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Mineral Economics

, Volume 31, Issue 1–2, pp 35–59 | Cite as

Neither Dutch nor disease?—natural resource booms in theory and empirics

  • Grant Mark Nülle
  • Graham A. Davis
Original Paper

Abstract

For several decades, economists have endeavored to determine whether a sudden surge in mineral and energy extraction activity poses an albatross or boon to an economy. The “Dutch disease” version of the resource curse originates in a traditional model postulating that extensive mineral and energy production induces inter-sectoral adjustments among traded and non-traded industries and that these adjustments tend to crowd out traditional export industries such as manufacturing. This can be acutely detrimental to the long-run growth of an economy when the traditional industries produce positive learning by doing externalities. This chain of events is so frequently cited as being evident in resource-based economies that it has become stylized wisdom. This paper reviews whether modern theoretical models and empirical evidence actually support the Dutch disease. Overall, we find that the Dutch disease is by no means theoretically predicted or empirically evident within resource-based economies.

Keywords

Dutch disease Learning by doing Natural resources Resource curse Booming sector Economic growth 

JEL codes

O13 Q32 Q33 Q43 

Introduction

Do economies willing and able to exploit significant natural resource endowments suffer shrinking traditional sector output? If so, does this in turn have negative long-run consequences for growth and welfare? Often conflated with the broader miseries associated with the resource curse, the Dutch disease (DD) specifically refers to the shrinking of traditional export industries as a result of a “boom” in the extractive sector (Davis 1995). In the traditional model, a disease occurs because the shrinking sectors exhibit positive externalities, while the booming sector does not. Lost production in a sector with positive externalities permanently lowers the long-run path for income, resulting in lost present-value welfare when the short-run gains from the boom are insufficient to overcome the long-run losses.

A consensus in economic and policymaking communities is that the DD is evident in resource economies. For example, the DD, combined with lost positive externalities in manufacturing, has been taken up as one of the main culprits leading to the slower growth associated with the resource curse.1 Recent worries about the de-industrialization associated with resource booms have arisen in resource-rich developing countries like Mongolia (Jacob 2013), where traditional manufacturing and agriculture are shrinking, and in developed countries like Canada, where politicians blame lost manufacturing jobs on a booming and unregulated oil industry (Gollom 2012; Howlett and Walton 2012).

Marian Radetzki, a long-time student of the development challenges in mineral economies, astutely differentiates between the DD and the resource curse. In his books on global commodity markets, Radetzki focusses on the problems of extractive monoeconomies, whose booms “end with a bang” (Radetzki 1990, 2008; Radetzki and Wårell 2016). Painful adjustments attributable to the loss of fiscal and export revenue ensue. According to Radetzki, the DD, via its shrinking tradeable sector, accentuates the concentration of economic activity around resource extraction, making matters worse. This, of course, is a different take on the effects of resource booms. Rather than focus on the impacts of lost externalities on long-run growth, Radetzki focusses on the export and fiscal revenue instability associated with more concentrated domestic production. This is a valid concern, but is not what the traditional DD models emphasize.

There have been many reviews of the resource curse literature, but to our knowledge none of the DD literature. This paper conducts such a review. As part of our review, we will comment on whether or not traditional export sectors do shrink during a boom, addressing in part Radetzki’s worries about increasing economic concentration in resource-rich economies.

While the DD has come to be treated as a fait accompli in booming resource economies, our review reveals that the evidence is not so clear. The traditional theoretical models plausibly and intuitively anticipate negative macroeconomic consequences of resource booms in selected circumstances. At the same time, underemphasized extensions of the models’ core assumptions emphasize that adverse economic outcomes are by no means inevitable. A number of recent papers challenge even the most basic sectoral predictions of the seminal models. There is also scant empirical evidence of shrinking non-extractive sectors in booming mineral economies, let alone that these shrinking sectors have positive externalities, the second but crucial ingredient for the DD to manifest as a disease. In our view, all of this calls into question the stylized wisdom of an inextricable link between extractive activities and the DD. More importantly, it calls into question policy aimed at directing economic activity in booming resource economies to avoid the predicted macroeconomic adjustments. In this sense, the DD is what Manski (2011) calls a conventional certitude that is likely to misinform policy.

Radetzki was always skeptical of government actions to control the DD effects of a resource boom; “The temptations and potential benefits of a resource boom are simply too valuable to be missed” (2016, p. 269). He will be relieved to learn that there is no compelling evidence that such efforts are necessary in the first place. This is not to say that a resource curse does not exist, or that monoeconomies do not have special development challenges. It does say that the DD is not necessarily complicit in creating monoeconomies by shrinking the traditional sectors during a resource boom.

Our paper commences with a review of the seminal models of resource economies and how a booming resource sector may lead to DD. We then synthesize via a “layered” analysis (Manski 2011) the model extensions that demonstrate the anticipated sectoral shrinkages are not axiomatic.2 The paper concludes by examining empirical work regarding the presence or absence of DD in booming resource economies.

The seminal models of booming resource economies

Corden and Neary (1982) established the core model of booming resource economies via application of the Ricardo-Viner specific-factors model (Jones 1971).3 Precursor papers model the same phenomenon in Norway (Eide 1973; Bjerkholt et al. 1981), Kuwait (McKinnon 1976), Australia (Gregory 1976; Snape 1977; Stoeckel 1979), the Netherlands (Ellman 1981), and the UK (Corden 1981a, b; Forsyth and Kay 1980, 1981; Kaldor 1981).4 The core model features a small open economy (SOE) producing three goods (non-tradables, energy, and manufactures) with energy and manufactures traded internationally at exogenously determined world prices. Output in each sector is produced by a factor specific to that sector (capital) and by domestic labor, which is both fixed in supply and mobile across all three sectors. There are four scenarios by which an energy boom may transpire: (1) a Hicks-neutral or non-neutral improvement in extraction technology, (2) extraction of newly-discovered deposits, (3) an exogenous inflow of foreign capital into the energy sector, or (4) an increase in the world price of energy. Analytically, the inter-sectoral adjustments triggered by any of these four catalysts are the same, albeit to the extent the earnings on foreign capital are repatriated abroad under the third scenario, the spending effect, described below, may dissipate relative to the other scenarios.

Focusing on Hicks-neutral technological progress in the energy sector as the initiator of a “boom” in the sector, the Corden and Neary model identifies two distinct impacts on the economy: a resource movement effect (RME) and a spending effect (SE).5 The RME represents a rightward shift in the energy sector’s demand schedule for labor proportional to the amount of Hicks-neutral technological progress it enjoys. The energy sector’s demand for additional labor raises the domestic wage rate and draws labor away from the non-tradable and manufacturing sectors. In turn, both the manufacturing and non-tradable sectors experience a decline in output attributable to the reduction in the availability of the mobile factor. In the Corden and Neary terminology, the RME constitutes direct “de-industrialization” via the shrinking manufacturing sector.6

By contrast, the SE is a reflection of specific-factor owners in the energy sector accruing rents and using their windfalls to drive up demand for the non-tradable good relative to the price of manufactures. This increases the price of the non-tradable good relative to the tradable good. In a model with government, this change in relative prices can also come about through government spending of tax revenues derived from energy production. The change in relative prices can be seen as an appreciation of the real exchange rate, the price of the non-tradable good in terms of manufactures. In a supply response to the shift in relative prices, labor is drawn out of manufactures and into the non-tradable sector. A secondary effect of the relative price change is that some demand for the non-traded good shifts towards the relatively less expensive energy and manufacturing sectors.

As we noted above, the RME draws the mobile factor out of both the non-tradable sector and manufactures, reducing output in both sectors. In the absence of an exchange rate adjustment, the reduction of output in the non-tradable sector stokes excess demand in the sector. The excess demand can only be choked off if a rise in the relative price of the non-tradable output occurs—a real appreciation of the exchange rate. Consequently, both the RME and the SE induce a real exchange rate appreciation.

Given the combined RME and SE, one can estimate the impacts of an energy boom on the two non-resource sectors. The manufacturing sector is roundly buffeted. First, direct de-industrialization arises from the RME as mobile labor leaves for higher wages in the booming energy sector. Second, the SE stimulates a real exchange rate appreciation and resultant expansion of the non-traded sector, additionally drawing labor out of manufactures. Consequently, manufacturing output unambiguously declines.

The net response in the non-tradable sector is indefinite. Output relative to the pre-boom equilibrium will expand or contract depending on the relative intensity of the counteracting RME and SE effects. When the RME eclipses the SE, non-tradable output falls. Non-tradeable output rises when the SE dominates the RME.

The DD moniker, attributable to The Economist in 1977,7 was linked to the discovery of the Groningen gas fields and subsequent shrinking of the Dutch manufacturing sector in the 1970s. The DD garnered additional interest in the early 1980s as a result of the experiences of North Sea oil exporters grappling with similar bouts of real exchange rate appreciation, fiscal windfalls, inter-sectoral output and factor of production adjustments, and de-industrialization of traditional export sectors. With industrialization seen as the road to economic growth in the past century, de-industrialization was feared to be growth inhibiting, though the exact mechanism by which growth would be slowed was not made explicit.

Shrinking sectors, learning by doing, and welfare effects of a resource boom (in theory, at least)

In the Corden and Neary (1982) model, an expansion of resource exports on account of enhanced sectoral productivity unambiguously and permanently raises national income, regardless of how output changes in the other two sectors.8 Any factor reallocations and the consequent changes in output in the tradable and non-tradable sectors are simply optimal responses that allow the sector with the fortuitously enhanced productivity to boom. In essence, and a point that is missed by many, the Corden and Neary model merely describes a series of economic mechanisms (changes in wage rates, exchange rate appreciation) that facilitate an optimal reallocation of the mobile factor to the sector(s) most willing and able to pay for its services in the light of changes in demand (Findlay 1995; Neary and van Wijnbergen 1986).

Nevertheless, there is a long history of fascination with agriculture and manufacturing, two traditional sectors that are likely to shrink according to this model, as being especially beneficial to economic growth and development in ways not considered in these seminal models. Davis (1995, 1998) reviews these claims as juxtaposed against the relative ambivalence towards a booming resource sector. Manufacturing activity has also been suggested to be pro-poor, while growth in resource extraction not pro-poor.9

Because the seminal model is static, it cannot take into account any dynamic mechanisms that the boom may initiate. Beginning with van Wijnbergen (1984), a strand of literature developed examining whether the shift in factors of production to the energy and non-tradable sectors and concomitant expansion of output in one or both sectors at the expense of manufactures could present a threat to long-term economic growth, a DD effect. If economic growth is in large part generated by learning by doing-induced technological progress concentrated in the non-energy traded good sector, if that technological progress is external to any one firm, if there are impediments to natural reactions that internalize such externalities such as geographic clustering, and if there is no recognition by policy makers that market interventions are optimal before and after a boom when positive externalities exist, then temporary or sustained declines in that sector may permanently retard economic growth. There are plenty of “ifs” here, revealing that the DD requires a “perfect storm” of economic circumstances.

