Tag: Dutch disease

Is There a Dutch Disease in Russian Regions?

Posted on March 19, 2018 | Policy Brief

20180319 Is There a Dutch Disease Image 01

The low economic diversification in Russia is commonly blamed on the abundance of energy resources. This brief summarizes the results of our research that investigates the presence of Dutch disease effects across Russian regions. We compare manufacturing subsectors with different sensitivity to the availability of natural resources across Russian regions with varying natural resource endowments. We find no evidence of differential deindustrialization across subsectors, thereby offering no support for a Dutch disease. This finding suggests that the impact of energy resources on Russian manufacturing is more likely to go through the “institutional resource curse” channel. Thereby, we argue that more efficient policies to counteract the adverse effect of resources on the Russian economy should focus on improving the institutional environment.

Russian abundance in oil and gas, and the ways it could negatively affect long-term economic performance and institutional development is not a new debate. One of the key concerns is the influence of energy resources on Russian industrial structure. Energy resources are often blamed for the low diversification of the economy, with an extensive resource sector and the dominant oil and gas export share.

In a forthcoming chapter (Le Coq, Paltseva and Volchkova), we contribute to this debate by exploring the channels through which abundance in energy resources influences the industrial structure in Russia. Our main focus is on the deindustrialization due to the expansion of the natural resource sector, the so-called ‘Dutch disease’. Specifically, we explore the impact of energy resources on the growth of manufacturing subsectors in Russian regions. Adopting a regional perspective allows us to separate the Dutch disease mechanism from the main alternative channel of the institutional ‘resource curse’. This brief summarizes our findings.

Dutch disease vs. institutional resource curse

The Dutch disease and the institutional resource curse are, perhaps, the most discussed mechanisms proposed to explain the influence of natural resources on economic performance (see e.g., earlier FREE brief by Roine and Paltseva for a review). In an economy facing a Dutch disease, a resource boom and resulting high resource prices shift production factors from manufacturing industries towards resource and non-tradable sectors. As a result, a country experiencing a resource boom would end up with a slow-growing manufacturing and an under-diversified economic structure. Since the manufacturing sector is often the main driver of economic growth, the economic development may be delayed. If, instead, an economy is suffering from the institutional ‘resource curse’, it is the interplay of weak institutions and adverse incentives created by resource rents that leads to a slow growth of manufacturing and delayed development.

Importantly, offsetting the potential negative impact of these two channels requires different policy interventions. In the case of a Dutch disease, a state can rely on direct industrial policy mechanisms targeted towards increasing the competitiveness of the manufacturing sector and isolating it from the effect of booming resource prices. For example, it can use subsidies or targeted trade policy instruments, or channel money from increased resource prices out of the economy through reserve fund investments abroad.

In the case of an institutional resource curse, on the other hand, resource rents and weak institutions may undermine and disrupt the effect of such policies. In this case, state policies should be targeted, first and foremost, towards promoting good institutions such as securing accountability and the transparency of the state, and protecting property rights. This suggests that properly understanding the channels through which resource wealth impacts the economy is necessary for choosing appropriate remedial measures.

In our analysis, we address the differential impact of energy resources in Russian regions. This regional perspective allows us to single out the Dutch disease effect, and disregard the mechanisms of a political resource curse to the extent that the relevant institutions do not differ much across regions.

Resource reallocation effect vs. spending effect

The mechanism of a Dutch disease implies two channels through which a resource boom negatively affects the manufacturing sector. First, a resource boom implies the reallocation of production factors from other sectors of economy such as manufacturing or services to the resource sector, a so-called ‘resource reallocation effect’. Second, an additional income resulting from a boom in the resource sector leads to an increase in demand for all goods and services in the economy. This increase in demand will be accommodated differently by different sectors, depending on their openness to world markets. Namely, in non-tradable sectors, isolated from international competition, there will be an increase in prices and output. This, in turn, will increase the prices on domestic factor markets. For tradable manufacturing sectors the price is determined internationally and cannot be adjusted domestically. As a result, production factors will also reallocate away from manufacturing to non-tradable sectors, a so-called “spending effect”.

The strength of either effect is likely to be different across different subsectors of manufacturing depending on the sectoral specificities. In particular, subsectors with higher economies of scale are likely to be more affected by the outflow of factors towards the resource sector through the “resource reallocation effect”. Similarly, subsectors that are more open to international trade are likely to be affected by the “spending effect”.

These observations give raise to our empirical strategy: we access differences in growth of regional manufacturing subsectors with different sensitivity to the availability of energy resources, where sensitivity reflects economies of scale, for the first mechanism, and openness to the world market, for the second mechanism. In other words, we test whether manufacturing subsectors with higher economies of scale (or openness) grow slower than subsectors with lower economies of scale (or openness) in regions rich in energy resources, as compared to the regions poor in energy resources. Observing differential deindustrialization, depending on the industry’s exposure to the tested mechanism, would offer support to the presence of a Dutch disease.

Note that the validity of our empirical strategy relies on the fact that there is high variation in resource abundancy and structure of the manufacturing sectors across Russian regions (as illustrated by Figures 1 and 2).

Figure 1. Geographical distribution of fuel extractions relative to gross regional product; 2014, percent.

