be brought back through lowering home costs and prices relative to those of overseas. This kind of adjustment can be made via a devaluation of local currency, as well as an additional cut in expenditure.

• A rise in world prices:

As long as the price of traded goods is determined exogenously, any increase in the price of tradable goods (e.g. an increase in world prices) will lead to an increase in the price of domestically traded goods as well. Subsequently, on the supply side, the amount of production of traded goods will enlarge at the expense of non-traded goods. However, on the demand side, in the case of increasing prices in the global market, the influence of expenditure will rely on the behavior of consumers’ domestic income. Let us assume that expenditure is constant. In this case, the real expenditure drops, leading to a drop in the quantity demanded. The unemployment rate might increase, especially when the elasticity of demand is greater than one. This implies that the real expenditure effect is more influential than the price effect, which may reduce the relative prices (Aliyu 2009). The remedy for this is the appreciation of the exchange rate. In contrast, if expenditure increases, income increases as well, triggering over-employment and a surplus in the balance of payments. This consequence can be dealt with via an appreciation of the exchange rate to decrease expenditure.

• Overseas capital inflows:

Such an inflow of capital (either in the form of FDI or portfolio) will cause an upsurge in expenditure, shifting its line upwards and generating overemployment. Additional expenditure will cause an excess demand for non-traded goods and/or an increase in investment, leading to an increase in relative prices (Lartey 2008). In this case, the volume of imports may also increase due to extra expenditure, if the internal balance exists (meaning no changes in local prices and levels of production of non-traded goods) bringing about structural changes. The cure will transfer the additional expenditure to the balance of payments, allowing the internal balance to reconcile with capital inflow.

The previous analysis is related with the hypothesis that the terms of trade remain fixed. However, internationally, the prices of goods and services change over time and may influence the terms of trade, both importable and exportable. Hence, a drop in the export price brings about an adverse movement of the terms of trade; as a consequence, national income will drop when it is measured in terms of importable trade and increase in terms of exportable trade. To return the external and internal equilibrium, a devaluation of domestic currency is increasingly possible in the case of a decrease in exportable prices to increase non-traded goods prices and increase demand for traded goods. Depreciation occur when an increase in the importable price triggers the price of non-traded goods to be cheaper and the price of traded goods to be more expensive. Moreover, Salter has mentioned the influence of import restrictions in order to stop, or at least minimize, a drain on foreign funds. However, that policy could bring about growth in demand for non-traded goods, which eventually leads to internal imbalance. To prevent extra demand from being transferred to non-traded goods, governments must cut some of their expenditure until changes in the relative price return to an equilibrium point.

A partial equilibrium model (apparently the first model after increasing oil price in 1976) has been developed by Gregory (1976) in order to examine the effect of the discovery of mineral resources, on the structure of different economic sectors. Due to the discovery of mineral resources, the surplus in the balance of payments has influenced the volume of exports and import-competing industries via an adjustment in the exchange rates, and also raises the price of non-tradable goods relative to the price of exports and imports. Therefore, import-competing and pre-existing export industries are squeezed. Moreover, Gregory’s analysis demonstrates that the influence of the mineral exports sector is equivalent to a doubling of the tariff on the traditional export sector, whereas the fast growth of the mineral resources export sector, from the standpoint of the import competing sector, is equivalent to the elimination of the tariff. The increase in the mineral resources sector has resulted in a 25% overall decrease in tariffs coming from different economic sectors.

However, Gregory examined only the influence of the spending effect which comes from new mineral discoveries. The changes, essential in order to bring back equilibrium, depend on the extent of the mineral resource discoveries, the price of demand and supply elasticities of mineral resource exports, and the price elasticity of supply and demand of imports and traditional exports sectors. In Gregory’s analysis, the impacts of income and the costs of import-competing and traditional exports industries have been ignored. This is one of the main limitations of Gregory’s model.

Later, Gregory’s model would be amended by other scholars such as Snape (1977), Eastwood and Venables (1982), Corden and Neary (1982) and Long (1983). For instance, the theoretical equilibrium model has been developed by Snape (1977), resting on Gregory’s assumptions. Snape aimed to investigate the impact of natural resource exploitation income on the costs of import-competing export sectors. Moreover, he tested the influence on price elasticity of demand and supply for imports, mineral exports and non-mineral tradable goods exports sectors. His outcomes regarding the influence of the expenditure effect and resource effects can be summarised as follows:

(i)  Output of non-mineral tradable goods may decline.

(ii) Social benefits can be achieved even if the output of non-mineral goods remains constant.

(iii) While the price of non-tradable goods can be expected to rise, their production may increase.

On the other hand, a theoretical macroeconomic model has been developed by Eastwood and Venables (1982) to examine the influence of the exploitation of oil on an open economy. This model assumes that oil is completely tradable and its receipts are independent of an oil depletion strategy. This infers that there is no cost in extracting the oil. Other assumptions about the economy include full employment, zero inflation, and that capital and current accounts are balanced. These assumptions are based on Dornbusch’s (1976) method which uses five equations concerning domestic non-oil demand, money market equilibrium, output, foreign exchange equilibrium and the Phillips curve. Eastwood and Venables concluded that predicted oil earnings bring about an increase in the rate of unemployment, as well as price deflation, if spending is not adjusted immediately after the exploitation of oil.

However, Eastwood and Venables’ study examined only the influence of oil receipts on the spending effect, and ignored such an effect on money demand. Neary and Van Wijnbergen (1986) pointed out that this omission is logical because oil discovery would increase expenditure, and constant money demand permanently would be inconceivable. Neary and Wijnbergen justified their opinion on liquidity grounds. The huge revenue which comes from exporting oil will significantly influence money demand either directly via the private sector or indirectly via the government. Therefore, the wealth of the private sector should increase via the predicted decrease in future tax liabilities.

In order to minimize this gap, Neary and Van Wijnbergen (1986) applied Eastwood and Venables’ model incorporating the wealth effect on asset markets. Assuming a positive wealth elasticity of money demand, the higher wealth resulting from oil revenue would encourage excess money demand. To accommodate this condition, an increase in real money stock has to occur, causing an appreciation of the exchange rate and a lower post-shock price level than given by Eastwood and Venables (1982). Neary and Van Wijnbergen (1986) concluded that greater oil receipts might bring about a downturn, even without a spending lag. They justify this conclusion by arguing that an increase in wealth will raise the demand for money.