Because this notion of lost learning by doing (LBD) is at the crux of whether booming resource sectors lead to DD in these models, it is useful to examine a dynamic DD model in detail. Matsuyama (1992), one of the modern models of the DD that has been very well cited, best elucidates how a spike in productivity in a non-LBD sector may be inimical to long-term, LBD-led economic growth. The paper also illustrates how DD model outcomes depend crucially on the assumptions made. The model includes two sectors (Agriculture—A and Manufacturing—M), non-homothetic preferences, a constant population, full employment with labor supply fixed and normalized to 1 in our mathematical representation of the model, less than unitary income elasticity of demand for food, and increasing manufacturing output due to LBD productivity increases. The model is set up as follows, beginning with the production and equilibrium labor market conditions:

$$ {X}_t^{\mathrm{A}}= AG\left(1-{n}_t\right)\kern3.75em G(0)=0,{G}^{\prime }>0,{G}^{\prime \prime }<0,\kern0.75em $$
(2.1)
$$ {X}_t^{\mathrm{M}}={M}_tF\left({n}_t\right)\kern5em F(0)=0,{F}^{\prime }>0,{F}^{\prime \prime }<0,\kern1em $$
(2.2)
$$ {\dot{M}}_t=\delta {X}_t^{\mathrm{M}},\kern7.25em \delta >0,\kern1.25em $$
(2.3)
$$ A{G}^{\prime}\left(1-{n}_t\right)={p}_t{M}_t{F}^{\prime}\left({n}_t\right).\kern0.5em $$
(2.4)

Output at time t in the agriculture sector, \( {X}_t^{\mathrm{A}} \), is a function of A, a constant exogenous technological agricultural productivity term, and the amount of labor (1 − n t ) not employed in the manufacturing sector. Manufacturing output,\( {X}_t^{\mathrm{M}} \), is a function of the number of laborers employed in the sector, n t , and the endogenously determined level of knowledge capital M t . Knowledge capital accumulates based on the volume of manufacturing production as indicated by Eq. (2.3). This is the LBD effect. Each firm takes M t to be exogenous, and so will not plan for and internalize the benefits of increased cumulative output—a market failure. Both sectors exhibit diminishing returns, though the size of the economy does not affect the model outcome. In Eq. (2.4), a first-order equilibrium in the labor market is generated by inter-sectoral competition for labor, where p t is the relative price of the manufacturing good. The price of agriculture is normalized to 1.

The representative consumer’s preferences are represented as follows:
$$ W={\int}_0^{\infty}\left[\beta \mathit{\log}\left({c}_t^{\mathrm{A}}-\gamma \right)+\mathit{\log}\left({\mathrm{c}}_{\mathrm{t}}^{\mathrm{M}}\right)\right]{e}^{-\rho t} dt,\kern0.5em \beta, \gamma, \rho >0,\kern1em $$
(2.5)
$$ AG(1)>\gamma >0,\kern20.5em $$
(2.6)
where \( {c}_t^{\mathrm{A}} \)and \( {c}_t^{\mathrm{M}} \) constitute consumption of the agricultural and manufacturing good in a given period and γ represents the subsistence level of food consumption per capita. Equation (2.6) ensures that the agricultural sector is productive enough to provide a subsistence level of food to all consumers. Aggregate demand for the two goods across all consumers is
$$ {C}_t^{\mathrm{A}}=\gamma +\beta {p}_t{C}_t^{\mathrm{M}}. $$
(2.7)
Applying a closed economy assumption,
$$ {C}_t^{\mathrm{M}}={X}_t^{\mathrm{M}}\ \mathrm{and}\kern0.50em {C}_t^{\mathrm{A}}={X}_t^{\mathrm{A}}. $$
(2.8)
Combining Eq. (2.8) with Eqs. (2.4) and (2.7) gives
$$ \varphi \left({n}_t\right)>\frac{\gamma }{A}, $$
(2.9)
where
$$ \varphi \left({n}_t\right)\equiv \frac{G\left(1-{n}_t\right)-\beta {G}^{\prime}\left(1-{n}_t\right)F\left({n}_t\right)}{F^{\prime}\left({n}_t\right)},\kern4.5em \varphi (0)=G(1),\varphi (1)<0,{\varphi}^{\prime }<0. $$
(2.10)
From Eq. (2.6), Eq. (2.9) has a unique solution for n t  ∈ (0, 1) that is increasing in A:
$$ {n}_t=n=v(A),\kern1.75em $$
(2.11)
with v´(A) > 0.  In the closed economy model, the share of labor in manufacturing is constant, as is agricultural output. Over time, manufacturing output grows at a constant rate due to the LBD effect, decreasing the relative price of manufactures via Eq. (2.4) given the constant agricultural output. Economic output, the sum of the value of agricultural output and manufacturing output valued at p t , is constant given that food is the accounting unit.10

An increase in agricultural productivity, A, (akin to a Hicks-neutral technological change in the Corden and Neary model) increases the steady-state level of agricultural output, increases the relative price of manufactures, and releases labor to manufacturing, which increases manufacturing output and its rate of growth as more rapidly accumulating experience enhances manufacturing prowess. This is the obverse of the DD model. The steady-state level of overall economic output increases, as does present-value welfare, W.

Matsuyama then adapts the model from that of a closed economy to that of a small open economy (SOE) to show how the DD can arise. Labor is immobile between the SOE and the rest of the world (RoW); manufacturing LBD externalities, which did not spill across sectors in the closed economy model, now do not spill across international borders. The RoW only differs from the SOE in terms of initial agricultural and manufacturing productivity (A—home versus A*—RoW and M t versus M t *). The RoW has the same dynamics that the closed economy had—steady-state agricultural and total output are constant, and manufacturing grows at a constant rate. Now, because the relative price of manufacturing is fixed, an increase in agricultural productivity in the SOE shifts production away from the manufacturing sector to agriculture via competition for labor. The booming agriculture sector leads to a smaller manufacturing sector and slower manufacturing growth. Since manufacturing was the only engine of growth, the SOE now experiences slower overall growth, which is negative when food is the accounting unit.

To see this within the model, suppose the RoW operates along the original equilibrium path of the erstwhile closed economy, as indicated by the RoW labor market equilibrium equation:

$$ {A}^{\ast }{G}^{\prime}\left(1-{n}^{\ast}\right)={p}_t{M}_t^{\ast }F\left({n}^{\ast}\right)\kern0.5em \mathrm{where}\kern0.5em {n}^{\ast }=v\left({A}^{\ast}\right). $$
(2.12)
In the absence of full specialization, manufacturing employment in the SOE is now determined jointly by Eqs. (2.4) and (2.12). Taking ratios of both equations, n t satisfies
$$ \frac{F^{\prime}\left({n}_t\right)}{G^{\prime}\left(1-{n}_t\right)}=\frac{A{M}_t^{\ast }}{A^{\ast }{M}_t}\times \frac{F^{\prime}\left({n}^{\ast}\right)}{G^{\prime}\left(1-{n}^{\ast}\right)}\kern0.75em . $$
(2.13)
Differentiating Eq. 2.13 with respect to time yields
$$ \dot{n_t}=\frac{\delta \left\{F\left({n}^{\ast}\right)-F\left({n}_t\right)\right\}}{\left[\frac{G^{\prime \prime}\left(1-{n}_t\right)}{G^{\prime}\left(1-{n}_t\right)}+\frac{F^{\prime \prime}\left({n}_t\right)}{F^{\prime}\left({n}_t\right)}\right]\ }. $$
(2.14)

The expression in the square brackets is negative, meaning equilibrium employment in the SOE’s manufacturing sector will increase over time if n t  > n* and decrease if n t  < n*.

Since in Eq. (2.13) \( \frac{F^{\prime}\left({n}_t\right)}{G^{\prime}\left(1-{n}_t\right)} \) is decreasing in n t , an increase in agricultural productivity in the SOE now shifts labor away from the manufacturing sector and to agriculture. This lower equilibrium path of labor allocated to the manufacturing sector, be it growing or declining, and the corresponding reduction in manufacturing output via Eqs. (2.2) and (2.3) reduces the only mechanism for productivity growth in the SOE. Because the SOE is exposed to world prices, there is no domestic price mechanism that can temper these adjustments. Thus, under the open economy assumption, and given that the level of M t is divorced from the level of M t * because of the assumption of no international knowledge spillovers, the model predicts a negative link between an agricultural boom and economic growth.

Present-value welfare effects, however, are ambiguous. The productivity shock in agriculture has increased production of that good and resulted in a temporarily higher level of national income, though that national income is growing more slowly as a result of the slower manufacturing growth. Matsuyama (1992) specifically cautions that “An economy with a rich endowment of arable land (and natural resources), such as Australia (and Kuwait), may grow slower, but does not necessarily have a lower standard of living” (p. 327). Depending on the relative magnitudes of the two effects, present-value welfare may increase or decrease as a result of the boom. Decreased present-value welfare is more likely the lower the productivity shock, the higher the reallocation of labor out of manufacturing for a given shock, the greater the knowledge accumulation through manufacturing activity, and the lower the intertemporal discount rate.11 The point is, one cannot simply look at the one negative effect (a shrinking manufacturing sector and its slower growth) and interpret the welfare impact of a resource boom.

Leveraging Matsuyama’ model, Sachs and Warner (1995) model a three-sector booming resource economy where positive externalities generated from human capital employed in the traded sector induce endogenous growth in both the traded and non-traded sectors. Increased natural resource exports, from a third sector, not only lower employment in the traded sector, attenuating LBD and thereby hampering economic growth, but also negatively affect the growth of the non-traded sector that is dependent on LBD effects spilling over from the traded sector. De-industrialization is the foreseen outcome, and this slows growth. Present-value welfare effects once again remain ambiguous since long-run growth effects may be offset by a short-run booming income, the manifestation of the latter depending on capital intensities in the traded and non-traded sectors.

The Matsuyama model is popular because of its strong conclusions regarding the effects of a booming sector. Yet the model is paradoxical—a world with LBD in manufacturing can make a booming SOE worse off than a world without LBD, regardless of whether or not the SOE has a comparative advantage in manufacturing. It also suggests that a SOE that destroys its agricultural sector or at a minimum prohibits labor movements between sectors will have faster growth, though not necessarily higher welfare. The model also contains the assumption that there is no policy response to the boom. As with any production activity that has positive externalities, the first-best policy solution is subsidization of the activity. The first-best response to the onset of DD associated with a resource boom is then to escalate the existing level of subsidies for the LBD-oriented tradable sector (van Wijnbergen 1984; Roemer 1985). The application of even a second-best trade distortion that raises the domestic price of manufactures can assure the SOE that it benefits not only from the LBD in that sector (Rodríguez and Rodrik 2001), but that it also benefits from the resource boom.12 Autarky would also be welfare improving. Second-best policy responses frequently manifest (e.g., Davis 1995). This penchant for protectionism is apparent in the data, where non-renewable resource-exporting nations are less open to trade than resource-poor nations (Sachs and Warner 1997; Davis and Vásquez Cordano 2013). Though not intended as a policy to combat DD, protectionism turns out to be effective at diminishing DD effects according to this model.13

Manski (2011) proposes that there is a Law of Decreasing Credibility associated with models such as Matsuyama’s, where “stronger assumptions yield conclusions that are more powerful but less credible” (p. F262). Matsuyama (1992) himself warns that his model “is extremely special and should be interpreted with caution” (p. 330). In our view, modeling exercises such as this ask the question “Just how far down the path of extreme assumptions does one need to go before a paradoxical result is obtained?” The Matsuyama modeling exercise is useful, but the strong assumptions needed to get to the paradoxical results are quickly forgotten in subsequent citations.

In an early modeling exercise that is diametrically opposed to Matsuyama, Gelb (1985a, b) simulates via computable general equilibrium the impacts of a temporary oil boom on an Indonesia-like economy under a series of assumptions concerning market and policy imperfections. He does not model LBD. Manufacturing output increases substantially due to exogenous technological progress, public infrastructure investment, increasing labor force participation, and import substitution policies. He finds that in most scenarios, present-value welfare rises as a result of the temporary boom, even given macroeconomic rigidities and suboptimal policy responses to the boom. As Gelb makes clear, almost anything can happen in a booming economy, depending on what imperfections are modeled and the policy responses to those imperfections. Gelb has a relatively tough time obtaining negative outcomes from the boom, highlighting just how important the assumption of LBD externalities are in the DD model.  

We will later examine other forms of LBD and relax other restrictions of the Matsuyama model to provide a more nuanced analysis of booming economy outcomes, but first we pause briefly to relate what the actual impacts of resource booms were thought to be at the time that the early DD models were being formulated.

Early realized welfare effects of a resource boom

The models we have reviewed thus far anticipate an increase in income due to a resource boom, but vacillate on whether manufacturing production decreases in booming economies, and if it does, whether this damages long-run aggregate growth. Let us pause briefly to reflect on what, early on, had been observed regarding economic outcomes in the booming resource economies that were the driving force behind the DD modeling.