Source: Authors’ calculation based on Rosstat data. Note: Figures for regions exclude contribution of autonomous okrugs where applicable.

Figure 2. Regional diversity in manufacturing structure, 2014.

Source: Rosstat.

Data and results

Our empirical investigation covers the period 2006—2014. The data on manufacturing subsector growth and regional energy resource abundancy come from Rosstat, the sensitivity measures across different manufacturing sectors are approximated based on data from Diewert and Fox (2008) (economies of scale in US manufacturing), and OECD (sectoral openness to trade).

The results of our estimation show that the differences in growth rates of manufacturing subindustries across Russian regions with varying natural resource endowments cannot be explained by the sensitivity of these subindustries to the availability of energy resources. This can be seen from Table 1, where the coefficient of interest – the one of the interaction term between the measure of sectoral sensitivity if resource abundance and regional energy resource wealth – is not significantly different from zero, no matter how we measure the sensitivity: by the returns to scale or by openness to international trade.

Table 1. Estimation of Dutch disease effect with different sensitivity measures.

	Dependent variable: average annual growth index of sectoral output
	Sensitivity measure: Economies of scale	Sensitivity measure: Openness
Subsector sensitivity * Size of the fuel extraction sector in the region	-0.0353 (0.0873)	0.0674 (0.0954)
Subsector fixed effect	YES	YES
Region fixed effect	YES	YES
Observations	1,185	1,185
R-squared	0.1574	0.1577

Source: Authors’ calculations.

These results hold true if we control for differences in regional taxes, labor market conditions, and other region-specific characteristics by including regional and sectoral dummy variables, if we consider alternative measures of energy resource wealth, and if we use other, non-parametric estimation methods.

In other words, our data robustly offers no support for the presence of a Dutch disease in Russian regions.

Conclusion and policy implications

Diversification is often mentioned by the Russian government, as one of the top economic policy priorities, and the need for ‘diversification’ has been used in the political debate as an argument for an active industrial policy.

However, the policy measures that are necessary to counter the effect of abundant energy resources on diversification and, more generally, on economic development may be highly dependent on the prevailing channel through which resources affect the economy. In particular, while active industrial policy may be justified as a remedy in the case of a Dutch disease, industrial policy may well be ineffective, or even harmful, in the presence of an institutional resource curse mechanism.

In our study, we find no support for the Dutch disease effect when looking at the impact of energy resources on the growth of regional manufacturing sectors. Thereby, to counterbalance the resource curse effect on the Russian economy, we argue that it may be more efficient to improve the institutional environment than to use active government policies affecting industrial structures.

References

Diewert, W. E and Fox, K. J. (2008) ‘On the estimation of returns to scale, technical progress and monopolistic markups’, Journal of Econometrics, 145(1-2): 174-93.
Le Coq, C., Paltseva E., and Volchkova N., forthcoming. “Regional impacts of the Russian energy sector”, in Perspectives on the Russian economy under Putin, eds. Becker and Oxenstierna, London, Routledge.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Decomposition of Economic Growth in Belarus

Posted on October 21, 2013 | Policy Brief

During the last decade Belarus was one of the leaders of growth in the CEE region. Kruk and Bornukova (2013) have analyzed the sources of growth and found that capital accumulation was the main contributor to growth. The contribution of total factor productivity (TFP) to growth was, on the contrary, quite modest. On the sectoral level, capital accumulation was not always accompanied by the increases in TFP. Hence, the new growth policy, modernization, with the bottom line “more capital” may not be the best option for enhancing productivity-based growth. The competitive advantages of Belarus lie in the resource-based and non-tradable sectors, while the majority of the manufacturing sectors are lagging behind in productivity. Belarus has symptoms of a Dutch disease without the trade surplus, and the devaluation of 2011 did not cure it.

During 2003-2012, Belarus had an average growth rate of 7.1%, and during the ‘fat years’, i.e. 2003-2008, it was even higher – 9.5%. Intuitively, this prominent growth is questionable, as it was achieved in the context of dominating state ownership, centralized allocation of resources, government’s control at the factor and goods markets, as well as poor infrastructural reforms (for instance, according to the indices of the EBRD). The Belarusian case challenges the mainstream paradigm of growth in transitional countries, which assumes that the progress in market reforms is the key factor for high and sustainable growth.

The simplest and most widespread explanation of the Belarusian phenomena is based on ‘non-standard’ gains in productivity. This approach assumes that productivity is the engine of growth (World Bank (2012); Demidenko and Kuznetsov (2012)). To a large extent, these gains in productivity are seen as “artificial”, resulting from Russian injections into the Belarusian economy: cheap gas, specific schemes of oil trade, and preferences in access to the Russian markets (Kruk (2010)). However, under this approach, decomposing the growth in productivity by ‘natural’ and ‘artificial’ parts is hardly possible, as the impact of these factors is already hidden in the available data.

The IMF (2010) gave a substantially different explanation of Belarusian growth. They claimed that the average growth of 8.3% over the period of 2001-2008 was mainly capital-based with a contribution of 4.8 percentage points, while the contribution of productivity growth was only 3.0 percentage points (the rest of growth was explained by labor and cyclical factors).