Based on what has been analysed and argued above about relative price and economic structure due to a booming sector, Corden and Neary (1982) develop a core model with both direct and indirect de-industrialization effects of energy discoveries. They assume that the economy is divided into three sectors, a booming sector, a tradeable sector and a non-tradeable sector. The prices of the first two sectors (booming sector and tradable sector) are determined by the international market (exogenously), while prices for non-tradable goods are determined by the domestic market. Output in each sector is produced by a factor specific to that sector and by labor which is assumed to be mobile between all three sectors. The direct impact of the boom on the economy is referred to as the Resource Movement Effect. The greater labor demand in the booming sector will trigger the movement of labor from the tradable sector to the booming sector (oil sector) and will therefore lead directly to lower output in the tradable sector. In other words, the resource movement effect means that the boom in the natural recourses sector (oil) increases the price of the marginal products of mobile factors (labor), and so draws resources out of the tradable sector (i.e. agriculture or manufacturing sector) and moves them to the booming sector. Moreover, the movement of labor from the non-tradable goods sector to the booming sector will reduce the supply of non-tradable goods and create an excess demand for them. In this situation, the price for non-traded goods, in terms of traded goods, will increase (a real appreciation) and further movements of resources out of the traded sector into the non-tradable sector are expected (indirect de-industrialization).

Although the analysis described above predicts that the output of the tradable sector will finally contract because of the expansion of the booming sector, there are some ways in which the core model might be altered. This can be done by changing some of the fundamental assumptions in the model: the potential effects of the boom on the tradable sector may be less severe and in some cases, there may not even be a Dutch disease at all. For example, as Corden (1984) pointed out, if one is initially in a condition where all domestic resources are not fully employed before the boom period, the boom may deliver a stimulating effect on the tradable sector. Consequently, the traded goods can be recovered and then, to some extent, the appreciation of the real exchange rate could be avoided.

Bruno and Sachs (1982) extend Corden and Neary’s core model by changing it to a dynamic model. They claim that the effects of the boom on the non-traded and traded goods sectors are inherently dynamic. An increase in national income moves demand towards the non-tradable sector from the tradable sector and, hereafter, will result to profitability on capital in the two sectors diverging and differing from the rate of return given on world capital markets. Therefore, according to the dynamic model, a capital-accumulation process in the non-tradable sector and dissimulation in the tradable sector will be predictable. To account for this matter, Bruno and Sachs assume that physical capital moves freely between sectors and from abroad to the local economy and in this case the marginal product of capital is always equal to the rate of return given on international capital markets. Based on this assumption, Bruno and Sachs demonstrate that, although the basic outcome of the Dutch-disease analysis is confirmed again, international-capital mobility profits to the point where the relative price rise of non-traded goods is fully eliminated.

Long (1983) also develop a model to clarify the influence of an export boom industry through resource-movement effects and spending effects on the rest of the economy. He concentrated on changes in prices of non-traded to traded goods and the volume of production of each industry. Such changes will happen via moves in the demand curve, as well as the supply curve. To some extent, the outcome of this analysis contradicts what is predicted with the Dutch disease. According to Long’s (1983) analysis, the following economic effects may occur:

(i) The price of non-traded goods can decrease relative to the price of traded goods (except the mining sector).

(ii) The non-mineral traded goods can enlarge, even in the case of decreases in price relative to the price of non-traded goods.

(iii) The enlargement in the non-mineral traded goods might be associated with a contraction of production of the booming mining sector relative to its pre-boom equilibrium output.

Roemer (1994) re-examined Corden and others’ medium-run and Long’s long-run theoretical Dutch disease framework for developing countries. He concentrates on the influence of an export boom in the oil and gas sectors on the developing nations’ economies. The characteristics of the developing countries’ environment in relation to the Dutch disease have been considered. Roemer argues that, in developing countries, one cannot generalise the predictions of the Dutch disease phenomenon. Such a conclusion arises from the fact that these nations have different structures and features, which bring about an unpredictable influence compared to what occurs in developed countries. For instance, developing countries are suffering from underemployment (particularly disguised unemployment) instead of full employment.

On the other hand, Edwards (1983) and Edwards and Akoi (1983) introduced a model of a developing country that relies heavily on the exports of a particular goods is fully worked out. The model highlights the impact of changes in commodity export prices on money creation, inflation and real exchange rate. In this model, the monetary side is also included into real side. The monetary aspects of the boom are parallel forces by which the boom revenue is accommodated into the local economy, and which reinforce the real effects (Edwards 1983, 1984 and 1986). For instance, if this revenue (oil revenue) accrues to the government and is deposited in the central bank, the money supply of domestic currency will not be increased unless the government increases expenditure at home, or local credit to the private sector is increased (for instance if revenue accrued to the private sector is deposited in the commercial banks, the money supply of domestic currency will increase). If government expenditure is absent, the rise in net foreign assets of the central bank is precisely offset by the decline in net local credit to the government. Therefore, the supply of money will rise because government domestic spending monetizes the boom revenue. Indeed, the extent of change in the local money supply relies on the extent of foreign exchange revenue from oil revenue, as well as the extent to which the monetary authorities sterilize these proceeds to neutralize their impact on the money supply of domestic currency. As we can see:

Change in Monetary = Change in Domestic credit + change in foreign reserve

Any change in oil price leads to an increase in foreign reserve at home (change the second term in the right-hand side of the equation above). The net monetary effect of oil revenues relies on how much domestic credit (the first term in the above equation) would be influenced by oil revenue, and also depends on the authorities’ decision to accommodate the monetary impact (Edwards and Akoi 1983).

Based on this model the relative prices will overshoot, and that the tradable sector will have a greater loss of competitiveness in the short-run than in the long-run. Edwards (1983 and 1984) argues that unexpected increases in price of coffee in Colombia by increasing international reserves, led to increasing the quantity of money with consequent increases in the general price level and a decrease real exchange rate (real exchange rate appreciation). As a result of this appreciation, tradable goods become relatively more expensive and then become less competitive, both domestically and in international markets. Simultaneously, a strong local currency, against foreign currencies, will bring about an increase in imports as they become relatively cheaper than domestic goods, causing domestically produced goods to be squeezed out of the domestic and global market. The end consequence, according to Carneiro et al. (2007) will be the withering of the agricultural, manufacturing, and other tradable sectors of the economy, as well as possible loss of jobs in these sectors, and even greater economic dependence on the oil industry.