Whereas the DD literature arose in response to the maladjustments of the manufacturing sectors in Northern European oil and gas exporters in the 1970s and early 1980s, manufacturing across the world’s major developing country oil exporters did not shrink during that epoch. Indeed, only Kuwait and Saudi Arabia experienced declining manufacturing production during the period of exceedingly high oil prices from 1974 to 1982 (Fardmanesh 1991). Kremers (1986) notes that it is a stretch to pin the Netherlands’ shrinking manufacturing sector between 1973 and 1977 on natural gas extraction. Rather, he points to a general economic downturn blighting Western Europe during that era, which was acutely experienced in Germany and France, both primary Dutch export destinations and nations bereft of hydrocarbons. A closer examination of the statistical evidence from the 1970s to mid-1980s indicates the Dutch guilder depreciated in real terms and manufacturing output and exports failed to contract in real terms for the same period, despite the alleged ravages of the natural gas boom (Kojo 2014). Forsyth (1986) notes Britain’s manufacturing sector recovered after the 1982 recession, implying macroeconomic conditions were important in explaining the sector’s poor performance in the early 1980s. Krzepkowski and Mintz (2013) show that Canada’s manufacturing sector had been shedding jobs since the end of the Second World War, as virtually all other Western countries had, and this secular decline began well before the oil boom of the 1970s and the development of Alberta’s oil sands in subsequent decades.

Going back even further in history, to the economic effects of gold discovery in Australia and California, Cairnes (1921, p. 95) writes

The importance of thus conceiving the commercial effects of the gold discoveries is, that it enables us at once to perceive the precise nature and bounds of the advantage which Australia and California reap from their gold-fields. By means of them they are enabled to obtain their gold at rather less than one-half the sacrifice formerly necessary; and….they can obtain through the medium of it all their other commodities on terms proportionally easier.

In 1890, the USA exceeded the UK in GDP per capita and attained preeminence in world productivity per capita by 1913. According to Wright and Czelusta (2007), it was not an anomaly that in 1913 the USA was also the world’s largest producer of almost every major industrial mineral of that era. The coefficient of relative mineral intensity in US manufacturing exports increased sharply between 1879 and 1914, the very period in which the country became the world’s manufacturing leader (Wright 1990).

While there is a great deal more to say regarding the empirics of the DD, even the early cases call into question the conventional certitude that DD is inescapable. We now turn to a modern day model of the DD that allows for a booming resource sector to enhance manufacturing productivity.

Is growth-reducing de-industrialization inevitable?

The Corden and Neary model sees de-industrialization as an inevitable and desirable response to a resource boom. The open economy version of the Matsuyama model comes to the same conclusion regarding inevitability, though the result is not desirable. The dynamics of DD is crucially introduced into the Matsuyama model via the assumption that the only sector to enjoy LBD is manufacturing. Van Wijnbergen (1984), Krugman (1987) and Sachs and Warner (1995), and Gylfason et al. (1999) also assume that LBD only occurs in the manufacturing sector, though Sachs and Warner (1995) allow for LBD to perfectly spill over to the non-traded sector. In each case, a growth-reducing DD is initiated by a resource boom.

The role of de-industrialization and LBD in causing the DD is critically examined by Torvik (2001). Torvik contests the premise that LBD externalities singularly originate from and only inures the manufacturing sector. This is based on his observation that the industries constituting the non-resource traded sector vary markedly across nations. In some cases, manufacturing is indeed the tradable industry, but in other nations where trade restrictions protect the manufacturing sector from international competition the manufacturing sector more loosely resembles a non-tradable sector.14 This observation provides the impetus to ascribe direct LDB to both the traded good and non-traded good in the two-sector model. Furthermore, his model allows for indirect LBD in that a proportion of the LBD externality generated in one industry spills over to the other industry.

In Torvik’s model, an SOE produces a non-traded good (N) and a traded good (T) according to the respective production functions
$$ {X}_t^{\mathrm{N}}={H}_{\mathrm{N}t}F\left({n}_t\right)\kern7em F(0)=0,{F}^{\prime }>0,{F}^{\prime \prime }<0 $$
(2.15)
$$ {X}_t^{\mathrm{T}}={H}_{\mathrm{T}t}G\left(1-{n}_t\right)\kern5em G(0)=0,{G}^{\prime }>0,{G}^{\prime \prime }<0. $$
(2.16)
The production functions are similar to Matsuyama (1992) in that both non-traded and traded production operates under the assumption of decreasing returns to scale and are functions of a productivity term (H) and proportion of a fixed labor supply normalized to one. Whereas Matsuyama (1992) made productivity exogenous in one sector and endogenous in the other and prohibited LBD spillover effects from one sector to the other (see Eqs. 2.1 and 2.2 above), Torvik (2001) allows the following for growth in productivity in each sector:
$$ \frac{{\dot{H}}_{\mathrm{N}t}}{H_{\mathrm{N}t}}=u{n}_t+{\delta}_{\mathrm{T}}v\left(1-{n}_t\right),\kern0.5em 0\le {\delta}_{\mathrm{T}}\le 1 $$
(2.17)
$$ \frac{{\dot{H}}_{\mathrm{T}t}}{H_{\mathrm{T}t}}=v\left(1-{n}_t\right)+{\delta}_{\mathrm{N}}u{n}_t,\kern0.5em 0\le {\delta}_{\mathrm{N}}\le 1. $$
(2.18)

Equations (2.17) and (2.18) allow for spillover effects across sectors via fractions δT and δN. More explicitly, one unit of labor used in the non-traded sector augments the productivity growth rate of the sector by u, while one unit of labor used in the traded sector contributes to the productivity growth rate of the traded sector by v. These are the direct productivity effects. A fraction (δT) of the LBD from employment in the traded sector spills over to the non-traded sector, and a fraction (δN) of the LBD from employment in the non-traded sector spills over to the traded sector, with the caveat that these indirect spillover effects generated by an industry cannot exceed the direct LBD effects.

Torvik shows that as a result of this simple redefinition of technology spillovers the steady-state allocation of labor across sectors is not affected by a resource boom. The steady-state rate of growth in the economy is likewise not affected. What is affected is the productivities of the two sectors as generated by the transitory reallocation of labor from the traded sector to the non-traded sector during adjustment to the new steady state that results from the resource boom. The continuum of outcomes is illustrated in Fig. 1. When the direct LBD effect in the non-traded sector exceeds the indirect effect it receives from the traded sector, (u − δTv) > 0 or \( \frac{u}{v}>{\delta}_T, \)and if in the tradable sector the indirect effect dominates, (δNu − v) > 0 or \( \frac{u}{v}>\frac{1}{\delta_N}, \)productivity and production permanently increase in both sectors respectively as a result of the labor reallocations associated with the transition after a resource boom. In that instance, pro-industrialization occurs in the traded sector. The level of income rises in all periods compared with the case of no boom, and welfare is enhanced. In the event that \( \frac{u}{v}<{\delta}_T, \)the direct LBD dominates in the traded sector and the indirect LBD effect dominates in the non-traded sector, resulting in a new steady state where productivity in both sectors is lower than it would have been had the natural resource windfall not happened—the resource curse anticipated in Sachs and Warner (1995). Here, however, steady-state growth does not slow. Instead, the level of income declines from the no boom case over all periods to create a DD. Should \( {\delta}_T<\frac{u}{v}<\frac{1}{\delta_N}, \)the direct LBD effects dominate in both sectors, augmenting productivity in the non-traded sector and diminishing it in the traded sector relative to the pre-boom equilibrium—the de-industrialization outcome anticipated by van Wijnbergen (1984), Krugman (1987), and Matsuyama (1992).
Fig. 1

Predictions of increased or decreased traded sector output (Torvik 2001)

The significance of these results is manifold. First, relaxation of the restrictive LBD assumptions in Matsuyama produces a richer set of outcomes that not only incorporates and generates the results of the precursor models but also generates insights not otherwise attainable when LBD was confined strictly to the traded sector. Importantly, traded sector output could actually increase as a result of a natural resource windfall. Whereas the models presented by van Wijnbergen (1984), Krugman (1987), Matsuyama (1992), and Sachs and Warner (1995) gloomily predict with near certainty that an energy boom—indeed, any such “foreign exchange gift”—will cripple tradable sector productivity and retard economic growth, the Torvik model demonstrates instances where not only will the non-traded sector production and productivity grow, but the traded sector could as well, the boom thereby stimulating pro-industrialization. The most important insight from the model is that anything can happen depending on the magnitudes of the direct and indirect LBD effects, highlighting that the determinism of the earlier models is due to their restrictive assumptions about LBD. Nor is the analysis purely theoretical. In an empirical analysis that allows for additional LBD spillovers from the resource sector directly to the traded and non-traded sectors, Bjørnland and Thorsrud (2016) find that such spillovers are material in Norway and Australia.

Neither disease nor destiny

The Torvik model underscores the notion that the “Dutch disease” is not always a disease destined to blight the non-resource traded sector in models that contain LBD externalities; LBD externalities are necessary, but not sufficient, for DD to occur. But LBD itself has scarce empirical support. Several instances of the early DD literature do not assume any LBD effects and show that a resource boom and attendant sectoral adjustments may simply induce a temporary or permanent shift in a nation’s comparative advantage unhindered by the predicted and undesirable ramifications for the non-energy traded sector. The focus was again the impact of a boom on manufacturing, but only as a curiosity, perhaps because of the implicit belief in LBD effects.

To begin elaborating this point, one may simply return to the seminal work of Corden and Neary (1982). While the initial sections of that paper set up the core model that assumes factors are specific to each sector, subsequent sections relax the factor specificity to varying degrees, beginning with allowing capital to be mobile across the non-tradable and manufacturing sectors, a caricature of the Heckscher-Ohlin economy facing a variable supply of labor equal to the total endowment of labor in the economy less the amount employed by the energy sector. In this case, the RME, in isolation, draws labor away from the non-traded sector and manufactures, generating the insights of the Rybczynski theorem15: output falls in the sector using labor most intensively while output increases in the capital-intensive industry. Assuming the manufacturing sector is the relatively capital-intensive sector in line with the stylized facts, the RME prompts manufacturing output to increase, inducing pro-industrialization—a possibility obviated in the “core” DD model.

What is more, should one turn the stylized facts concerning the assumed factor intensity on its head and analyze the RME again, capital intensity in the non-tradable increases its output and reduces its price, with the opposite occurring in the labor-intensive manufacturing sector. While de-industrialization is again the result of the RME, what the relaxation of the fixed capital assumption and reversal of the stylized facts concerning capital intensity shows is that the real exchange rate falls due to the decline in the price of the non-tradable good—a real depreciation previously unattainable in the Corden and Neary paper—ensues.

Although the SE effects on the non-traded and manufactures are the same whether capital is industry specific or mobile, substituting a miniature Heckscher-Ohlin framework for the basic specific-factors model nevertheless generates two surprises—the prospect of pro-industrialization in manufactures and a depreciation of the real exchange rate, depending on the assumed factor intensities in the traded and non-traded sectors.

Corden and Neary (1982) then proceed to a situation where labor and capital are mobile across all three sectors: non-tradable, manufactures, and energy, implying that each of the three sectors can be ranked in various orders depending on the assumed capital and labor intensities. Under the assumption of constant returns to scale and all three goods being produced subsequent to the expansion of the energy sector, this model’s insights hinge solely on the local factor price equalization property: the number of sectors equals the number of endogenously determined prices (capital rental rate, the wage, and the price of services) all of which are determined by technology and traded good prices, rather than factor endowments and consumption patterns.

What is critical here are the insights gleaned from this model. First, the unique circumstances of the model necessarily obviate the SE, as prices are completely determined by the conditions for factor-market equilibrium and changes in prices brought about by the boom are unrelated of the magnitude of the income elasticity of demand for services. Analyzing the sectoral changes by the RME only, the results hinge on the assumed capital and labor intensities of production, giving rise to six distinct possibilities. In four out of the six cases of varying capital intensities, expansion of manufacturing output is a potential outcome. Only in the case where the manufacturing sector is more capital intensive than the non-traded sector but less capital intensive than the energy sector is a contraction in manufacturing output inevitable. Like the Heckscher-Ohlin permutation of the Corden and Neary (1982) model described above, pro-industrialization is a valid expectation when labor and capital are rendered mobile across all three production sectors.