The main reason behind the substantial difference in the explanation of growth factors is the statistical data on capital used during the growth accounting exercise. Belarusian official statistics reports the data on capital stock based on a direct survey of capital assets according to both gross and net (wealth) capital concept. However, the growth rates of capital are reported only for the gross stock of capital. These growth rates are questionable as they demonstrate ‘unnatural stability’ – they fluctuate around 2% for the last 20 years, despite the fact that investments during this period has displayed huge and volatile growth. Statistical offices in other CIS countries have reported similar dynamics of the capital stock. Voskoboynikov (2012), and Bessonov and Voskoboynikov (2008) show that this trend is a consequence of the statistical methodology used in Russia (which the Belarusian methodology is very similar to). In particular, the trend is driven by biased capital investments deflators (which are overestimated) from the periods of high inflation (1990-s and early 2000-s).

If official data is used as the capital input for the growth accounting exercise, the contribution of TFP to growth will be overestimated. Hence, in the studies of the World Bank (2012) and Demidenko and Kuznetsov (2012), the leading role of TFP may be due to the use of the official data on the capital stock.

Motivated by this concern, we use two different methods to evaluate the value of capital inputs (see Kruk and Bornukova (2013) for more details). The first alternative to using the data from direct capital survey is to exploit a perpetual inventory method (PIM): the historical assessment of initial capital stock is further adjusted by the flow of investments and depreciation. However, if there is a bias in deflators within the sample, the series will also be distorted. This problem may be eliminated if the initial stock will be selected at the moment when there is no bias in investment deflator, in the period of moderate inflation. We call this approach PIM-backward.

The second approach to constructing capital series exploits the concept of productive capital and the data on the flow of capital. It assumes that the productive capacity of a capital good depends on its age. The productive stock of a capital good (i.e. the gross stock adjusted by the age-efficiency profile) generates a flow – capital services. The latter is the productive stock adjusted by the user cost of the individual capital good. For the total output of an industry (or economy) one should aggregate the inputs by different capital goods, which in contrast to the net (wealth) concept depends not only on the value of capital goods, but also on their user costs. This approach has solid theoretical foundations, which is the reason it is prioritized in productivity studies.

From the view of available data in the case of Belarus, this approach has a number of powerful advantages. First, we use individual deflators for individual capital goods, which are expected to be less biased than total deflators for the industry. Second, we use heterogeneous depreciation rates for each capital good in each industry based on actual data of ‘accounting depreciation’, while we would have to use homogenous assumptions for each industry in the case of net (wealth) concept. Third, we can exclude residential housing from our measure of capital input.

There are, however, also disadvantages. First, data of newly employed capital goods (in direct surveys of capital assets) and data on capital investments differ rather substantially. Traditionally, the data on capital investments is treated as more reliable, but based on the direct surveys of capital assets we have to use the series of newly employed capital goods as a flow variable when running PIM. Second, we use exogenous real interest rate for computing unit user costs, but the results are very sensitive to our assumptions on the real interest rates across industries. Third, the necessity to exclude residential housing from the data (because of ‘mixed historical prices’) may be interpreted as a loss of information. Given the strengths and weaknesses of the approach, we prioritize it on the industrial level, but prefer the PIM-backward approach for an aggregate economy analysis.

Based on the PIM-backward measure for the total economy (see Figure 1), we may argue that the contribution of TFP to growth was more modest during the last decade than what was reported in the majority of previous studies on Belarusian growth. This finding is of fundamental importance for the growth agenda: only productivity-based growth may be treated as sustainable, since capital growth will slow down as the capital approaches its stationary value. We argue that only the policy directed to promotion of productivity is vital for growth prospects.

Figure 1. Contribution of Production Factors and TFP to the Growth of Gross Value Added (PIM-Backward Approach)

The dynamics of productivity divided according to industries (see Table 1) display that the leaders in productivity growth are either industries that produce non-tradable goods (communications, finance, construction) or those that have a chance of ‘artificial productivity gains’ (chemical and petrochemical manufacturing, and fuel).

Table 1. Initial Level and Growth Rates of Productivity in Major Industries

However, the theory suggests that the leaders in productivity growth should be the industries producing tradable goods. . This contradiction may be interpreted in two ways. First, one may argue that a more competitive environment and larger share of private ownership (which are seen in the financial industry, trade and catering) are the core reasons for high productivity level and growth rates in ‘domestic industries’. Second, an attractive position of ‘domestic industries’ may reflect a high level of domestic prices rather than ‘natural’ productivity. The base year for our computations is 2009, in which both the real effective exchange rate of the national currency and income were relatively high. The devaluation of 2011 fixed the problem only temporarily, since the inflation in 2011-2013 quickly eroded the benefits of the devaluation. Therefore, the indicators, in terms of 2009 prices, may capture the changes in nominal values as the main component of the productivity gains, while from a longer-term perspective it would be seen as mainly price movements without substantial progress in productivity. In our view, the second explanation is the main reason for the non-standard disposition of productivity levels and growth rates among industries.

If that is the case, the bigger picture looks as follows. Industries producing tradable goods suffer from the lack of progress in productivity, i.e. lose their competitive advantage; enhancements in total productivity are mainly due to industries with ‘artificial productivity gains’. The latter allows domestic prices to grow, making a productivity illusion of domestic industries. All together these symptoms are quite similar to the Dutch disease.