Apart from Edwards, Noorbakhsh (1990) also argues that the conversion of oil revenues from foreign-exchange earnings into domestic currency becomes the most significant source of increases in the money supply in oil-exporting developing countries. But he also says that the size of change in the domestic money supply depends on the size of foreign-exchange proceeds from oil revenue and the extent to which the money authorities sterilize these proceeds so as to neutralize their effect on the domestic money supply. Aghevli and Sassanpour (1982: p.792) argue that “Unlike domestic taxes, foreign revenues in the form of royalties on natural resources do not induce a reduction in disposable income, and their domestic spending leads to the creation of additional money”. However, the consequences of such a boom rely on the responses of the money demand to the boom. If the change in the demand of money is equal to the supply of money, then the monetary effects might be of little consequence for the rest of the economy. Moreover, in the case of oil, it is not certain that a money-supply growth will follow the boom. In some oil-exporting developing countries, oil revenue typically accrues to the state; only if the government injects this money into the economy, for example through expanding the budget deficit, will the money supply expand (see Morgan, 1979). This raises the possibility of some deflationary pressure following the booming period if the money supply fails to increase as fast as the money demand.

Moreover, Gylfason (2001) and Davis and Tilton (2005) criticized the structural adjustments that occur within a country throughout a natural resource boom. Some developed nations like Norway and the United Kingdom have gone through similar experiences. In reality, the Dutch disease allows a nation to take advantage of its newfound mineral wealth by enhancing resources to flow from other sectors to the booming sector. If natural (capital) assets are changed into human or physical capital, then they can encourage the economic growth rate; however, if natural (capital) assets are consumed without converting these assets into other productive capital, then the economic activity will slow down. Therefore, in both cases, natural resources may improve economic development since these natural resources offer chances and opportunities to developing nations (Hutchison 1994).

In summary in terms of theoretical framework, the consequences of the Dutch disease are summarized as follows:; (i) the appreciation of real exchange rate; (ii) a decline in the production of traded goods; and (iii) an enlargement in the output of the non-traded goods sector. However, empirically different conclusions have been resulted, therefore in order to complete the picture about the impact of booming sector on real exchange rate and structure of economic, the next section will be about the empirical studies and how the booming sector affect the domestic economy in both developed and developing countries.

SOME EMPIRICAL STUDIES ON THE DUTCH DISEASE

The impacts of natural resource receipts on the economic structure have been covered by several scholars. All the studies conducted have been suited to the macroeconomic study in industrial natural resource-producing nations, such as Norway, the Netherlands, the UK, and Australia. Nonetheless, there are many studies which have drawn attention to specific natural resource-producing developing countries, and the differences in economic structure between developed and developing nations. Our aim is to analyses certain macroeconomic studies and examines the impact of a natural resource export boom on developing economies. In this section, the author is going to analyses some previous empirical research studies linked to the Dutch disease and present their main outcomes. This section also provides some references in order to compare our results. This review of the literature helps us to consider the related variables and to select a suitable measure for them. Gelb (1988) conducts a widespread empirical cross-country study of the Dutch disease, examining the impact of the booming period in oil for a group of oil-exporting developing countries. However, almost all countries in the study demonstrated no Dutch-disease phenomena in the manufacturing sectors, whereas in nearly all the countries the output of the agricultural sector has shrunk during the periods of study. A possible explanation for the missing Dutch disease in the manufacturing sectors was that these sectors were initially too small and that the subsidies and the price controls by the government, along with active promotion of the sectors, kept the manufacturing sectors from being harmfully affected by the booming sector.

Benjamin (1990) developed a multi-sectoral, computable general equilibrium model to examine the influence of the high revenue from the oil sector on the Cameroon economy in 1979. He found that the appreciation of real exchange rate by 8.5% brought about an increase in imports by 10.5 percent and a decrease in exports of tradable goods by 6.1 percent. This was a result of the appreciation of the real exchange rate, which reduced the competitiveness of the country’s exports and domestic production of import-competing products. Benjamin concluded that there was a reduction in the traditional tradable goods sectors and an upsurge in the non-traded goods sectors. Fardmanesh (1991) developed a model to examine the effect of global prices on the share of manufacturing, agricultural and service sectors in the non-oil GDP in five oil-producing nations in OPEC. In these countries, oil made up the greatest contribution to GDP. Fardmanesh concluded that the oil sector has a negative influence on the agricultural sector and a positive influence on the services sectors. However, he mentioned that the impact of the oil sector is hard to evaluate, not only because of the dominance of the oil sector in the economy, but also because the non-oil tradable sector (agriculture) is highly underdeveloped (agriculture was the most important activity before the discovery of oil). In addition, oil rents may have decreased the incentives to proceed with reforms in the agricultural sector. However, Fardmanesh’s study has ignored the influence of the spending effect and local prices on tradable and non-tradable production.

AI-Gaeed (1991) created a two-sector macroeconomics model to examine the influence of the oil on the structure of the economy in Saudi Arabia. He used a factor of spending effects. He found that the spending effect positively influenced the investment and consumption, and negatively affected the real exchange rate. As well as this, the traditional sectors contracted in favour of the non-traded goods. However, this study has ignored the resource movement effects, testing only the influence of the spending effect on relative prices and real exchange rate.  Usui (1997) provides a comparison study between Mexico and Indonesia when both countries changed their policy adjustments due to an increase in the international oil price, with special reference to the Dutch disease phenomena. He found that Mexico shows a clear-cut example of the Dutch disease; however, Indonesia has not faced the Dutch disease phenomena. This outcome illustrates a striking contrast, particularly in the two countries’ monetary and fiscal policy, and emphasizes that good macroeconomic management (as was the case in Indonesia) is a very important factor in avoiding the Dutch disease phenomena. Besides subsidizing investment by using oil returns to support the tradable sector is another factor helped for Indonesian success. This means that good management in fiscal policy may be a very significant factor in avoiding the Dutch disease syndrome.