In the penultimate section of the paper, Corden and Neary also note that if the technological improvement in the extractive sector catalyzing the “boom” in energy production happens not to be Hicks-neutral, but instead biased in favor of capital, the technological progress could sufficiently economize on labor that it could depress rather than augment the energy sector’s demand for labor at the pre-boom wage. While this capital biased technological progress would not necessarily alter or dampen the SE, it could however reverse the RME, releasing labor to the non-energy traded sector, thereby promoting pro-industrialization.

Similar qualifications to the assumed inevitability of dire DD effects on the manufacturing sector are found elsewhere in the literature. In a footnote, Corden (1984) discusses the implications of allowing the marginal propensity to consume (MPC) the non-traded good to vary across the specific factors featured in the original model. Should the appetite for non-traded goods among the specific-factor owners of the contracting sectors (capital owners in the non-traded and manufacturing sectors hurt by the RME) be sufficiently higher than the specific-factor owners benefiting from the RME (capital owners in the energy sector or the government via resource tax revenues), the SE could be negative, thereby at least mitigating, if not negating, the de-industrialization and the real exchange rate appreciation generated by the RME. Relaxing the assumption of a uniform and positive MPC for non-traded goods across all factors in the core Corden and Neary model explicitly makes the SE dependent on the RME and negates the assurance that the SE will always engender a real appreciation and de-industrialization—as enumerated in another footnote in Corden (1984).

Briefly returning to the conditions in which real exchange depreciation may come about, Neary and Purvis (1982) adopt a model similar to the specific-factors/Rybczynski theorem set-up utilized by Corden and Neary (1982). In the event the traded sector is relatively capital intensive, labor and capital are mobile across all three sectors, and capital is required for the exploitation of natural resources, less capital available to the capital-intensive traded sector reduces its demand for labor, freeing labor for the labor-intensive non-traded sector. Applying the Rybczynski theorem, output expands in the non-traded sector, resulting in a depreciation of the real exchange rate.

In sum, from the foregoing examination of the literature, it is far from clear that slower economic growth, diminished welfare, and emaciation of an economy’s traditional export sectors are inevitable when an economy experiences a boom in mineral- or energy-extractive activities. Whether assessing the implications of the Torvik (2001) model with regard to the LBD strand of DD literature or simply delving beyond the initial model presented in Corden and Neary (1982), one can readily see pro-industrialization and real exchange rate depreciation are equally plausible analytical results. Those who champion the DD model as implying that it equals de-industrialization (or slower growth, or lower welfare, again recounting the various distinct effects) are calling up just one special, and not necessarily valid, set of assumptions about an economy.

The remainder of this paper describes recent theoretical models and empirical studies of DD, showing that relaxation of the reference models’ simplistic and restrictive assumptions yields richer insights suggesting that adverse DD impacts can be alleviated if not surmounted altogether. The empirical evidence is correspondingly indicative of muted or non-existent DD impacts on traditional export sectors (e.g., manufacturing), thereby seriously questioning the orthodoxy that this supposed economic malady will inexorably afflict mineral- and energy-intensive economies.

More recent theoretical contributions

Despite almost three decades since original publication, the seminal papers by Corden and Neary (1982), Neary and Purvis (1982), Corden (1984), van Wijnbergen (1984), and Neary and van Wijnbergen (1986) describing and assessing the implications of a boom in the resource sector continue to be the starting point of theoretical and empirical work concerning the DD. Like these seminal papers, recent work shows that the adverse impacts of the DD on the traded sector are only tenable under particularly restrictive model assumptions and are averted altogether under alternative model specifications.

For example, applying the Rybczynski theorem in a manner akin to the approaches described above, Beverelli et al. (2011) model an SOE consisting of the standard three production sectors. The distinction from prior models is that the non-traded sector relies only on labor as an input in its production function, while the manufacturing sector uses the inter-sectorally mobile labor and a proportion of domestic energy production as inputs into the production function. In addition, the manufacturing sector consists of a set of sub-industries producing manufactured goods ranked according to the degree of energy intensiveness relative to labor in production.

Given unfettered movement of labor across the non-traded and manufacturing industries and recalling the specification of services output being based singularly on a fixed proportion of labor, the wage rate of the SOE is equal to the domestic price of services while the manufactures price is the numéraire. Taking these conditions into account, the real exchange rate is defined as the ratio of manufactured goods price to the price of non-traded sector. The model indicates that as the natural resource sector expands due to the standard Hicks-neutral technological progress,16 the anticipated appreciation of the real exchange rate and de-industrialization can be potentially obviated if patterns of manufacturing specialization within the country shift towards sectors utilizing energy most intensively. Beverelli et al. suggest that if countries wish to grow their manufacturing sector in the presence of a resource boom, it could be abetted by ensuring a portion of the expanded resource production is employed domestically in the resource-intensive manufacturing industries. Despite this insight, Beverelli et al. eschew providing specific remedies, whether policy instruments or market-based signals, that nations concerned about insulating its manufacturing industry could use.

Looking at the exchange rate adjustments in the presence of a resource boom, Mahbub Morshed and Turnovsky (2004) examine the effects in a dynamic, two-sector-dependent economy, with labor and capital mobility, albeit with adjustment costs for investment and costly sectoral reallocation of capital between non-traded and traded good sectors. The intuition one may glean from this model is that modification of one form of capital assembled to suit an entirely different sector is costly, if not prohibitively so, since the transformation may entail partial demolition of existing capital. Indeed, this restriction on the malleability of capital across sectors alters the real exchange rate (again defined as the price of non-traded goods in terms of traded goods) determination in both the short and medium run as the real exchange rate is no longer fully determined by the supply side and is precluded from instantaneous adjustment. Given the impediments to instantaneously and costlessly reallocating capital from one sector to another, Mahbub Morshed and Turnovsky show that the resultant real exchange rate appreciation/depreciation (depending on the relative factor intensities in the non-traded and traded sectors) anticipated in the Heckscher-Ohlin variant of the Corden and Neary (1982) model may not manifest immediately.

In a similar vein, van der Ploeg and Venables (2011) and van der Ploeg (2011a) model DD with a dynamic, three-sector, specific-factors international trade model featuring capital accumulation. Their model shows that if a boom in a country’s natural resource sector is large, but insufficient to allow the country to import capital goods, capital must be produced in-country to feed production in the expanding non-traded sector. More specifically, the papers show that the classic DD symptoms are apparent, as the model indicates that the optimal response to a resource boom is for the real exchange to appreciate straightaway in order to induce the mobile factor—again labor—to migrate from the traded to the non-traded sector and shift demand to traded goods and away from non-traded goods. Over time, the model indicates that investment induces a gradual expansion in home-grown capital available for use in the expanded non-traded sector, permitting a gradual reversal of the initial appreciation of the real exchange rate. In this case, where the home country cannot afford to import capital, one can ascertain that all of the traditional DD effects are present—a real appreciation of the exchange rate and de-industrialization of the traded sector. However, if a country is small and the natural resource boom is sufficiently large to finance the importation of capital and labor to satiate the expanding non-traded sector, then the SOE may be able to avoid the alleged ravages of the DD on the traditional traded sector.

Mobile factors of production

The ability of a booming energy economy to attract factors of production located outside its borders to satiate demands for labor and capital presents a rich set of possibilities that tend to mitigate DD. Vermeulen (2011) and Beine et al. (2015) assess the sectoral effects of permitting migration of laborers to regions experiencing an energy boom in a general equilibrium context. The basic model used in both studies features a two-firm, two-production sector SOE, where representative firms in the traded goods and non-traded goods sectors operate according to a Cobb-Douglas production function featuring a productivity term, sector-specific capital, and inter-sectorally mobile labor that is exogenous, but variable. More specifically, the SOE can import an exogenous labor supply from outside the SOE to supplement its indigenous supply, thereby relaxing the fixed labor supply assumption of Corden and Neary (1982). Household consumption assumes homothetic preferences over the traded and non-traded goods via a standard CES utility function. The corresponding budget constraint, where consumption equals income, features spending on the traded and non-traded goods where the price of the former is the numéraire and the real exchange rate is expressed as the price of non-traded goods in terms of traded goods. Income is derived from labor income and capital income earned by citizens on their total fixed capital as well as exogenous capital generated from resource rents.

The main implications from the model are twofold. First, non-tradable output, as measured by the proportion of labor employed in the non-traded sector, increases with augmentations of wealth—both the total fixed capital and exogenous capital accrued from resource rents. Since the former source of wealth is held fixed in the model, an increase in exogenous resource wealth arising from an energy boom means the share of non-traded employment and output increases and the traditional non-energy traded sector (e.g., manufacturing) decreases. As anticipated by the seminal DD models, demand for non-tradable output rises with a general income increase due to a resource boom, stoking additional demand for non-tradable sector labor employment.

On the other hand, non-tradable (tradable) sector output and employment decrease to the extent the total labor supply (indigenous plus exogenous labor supply) increases. The influx of supplementary labor from outside the SOE diminishes aggregate marginal labor productivity, which abets the tradable sector absorbing a proportion of the additional labor, thereby expanding output in the tradable sector.

The impact of the model’s insights is significant. The countervailing impacts of exogenous income increases due to a resource boom and an exogenously increasing labor supply are readily apparent. Both would likely accompany a resource boom with the former aggravating and the latter mitigating, de-industrialization in the traditional, non-resource tradable sector.

Admitting importation of mobile capital rather than labor, Raveh (2013) builds a two-region capital tax competition model to show how resource-rich economies can effectively compete for inter-regionally mobile capital and, as such, overcome the traditional DD impacts—especially the RME. Keeping the labor supply immobile and fixed in both the resource-rich and resource-poor region, the model identifies a threshold cost of factor mobility below which resource-abundant regions may generate an “Alberta Effect.”

The Alberta effect describes a situation whereby a natural resource region, like the effect’s namesake, exploits the fiscal policy advantage conferred by taxation of natural resource rents to compete more assertively in the inter-regional competition for factors of production. The result is that such regions are successful at attracting significant amounts of capital—potentially reversing the classic DD effects and in turn transmitting the “disease” to resource-poor regions unable to compete as robustly. In turn, the loss of capital by the region bereft of a natural resource-induced fiscal advantage assures its own DD effects as the traditional traded sector is crippled due to the mobile factors re-locating to the resource-rich economy. Helliwell (1981) was among the first to identify the tendency of Canada’s largest energy-producing province to wield its fiscal policy advantages to enhance its attractiveness to non-energy businesses and residents in the form of tax incentives and expenditures on public amenities. Consequently, the “Alberta Effect” appears to be a capital-focused version of the same impacts labor migration produced in the Vermeulen (2011) and Beine et al. (2015) models.

Finally, Nülle (2016) relaxes the classic assumption of a fixed, but sectorally mobile, domestic labor supply by adding an endogenous labor supply response based on inter-regional wage differentials. A boom in a natural resource-producing economy induces laborers in a non-booming economy to move to the booming economy to obtain a higher wage, with labor migration ceasing when the regional wage rates return to parity. Specified as a CGE model, he shows that labor migration allows the booming economy to “export” some, but not all, of the DD effects (higher wages, smaller manufacturing sector) to the non-booming economy via labor migration flows, The model’s findings somewhat contrast with Corden (1984), which anticipates that labor migration will virtually obviate the RME in the booming economy, as the supplementary labor supply will help fill jobs in all production sectors. However, because the Corden (1984) model is focused on one economy only and does not explicitly model inter-regional labor migration, it does not recognize that an outflow of labor from one jurisdiction to the other raises the real wage rate in the former and decreases the latter until inter-jurisdictional wage parity is reached, presenting a natural check on the amount of labor that will migrate to the booming economy to obviate the RME. In any event, Nülle (2016) suggests that when economists look for DD effects in the presence of inter-jurisdictional labor migration, they may need to look for DD effects manifesting in adjacent, non-booming jurisdictions.