One more finding from the productivity analysis at the national level is the lack of productivity gains from reallocation of resources from less productive industries to more productive ones. A scatter-plot between capital accumulation growth rates and TFP growth rates (see Figure 2) demonstrates no clear relationship between them.

Figure 2. Growth Rates of Capital Input vs. TFP Growth Rates in Manufacturing Branches, 2006-2010.

Notes: The sizes of the circles correspond to industry shares in value added.

However, if there was a free allocation of resources, more productive industries would accumulate more capital. Moreover, the same indicators under the PIM-backward approach demonstrate clear negative relationship. A ‘soft’ interpretation of this phenomenon assumes that the lack of reallocation of capital restrains the development of total productivity. A ‘tighter’ interpretation assumes that at least in some industries there is a trade-off between capital accumulation and productivity gains. For instance, in Kruk and Haiduk (2013) it is shown that spurring capital accumulation through the practice of directed lending leads to losses in efficiency through a number of channels. Hence, the simplest way to increase aggregate productivity is to depart from the centralized allocation of capital and unblock capital inflows to more productive industries and vice versa.

Figure 3 documents the mobility of labor markets across the manufacturing industries in Belarus. While one can expect that labor flow into more productive industries, it is not completely true for the Belarusian manufacturing sector.

Figure 3: Labor growth and TFP growth in industries of Belarusian manufacturing, (capital services approach).

Notes: The sizes of the circles correspond to industry shares in value added.

Two distinct trends emerge in the labor market. On the one hand, some industries exhibit textbook behavior: increases in TFP are associated with increases in the number of people employed. The best example here is the fuel industry, which experiences TFP increases due to preferential oil prices. However, there are industries that gain TFP and lose labor at the same time. The chemical industry, machinery manufacturing and woodworking are examples of this pattern. These industries have experienced rapid capital accumulation, which, coupled with high gains in TFP, should have contributed to the increases in labor productivity. Surprisingly, though, these industries did not attract more labor. A possible explanation for this counterintuitive pattern is the excessive employment at the beginning of the period in question. In this case, a decrease in the number of people employed may have contributed to the increases of TFP.

Indeed, Figure 4 confirms our hypothesis: labor was flowing from the industries with lower labor productivity to the industries with higher labor productivity in general. Industries in which TFP increased and which were accompanied by a labor decrease, featured low labor productivity in the beginning of the period in consideration, more precisely in 2005. Only the chemical industry exhibited the unexpected behavior: it lost labor despite high initial productivity. By getting rid of excessive employment they were contributing to an increase in TFP.

Figure 4: Labor shifts into the sectors with higher labor productivity.

Notes: The sizes of the circles correspond to industry shares in value added.

How is Belarus doing relative to other countries? We have compared Belarusian TFP to the TFP of the leader of transition, the Czech Republic, and to the regional leader, Sweden. The Czech Republic is more developed than Belarus (in 2010 Czech GDP per capita (PPP-corrected) was 1.73 times higher than in Belarus), and, theoretically, it should be much more difficult and costly for it to continue approaching the technological frontier. However, our findings suggest that the Czech Republic is catching up with Sweden in terms of TFP, and doing it faster than Belarus (see Figure 5).

Figure 5: TFP of Belarus and the Czech Republic relative to TFP of Sweden, (PIM-backward approach).

Over the last 10 years, Belarus has closed only 5 percentage points of the gap with Sweden. The Czech Republic, where the contribution of TFP to growth was more substantial, has managed to close 8 percentage points of the gap.

In absolute numbers (in ‘international’ dollars of 2010), aggregate TFP in Belarus in 2010 was 2.92 versus 4.66 in the Czech Republic and 9.38 in Sweden (according to the PIM-backwards method). However, the aggregate picture does not reflect the situation in the sectors of the economy and industries of manufacturing.

Table 2: Comparative advantage of Belarusian industries: winners and losers (capital services approach)

Table 2 documents the comparative advantages and disadvantages of the Belarusian economy in 2010 according to the capital services approach. Both the capital services approach and the PIM-backwards approach produce the same winners and losers list with the only difference being that the PIM-backwards method has the construction sector among winners. It is not surprising to see resource-based industries among the winners (mining and quarrying mainly reflects the extraction of potash, while the chemical industry benefits both from potash and from preferential process for Russian oil). Food manufacturing is among the winners mostly due to the price scissors in agriculture: food producers buy their inputs at very low prices. The non-tradable sectors are among winners, and the majority of the manufacturing sectors are among the losers. Again, this is similar to the symptoms of the Dutch disease. It is ironic that Belarus has symptoms of a Dutch disease without the trade surplus. Instead, the desire of the government to inflate wages combined with the preferences for Russia led to the development of the same diagnosis.

Belarusian economic growth is less TFP-led than is commonly believed. While the labor market proves to be relatively successful in its reallocation of employees and its contribution to aggregate increases in efficiency, the capital market is distorted by government interventions. Capital accumulation does not necessarily lead to increases in TFP, and the new modernization policy with the bottom line of “more capital” may not be the best option for enhancing growth. Our conclusion is that Belarus should find new sources for TFP-led growth.