An empirical study was carried out by Larsen (2004) in Norway to demonstrate that Norway was able to avoid the effects of Dutch disease after the discovery of massive natural resources during the 1970s, and to examine the policies behind this success. He highlighted that a dominant centralized wage system limited increases in wages in all sectors from an expanding resource sector. The spending effect, in turn, was restricted since the government protected the economy through fiscal discipline and investing oil revenue abroad. Mogotsi (2002), however, analyzed the impact of Botswana’s booming sector (as a result of its diamond boom of 1982-90) on its economic structure. He found that Botswana did experience Dutch disease, as proved by an appreciation of real exchange rate, the effect of which was a drop in most of the manufacturing sector, particularly the textiles industries. However, Mogotsi concludes that the manufacturing industries did not drop in absolute terms, though there is an indication of a diminishing growth rate in the manufacturing sector during the boom period. Subramanian and Sala-i-Martin (2003) were unable to find strong evidence of the Dutch disease in Nigeria because of oil-price fluctuation. Moreover, they found that the real exchange rate was insensitive to oil prices. They also emphasized an issue all too common in examining the effect of changing international oil prices on macroeconomic variables in oil-exporting countries, namely the importance of knowing the nature of government expenditure and not only the quantity of that expenditure. The spending of oil revenues on tradable goods has no influence on the real exchange rate. Thus, if the bulk of the windfall is spent on traditional tradable goods, any signs of a Dutch disease may be weak. Ebrahim-Zadeh (2003) studied the Dutch disease effects in Kuwait throughout the oil boom. He applied a computable general equilibrium model to clarify the changing economic structure as a result of the boom. He found that (i) a relative increase in the output of non-tradable goods sector is accompanied by a decrease in the output of tradable goods sector; and (ii) a reallocation of resources results from an appreciation in the real exchange rate. Oomes and Kalcheva (2007) investigated the Dutch disease phenomenon in Russia, testing whether economic developments have been symptomatic of Dutch disease. After taking two symptoms (appreciation of real exchange rate and the declining manufacturing sector), Oomes and Kalcheva found that a 1% increase in the oil price brings about a 0.50 percent appreciation of the real exchange rate. In order to examine the second symptom of Dutch disease (declining manufacturing output), they used sector-level data to compare growth rates across Russian economic sectors for production level and employment across different economic sectors. They evaluated the impact of higher oil prices on five non-oil manufacturing sectors. The authors found that Russia showed this symptom of Dutch disease. In particular, their sectorial data demonstrated that the growth of the manufacturing sector slowed down compared to other economic sectors. In addition, since 2001, the manufacturing employment growth rate had dropped (Yaqub 2019).

However, Oomes and Kalcheva (2007) highlight that it is difficult to conclude whether Dutch disease was the only factor that led to a slowing down in the manufacturing sector, because this can also be affected by other factors. For instance, de-industrialisation has been a natural phenomenon even in developed countries, such as the US and European developed countries. These countries are not necessarily resource-rich; as long as the households become richer, then demand naturally tends to shift away from goods toward services. Beine et al. (2009) analyse the Dutch disease phenomena because of the increase of Canada’s oil production during 2000s. Their research uses a Bayesian approach to evaluate how much the Canadian Dollar is related with commodity prices, and then uses that to display the level of changes in commodity price on employment rate. They found that 42% of the industrial employment loss between 2002 and 2007 is linked to the Dutch disease phenomenon. Ismail (2010) examines a model using microeconomic data for the existence of Dutch disease. In addition, he used annual data from 1977 to 2004 in 90 countries, and the data only related to the manufacturing sector because of data availability issues. He found that a permanent oil shock led to a reduction in the manufacturing sector. Moreover, these effects are stronger in countries with more open capital accounts. Apart from that, the relative factor price of labor rises in regard to capital. Subsequently, capital intensity rises in the natural resource shock, which is consistent with this labor-intensive sector. He also found that an oil price shock affects sectors with higher capital intensity less than the labor-intensive non-tradable sector (Ali 2024). Ruehle and Kulkarni (2011) study potential Dutch disease effects in Chile following the copper boom in the early 2000s. They found that there was an insignificant adverse effect in the manufacturing sector. However, the agriculture sector has been severely affected negatively (declined output) and therefore concludes that the Dutch disease did occur. Their study, however, uses basic correlation matrices and single factor regression models that notably do not include real exchange rate as a dependent variable, meaning that de-agriculturization may have occurred for a host of other reasons (Ahmed et al. 2023).

Overall, the results of the above studies show that the Dutch disease phenomenon is not common in all countries, particularly in oil-exporting countries, although it has been observed in several cases. In most empirical studies, good fiscal policies and prudent management of the real exchange rate, as well as strong government policies, have been identified as the main factors for the absence of the Dutch disease phenomenon. Although some of these nations (developed nations) involved in the study are oil-producing nations, these countries are not similar to oil-exporting developing nations in terms of economic structure, size of government intervention into economic activities, population size and the size of the manufacturing output. Subsequently, the outcomes of studies in developed nations may not correspond to an oil-exporting developing nation’s economy. Almost all empirical studies found that oil-exporting developing nations faced a changing economic structure as a result of their high dependence on oil revenue (due to high oil price in international markets).

REAL EXCHANGE RATES AND OUTPUT OF TRADABLE AND NON-TRADABLE GOODS

We have established the fact that an appreciating real exchange rate did take place during periods of high oil prices and high real government expenditure. Conversely, during period of low oil prices and low real government expenditure, the real exchange rate appreciates. This section seeks to investigate whether relationships exist between the real exchange rate and the output of tradable and non-tradable goods sectors. According to the Dutch disease theory, a positive relationship should exist between the real exchange rate and the rate of growth of tradable goods sectors. This would mean that, when the real exchange rate appreciates, and the output of tradable goods sector shrinks, a deprecation of real exchange rate results in an increase in the rate of growth of tradable goods. On the other hand, the relationship between the appreciations (depreciation) of real exchange rates will be analyzed in this section. Here, it is expected that there is a negative relationship between the growth rate of non-tradable goods and real exchange rate.

As Figure 1, shows, during the 1970s, when the real exchange rate appreciated, the output of tradable goods (manufacturing and agricultural sectors) fluctuated slightly and, during some periods, increased. During that time, the Iraqi government attempted to protect domestic producers via subsiding domestic producers (particularly the agricultural sector). This action at least protected the tradable goods sector from collapse (Foote et al. 2004).   However, the output of tradable goods sector began to increase sharply in 1980 and, in 1987, reached $3650 million from $1366 million in 1977. Two main reasons led to the increase in output of tradable goods. First, the Iraqi government introduced a development plan, in use since 1975, to support and encourage the manufacturing and agricultural sectors. Part of the oil revenues during the 1970s were used to subsidies the tradable sector. The second factor, which is the most important factor, was related to the depreciation of nominal and real exchange rates in the 1980s, which caused domestic products to be cheaper than international products for similar products.

However, it can be seen that there is a sharp drop in the tradable goods output from 1990 to 1992, despite a sharp depreciation in real exchange rate during the same period. This was because of the Iraq-Kuwait war (invasion of Kuwait) during that time (Schnepf 2003). Apart from that, the 1991 uprisings in Iraq were a series of popular rebellions in northern and southern Iraq, in March and April 1991. Thus, the economy was disrupted and most production units in almost all economic sectors were halted due to political instability, which dominated the whole country. When the war ended, at the beginning of the 1990s, the output of the tradable goods sector started to increase with increased real exchange rates (depreciation RER), which are expected according to the Dutch disease theory.