Empirical evidence of the Dutch disease at the national level

The multiplicity of models explaining and predicting the circumstances in which the alleged adverse impacts—real exchange rate appreciation, de-industrialization of the traded sector, expansion of the non-traded goods sector—of the DD will or will not transpire leads one to look to empirical evidence for or against the presence of DD impacts in natural resource-exporting economies. In looking at the empirical evidence, it is important to recall that the theoretical models predict certain outcomes associated with a resource boom that deviate from the counterfactual of no resource boom. Simply observing an outcome in a resource-based economy, without being able to test for the counterfactual, is insufficient evidence to support a model’s predictions.

Like the theoretical models examined in the foregoing sections of this paper, the empirical evidence is ambivalent—adverse DD effects are not a pre-ordained outcome. As anticipated by the seminal models of DD, there does appear to be a substantial increase in near-term income associated with a resource boom. Davis (1995), Alexeev and Conrad (2009), and Mideska (2013) show that the increase lasts for decades, indicating that any slower aggregate growth due to DD effects is not strong. The near-term income effect combined with the lack of substantial negative impact on long-run growth reduces the chance that the present-value welfare effect from any lost LBD and the slower resultant growth effect overwhelm the near-term positive impact of booming resource production.

Conversely, there is some evidence that a booming resource sector causes the growth of the traditional traded sector to slow compared with the counterfactual (Sachs and Warner 1997; Davis 2011). Corden (1982) points to non-energy examples such as the rise of Japan’s manufacturing sector in the 1960s that reduced the output of traditional non-tradable sectors and how the sudden exportation of Swiss financial instruments in the 1970s stoked a real exchange rate appreciation that induced significant competitive pressures on more established export-competing sectors. Gelb (1988) measures less than normal manufacturing and agricultural output in seven temporarily booming oil economies. Curiously, the deficit widened in four of the economies as the boom subsided. He does note that in each economy there were substantial policy-induced distortions that impacted the production mix. This is one of the problems with testing model predictions against observed macro adjustments to the boom—the models cannot possibly include all of the ways that the government sector spends revenue windfalls and how that can alter economic activity.

There is also evidence that the boom slows aggregate growth in at least the first few decades subsequent to the boom (e.g., Sachs and Warner 1997; Butkiewicz and Yanikkaya 2010), though some of that evidence has been contested (van der Ploeg and Poelhekke 2010). There are many explanations of why growth slows that do not include lost LBD. For instance, it has been proposed that the cause is the short-run drag effect from a booming sector that has constant or declining output subsequent to the initiation of the boom (Davis 2011; James and James 2011; James 2015). Rodriguez and Sachs (1999) show that the equilibrium path for a booming economy may well involve a temporary overshooting of steady-state growth that manifests as temporarily slower growth upon the return to the steady state. That the slower growth has not caused incomes in resource economies to drop below the counterfactual (Alexeev and Conrad 2009) is not entirely surprising given that structurally slower growth in the Matsuyama model hinges on the degree of trade openness of an economy. Most booming resource economies are relatively closed (Sachs and Warner 1995). The degree of slower growth due to lost LDB, which poses long-run losses that can never be regained, may also be beyond measurement given that booming mineral production draws a labor away from manufacturing by the thousands, not millions (Davis 2011). Moreover, strong LBD effects in manufacturing are not guaranteed.

In sum, at the national level, there is ample evidence that resource booms increase current income, some evidence that resource booms negatively impact tradable sector output, but little evidence that the long-run path of income is below the counterfactual. This infers that aggregate welfare effects of resource booms are positive, though there may be other negative effects, like increased income inequality or lost opportunities for the poor in manufacturing employment, that are not captured in the traditional welfare function.

To more fully elucidate the empirical evidence for and against DD effects, presented below is a summary of the empirical research focused at the national level. The sections below commence with the most elementary empirical research (single country, case study exercises) to more sophisticated undertakings (multi-country cross-section and panel regressions). The results of this review show that empirical evidence of booming economy effects at the national level is weaker than commonly assumed.

Case study evidence

Looking at the empirical evidence on the international level, case studies of single nations and cross-country research are broadly inconclusive in pointing unequivocally to the prevalence or absence of DD effects.

Using a computable general equilibrium (CGE) framework for Cameroon, Benjamin et al. (1989) show a boom in that nation’s oil sector tends to expand production of manufacturing but depresses agricultural exports, and causes a modest appreciation of the real exchange rate—a partial Dutch disease outcome. A similar finding with respect to the deleterious impact of booming natural resource exports on agrarian exports was shown a decade earlier in the Asia-Pacific region (Timmer 1984), albeit without the use of a CGE model. Carbone and McKenzie (2016) apply a CGE model to the Canadian economy and show that an increase in oil prices enhances Canadian GDP and consumer welfare as measured by equivalent variation, while shrinking the manufacturing sector and its exports. None of these CGE models estimates the longer-run outcomes due to LBD externalities, and so we cannot infer from them whether the predicted long-run lost growth effects occur. It is possible, however, that the stage for these deleterious growth effects is set in the models where manufacturing (and any other sector that has LBD externalities) shrinks.

Cuddington (1989) assessed whether the macroeconomic experiences of commodity-exporting nations undergoing commodity booms in the late 1970s, Colombia, Cameroon, Kenya, Nigeria, and Jamaica, adhered to the DD classic expectations. With respect to Columbia, the country’s real exchange rate appreciated markedly on the heels of a boom in the coffee price in the late 1970s, which abetted significant real wage gains domestically. The international competitiveness of non-coffee exports declined concomitantly, with the ratio of non-coffee exports plummeting from 10.7% of real GDP in 1976 to roundabout the same share as in the mid-1960s (6.6%). Similar outcomes held in the case of Kenya (coffee), Nigeria (petroleum), and Jamaica (Bauxite). Cuddington identified these aftermaths as being exacerbated by a penchant for fiscal profligacy following export booms, particularly in augmented public and private consumption expenditures and ineffectual capital investment programs. By contrast, fiscal restraint on the part of Cameroon’s government and its dedication of a share of oil revenues to finance price supports for traditional agricultural exports prevented the country’s real exchange rate from appreciating in the early 1980s, thereby “immunizing” the traditional export sector from “contracting” DD (Devarajan and DeMelo 1987).

Pinto (1987) draws a similar distinction when comparing the policy responses of Nigerian and Indonesian governments to the oil price spikes of 1973 and 1979. While both countries were similar in population density, reliance on agricultural activities as the chief non-oil export sector, and proclivity to impose import substitution policies to support manufactures, Pinto provides empirical evidence showing Nigeria’s cavalier fiscal, exchange rate, agrarian, and borrowing policies tended to exacerbate classic DD effects relative to Indonesia, which exercised far more fiscal restraint, habitually devalued the exchange rate, and employed market-based mechanisms in the agricultural sector to offset potential DD effects. Substituting Mexico for Nigeria as the comparison nation, Usui (1997) shows an analogous contrast. Usui (1996), Roemer (1994), and van der Meulen Rodgers (1998) also arrive at similar conclusions regarding Indonesia’s success in staving off DD effects.

Similarly, other case studies such as Kremers (1986), Looney (1991), Mikesell (1997), Bjørnland (1998), Rodriguez and Sachs (1999), Larsen (2006), Domenech (2008), Pegg (2010), Mainguy (2011), and Al Rawashdeh and Maxwell (2013) are mixed in their assessment of whether the de-industrialization attributable to DD is empirically discernible or not. While by no means exhaustive, the bevy of case studies underscores how underwhelming the empirical evidence is in demonstratively asserting the inevitability of adverse DD effects in the presence of natural resource booms. Table 1 summarizes the results of these studies. For Norway alone, there are at least eight different articles (Bye et al. 1994; Hutchison 1994; Brunstad and Dyrstad 1997; Bjørnland 1998; Cappelen et al. 2000; Larsen 2006; Gylfason 2007; Bjørnland and Thorsrud 2016) examining whether the country has suffered from DD effects, with three showing evidence supportive of adverse DD impacts, four finding no statistical evidence, and one identifying weak signs of DD.
Table 1

Results of case studies investigating DD impacts

Publication

Case study country(ies)/time period

DD effects present?

Forsyth (1986)

The UK (1970s)

DD effects present, but nettlesome to measure the extent of DD-induced structural changes

Kremers (1986)

The Netherlands (1970s)

DD effects present, although RME absent as gas sector required little indigenous labor or capital

Kamas (1986)

Colombia (1970–1984)

DD effects strongly present

Looney (1988/1989)

Saudi Arabia (1970–1981)

Strong evidence of DD including exchange rate appreciation and reduced traded sectors’ output

Looney (1991)

Kuwait (1970–1986)

Strong evidence of DD from 1973 to 1982; muted thereafter

Younger (1992)

Ghana (1970–1990)

DD effects arise from influx of international aid flows

Feltenstein (1992)

Mexico (1974–1987)

DD effects evident by labor migration out of the traditional traded sector

Mikesell (1997)

7 hydrocarbon-exporting developing nations; 7 mineral-exporting developing countries (1960–1993)

No evidence in more than half the case study countries; DD notably absent in Chile, Papua New Guinea, and Botswana

Bjørnland (1998)

Norway and the UK (1976–1994)

DD weakly present in the UK, while oil production tended to strengthen Norway’s manufacturing sector

Rosenberg and Saavalainen (1998)

Azerbaijan (1994–1997)

No evidence of loss of international competitiveness in the non-energy traded sector

Roca (1999)

Colombia (1910–1950)

DD effects present

Rodriguez and Sachs (1999)

Venezuela (1972–1993)

DD effects present

Spilimbergo (1999)

Chile (1960–1998)

No DD effects; copper a macroeconomic boon

Sala-i-Martin and Subramanian (2003)

Nigeria (1970–1998)

Corruption, not DD, responsible for Nigeria’s anemic economic performance

Olusi and Olagunju (2005)

Nigeria (1980–2003)

DD effects detected, albeit delayed onset in terms of adversely affecting traditional agricultural sector

Larsen (2006)

Norway (1960–2002)

DD effects avoided

Ahrend et al. (2007)

Russian Federation and the Ukraine (1995–2004)

Real exchange rate appreciation detected, but de-industrialization of the manufacturing sector avoided

Domenech (2008)

Spain (1860–2000)

Mining activities had positive effect on industrialization and manufacturing sector

Priyati (2009)

Indonesia (2002–2008)

Mixed DD effects; agriculture and manufacturing exports expanded, despite real exchange rate appreciation

Dobrynskaya and Turkisch (2010)

Russia (1999–2007)

Mixed DD effects; no evidence of de-industrialization despite real exchange rate appreciation

Pegg (2010)

Botswana (1966–1999)

Mixed; no RME, but SE effect present

Mainguy (2011)

Mali (1995–2008)

Inconclusive; traditional export sector (agriculture) shrunk but not due to DD channels

Algieri (2011)

Russian Federation (1993–2009)

DD effects present

Al Rawashdeh and Maxwell (2013)

Jordan (1950–2010)

Scant DD effects

Dülger et al. (2013)

Russian Federation (1995–2011)

Real exchange rate appreciation and de-industrialization detected

Hasanov (2013)

Azerbaijan (2000–2007)

Mixed results; slower growth, but no absolute decline in non-oil tradable sector and substantial expansion of the non-traded sector

Bjørnland and Thorsrud (2016)

Norway (1991–2012)

DD effects avoided

Bjørnland and Thorsrud (2016)

Australia (1996–2012)

DD effects present

As pointed out by Cuddington (1989), the onset of DD may be averted or aggravated by policy decisions made by resource-exporting countries. Mikesell (1997) recommends the following policy prescriptions: channeling export receipts to a revenue stabilization fund, central bank intervention to arrest the appreciation of the real exchange rate, and allocating energy export receipts to long-term public infrastructure projects. While largely concurring with the recommendations above, Hjort (2006) argues against the necessity of a revenue stabilization fund, noting the sustained success Indonesia’s and Botswana’s multi-faceted policy responses have achieved in averting DD-induced de-industrialization and real exchange rate appreciation without recourse to a so-called citizens' revenue distribution fund. All of the papers in Table 1 simply describe realized outcomes net of policy responses. The lack of consistent empirical evidence of DD effects may well be because of policy reponses in certain countries that mitigate and even offset the effects of the boom.