▪

References

Bessonov, V., Voskoboynikov.I. (2008). “Fixed Capital and Investment Trends in the Russian Economy in Transition.”, Problems of Economic Transition, 51(4), pp. 6-48.
Demidenko, M., Kuznetsov, A. (2012). “Ekonomicheskiy rost v Respublike Belarus: factory i otsenka ravnovesiya” (Economic Growth in Belarus: Factors and Equilibrium Assessments), National Bank of the Republic of Belarus, Working Paper No.3.
IMF (2010). “Sources of Recent Growth and Prospects for Future Growth”, IMF, Country Report No.10/16.
Kruk, D., Bornukova, K. (2013). “Belarusian Economic Growth Decomposition”, unpublished manuscript.
Kruk, D., Haiduk, K. (2013). “The Outcome of Directed Lending in Belarus: Mitigating Recession or Dampening Long-Run Growth?”, BEROC Working Paper Series, WP No.22
Kruk, D. (2010). “Vliyanie krizisa na perspectivy dolgosrochnogo ekonomisheskogo rosta v Belarusi” (The Impact of Crisis on the Perspectives of Long-term Growth in Belarus), IPM Research Center Working Paper Seies, WP/10/07.
World Bank (2012). “Belarus Country Economic Memorandum: Economic Transformation for Growth”, Country Economic Memorandum, Report No. 66614
Voskoboynikov, I. (2012). “New Measures of Output, Labour and Capital in Industries of the Russian Economy”, Groningen Growth and Development Centre, Research Memorandum GD

Are Natural Resources Good or Bad for Development?

Policy Brief

Open pit mine industry representing natural resources and development

Natural resources undoubtedly play an important role in the economy of many countries. Whether their contribution to development is positive or negative is, however, a contested and difficult question. Arguably countries like Australia, Botswana and Norway have gained enormously over long periods from their natural resources, others like Azerbaijan, Kazakhstan and Russia have gained in economic growth terms but maybe at the expense of institutional development, while in some countries, such as Angola and Sierra Leone, natural resources have been at the heart of violent conflicts with devastating effects for society. With many developing countries being highly resource-dependent a deeper understanding of the sources and solutions to the potential problem of natural resources is highly relevant. This brief reviews the main issues and points to key policy challenges for turning resource rents into driver rather than a detriment for development.

Is it good for a country to be rich in natural resources? Superficially, the answer to this question would obviously seem to be “yes”. How could it ever be negative to have something in addition to labor and produced capital? How could it be negative to have something valuable “for free”? Yet, the answer is far from that simple and one can relatively quickly come up with counterarguments: “Having natural resources takes away incentives to develop other areas of the economy which are potentially more important for long-run growth”; “Natural resource-income can cause corruption or be a source of conflict”, etc.

Looking at some of the starkest cases, the “benefits” of resources can indeed be questioned. Take the Democratic Republic of Congo for example. It is the world’s largest producer of cobalt (49% of the world’s production in 2009) and of industrial diamonds (30%). It is also a large producer of gemstone diamonds (6%), it has around 2/3 of the world’s deposits of coltan and significant deposits of copper and tin. At the same time, it has the world’s worst growth rate and the 8^th lowest GDP per capita over the last 40 years.[1] The picture for Sierra Leone and Liberia is very similar – they possess immense natural wealth, yet they are found among the worst performers both in terms of economic growth and GDP per capita. While the experiences of countries such as Bolivia and Venezuela are not as extreme their resource wealth in terms of natural gas and oil respectively seem to have brought serious problems in terms of low growth, increased inequality and corruption. When one, on top of this, adds that some of the world’s fastest-growing economies over the past decades – such as Hong Kong, South Korea and Singapore – have no natural wealth the picture that emerges is that resources seem to be negative for development.

These are not isolated examples. By now, it is a well-established fact that there is a robust negative relationship between a country’s share of primary exports in GDP and its subsequent economic growth. This relationship, first established in the seminal paper by Sachs & Warner (1995) is the basis for what is often referred to as the resource curse, that is, the idea that resource dependence undermines long-run economic performance.[2]

Based on the World Development Indicators database (World Bank). Primary exports consist of agricultural raw materials exports, fuel exports, ores and metals, and food exports.

At the same time, there are numerous countries that provide counterexamples to this idea. Being the second largest exporter of natural gas and the fifth largest of oil, Norway is one of the richest world economies. Botswana produces 29% of the world’s gemstone diamonds and has been one of the fastest-growing countries over the last 40 years. Australia, Chile, and Malaysia are other examples of countries that have performed well, not just despite their resource wealth, but, to a large extent, due to it.