On the other hand, when the real exchange rate began to appreciate again in 1996 (due to OFFP), the output of the tradable goods sector began to decline. However, after 2003, the situation was different when the real exchange rate continued to appreciate, but the output of tradable goods increased instead of decreasing (the opposite of what the Dutch disease predicts). This was because the Iraqi government subsides financed the agricultural and manufacturing sector via development plans. This occurred after the government gained a huge amount of oil revenue after 2003. Despite a rapid increase in tradable goods output, its percentage to GDP is relatively small compared to other sectors, particularly the service sector.

The correlations between the real exchange rate and non-tradable goods output are shown in Figure, 2. During the first and second oil shocks in the 1970s, the output of the non-tradable goods sector grew sharply due to an appreciating real exchange rate, bearing in mind the appreciation of a real exchange rate occurred when both oil prices and government expenditure increased. However, when the real exchange rate started to depreciate (increase) in 1980, the output of non-tradable goods sector started to decline, as the Dutch disease theory predicts. During the first half of the 1990s, the real exchange rate reached its highest level of depreciation and the non-tradable goods sector was steady in low output until 1996. When the OFFP was introduced by the UN in 1996, the real exchange rate appreciated sharply.

Figure, 1 Correlation between output of tradable goods sector and RER (1970-2013)

Source: Authors’ calculations using data from the World Bank and Central Bank of Iraq,

Figure, 2 Correlation between output of non-tradable sector and RER (1970-2013)

Source: Authors’ calculations using data from the World Bank and Central Bank of Iraq,

As a result, the output of non-tradable goods sector gradually increased, except in 2003, as a result of the Iraq-US war. After the war, the situation was different, particularly when the UN decided to lift economic sanctions on Iraq. The real exchange rate declined (appreciate) sharply, which led to a significant increase in the output of the non-tradable goods sector (Yaqub 2024).

In general, it can be said that there is a negative correlation between the real exchange rate and output of the non-tradable goods sector. When the real exchange rate increases, the output of non-tradable goods sector decreases. During appreciation of the real exchange rate, the output of non-tradable goods sector increases. This phenomenon has been explained by the Dutch disease theory.

METHOD OF OLS, TSLS AND EMPIRICAL TEST

Different methods have been used in economic literature to estimate equations; one of the common methods is the Ordinary Least Square (OLS). However, the econometric analysis suggests various problems while using estimation of equations when using OLS. The main condition for the OLS regression to be unbiased and consistent is that there is no correlation between the error term and explanatory variables.

Two-stage least squares (2SLS), as the name suggests, involves using OLS regression in two stages. In the first stage, a reduced form of the structural equation is estimated where the RER variable is regressed on all the explanatory variables in the equation system. Then, in the second stage, a fitted value of the RER variable is calculated by subtracting the residual of the regression from the actual value of the regressed endogenous variable. The fitted value is a linear combination of all explanatory variables; and, since the explanatory variables are uncorrelated with the error term, a linear combination of them will also be uncorrelated with the error term.

The direct impact of oil revenue on real exchange rate.

It is valuable to examine the direct impact of oil revenue on Real exchange rate. As long as the contribution of oil revenue to total government revenue is relatively high at least in Iraqi economy, thus the oil revenue becomes the main source of national income. Any fluctuation takes place in oil revenue can significantly affect the whole Iraqi economy. Before testing the impact of real exchange rate on non-tradable and tradable goods, it is important to test how real exchange rate fluctuates in Iraqi economy as a single commodity exporting country.

• Specification of the real exchange rate

One of the symptoms of the Dutch disease is an appreciation of the real exchange rate resulting from oil revenue (oil boom). The real exchange rate in oil-exporting developing countries is a main key variable that has been affected by changing oil revenue, which in turn affects the whole structure of the economy. In the economic literature, different indices have been used to measure the price of tradable and non-tradable goods. The Dutch disease theory and economic literature predict that a change in commodity exports, for instance crude oil revenue may significantly affect the appreciation and depreciation of RER. In this section, the oil revenue will be employed in this equation. Thus, the null and alternative hypothesis can be written as follows:

It is hypothesized that there is a negative relationship between oil revenue and real exchange rate as a dependent variable. While there is a positive relationship between international price of tradable goods and both dummies in one hand and real exchange rate on the other hand.

H₀ : β_t = 0                  Null hypothesis

H₁ : β_t ≠ 0       Alternative hypothesis

Where  represents the coefficient of oil revenue, international price of tradable goods variables.

• Statistical Results Model

The results of the RER model are presented in Table, 1 from this estimation, all explanatory variables, including dummies, are significant at 1% level and their signs are consistent with the economic theory prediction. However, before analyzing the outcome of the tests, it is vital to check the following diagnostic tests: heteroscedasticity, serial correlation and normality.

Regarding the result of heteroscedasticity shows that the P value is more than 5%, which is equal to 33% and means we cannot reject null hypotheses. This demonstrates that there is a homoscedasticity (no heteroscedasticity exists) in this model. On the other hand, the serial correlation, the result below shows that the P value is more that 5% which is equal to 40%. We can easily accept null hypotheses, as this means there is no serial correlation in the model, which is also desirable.  However, the normal distribution of the RER model, the outcome shows that the probability value is more than 5% which is equal to 21%. This means that we cannot also reject null hypotheses, meaning that the data is distributed normally. Based on the above analyses, it can be said that the model (RER model) is satisfied in terms of heteroscedasticity, serial correlation and normality.

Table, 1 Regression Results of the Real Exchange Rate

Diagnostic Test:

Heteroscedasticity = 0.33

Serial Correlation LM = 0.40

Normality (J.B) = 0.21

We begin with the first explanatory variable, which is oil revenue. Table 1 shows that the variable oil revenue has the correct negative sign and is statistically significant at the 1 percent level. A 10 percent increase in oil revenue, ceteris paribus, leads to approximately an 11.8 percent decrease in the real exchange rate. This indicates that the RER appreciates by 11.8 percent when oil revenue increases by 10 percent. Notably, the coefficient of oil revenue is the strongest among all coefficients in this equation (RER model).

The estimated coefficients of the international price of tradable goods (PT) are also significant at the 1 percent level and have the expected negative sign. The results demonstrate that a 10 percent increase in PT, ceteris paribus, leads to a depreciation of the RER by 7.2 percent; this finding is consistent with economic theory. Collectively, all explanatory variables account for about 89 percent of the variation in the RER. Consequently, the null hypothesis of all explanatory variables is rejected in favor of the alternative hypothesis.