Cross-country regressions results

Turning to cross-country regressions, which tend to more adequately control for the counterfactual scenario, clear evidence of the DD prompting de-industrialization is conspicuously lacking. In addition to addressing the resource curse question itself, Sachs and Warner (1997) empirically test whether symptoms of the DD are observable in their cross-country data set of developing countries. Using growth in services and manufacturing valued added as a dependent variable, the regressions showed slower growth in non-resource sector output in natural resource-intensive economies. The results hold when controlling for the initial share of manufacturing exports in total exports, propensity to open trade, real gross domestic investment, institutional quality, and initial per capita income. In accordance with the traditional DD expectation that the ratio of non-traded output to non-renewable resource sector traded output will be more prolific in resource-abundant economies, a related regression shows that resource-intensive economies did exhibit a higher ratio of output of services to output of manufactures. Despite this apparent empirical finding, the results suffer from a fatal flaw: Davis (2013) shows the result of resource production’s impact on manufacturing output is not robust to country sample.

Results distinctly converse to Sachs and Warner (1997) are found in a contemporary, albeit considerably less cited, paper. Hutchison (1994) adopts co-integration analysis and vector error correction modeling (VECM) to empirically test whether the development of offshore oil and gas sectors systematically exerted adverse effects on the manufacturing sectors in the Netherlands, the UK, and Norway—the very North Sea oil exporters that arguably gave rise to the DD phenomenon in the first place. The co-integration/VECM empirical techniques allow one to estimate the impact of a booming energy sector on traditional tradable good output while disentangling it from other macroeconomic factors that may be influencing the waxing or waning of North Sea oil exporters’ manufacturing sectors. More specifically, the other “candidate” factors influencing the apparent decline in manufacturing output include hawkish monetary policy, spikes in world energy prices, and exchange rate appreciation unrelated to the advent of North Sea oil and gas extraction itself.

For all three countries investigated by Hutchison (1994), other macroeconomic factors distinct from the onset of oil and gas extraction activities were responsible for the deterioration of their manufacturing sectors. With regard to the UK, tight monetary policy and energy price swings were instrumental in stifling UK industry, while in the Netherlands—the namesake of the “Dutch disease” phenomenon—the extraction of natural gas was found to apply a positive, albeit small, influence on the country’s manufacturing sector. While the results did indicate an adverse effect of hydrocarbon extraction activities on manufacturing in Norway, it was found to be short term in nature.

To further underscore the distinction between the papers above, working papers by Andrew Warner both prior and subsequent to the publication of Sachs and Warner (1997) show scant impacts of DD effects on panel regressions. Spatafora and Warner (1995, 1999), in World Bank and IMF working papers, respectively, perform regressions on data comprised of 18 low-income oil exporters from 1965 to 1989. The results indicate that whereas non-tradable output growth is spurred by real exchange rate appreciation attributable to energy booms, DD effects on the traded sector are scarcely perceptible. Spatafora and Warner attribute the sparse tradeable sector findings to the assumed “enclave” sector status of oil; nevertheless, they also show that provided capital is markedly importable and domestic labor supply is sufficiently elastic, aggregate investment increases in oil-intensive economies.

More recently, Harding and Venables (2010) find in a sample of 135 countries spanning nearly five decades (1975–2007) that every $1 increase in natural resource exports results in a decrease in non-resource exports by half that amount, an increase in non-resource imports by 15 cents, and an overall decline in a country’s non-resource net trade balance by 65 cents. A follow-on paper, Harding and Venables (2016), applies the same panel estimation approach to 41 mineral and energy exporters for the period 1970–2006. The results show a dollar of resource revenue receipts decreases non-resource exports by 65 cents, increase imports by 20 cents, with the remaining 15 cents being saved. In neither paper do Harding and Venables establish whether separate fiscal policy actions undertaken by nations in the sample separately influenced the exchange rate during this period.

Comprehensively examining the fate of 81 separate manufacturing sectors across 90 countries (including 15 oil-exporting nations) over the period 1977–2004, Ismail (2010) arrives at the following empirical insights. First, sustained increases in the oil price negatively affect manufacturing output, as predicted by the traditional DD model. The econometric estimates indicate a 10% increase in an oil windfall results in a 3.4% decline across manufacturing sectors’ value added. Second, capital intensity of manufacturing and the relative factor price of labor to capital increase as the size and the duration of the oil windfall increase. Third, the DD effects of oil windfalls tend to be less acute in manufacturing sectors possessing relatively higher capital intensity. Finally, oil windfall shocks exert a stronger influence on manufacturing sectors in countries more open to foreign direct investment.

The implications of the second and third findings are that both a more diversified and more capital-intensive manufacturing sector tends to ameliorate the undesirable effects of the Dutch disease—similar to the contention advanced by Beverelli et al. (2011). The Beverelli et al. model showed that under the assumption of energy being used in varying intensities as an input in the multi-industry manufacturing sector, the manufacturing industries most resilient amid a boom in natural resource production are those using energy most intensively. To that point, the econometric specification and 132-country data set employed by Beverelli et al. find evidence generally supportive of their hypothesis that the negative effect of an increase in the endowment of natural resources on industry-specific outputs is attenuated in manufacturing industries that intensively use natural resources.

Dutch disease evidence in exchange rate data

Rather than focusing on the size of the tradable or non-tradable sector as the main variable of interest, another strand of empirically oriented papers attempts to ascertain whether a real exchange rate appreciation is incurred by countries undergoing a natural resource boom initiated by increases in exogenously determined world commodity prices. As shown by Rodrik (2008), a high value of the exchange rate, or “overvaluation” in Rodrik’s terms, tends to hinder long-term economic growth, particularly non-energy tradable good production in developing nations.

In this vein, Koranchelian (2005), Zalduendo (2006), and Issa et al. (2008) generate econometric evidence in support of a positive and statistically significant impact of oil prices on real exchange rates in Algeria, Venezuela and Canada,17 respectively. Beine et al. (2015) find that a third to two fifths of Canada’s manufacturing job losses from 2002 to 2007 are attributable to an energy price-induced real exchange rate appreciation. Kalcheva and Oomes (2007) estimate the long-run elasticity of the real exchange rates with respect to the real oil price to be approximately 0.5 in Russia, while Korhonen and Juurikkala (2009) arrive at a similar estimation value using a pooled mean group estimator applied to a panel data set of nine OPEC countries. Kutan and Wyzan (2005) use the Balassa–Samuelson model to ascertain whether Kazakhstan suffers from DD, finding oil production exerted a significant impact on the country’s exchange rate from 1996 to 2003. Applying an autoregressive distributed lag model, Jahan-Parvar and Mohammadi (2008) examine the possibility of Dutch disease in 14 oil-exporting countries, finding only marginal statistical evidence to support long-run elasticities in only four of the countries. Similarly, Hodge (2011) examines the relationship between manufacturing in South Africa and the real exchange rate, metal commodity prices, world economic growth, labor costs, and monetary policy from 1980 to 2010. The panel co-integration approach shows that manufacturing labor costs and world economic growth determine the level of South African manufacturing over the short and long run and that the real exchange rate and metal commodity prices do not.

Mohammadi and Jahan-Parvar (2012) challenge the findings described above, arguing that a critical assumption underlying the preceding econometric models —symmetric adjustments of exchange rates in response to positive and negative deviations from equilibrium—does not hold with respect to real exchange movements in oil-exporting countries pursuing official currency interventions such as a managed float or exchange rate targeting. Consequently, Mohammadi and Jahan-Parvar contend asymmetric adjustments that go unaccounted for precipitate mis-specification and biased results in the prior models. Accommodating asymmetric responses in exchange rates by applying threshold autoregressive (TAR) and momentum threshold autoregressive (MTAR) models, Mohammadi and Jahan-Parvar apply the econometric models to examine the long-run relation and short-run dynamics between real oil prices and real exchange rates in a sample of 13 oil-exporting countries. Tests of co-integration using the TAR and MTAR models indicate oil prices have a long-run effect on the exchange rates in only 3 out of 13 oil exporters—Bolivia, Mexico, and Norway. For these countries, there is no evidence in either direction of short-run causality between real exchange rates and real oil prices. Taken together, the exchange rate findings suggest, at best, a weak link between oil prices and real exchange rates and limited evidence in favor of the Dutch disease.

Empirical evidence of the Dutch disease on the sub-national level

While most theoretical and empirical works concerning both the resource curse and the DD have focused on the national case study or the cross-country comparison level, more recent literature has begun examining whether the alleged economic phenomena is observable at a more localized level. The advantages of sub-national analyses are compelling. First, sub-national units are largely homogenous political jurisdictions—one can easily control for otherwise nettlesome effects such as heterogeneous institutions, languages, currencies, and governments that afflict cross-country estimations. Second, patent heterogeneity within sub-national economies to include industry mix, population composition, and amenities is richer than international variations. The jury is still out as to whether the resource curse or the DD effects are manifest in mineral- and energy-abundant economies when econometrically estimated at the sub-national level.

State/provincial evidence

At the state/provincial level, most of the work has focused on the Americas, spanning from Canada down to the Southern Cone. Carrington (1996) examines the substantial, albeit seasonal and short-lived, macroeconomic effects of the construction of the Trans-Alaska pipeline from 1974 to 1977. Using aggregated earnings and employment time series data, Carrington found the sudden and seasonal influx—pipeline construction could only occur during the abridged Alaska summers—of 50,000 highly remunerated construction jobs had significant spillover effects by increasing wages and hours worked in related industries. Earnings in traditional non-traded sectors, such as services and retail trade, also grew markedly on account of the surge in seasonal workers. Non-related industries were not impacted, including the government sector and the state’s manufacturing sector that were both geographically concentrated in a location far removed from the construction and engaged in value-added activities (canning, logging) insulated from pipeline construction.

James (2016) extends the analysis to the impact of the pipeline on subsequent oil production in Alaska, which peaked in 1988, and extends the period of analysis to 2015. He finds significant inward migration in response to the wage increases associated with the production boom. That migration inflow slowed or even stopped in about 1990, as production declined. While he does not explicitly test for DD effects, the evidence of inward labor flows during the boom validates Nülle’s (2016) proposition that at the sub-national level, any model of DD must allow for mobile labor. James does find a long-run (as of 2015) decrease in Alaskan growth compared with the counterfactual of no oil boom. Given that Alaska was hardly a manufacturing mecca prior to the boom, and given the in-migration that should alleviate any shrinking of the manufacturing that existed prior to the boom, it is difficult to imagine that DD is responsible for the slower long-run growth.

Rather than using econometric specifications to ascertain how natural resource abundance may affect earnings and employment, Haddad and Giuberti (2011) use an inter-regional CGE model to assess the impacts of an extensive oil and natural gas discovery off the coast of the Brazilian federal state of Espírito Santo. Specifically, Haddad and Giuberti use a two-region (Espírito Santo and the rest of Brazil), two-representative consumer, two-government, 55-sector, 110-commodity, 480,000-equation CGE model to evaluate whether negative DD sectoral impacts will arise when extraction and export of the large offshore oil and gas fields commences. A one-time Hicks-neutral technological improvement in oil and gas production—similar to the Corden and Neary terminology—is modeled as an abrupt 50% reduction in the amount of inputs needed to produce a given quantity of hydrocarbons in Espírito Santo.

Consistent with the classic DD anticipations, aggregate regional income increases in Espírito Santo and the rest of Brazil, rents to factor owners of the specific factor in the energy sector rise, the state’s real exchange rate appreciates, and non-traded sectors expand, while non-resource tradable sectors contract. The CGE fully anticipates adverse DD impacts as an outcome of offshore oil extraction.

