Chapter II: Literature review
2.1 Causal Mechanisms
2.1.1 Dutch Disease. The Dutch Disease (coined by the Economist, 1977) model provides an explanation to the resource curse thesis by explaining how a resource
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Chapter II: Literature review
2.1 Causal Mechanisms
2.1.1 Dutch Disease. The Dutch Disease (coined by the Economist, 1977) model provides an explanation to the resource curse thesis by explaining how a resource boom could lead to deindustrialization and change the composition of production, then lower the growth rate. Because the level of productivity is determined by the composition of the production (Torvik, 2002), which will more favor primary resources, and cause a drop in total exports (Gylfason, 2001).
This term was originally used to describe the phenomenon of the development of the natural resource sectors and the decline of the tradable sectors in Dutch triggered by the discovery of the natural gas resource in 1960s. When the resource boom is over, it will cost high to move back to the former sector structure since the
diversification of the economy has been impeded and it is now highly relying on the volatile mineral markets (Davis & Tilton, 2005).
Corden and Neary (1982) developed the Dutch Disease model which divides a country’s economy into three sectors, which respectively are tradable natural resource sector, tradable manufacturing sector, and non-tradable sector. The disease refers to the impact that a resource boom will lead to the contraction of the manufacturing sector and the expansion of the non-traded sector. The side effects are named: the Resource Movement Effect and the Spending Effect. Both effects will result in a shift to the non-primary tradable sectors.
The Resource Movement Effect is the shift of labor and capital from the
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manufacturing sector to the primary sector. A resource boom will cause a rise in wages in the mineral sector to meet the need of labors in order to expand. The
Spending Effect is that a resource boom will lead to a rise in the resource export, thus causing an appreciation to the domestic currency (Corden & Neary, 1982; Corden, 1984). By testing the two-sector economy model, Van der Ploeg (2011) also get similar findings that a resource boom will lead to a rise in the exports of natural resources. This will cause an appreciation of the real exchange rate and ensuing contraction of the traded sector. Krugman (1987), Matsuyama (1992), and van
Wijnbergen (1984) also find that a mineral discovery or a terms-of-trade improvement will consequently drive the production factors, such as labor and capital out of the manufacturing sectors. Meanwhile, cause inflation pressure, which decreases the competitiveness of the manufacturing industries.
Early evidence on the Dutch Disease is ambiguous (Leite & Weidmann, 2002, Sala-i-Martin & Subramanian, 2003). However, Caselli and Micheals (2009) find evidences to support the Dutch Disease by conducting a within-country study. By testing the oil dependence of the municipalities in Brazil, they find that onshore oil has a modest effect on non-oil GDP composition. Empirical evidences are also fund in Auty and Evia (2001) for Bolivia, Pegg (2010) for Botswana, Papyrakis and Raveh (2014) for Canada.
The Dutch Disease model cannot always explain the curse because Corden and Neary’s model assumes that the flow of labor is based on full employment, when most developing countries are faced with the problem of labor redundancy and capital deficiency. Dutch disease is more likely to happen in countries with corruption, bad institution, and lacks rule of law (van der Ploeg, 2011).
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2.1.2 Crowding-out effect. Similar to the Dutch Disease model, the natural resource abundance could crowd out other growth-promoting tradable sectors such as manufacturing industries, thus cause a curse on the economy (Sachs & Warner, 1995;
Matsuyama, 1992).
Sachs and Warner (1995) conduct a cross-country study to exam the impact of natural resource dependence on economic growth during time period 1970 and 1989.
Find that countries with higher share of primary products exports at 1970 is associated with lower economic growth in the following twenty years. They develop an
endogenous growth model to explain this phenomenon. The general idea is that industrial structure affects growth. A positive income shock in the natural resource sector could cause an increasing demand for non-traded products, including wages.
This will lead to a decrease in the profits of the traded sectors, such as manufacturing and other sectors that use non-traded products as their inputs (Sachs & Warner, 1995, 1999, 2001).
The crowding out of the manufacturing sectors will cause deindustrialization, and then hinder the economic growth and cause a curse to the economy (Frankel, 2010), because the expansion the of natural resource sector cannot compensate the adverse effect of deindustrialization on the economy.
If the reward in the natural resource sector is high enough, it will stimulate more people, in particular the potential innovators and entrepreneurs to work in the resource sector. Thus, natural resource crowd out innovation and entrepreneurs (Gylfason et al., 1999; Sachs & Warner, 2001).
2.1.3 Learning by doing. How could a decline in traded sector lead to a resource curse? Both Dutch Disease and Crowding-out Effect cannot fully explain the resource
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curse thesis. Another popular explanation is the Learning by Doing model, that the traded sector is the driving force for economic growth and can benefit more from the Learning by Doing (Krugman, 1987; Balassa, 1964). Most economic growth is driven by technological progress and accumulating experience, which is faster in the traded sector than in the non-traded sector triggered by Learning by Doing.
Since most economic growth is caused by technological progress, which is mostly triggered by Learning by Doing in the traded sector, the forces that push factors away from traded sectors such as the manufacturing industries will reduce the learning-induced growth and lower the economy growth (Sachs & Warner, 1995).
Van Wijnbergen (1984) finds evidence to support the Learning by Doing model by analyzing the relation between the subsidies to the traded sector and the temporary oil revenue. And find that an expansion in the non-traded sector and a temporary decline in traded goods sector caused by a resource boom will lead to long-term lower growth.
Cross-sectional study in Gylfason et al. (1999), base on data from 125 countries during time period 1960 and 1992 find a strong negative relation between the size of primary sector and economic growth. A relative rise in the price of traded goods in terms of non-traded goods (a real appreciation) will lower long-term economic growth because the natural-resource sector does not experience Learning by Doing as the manufacturing sector does.
The precondition of this model is that Learning by Doing only happens in traded sector. Sachs and Warner (1995) assume that there are spillovers to the rest of the economy. An expansion in natural resource sector will lower productivity growth in all sectors. However, Torvik (2001) found that all sectors can contribute to Learning by Doing and there are learning spillovers between sectors. Weather the natural
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resource abundance will hinder growth depends on the structural characteristics of the economy. More natural resources will lower production if the traded sector has
increasing return to the scale (Torvik, 2001, 2002).
2.1.4 Volatility effect. Another explanation of the resource curse is the volatility effect. The primary products markets are competitive and the buyers enjoy the market power. Countries that are dependent on primary products export may face the problem of the declining terms of trade of the primary commodities. The primary products markets are instable. The highly volatile price may also create uncertainty for the domestic and overseas investors thus hindering further investment (van der Ploeg &
Poelhekke, 2010). The instability of the primary market will also cause volatility in government revenue (Auty, 1998), and make the government hard to carry out prudent fiscal policy (Pieschacón, 2012).
Countries highly rely on a single mineral resource export are most likely to be badly affected by the volatile resource market (Davis & Tilton, 2005). Oil dependent countries such as Venezuela, Russia, and Brazil suffered from an economic downturn because of the long-term relative low oil price. However, evidences have been found in the US copper industry that better governance can prevent the diverse effect of the volatile resource market (Tilton & Landsberg, 1999). Countries like Canada, Chile, Ghana, and Norway have established stabilization funds from the commodity revenue to respond to the possible depressed markets.
2.1.5 Rentier effect. A rentier effect is that considerable revenues from the