The direct impact of real exchange rate on non-tradable and tradable goods,

• Specification of the Nontrade Goods Sector

The first symptom of the standard Dutch disease is the expanding of the output of the non-traded goods sector. The theory assumes that the reallocation of resources in favor of non-tradable goods at the expense of tradable goods resulting from the oil boom takes place through two channels. The second channel is related to the first, and is that of a potential appreciation in the RER. The impact of the appreciation of the RER tends to be one where the demand for non-tradable goods is substituted for that of tradable goods to encourage the latter at the expense of the former one. These channels however are affected by booming sector which is oil revenue in our case (Abdlaziz et al. 2022).

On the other hand, in this model dummy variables are also introduced to capture the impact of the Iraq-Iran war during the 1980s and economic sanctions, which impacted on Iraq during the 1990s. It is expected this has a negative impact on the output of the non-tradable goods sector. It is expected (based on theoretical framework) that the RER and international price of tradable goods should have a negative relationship against the output of non-tradable goods output. The equations should be formulated as follows:

Thus, the null and alternative hypotheses of the coefficients of the output of non-traded goods can be formulated as follows:

Where γ₁,γ₂ γ₃ and γ₄ represent the coefficient of each real exchange rate, international price of tradable goods, DUMW variable for Iraq-Iran war and DUMS dummy variable for economic sanction (see; model 3). In the following section, the statistical result will be analyzed.

• Statistical Results
• The empirical results from regressing the output of the non-traded goods sector against all regressors included in Equation 6.5 indicate that these variables are both individually and jointly significant, aligning with predictions from economic theory. Additionally, the variation in all explanatory variables accounts for approximately 95 percent of the variation in the supply of non-traded goods.
• Table 1 presents the findings from the two-stage least squares (2SLS) method. Tests for heteroscedasticity, serial correlation, and normal distribution reveal P-values greater than 5%—specifically, 0.55, 0.11, and 0.75, respectively. This suggests acceptance of the null hypotheses, indicating that there is no heteroscedasticity or serial correlation in the model and that the data is normally distributed, which is desirable for the analysis.
• Furthermore, Table 1 demonstrates that the real exchange rate impacts the output of the non-traded goods sector, consistent with economic literature. An appreciation of the real exchange rate results in an increase in the production of non-tradable goods. The coefficient for the real exchange rate is statistically significant at the 1 percent level, indicating that a 10 percent appreciation (decrease) in the real exchange rate induces an 11.9 percent increase in the output of non-tradable goods. Thus, we can confidently reject the null hypothesis in favor of the alternative.
• The regression results regarding the impact of all explanatory variables on the output of non-traded goods confirm consistency with economic theory predictions, particularly regarding coefficient signs and significance levels. Table 2 shows that the coefficient of the real exchange rate has a negative sign and is statistically significant at the 1 percent level. A 10 percent decrease (appreciation) in the real exchange rate, ceteris paribus, results in a 2.3 percent increase in the output of non-tradable goods; this finding aligns with predictions in the literature. Consequently, the null hypotheses can be easily rejected, supporting acceptance of the alternative hypotheses.

Table 2 Regression Results of the Non-Tradable Goods Output

Diagnostic Test:

Heteroscedasticity = 0.55

Serial Correlation LM = 0.11

Normality (J.B) = 0.79

In the same way, the outcome of the impact of both dummies also consistent with the economic theory prediction. Its coefficient has shown the negative sign and is statistically significant at one per cent level. A ten per cent decrease in the DUMW ceteris paribus, caused an increase the non-tradable goods output by 2.1 percent. This is the weakest coefficient in model 3. While a ten percent of increase in the DUMS ceteris paribus, led to decrease the output of non-tradable goods by around 3 percent. Thus, the alternative hypothesis for both dommies is accepted, and the null hypothesis is rejected.

• Specification of the traded goods

In the earlier section, the main symptom of the consequences of increased oil revenue (prices of oil) in oil-exporting developing countries is appreciation of the RER. According to the theoretical framework, this symptom (appreciation RER) will lead to a production of the de-industrialisation phenomenon (contrasting tradable goods sector). An appreciation of the real exchange rate also leads to loss of competitiveness of the tradable goods sector in international markets. This phenomenon has also been known as de-industrialisation, or the tradable-squeeze effect. Similar to the previous models, two dummy variables is also included into the model to capture the impact of the Iraq-Iran war during the 1980s and economic sanctions during the 1990s. Therefore, the equation can be written as follows:

The null and alternative hypotheses of the coefficients of the output of traded goods can be formulated as follows:

Where represent the coefficient of each RER, dummy Iraq-Iran war and dummy economic sanction respectively (sees; model 4). It is expected theoretically that RER and DUMS has positive relationship against the output of tradable goods. While DUMW has a negative relationship since, the DUMW considered as a source of destroy factor of production particularly agriculture sector. in the following section, the statistical result will be analyzed.

• Statistical results
• To analyze the impact of fluctuations in oil revenue on the supply of traded goods, it is essential to regress the output of traded goods against the real exchange rate and both dummy variables. The two-stage least squares (2SLS) estimation method is employed to address the inherent simultaneity bias present in both the quantities produced and their prices. Additionally, the model accounts for serial correlation. The results of the tests for heteroscedasticity, serial correlation, and normal distribution are satisfactory, with P-values of 0.34, 0.25, and 0.45, respectively, all exceeding 5 percent.
• As shown in Table 1, the coefficient for the real exchange rate (RER) aligns with the theoretical predictions, displaying the correct sign and achieving statistical significance at the 1 percent level. Specifically, a 10 percent increase in RER (indicating depreciation) results, ceteris paribus, in an approximate 4.2 percent increase in the supply of the traded goods sector. This indicates that RER depreciation enhances the competitiveness of domestic products in international markets, thereby increasing the supply of tradable goods in the domestic economy.
• Consequently, the real exchange rate mechanism associated with the Dutch disease in Iraq appears to play a significant role. The diminished competitiveness of the tradable sector (particularly agricultural goods) in the international market leads to a decline in its contribution to non-oil GDP, consistent with predictions from Dutch disease theory. This finding corroborates the results of several previous studies, including those by Katouzian (1978), Corden and Neary (1982), Gelb (1986), Kamas (1986), Al-Sabah (1988), Looney (1990), Pinto (1991), Rowthorn and Ramaswamy (1999), and Olusi and Olagunju (2005), all of which indicate that rising oil prices can lead to an appreciation of the real exchange rate, resulting in a contraction of the traditional tradable sector, particularly agriculture.