Turning north to Canada’s provinces, Papyrakis and Raveh (2012) systematically attempt to empirically measure DD effects. They differentiate the RME from the SE impacts and propose three econometric specifications to measure each effect separately. The first specification attempts to ascertain whether inflation tends to be higher in resource-intensive provinces—a symptom of Dutch disease SE effect—controlling for resource abundance in resource-rich and resource-deficit provinces, lagged prices, provincial capital and labor stocks used in the non-resource tradable sector, migration of capital and labor, time and province fixed effects, and an error term. The succeeding two specifications test for the presence of the RME effect among resource-rich provinces. The first regresses labor migration on the very same regressors as in the first equation, testing the standard DD hypothesis that labor migrates away from the non-resource tradable sector to the resource and non-traded sectors, while the final equation determines whether capital migrates away from or to the non-resource traded sector—an explicit attempt to verify whether the postulated “Alberta effect” holds empirically. The Alberta effect describes a situation whereby a natural resource region, like the effect’s namesake, exploits the fiscal policy advantage conferred by taxation of natural resource rents to compete more assertively in the inter-regional competition for factors of production. Using provincial panel data spanning 1984–2008, Papyrakis and Raveh compute all three regressions simultaneously using the seemingly unrelated regressions technique.

The provincial regressions indicate resource-rich provinces tend to experience higher inflation at the 1% statistical significance level, giving credence to the SE aspect of DD. Regarding the RME, labor tends to leave the non-resource tradable sector, consistent with traditional DD literature; however, capital tends to migrate to the non-resource traded sector in resource-abundant provinces, suggestive of the “Alberta effect” being at play.

Papyrakis and Raveh then turn to measuring whether non-resource (intra-Canada and international) exports are diminished by a booming resource sector in resource-abundant provinces, where provincial non-resource export growth is the endogenous variable and is regressed on the independent and fixed effects terms featured in the previous three equations above, as well as a lagged endogenous variable term.

The results of the panel regression indicate that mineral abundance has a large and statistically significant negative impact on non-resource export growth in resource-intensive provinces. Combined, the three postulated DD symptoms (inflation, labor shift, capital shift) explain just over a fifth of the negative relationship between non-resource sector export growth and resource production in Canadian provinces. The DD channels most prominent in explaining this outcome were found to be higher inflation and labor shifts away from the non-resource export sector.

Also empirically testing the “Alberta Effect,” the basic two-region capital tax competition model used to illustrate the conditions by which resource-rich provinces effectively compete for inter-regionally mobile capital and surmount the traditional DD impacts, Raveh (2013) first compares the evidence for or against the resource curse hypothesis via inter-federal and intra-federal samples of nations in North America, Oceania, Europe, the Middle East and Asia.18 While natural resources tend to exert a negative influence on economic growth in the inter-federal regressions, the intra-federal regressions indicate the converse—that natural resource abundance is a boon to sub-national, regional economies. Raveh then proceeds to test empirically two DD hypotheses: (1) whether the Alberta effect applies and (2) whether DD symptoms are mitigated or even obviated by the Alberta effect. Rather than using Canadian data to test the Alberta effect, Raveh uses a panel data set of America’s 50 states and District of Columbia spanning 1977–2008 to perform a series of regressions measuring the propensity of states to attract foreign direct investment, establish a business-friendly tax regime, and stimulate its manufacturing sector.

The regressions indicate that resource-abundant states are more proficient at attracting FDI, furnish more competitive fiscal regimes for non-mineral enterprises, and as such, facilitate expansion of manufacturing sectors. The results reflect the reality that resource-intensive states such as Alaska and Texas lack an individual income tax altogether, and Wyoming, which boasts among the world’s largest coal reserves, has neither an individual nor corporate income tax. Raveh shows these positive benefits accruing to the resource-intensive states come at the expense of resource-poor states—thereby transmitting DD to resource-poor states.

Sub-state/sub-provincial evidence

Although relatively a small part of the DD the literature, empirical studies assessing the impacts of natural resource abundance on sub-state jurisdictions have begun to manifest in greater numbers in recent years. Sub-state analysis has all of the advantages of sub-national analysis in addition to other specific benefits. Intra-national, sub-state analysis eschews institutional and cultural differences between nations that may precipitate spurious correlation (van der Ploeg 2011b; Caselli and Michaels 2013). This level of analysis also presumes labor mobility costs are less than at the international level where immigration is more tightly restricted (Raveh 2013).

Methodological concerns are not the only motivator for looking at economic questions through a sub-national lens. Advances in information and communications technology, such as geographic information systems, have permitted non-governmental organizations to develop more detailed surveys at a micro level and national governments to collect and publish more granular levels of data. These advances allow researchers to acquire more extensive and specific data sets to test new or existing economic questions (Cust and Poelhekke 2015). A localized level of analysis offers a larger sample size of economic units that increases the reliability of econometric estimates. First-order subnational units like states and provinces for example number nearly 2000 across the globe (Mitton 2016). For second-order subnational units, the number is even greater. The USA has more than 3000 counties alone, a total 62 times greater than the number of US states and 15 times larger than the almost 200 countries of the world. Often, national statistical agencies are charged with collecting and maintaining data for sub-national governments, including total and per capita incomes and total and sectoral employment, allowing a researcher to assemble large and extensive panel data sets.

Not only do sub-national units present far more data points for a researcher, but the economic outcomes experienced by these entities ultimately sums to a net positive or benefit for the nation (Cust and Poelhekke 2015). Knowing this can help a researcher understand within-country effects, pinpoint where the effects do or do not transpire, and identify and test for potential intra-national transmission mechanisms. For these reasons, intra-national, sub-state analysis may indeed be the most appropriate level of analysis to empirically test for booming economy effects.

Booming economy literature at the sub-state level commences with Black et al. (2005), which studies labor market effects of boom and bust in the coal-producing counties of Kentucky, Ohio, Pennsylvania, and West Virginia. Identifying three epochs in the recent history of coal prices—the 1970–1979 “Boom,” 1978–1982 “Peak,” and 1983–1989 “Bust”—Black et al. compare jobs and earnings growth and spillovers across industries using the diff-in-diff counterfactual estimation technique. They find that while manufacturing earnings per worker in “treatment” (coal) counties experience a statistically significant increase during the boom, employment registers a minor and statistically insignificant decrease amid the boom relative to non-coal “control” counties. The empirical evidence concerning the non-traded sector is commensurately ambiguous, with employment growing during the boom in only two of three non-traded sectors examined in the paper and earnings not growing statistically significantly in any of the triad of non-traded industries. Black et al. also attempt to measure how many jobs in the non-coal sectors are created or destroyed for every one mining job generated or lost during the boom-peak-bust epochs. The estimated employment multipliers indicate that each mining job generates 0.174 non-traded sector jobs during the boom and manufacturing employment shrinks by 0.008 jobs for every one mining job created. Again, however, only the non-traded sector multiplier is statistically significant, depriving the Corden and Neary (1982) narrative of booming economy effects of empirical evidence at the local level.

Marchand (2012) finds similar results with respect to sub-provincial units in Western Canada engaged in oil and gas production. Using quasi-experimental diff-in-diff empirical techniques analogous to Black et al. (2005), he finds energy-abundant economies across two energy booms and one intervening bust avoid a shrinking manufacturing sector. The estimated employment multipliers for the non-traded goods sectors in the booming energy economies are higher than Black et al. (2005) while the multiplier for the manufacturing sector is virtually nil and statistically insignificant. Using the same diff-in-diff method, Weber (2012) and Brown (2014) look at income, employment, and job spillover effects arising from the natural gas boom occurring in counties situated in the Central and Rocky Mountain regions of the USA; Weinstein (2014) evaluates shale oil- and gas-producing counties in 20 US states; Fleming and Measham (2014) examine the same variables amid a boom in coal seam natural gas extraction in sub-provincial regions of Queensland, Australia; Komarek (2016) compares outcomes between natural gas-producing counties in Pennsylvania, Ohio, and West Virginia to New York counties in the Marcellus region prohibited from allowing fracking; and Ouedraogo (2016) compares virtually every county in the USA. As observed by Marchand and Weber (2017), each paper arrives at similar results to Black et al. (2005). Caselli and Michaels (2013) estimate how petroleum production affects fiscal and economic indicators among Brazilian áreas mínimas comparáveis (AMCs)—statistical areas comparable to US counties. Using panel data for more than 3800 AMCs, the econometric results for sectoral effects show only a scant negative impact of oil production on non-oil industrial sub-sectors (manufacturing, construction, utilities) and on non-industrial sectors (services, government, agriculture) when oil production occurs offshore. However, when onshore oil production increases by one Real, non-oil industrial output shrinks by one quarter of a Real, while non-industrial sectors’ output increases by one quarter of a Real. These onshore results are more consistent with the expectations of the seminal booming economy models. In a more recent and localized example, Aragón and Rud (2013) examine the economic impact of Yanacocha, a large gold mine located in Northern Peru. Analysis of annual household data spanning a decade yields robust evidence of a positive impact of the gold mine on local real income and associated welfare measures.

Michaels (2011) examines long-run oil production effects on employment and demographic outcomes in 220 oil-abundant US counties in the south-central USA versus the counterfactual non-oil control counties. Estimates of oil abundance on employment per square mile indicate that not only did the density of mining employment increase significantly over the period 1940–1990, but manufacturing and agricultural employment density increased as well. To explain this, Michaels turns to the estimates of population growth. Although population density in 1940 in oil-abundant counties was already higher than in control counties, population growth in the former outpaced the latter by as much as 1% per year through 1990. Stronger population growth fostered a larger population density in oil-abundant counties, thereby furnishing a larger indigenous labor supply19 capable of meeting demands in the oil and non-oil tradable sectors alike. Michaels speculates that labor migration is at play in the population growth, but refrains from empirically testing this contention. Overall, the expansion of oil, agriculture, and manufacturing appears to have been abetted by strong population growth in oil-abundant counties, providing the requisite labor supply to satiate input demand among these production sectors, providing scant evidence in favor of the traditional booming economy impacts at the county level.

These implications of population growth and movements in labor supply are echoed in Weber (2012, 2014), who shows US counties experiencing rapid expansion of shale gas production from 2000 to 10 did not register a crowding out of the manufacturing sector. Weber found rapid population increases in gas-abundant counties arrested sharp gains in wages, placing the RME in abeyance.20 Wilson (2016) and Nülle (2016) both explicitly tackle the question of whether migration is at play. Estimating the elasticity of in-migration in responses to changes in wage rates, Wilson (2016) shows that a 1% increase in the average wage increase the rate of in-migration by 4.6% in North Dakota, as opposed to only 3.1% in other states. The higher elasticity in North Dakota is attributed to the surge in oil and gas extraction activity occurring in the Bakken region. Looking at outcomes in nearly 3100 US counties across nearly three decades, Nülle (2016) finds that during periods of high natural resource prices, labor migrates at higher rates and manufacturing sector jobs grow faster in booming counties relative to non-booming counties, suggesting that labor migration helps arrest the RME in booming counties.

Rather than examining the economic impacts of the most recent boom in US oil and gas production, Jacobsen and Parker (2016) focus on the boom in the mid 1970s and its aftermath. In view of isolating the impacts of both a boom and bust over both short and long periods of time, Jacobsen and Parker evaluate changes in county-level data from 1969 to 1998 by dividing the timespan into pre-boom (1969–1974), boom (1975–1981), bust (1982–1985), and post-bust (1986–1998) intervals. Defining boom counties in the western USA based on hydrocarbon drilling activity and adopting the diff-in-diff empirical estimation method akin to (Black et al. 2005), they find that per capita income, extraction jobs, and non-tradable sector employment increased in the treatment counties faster during the boom than in control counties. They also find statistically significant evidence of a decline in tradable sector (agricultural) profits in treatment counties during the boom period, consistent with the stylized expectation that the traditional traded sector experiences reduced profits during a hydrocarbon boom. Lastly, the empirical results indicate that once the full 30-year boom-and-bust cycle was complete per capita income was nearly 6% lower in the treatment counties than it would have been if the boom had never occurred, evidence of a negative impact of hydrocarbons extraction on the competitiveness of traditional traded sector (agricultural) output.