Given these examples the relevant question becomes not “Are resources good or bad for development?” but rather “Under what circumstances are resources good and when are they bad for development?. As Rick van der Ploeg (2011) puts it in a recent overview: “the interesting question is why some resource-rich economies [.] are successful while others [.] perform badly despite their immense natural wealth”. To begin to answer this question it is useful to first review some of the many theoretical explanations that have been suggested and to see what empirical support they have received. Clearly, our overview is far from complete but we think it gives a fair picture of how we have arrived at our current stage of knowledge.[3]

Theories and Evidence

The most well-known economic explanation of the resources curse suggests that a resource windfall generates additional wealth, which raises the prices of non-tradable goods, such as services. This, in turn, leads to real exchange rate appreciation and higher wages in the service sector. The resulting reallocation of capital and labor to the non-tradable sector and to the resource sector causes the manufacturing sector to contract (so-called “de-industrialization”). This mechanism is usually referred to as “Dutch disease” due to the real exchange rate appreciation and decrease in manufacturing exports observed in the Netherlands following the discovery of North Sea gas in the late 1950s. Of course, the contraction of the manufacturing sector is not necessarily harmful per se, but if manufacturing has a higher impact on human capital development, product quality improvements and on the development of new products, this development lowers long-run growth.[4] Other theories have focused on the problems related to the increased volatility that comes with high resource dependence. In particular, it has been suggested that irreversible and long-term investments such as education decrease as volatility goes up. If human capital accumulation is important for long-run growth this is yet another potential problem of resource wealth.

The empirical support for the Dutch disease and related mechanisms is mixed. Some authors find that a resource boom causes a decline in manufacturing exports and an expansion of the service sector (e.g. Harding and Venables (2010)), others do not (e.g. Sala-i-Martin and Subramanian (2003)). But even the studies that do find evidence of the Dutch disease mechanism, usually do not analyze its effect on the growth rates. In principle, Dutch disease could be at work without this hurting growth. Another problem is that the Dutch disease theory suggests that natural resources are equally bad for development across countries. This means that the theories cannot account for the great heterogeneity of observed outcomes, that is, they cannot explain why some countries fail and others succeed at a given level of resource dependence. The same goes for the possibility that natural resources create disincentives for education. Gylfason 2001, Stijns (2006) and Suslova and Volchkova (2007) find evidence of lower human capital investment in resource-rich countries but the theory cannot explain differences across (equally) resource-rich countries.

As a result, greater attention has been devoted to the political-economic explanations of the resource curse. The main idea in recent work is that the impact of resources on development is heavily dependent on the institutional environment. If the institutions provide good protection of property rights and are favourable to productive and entrepreneurial activities, natural resources are likely to benefit the economy by being a source of income, new investment opportunities, and of potential positive spillovers to the rest of the economy. However, if property rights are insecure and institutions are “grabber-friendly”, the resource windfall instead gives rise to rent-seeking, corruption and conflict, which have a negative effect on the country’s development and growth. In short, resources have different effects depending on the institutional environment. If institutions are good enough resources have a positive effect on economic outcomes, if institutions are bad, so are resources for development.

Mehlum, Moene and Torvik (2006) develop a theoretical model for this effect and also find empirical support for the idea. In resource-rich countries with bad institutions incentives become geared towards “grabbing resource rents” while in countries where institutions render such activities difficult resources contribute positively to growth. Boschini, Pettersson and Roine (2007) provide a similar explanation but also stress the importance of the type of resources that dominate. They show that if a country’s institutions are bad, “appropriable” resources (i.e., resources that are more valuable, more concentrated geographically, easier to transport etc. – such as gold or diamonds) are more “dangerous” for economic growth. The effect is reversed for good institutions – gold and diamonds do more good than less appropriable resources. In turn, better institutions are more important in avoiding the resource curse with precious metals and diamonds than with mineral production. The following graph illustrates their result by showing the marginal effects of different resources on growth for varying institutional quality. Distinguishing the growth contribution of mineral production in countries with good institutions with the effect in countries with bad institutions, the left panel shows a positive effect in the former and a negative one in the latter case. The right-hand panel illustrates the corresponding, steeper effects when isolating only precious metals and diamond production.

Even if these papers provide important insights and allow for the possibility of similar resource endowments having variable effects depending on the institutional setting, two major problems still remain. First, the measures of “institutional quality” are broad averages of institutional outcomes (rather than rules).[5] Even if Boschini et al. (2007), and in particular Boschini, Pettersson and Roine (2011) test the robustness of the interaction result using alternative institutional measures (including the Polity IV measure of the degree of democracy) it remains an important issue to understand more precisely which aspects of institutions that matter. An attempt at studying a particular aspect of this question is the paper by Andersen and Aslaksen (2008), which shows that presidential democracies are subject to the resource curse, while it is not present in parliamentary democracies. They argue that this result is due to higher accountability and better representation of the parliamentary regimes.

A second remaining issue is that even if one concludes that the impact of natural resources differs across institutional environments it is an obvious possibility that natural resources have an impact on the chosen policies and institutional arrangements. For example, access to resource rents may provide additional incentives for the current ruler to stay in power and to block institutional reforms that threaten his power, such as democratization. In a well-known paper with the catchy title “Does oil hinder democracy?” Ross (2001) uses pooled cross-country data to establish a negative correlation between resource dependence and democracy.

However, one needs to be careful in distinguishing such a correlation from a causal effect. There are at least two issues that can affect the interpretation: First, there could be an omitted variable bias, that is, the natural resource dependence and institutional environment can be influenced by an unobserved country-specific variable, such as historically given institutions (which in turn could be the result of unobserved effects of resources in previous periods), culture, etc. For the same reason, cross-country comparisons may also be misleading. One way of dealing with this problem is to use fixed-effect panel regressions to eliminate the effect of the country-specific unobserved characteristics. This approach produces mixed empirical results: in the analysis of Haber and Menaldo (2011) the effect of resources on democracy disappears, while Aslaksen (2010) and Andersen and Ross (2011) find support for a political resource curse.