Table 3 Regression results of the tradable goods output (Model 4)

Diagnostic Test:

Heteroscedasticity = 0.34

Serial Correlation LM = 0.25

Normality (J.B) = 0.45

It is important to note that these findings contrast with those of Roemer (1985), who conducted a study on Nigeria, Mexico, and Venezuela. Similarly, Jazayeri (1986), focusing on Iran and Nigeria, also reported findings inconsistent with the Dutch disease theory. However, these studies assumed that the manufacturing sector was primarily affected, while agriculture remains the traditional export sector for most oil-exporting countries, including Iraq. In contrast, Chen and Ross (1986) investigated the symptoms of Dutch disease in the United Kingdom. Their study found that following the commercial exploitation of crude oil in the early 1970s, the real exchange rate appreciated by approximately 10 percent from 1973 to 1982, resulting in a decline in manufacturing output in the UK. Forsyth (1986) corroborated this, providing evidence of Dutch disease in the UK. The impact of the oil boom on the contracting manufacturing sector can be attributed to the fact that manufacturing was the primary export sector, making it particularly vulnerable to fluctuations in the real exchange rate.

On the other hand, the last two coefficients in the model correspond to the dummy variables. Both DUMW and DUMS exhibit positive coefficients, statistically significant at the 5 and 1 percent levels, respectively. Interestingly, the coefficient for DUMW is not aligned with theoretical expectations, being positive instead. The effects of these dummy variables differ in magnitude: a 10 percent increase in DUMW, ceteris paribus, results in a 7.3 percent increase in the supply of tradable goods. In comparison, the coefficient for DUMS is significantly stronger, with a 10 percent increase leading to a nearly 17 percent rise in tradable goods output.

While political instability is typically associated with a decrease in output, the situation in Iraq presents a different outcome. This may be attributed to rising prices for tradable goods, particularly in the agricultural sector, which was influenced by the devaluation of the nominal exchange rate during this period, alongside declining oil revenues. The sharp devaluation of the Iraqi dinar from 1980 to 1988 rendered tradable goods more expensive relative to non-tradable goods, enhancing their profitability. Consequently, investors shifted their focus from the non-tradable goods sector to the tradable goods sector. This trend was evident in the Iraqi economy throughout the 1980s and 1990s, as detailed in Chapters Three and Five.

Overall, the regression analysis for the output of tradable goods performed admirably, accounting for a substantial portion of the variation in the dependent variable. The coefficient of determination (R²) from Model 4 indicates that 66 percent of the variation in the supply of tradable goods is jointly explained by all explanatory variables (RER, DUMW, and DUMS). Although this R² value is lower than that of some previous regressions, it remains at a commendable level, indicating a good model fit. The null hypothesis for the coefficients of the real exchange rate and DUMS can be easily rejected. In contrast, while the null hypothesis for the coefficient of DUMW cannot be accepted, it is concluded that it is statistically significant and has the opposite sign. Overall, the regression analysis for the supply of tradable goods suggests that the Dutch disease phenomenon, along with various other factors, has contributed to the de-agriculturalization of the economy.

CONCLUSION

This paper employs a time-series technique to investigate and examine the impact of real exchange rates on changing the structure of economics. However, before conducting regression, the stationarity and Johannsen con-integration test have been examined. It is found that all variables are non-stationarity at level, but when they transfer to first difference they become stationarity. Stationarity, in the same order for all variables, requires a Johannsen co-integration test to be performed showing, in all equations, that there is a long-run relationship between variables. The issue of multicollinearity is also satisfactory according to VIF test. The empirical outcomes from the above estimated equations were based on the OLS and TSLS methods. Employing TSLS in this thesis is related to the problem with “endogeneity” which does exist in both model 2 and 3. Moreover, the outcome of this regression suggests that, in Iraq, the real appreciation resulting from increasing oil revenue has been accommodated partially by the “spending effect” (government expenditure and money supply, GDP per capita and international price of tradable goods). On the other hand, the impact of RER on changing the structure of economy has been examined. The non-tradable and tradable goods output were significantly affected by changing RER. In terms of sign and level of significant, the coefficient of RER is consistent with economic theory.

Overall, the analysis and outcome of this chapter is strongly suggestive that increased international oil prices are responsible for changing the relative price, real exchange rate and changing the structure of the economic sector from tradeable to non-tradeable. It is found that uncertainty about the international oil price implies uncertainty about the magnitude of the reduction in the tradable goods sector and enlarged non-tradable output sector. Moreover, how these changes are managed is important. In oil-exporting developing countries, the extent of the real exchange rates and structural changes will depend, among other things, on the fiscal policy response to how the oil revenue is spent, directly or indirectly by the authorities. Therefore, both fiscal and monetary policies are required to be used when oil prices increase and decrease in order to create a balance between the economic sector and expanding the production of exportable other than those of the oil sector, and at least to restore the initial structure of the pre-oil era.