Focusing on the impacts of a change on technology as the driver of an energy boom, Maniloff and Mastromonaco (2017) empirically assess the impacts of hydraulic fracturing on aggregate employment, income, and firm growth at the US county level. Utilizing the diff-in-diff method of counterfactual analysis and controlling for unobserved and time-varying factors of growth, the results indicate the advent of advanced oil and gas extraction techniques stimulated relatively faster job growth and wage growth in the treatment counties relative to control counties. More crucially, the evidence from the study is mixed regarding adverse impacts on the traditional traded sector in the counties where hydraulic fracturing occurs. Generally, the empirical estimates indicate that remuneration, employment, and the number of firms in the manufacturing sector were not negatively affected by shale oil and gas development. However, when controlling for the tightness of labor markets via unemployment rates and a county’s level of exposure to the oil and gas industry prior to the introduction of hydraulic fracturing, Maniloff and Mastromonaco find evidence of wage pressures on the traditional manufacturing sector when county unemployment is low and there is little prior industry exposure.

Similarly evaluating the economic impact of hydraulic fracturing on US county-level outcomes, Fetzer (2014) finds no evidence that the manufacturing sector in shale oil and gas producing counties shrinks. To explain these findings, Fetzer asserts, but does not empirically test, that shale oil and gas extraction lowers energy prices in the counties in which the activity occurs. To the extent manufacturing firms predicate site selection decisions on energy costs, a relatively lower cost of electricity provides shale oil and gas producing counties a comparative advantage in the inter-jurisdictional competition for manufacturing jobs. Once again, we see the premise that if there is a shrinking manufacturing sector it happens in areas that are resource poor, not resource rich.

Finally, Alcott and Keniston (2017) acquire restricted-use firm-level industrial output data to answer the following questions on the US county level: (1) whether counties experiencing a resource “boom” undergo a contraction in non-energy tradable output, (2) whether prices and production of counties’ non-tradable sectors increase amid an energy boom, and (3) whether LBD productivity in the manufacturing sector is impaired by resource booms such that non-resource-intensive counties’ manufacturing sectors grow significantly faster than energy-abundant counties. Alcott and Keniston find manufacturing productivity growth in energy-abundant counties is diminished relative to control counties, but manufacturing employment and output both nevertheless rise during booms. This finding is important, in that it implies manufacturing growth in energy-abundant counties is positively correlated with increases in employment and output in the counties’ oil and gas sector, suggesting the anticipated adverse effects of a non-renewable resource boom on the non-energy traded (manufacturing) sector are absent—just as Black et al. (2005), Michaels (2011), Marchand (2012), Weber (2012, 2014), Fleming and Measham (2014), Fetzer (2014), Ouedraogo (2016), Komarek (2016), and Maniloff and Mastromonaco (2017) show.

Conclusions and policy implications

In recent years, a great deal of academic attention has been devoted to explaining the causes of the resource curse. Among the most trenchant economic explanations is the Dutch disease. While the seminal models of Dutch disease are represented as predicting that resource booms will engender real exchange rate depreciation, expanded output of non-tradable goods, and an emaciated non-energy traded sector, with possibly negative present-value welfare effects due to slower growth, several variants and extensions of the core models present plausible scenarios where just the opposite occurs—real exchange rate depreciation, pro-industrialization, and increased welfare. These variants have been slowly erased from public memory, such that the only version now espoused is that of de-industrialization. Like the equivocal theoretical literature, empirical evidence concerning the presence of the Dutch disease on the national and sub-national level is decidedly mixed—with the Dutch disease effects attenuating, if not disappearing altogether, the smaller the political jurisdiction. Most recently, a few empirical studies have been published examining the Dutch disease on the intra-national level. Depending on the timespan and country examined, a surge in natural resource extraction tends to improve per capita income and other indicators of economic well-being and even facilitate, rather than mar, the expansion of traditional tradable sectors. If there is a Dutch disease associated with resource extraction, the place to look is the resource-poor areas, whose manufacturing output shrinks as labor is drawn to the booming regions.

Overall, the theoretical and empirical literature reviewed in this paper indicates that the Dutch disease is by no means inevitable at the national level, with or without the influence of policy responses. Where Dutch disease does occur there has yet to be an empirical investigation of learning by doing effects in the sectors affected and whether or not the Dutch disease leads to the immiserization predicted in certain models. We have also highlighted theoretical models with learning by doing effects that find that the Dutch disease responses to resource booms can be welfare enhancing. The oft-repeated concerns about Dutch disease in booming resource economies are thus not necessarily warranted.

Given this, national policy responses to protect exchange rates or to increase the subsidies to traditional traded sectors with leaning by doing externalities in the face of a resource boom are premature at best and at worst may inhibit the economic benefits of such booms by interfering with natural market mechanisms. For example, given Bjørnland and Thorsrud’s (2016) finding that there can be learning by doing externalities associated with resource production, it is this sector, and not the manufacturing sector, that should be subsidized. Subsidizing the manufacturing sector may reduce the crowding in that flows from the booming resource sector. This is not to say that there are no dislocations or other negative effects such as income inequality produced by a sudden resource boom, and that policy to ease these effects is not warranted. For example, Norway’s widely-heralded policy of capturing and saving a large portion of the rents from its oil boom can hardly be faulted for its attempts at intra and intergenerational equality. The fund’s value is now worth $US 200,000 per Norwegian and continues to grow.21 The Norwegian government was certainly aware of the Dutch disease early on, but shied away from any explicit attempt to inhibit the natural macroeconomic forces that accompany a boom other than to sterilize much of the rent through foreign investment (Holden 2013). Larsen (2006) unconvincingly ascribes the introduction of Norway's savings policy, as well as its industrial policy, as being to counteract the Dutch disease. He admits that the attempt at and effectiveness of industrial policy is difficult to assess, and that Norway’s growth may have happened in spite of, and not because of, its industrial policy. In any event there is ample evidence that Norway’s manufacturing sector did not shrink as a result of its boom, in large part due to technology spillovers from the resource sector and possibly unintended consequences from its welfare programs (Bjørnland and Thorsrud 2016). Interestingly, Australia’s latest mining boom, which happened at roughly the same time as Norway’s oil boom, appears to have caused some Dutch disease given weaker spillovers from the resource sector and an absence of industrial policy to combat the disease (Bjørnland and Thorsrud 2016; Hunter 2014).

Returning to Radetzki’s concerns about monoeconomies, it is not at all clear that the resource specialization that concerns him is exacerbated by a shrinking traditional sector. On the other hand, none of the models or empirical analyses we have reviewed concern themselves with the temporary nature of the resource boom, which is his other concern. What happens to these economies when the boom ends? It is unlikely that one can simply unwind the booming sector model, as stickiness and dislocations need to be considered. James (2016) finds that oil-rich Alaska was initially better off as a result of the oil boom, but is now worse off as its oil output declines to pre-boom levels. Its legislators are also grappling with how to deal with declining oil revenues (Semuels 2015). These are provocative indications that Radetzki’s concerns may be warranted even if they do not arise from Dutch disease effects. Post-resource-boom economics is a fertile area for future research, and one that will complete the picture of the welfare effects of resource booms in extractive economies.

Footnotes

  1. 1.

    Literature reviews of the resource curse include Ross (1999, 2015), Stevens (2003), Davis and Tilton (2005), Rosser (2006), Wick and Bulte (2009), Frankel (2010), Deacon (2011), van der Ploeg (2011b), Stevens et al. (2015), and van der Ploeg and Poelhekke (2017). The first paper to attribute the resource curse to the DD was Sachs and Warner (1995), based on Matsuyama (1992).

  2. 2.

    “A researcher who performs an instructive layered policy analysis and exposits the work clearly may see himself as having accomplished the objective of informing policy choice” (Manski 2011, F286).

  3. 3.

    See also Corden (1984).

  4. 4.

    Although DD policy discussions and research endeavors arose as a result of hydrocarbon discoveries in Northern Europe, formative research on the subject primarily occurred “Down Under” in response to a booming mining sector, giving rise to the nickname of “the Australian model”. As Corden (1996) explains, British economist James Meade is credited for first having written explicitly about the DD phenomenon when the notion was brought to his attention by Australian economist Eric Russell during Meade’s visit to Australia in 1956, which became the impetus for arguably the very first paper on the subject (Meade and Russell 1957).

  5. 5.

    The implications of the special case of non-neutral technological progress are addressed in the “Neither disease nor destiny” section below. Papers contemporary to the Corden and Neary model investigating the SE in isolation include Bruno and Sachs (1982), Buiter and Purvis (1982), Eastwood and Venables (1982), Enders and Herberg (1983), Maddock and McLean (1984), and van Wijnbergen (1982).

  6. 6.

    Cassing and Warr (1985) exhaustively assess the distributional impacts concerning income gains and losses from factor owners in the various sectors and the implications of alternative policy arrangements successfully pursued by aggrieved factor owners.

  7. 7.

    Gregory (2012) claims the original term for what became known as the Dutch disease was the “Gregory Thesis” originated by C. Hurford, a member of the Australian Parliament.

  8. 8.

    A critical point that is frequently missed in papers concerning DD.

  9. 9.

    There is limited empirical evidence examining this effect (Davis 2009; Davis and Vásquez Cordano 2013; James and Smith 2017) due to the lack of good data on income inequality (Ross 2007; Parcero and Papyrakis 2016).

  10. 10.

    If manufacturing were the accounting unit, the economy would be growing.

  11. 11.

    For large resource booms that are welfare improving, concerns about intergenerational welfare can be addressed via capital market activities such as sovereign wealth funds that are aimed at smoothing consumption. Norway has undertaken such consumption smoothing.

  12. 12.

    One can imagine a price distortion that adjusts to keep the proportion of labor in the manufacturing sector at the same level as before the boom. The boom then simply augments output in the agricultural sector, increasing welfare as measured at international prices.

  13. 13.

    Matsuyama (p. 330) is forced to recognize this implication of his model, but parries by suggesting that there are likely to be technological spillover effects in practice and that such protectionism could then slow economic growth by retarding these spillovers. He nevertheless conjectures that as long as the spillovers are incomplete, there will be a negative link between agricultural productivity and growth.

  14. 14.

    Corden (1984) makes a similar observation, pointing to agriculture—rather than manufactures—being the major non-energy/mineral export in countries like Nigeria and Australia.

  15. 15.

    See Rybczynski (1955) for the well-known theorem.

  16. 16.

    Corden and Neary (1982) examine the case where energy is used as an intermediate good in the manufacturing sector only under the assumption that a boom in the energy sector is triggered by an increase in the exogenously determined world price for energy. Under that scenario, manufacturing unambiguously contracts due to higher energy input prices.

  17. 17.

    Naim and Tombe (2013) show that rather than being a scourge to Canadian manufacturing, appreciation of Canada’s commodity price sensitive dollar may actually be a boon. Their examination of the Canadian manufacturing sector shows that in excess of 40% of the sector’s intermediate inputs are imported, among the highest in the OECD. Their analysis shows the imported intermediate goods obtained more cheaply with a stronger dollar tend to offset the countervailing negative effect a dearer dollar has on the sector’s competitiveness in global markets.

  18. 18.

    The heterogeneous sample includes Australia, Belgium, Brazil, Canada, Germany, India, Malaysia, Russia, United Arab Emirates, and the USA.

  19. 19.

    Smith (2015) finds that with the advent of oil and gas extraction several developing and developed countries experience significant increases in both the size and quality of its labor force. He postulates that the augmentation of these oil and gas producing countries’ labor forces may be attributable to an influx of migrant workers following the hydrocarbons boom.

  20. 20.

    Wilson (2016) explicitly tackles the question of whether migration is at play. Estimating the elasticity of in-migration in responses to changes in wage rates, the paper shows that a 1% increase in the average wage upped the rate of in-migration by 4.6% in North Dakota, as opposed to only 3.1% in other states. The higher elasticity in North Dakota is attributed to the surge in oil and gas extraction activity occurring in the Bakken region.

  21. 21.

Notes

Acknowledgements

The authors would like to thank two anonymous referees for their helpful comments on an earlier draft.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Arizona Department of RevenuePhoenixUSA
  2. 2.Division of Economics and BusinessColorado School of MinesGoldenUSA

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