Second, the measures of natural resource wealth may be endogenous to institutions and, in particular, its level of democracy. For example, the level of oil production and even the efforts put into oil discovery can be affected by the decisions of (and constraints on) those in power. Thereby one would need to find instrumental variables that influence the level of democracy only through the resource measures.[6] Tsui (2011) investigates the causal relationship between democracy and resources by looking at the impact of oil discovery event(s) on a cross-country sample. His identification strategy is based on using the exogenous variation in oil endowments (an estimate of the total amount of oil initially in place) to instrument for the amount of total discovered oil to date. The idea is that, while the amount of oil discovered could well be influenced by the institutional environment, the size of the oil endowment is determined only by nature. Tsui’s findings also support the political resource curse story.

There are also numerous studies about the effect of resources on particular institutional aspects and policies. For example, Beck and Laeven (2006) find that resource wealth delayed reform in Eastern Europe and the CIS, Desai, Olofsgård and Yousef (2009) point to natural resource income as central for the possibilities of autocratic governments to remain in power through buying support, Egorov et. al. (2009) show that there is fewer media freedom in oil-rich economies, with the effect being the strongest for the autocratic regimes. Andersen and Aslaksen (2011) find that natural resource wealth only affects leadership duration in non-democratic regimes. Moreover, in these countries, less appropriable resources extend the term in power (in line with the ruler incentive argument above), while more appropriable resources, such as diamonds, shorten political survival (perhaps, due to increased competition for power). Several papers show that in a bad institutional environment natural resources increase corruption (e.g., Bhattacharyya and Hodler (2010) or Vincente (2010)), and reduce corporate transparency (Durnev and Guriev (2011)).

Implications for Policy

Overall the literature points to potential economic as well as political problems connected to natural resources. Even if some issues remain contested it seems clear that many of the economic problems are solvable with appropriate policy measures and in general that natural resources can have positive effects on economic development given the right institutional setting. However, it seems equally clear that natural resource wealth, especially in initially weak institutional settings, tends to delay diversification and reforms, and also increases incentives to engage in various types of rent-seeking. In autocratic settings, resource incomes can also be used by the elite to strengthen their hold on power.

Successful examples of managing resource wealth, such as the establishment of sovereign wealth funds that can both reduce the volatility and create transparency and also smooth the use of resource incomes over time, are not always optimal or easily implementable. Using the money for large investments could be perfectly legitimate and consumption should be skewed toward the present in a capital-scarce developing setting (as shown by van der Ploeg and Venables, 2011). But no matter what we think we know about the optimal policy it still has to be implemented and if the institutional setting is weak the problems are very real. This is just because of potentially corrupt governments but also due to the difficulty to make credible commitments even for perfectly benevolent politicians (see e.g. Desai, Olofgård and Yousef, 2009).

Many political leaders in resource-rich countries have pointed to the hopelessness of their situation and have expressed a wish to rather be without their natural wealth. Such conclusions are unnecessarily pessimistic. Even if it is true that the policy implications from the literature more or less boil down to a catch-22 combination of 1) “Resources are bad (only) if you have poor institutions, so make sure you develop good institutions if you have resource wealth” and 2) “Natural resources have a tendency to impede good institutional development”, there are possibilities. Some countries have succeeded in using their resource wealth to develop and arguably strengthen their institutions. Even if it is often noted that Botswana had relatively good institutions already at the time of independence, it was still a poor country with no democratic history facing the challenge of developing a country more or less from scratch. And at the time of independence, they also discovered and started mining diamonds which have since been an important source both of growth and government revenue. This development has to a large part been due to good, prudent policy.

There is nothing inevitable about the adverse effects of natural resources but resource-rich developing countries must face the challenges that come with having such wealth and use it wisely. The first step is surely to understand the potential problems and to be explicit and transparent about how one intends to deal with them.