REFERENCES

1. Abdlaziz, R. A., Ahmed, Y. A., Mohammed, B. A., & Yaqub, K. Q. (2022). The impact of oil price shocks on economic growth: Iraq case study for the period (1968-2019) using symmetric and asymmetric co-integration analysis. Qalaai Zanist Journal, 7(2), 1045-1074.
2. Ahmed, Y. A., Abdlaziz, R. A., Yaqub, K. Q., & Mohammed, B. A. (2023). The impact of economic planning in improving the profits of beekeeping fields: Halabja governorate as a model. University of Kirkuk Journal for Administrative and Economic Science, 13(1).
3. Aghevli, B., & Sassanpour, C. (1982). Prices, output and the trade balance in Iran. World Development, 10(9), 791-800.
4. Al-Gaeed, A. (1991). Oil windfalls, the Dutch disease, and the Saudi Arabian experience: 1970-1987. Missouri University, Columbia, MO (United States).
5. Aliyu, S. (2009). Impact of oil price shock and exchange rate volatility on economic growth in Nigeria: An empirical investigation.
6. Ali, B., Nazari, F., Mustafa, K., Yaqub, K. Q., & Alyani, M. A. (2024). Impact of trade liberalization on economic growth in developing countries. Bulletin of Business and Economics, 13(2), 1128-1133.
7. Al‐Sabah, M. (1988). The ‗Dutch disease‘ in an oil‐exporting country: Kuwait. OPEC Energy Review, 12(2), 129-144.
8. Beine, M., Janssen, G., & Lecourt, C. (2009). Should central bankers talk to the foreign exchange markets? Journal of International Money and Finance, 28(5), 776-803.
9. Benjamin, N. (1990). Investment, the real exchange rate, and Dutch disease: A two-period general equilibrium model of Cameroon. Journal of Policy Modeling, 12(1), 77-92.
10. Bruno, M., & Sachs, J. (1982). Energy and resource allocation: A dynamic model of the Dutch disease. Review of Economic Studies, 49, 845-859.
11. Carneiro, P., Crawford, C., & Goodman, A. (2007). The impact of early cognitive and non-cognitive skills on later outcomes. London: Centre for the Economics of Education, London School of Economics.
12. Ross, S., Chen, N. F., & Roll, R. (1986). Economic forces and the stock market. Journal of Business, 59(3), 383-403.
13. Corden, W. (1981). The exchange rate, monetary policy and North Sea oil: The economic theory of the squeeze on tradables. Oxford Economic Papers, 33, 23-46.
14. Corden, W., & Neary, P. (1982). Booming sector and de-industrialisation in a small open economy. The Economic Journal, 92(368), 825-848.
15. Corden, W. (1984). Booming sector and Dutch disease economics: Survey and consolidation. Oxford Economic Papers, 36(3), 359-380.
16. Dornbusch, R. (1976). Expectations and exchange rate dynamics. Journal of Political Economy, 84(6), 11611176.
17. Eastwood, R., & Venables, A. (1982). The macroeconomic implications of a resource discovery in an open economy. The Economic Journal, 92(366), 285-299.
18. Ebrahim-Zadeh, C. (2003). Dutch disease: Too much wealth managed unwisely. Finance and Development, 40(1), 1-41.
19. Edwards, S. (1983). The short-run relation between growth and inflation in Latin America: Comment. American Economic Review, 73(3), 477-488.
20. Edwards, S., & Aoki, M. (1983). Oil export boom and Dutch disease: A dynamic analysis. Resources and Energy, 5(3), 219-242.
21. Edwards, S. (1984). The order of liberalization of the external sector in developing countries. International Finance Section, Department of Economics, Princeton University, Princeton, New Jersey.
22. Edwards, S. (1986). Are devaluations contractionary? The Review of Economics and Statistics, 68(3), 501-508.
23. Fardmanesh, M. (1991). Dutch disease economics and oil syndrome: An empirical study. World Development, 19(6), 711-717.
24. Foote, C., Block, W., Crane, K., & Gray, S. (2004). Economic policy and prospects in Iraq. The Journal of Economic Perspectives, 18(3), 47-70.
25. Forsyth, P., & Kay, J. (1980). The economic implications of North Sea oil revenues. Fiscal Studies, 1(3), 1-28.
26. Gelb, A. (1986). From boom to bust: Oil exporting countries over the cycle 1970-1984. IDS Bulletin, 17(4), 1-8.
27. Gelb, A. (1988). Oil windfalls: Blessing or curse? Oxford: Oxford University Press.
28. Gregory, R. (1976). Some implications of the growth of the mineral sector. Australian Journal of Agricultural and Resource Economics, 20(2), 71-91.
29. Gylfason, T. (2001). Natural resources, education, and economic development. European Economic Review, 45(4), 847-859.
30. Hutchison, M. (1994). Manufacturing sector resiliency to energy booms: Empirical evidence from Norway, the Netherlands, and the United Kingdom. Oxford Economic Papers, 46(2), 311–329.
31. Jazayeri, S. (1986). Economic adjustment, prices, and output in two oil exporting countries: The case of Iran and Nigeria (Doctoral dissertation, University of Sussex).
32. Katouzian, M. (1978). Oil versus agriculture: A case of dual resource depletion in Iran. The Journal of Peasant Studies, 5(3), 347-369.
33. Lartey, E. (2008). Capital inflows, Dutch disease effects, and monetary policy in a small open economy. Review of International Economics, 16(5), 971-989.
34. Long, N. (1983). The effects of a booming export industry on the rest of the economy. Economic Record, 59(1), 57-60.
35. Looney, R. (1990). Oil revenues and Dutch disease in Saudi Arabia: Differential impacts on sectoral growth. Canadian Journal of Development Studies, 11(1), 119-133.
36. Mogotsi, I. (2002). Botswana’s diamonds boom: Was there a Dutch disease? South African Journal of Economics, 70(1), 128-155.
37. Morgan, D. (1979). Fiscal policy in oil exporting countries, 1972-78. Staff Papers, 26(1), 55-86.
38. Neary, P., & Wijnbergen, V. (1986). Natural resources and macroeconomy. Oxford: Basil Blackwell.
39. Noorbakhsh, A. (1990). Inflation in oil-exporting least-developed countries (OELDCs): An empirical study, 1959-1985. The Journal of Development Areas, 24, 367-376.
40. Olusi, J., & Olagunju, M. (2005). The primary sectors of the economy and the Dutch disease in Nigeria. The Pakistan Development Review, 159-175.
41. Oomes, N., & Kalcheva, K. (2007). Diagnosing Dutch disease: Does Russia have the symptoms? Bank of Finland, Institute for Economies in Transition Discussion Paper, 12.
42. Roberts, K., & Crampton, A. (1992). The impact of Dutch disease on the UK economy: An econometric analysis. Scottish Journal of Political Economy, 39(4), 411-424.
43. Robinson, S., & Thierfelder, K. (2008). The impact of resource boom on the economy: A general equilibrium analysis for the South African economy. Journal of Economic Modeling, 25(4), 657-673.
44. Sachs, J. D., & Warner, A. M. (1995). Natural resource abundance and economic growth. NBER Working Paper, 5398.
45. Stiglitz, J. (1986). The economic theory of resource allocation. American Economic Review, 76(2), 301-305.
46. Taqi, Z., & Bahrami, H. (2023). The effect of oil price fluctuations on economic growth: A comparative analysis of Iraq and Iran. Economic Research Journal, 10(2), 234-252.
47. Teixeira, P. (2003). Dutch disease and economic growth in the Gulf States. Economic Systems, 27(4), 383-396.
48. Van Wijnbergen, S. (1984). The Dutch disease: A second look. The Economic Journal, 94(376), 85-99.
49. Wijnbergen, S. (1986). Dutch disease and industrialization in the Netherlands: An econometric model. Journal of Development Economics, 23(1), 187-204.
50. World Bank. (2020). World Development Indicators.
51. Yaqub, K.Q., 2019. Impact of oil revenue volatility on the real exchange rate and the structure of economy: Empirical evidence of “Dutch disease” in Iraq(Doctoral dissertation, University of Bradford).
52. Yaqub, K.Q., 2024. Volatility of Oil Revenue and the Real Exchange, Empirical Evidence from Iraq. Education, 4(9), pp.1063-1072.
53. Yaqub, K.Q., Mustufa, K., Shakoor, M.F., Sarwar, A. and Asif, M., 2024. Examining How Removing Trade Barriers Have Influenced GDP Growth in Emerging Economies. Bulletin of Business and Economics (BBE), 13(3), pp.338-344.

FOOTNOTES

[1] In econometrics, an endogeneity problem occurs when an explanatory variable is correlated with the error term.