References

Andersen, J. J. and Aslaksen, S., 2008. “Constitutions and the resource curse.” Journal of Development Economics, Volume 87, Issue 2.
Andersen, J. J. and Aslaksen, S., 2011. “Oil and political survival.” mimeo.
Andersen, J. J. and Ross, M., 2011, “Making the Resource Curse Disappear: A re-examination of Haber and Menaldo’s: “Do Natural Resources Fuel Authoritarianism?”.” mimeo.
Aslaksen, S., 2010. “Oil and Democracy – More than a Cross-Country Correlation?,” Journal of Peace Research, vol. 47(4).
Beck, T., and Laeven, L., 2006. “Institution Building and Growth in Transition Economies.” CEPR Discussion Paper 5718, Centre for Economic Policy Research:London.
Bhattacharyya, S., and Hodler, R., 2010. “Natural resources, democracy and corruption” European Economic Review, Elsevier, vol. 54(4).
Boschini, A.D., Pettersson, J. and Roine, J., 2007. “Resource curse or not: a question of appropriability” Scandinavian Journal of Economics, 109.
Boschini, A.D., Pettersson, J. and Roine, J., 2011. “Unbundling the resource curse” mimeo.
David, P. A., and Wright, G.. 1997. “The Genesis of American Resource Abundance” Industrial and Corporate Change 6.
Desai, R. M., Olofsgård, A. and Yousef, T., 2009. “The Logic of Authoritarian Bargains” Economics & Politics, Vol. 21, Issue 1.
Durnev, A. and Guriev, S. M., 2011. ”Expropriation Risk and Firm Growth: A Corporate Transparency Channel.”, mimeo
Egorov, G., Guriev, S. M. and Sonin, K., 2009. “Why Resource-Poor Dictators Allow Freer Media: A Theory and Evidence from Panel Data.” American Political Science Review, Vol. 103, No. 4.
Gylfason, T., 2001. “Nature, Power, and Growth” Scottish Journal of Political Economy, Scottish Economic Society, vol. 48(5).
Gylfason, T., Herbertsson, T. T., and Zoega, G., 1999. “A mixed blessing” Macroeconomic Dynamics, 3.
Findlay, R. and Lundahl M., 1999. “Resource-Led Growth: A Long-Term Perspective.” Helsinki: World Institute for Development Economics Research.
Frankel, J. A., 2010 “The Natural Resource Curse: A Survey.” HKS Working Paper No. RWP10-005.
Haber, S. H. and Menaldo, V. A., 2011. “Do Natural Resources Fuel Authoritarianism? A Reappraisal of the Resource Curse.” American Political Science Review, Vol. 105, No. 1.
Harding, T. and Venables, A.J., 2011. “Exports, imports and foreign exchange windfalls.” mimeo.
Hausmann R., Hwang J. and Rodrik, D., 2007. “What you export matters.” Journal of Economic Growth, Springer, vol. 12(1).
Leite, C. A. and Weidmann, J., 1999. “Does Mother Nature Corrupt? Natural Resources, Corruption, and Economic Growth.” IMF Working Paper No. 99/85.
Mehlum, H., Moene, K. and Torvik, R., 2006. ”Institutions and the resource curse.” Economic Journal, 116.
Montague, D., 2002. “Stolen Goods: Coltan and Conflict in the Democratic Republic of Congo.” SAISReview – Volume 22, Number 1, Winter-Spring, pp. 103-118
van der Ploeg, F., 2011. “Natural Resources: Curse or Blessing?.” Journal of Economic Literature, American Economic Association, vol. 49(2).
van der Ploeg, F. and Venables, A. J., 2011. “Harnessing Windfall Revenues: Optimal Policies for Resource-Rich Developing Economies.” Economic Journal, Royal Economic Society, vol. 121(551).
Ross, M.L., 2001. “Does Oil Hinder Democracy?” World Politics, 53(3).
Sachs, J. D. and Warner, A. M., 1995. “Natural Resource Abundance and Economic Growth.” NBER Working Papers 5398, National Bureau of Economic Research, Inc.
Sala-I-Martin, X., Doppelhofer, G. and Miller, R. I., 2004. “Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach.” American Economic Review, American Economic Association, vol. 94(4).
Sala-I-Martin, X., and Subramanian, A., 2003. “Addressing the Natural Resource Curse: An Illustration from Nigeria.” NBER Working Paper 9804.
Stijns, J.-P., 2006. “Natural resource abundance and human capital accumulation.” World Development, Elsevier, vol. 34(6).
Suslova, E. and Volchkova, N., 2007. “Human Capital, Industrial Growth and Resource Curse.” Working Papers WP13_2007_11, Laboratory for Macroeconomic Analysis, HSE.
Torvik, R., 2009. “Why do some resource-abundant countries succeed while others do not?”, Oxford Review of Economic Policy, vol. 25(2).
Tsui, K. K., 2011. “More Oil, Less Democracy: Evidence from Worldwide Crude Oil Discoveries.” The Economic Journal, 121.
Vincente, P., 2010. “Does Oil Corrupt? Evidence from a Natural Experiment in West Africa,” Journal of Development Economics, 92(1).
Wright, G., 1990. “The Origins of American Industrial Success, 1879-1940.” American Economic Review 80.

Footnotes

[1] Based on World Development Indicators database (World Bank).

[2] Its robustness has been confirmed in, for example, Gylfason, Herbertsson and Zoega (1999), Leite and Weidmann (1999), Sachs and Warner (2001) and Sala-i-Martin and Subramanian (2003). Doppelhoefer, Miller and Sala-i-Martin (2004) find that the negative relation between the fraction of primary exports in total exports and growth is one of 11 variables which is robust when estimates are constructed as weighted averages of basically every possible combination of included variables.

[3] The interested reader should consult more extensive overviews such as Torvik (2009), Frankel (2010) or van der Ploeg (2011).

[4] This assumption has been criticized by, for example, Wright (1990), David and Wright (1997), and Findlay and Lundahl (1999) who all point to historical examples where resource extraction has been a driver for the development of new technology. On the other hand others, e.g. Hausmann, Hwang and Rodrik (2007), provide evidence that export product sophistication predicts higher growth.

[5] The distinction between using institutional outcomes rather than institutional rules has been much debated in the literature on the importance of institutions in general. It is, for example, possible for a dictator to choose to enforce good property rights protection even if this is something typically associated with democracy.

[6] The studies by Boschini, Pettersson and Roine (2007) and (2011) also use instrumental variables to try to account for the potential endogeneity problems. The results are in line with the OLS results but instruments are weak in this setting.

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