Econ Change Restruct (2011) 44:243–277 DOI 10.1007/s10644-011-9101-4

The Dutch Disease: evidences from Russia

Bernardina Algieri

Received: 13 December 2007 / Accepted: 21 February 2011 / Published online: 8 March 2011 Springer Science+Business Media, LLC. 2011

Abstract The present study examines whether the Russian economy exhibits the symptoms of the Dutch Disease over the transition period begun in the early 1990s. Five warning signs have been detected, namely, a real exchange rate appreciation (1); a flourishing economic situation pushed by higher oil prices (2); a relative de- industrialisation (3); an export reduction in the non-booming-sector (4) and a real wage growth (5). The first three symptoms are estimated simultaneously in a VECM dimension. The results suggest the existence of three long-run cointegrating vectors, thus confirming the presence of the first three symptoms. Specifically, a 10% oil price shock leads to a real appreciation by 4%, a rise in GDP by 3% and a decline in domestic manufacturing production vis-a`-vis service production by another 3%. Finally, a number of manufacturing exports have been crowded out and real wages have recorded important increases. To a certain extent, this corroborates the pres- ence of symptom 4 and 5. The paper concludes that the risk of the Dutch Disease exists, and two preventive thrusts of action could be undertaken to reduce its threat: namely to diversify the economy and to hold back the appreciation of the exchange rate through targeted fiscal and monetary policies. These instruments would render Russia less vulnerable to exogenous shocks.

Keywords Dutch Disease Oil price VECM analysis

JEL Classification C22 F14 P27

B. Algieri (&) Department of Economics and Statistics, University of Calabria, Arcavacata di Rende, 87036 Cosenza, Italy e-mail: [email protected] 123 244 Econ Change Restruct (2011) 44:243–277

1 Introduction

When a country experiences a resource boom due to a tradable resource discovery and/or to an exogenous raise in a resource price, it normally undergoes a real appreciation of its exchange rate and, as a result of increasing wages, a relocation of some of the labour force to the resource sector. A real appreciation brings about a loss of international competitiveness in manufacturing which may lead to a progressive de-industrialisation (Krugman 1987). This phenomenon, known as ‘‘Dutch Disease’’, originated in the Netherlands in the late 1950s when natural gas discoveries in the North Sea eventually hurt the competitiveness of the Dutch manufacturing sector. Corden (1984) noted that the term was used for the first time in an article by The Economist dated November 26, 1977. Thereafter, the Dutch Disease has been used to explain the economic performances of countries facing similar conditions.1 Russia is a good candidate for Dutch Disease. In fact, the country is one of the main producers and exporters of natural resources. It owns the world’s largest natural gas reserves and supplies one fourth of all gas on the world market (International Energy Outlook 2009). The Russian Federation is a major world oil producer, sometimes producing even more than Saudi Arabia, and is the world’s second largest oil exporter2 (EIA 2010). Likewise, the country owns strategically significant reserves of raw materials: average per capita reserves of coal, iron, wood, copper, nickel, aluminium, pulp and the main ferrous metals by far exceed the world average. Against this background, the present study aims to evaluate whether the Russian economy exhibits the symptoms of Dutch Disease due to oil price booms. The paper provides several contributions to the Dutch Disease debate. It explicitly examines diversity across symptoms, distinguishing between five warning signs as derived from Krugman’s definition and the Dutch Disease literature, namely: a real exchange rate appreciation resulting from increased export revenues and, hence, demand for domestic currency; a dizzying period of economic expansion thanks to oil price surges which might be temporary; a slower growth in the non-booming sector than in other sectors (i.e. relative de-industrialisation); a reduction in the non- booming sector exports and a rise in real wage growth across sectors. While I am not the first to flag the possibility that Russia shows some signs of Dutch Disease, a systematic analysis, with the exception of Oomes and Kalcheva (2007), has yet to be undertaken. Most studies either have a theoretical nature (Moiseev 1999; Latsis 2005), or are mainly statistically oriented (Roland 2005; the World Bank 2005; Ahrend et al. 2007; Barisitz and Ollus 2007). Hitherto, one key reason for the absence of econometric works regarding the effects of oil prices on Russia’s economy has been data issues. Specifically, fragmented time series concerning trade variables, output and fiscal figures and recurrent data adjustments have been the

1 Mineral upturns in Sub-Saharan economies have been approached in Dutch Disease terms (Wheeler 1984; Gelb 1988). Also the gold discoveries in Australia during the nineteenth century were analysed according to the DD literature (Forsyth 1985). See Corden (1984) and Gylfason (2001) for a number of further examples. 2 The largest non-OPEC oil exporter-country is Venezuela. 123 Econ Change Restruct (2011) 44:243–277 245 main obstacles to carrying out research. Furthermore, the several institutional and structural adjustments that occurred in Russia during the process of transition to a market economy have further complicated empirical investigation. This study takes a step forward in the analysis of Dutch Disease with the aid of a wider availability of data. A further important novelty of the paper relates to the use of the VECM technique developed by Johansen (1988), which enables researchers to identify the long-run equilibrium path distinguishing between short and long term dynamics, and to test for the number of cointegrating relationships. Most of the previous studies have investigated some of the symptoms, and always separately; instead, the present work simultaneously estimates a set of equations in order to have a more comprehensive picture of the phenomenon. A final important element of this study relates to the use of monthly data allowing for a finer investigation of Dutch Disease dynamics. Most papers, except for the work done by Oomes and Kalcheva (2007) are based on more aggregated data. Hence, this analysis will provide a deep understanding of how the dependency on oil affects the Russian economy and its performance. This could help policy makers to better shape their policies in order to best take advantage of the benefits coming from the oil sector and guard against the threats hidden in the Dutch Disease trap. The rest of the paper is organised as follows. Section 2 presents the basic Dutch Disease model and reviews the key literature on the topic. Section 3 sets up the equations that will enter the vector error correction model. Section 4 provides evidence of a real appreciation, a positive period of economic expansion and a relative de-industrialisation. Section 5 displays the empirical findings on the losses in manufacturing exports and the gains in real wages across sectors. Section 6 concludes the study.

2 The Dutch Disease

2.1 The core model

Corden and Neary (1983) were the first economists to model Dutch Disease within the framework of a three-sector economy, namely a resource sector, a tradable manufacturing sector and a non-tradable service sector. While tradable goods are exposed to international competition and their prices are determined by the world demand and supply, service prices are not exposed to international competition and, thereby, their prices depend only on the domestic demand and supply. The model assumes that: (1) labour is perfectly mobile between all the three sectors, and makes sure that wages equalise across these sectors; (2) all goods are for final consumption; (3) trade is balanced every time, as national output always equals expenditures; (4) factor prices are flexible and all factors are internationally immobile. The two authors show that a resource boom (e.g. energy) affects the rest of the economy through two channels: the resource movement effect and the spending effect. An increase in energy prices raises the value of the marginal product of labour in the energy sector, and pushes the equilibrium wage rate up, bringing about a movement of labour from both the manufacturing and non-tradable 123 246 Econ Change Restruct (2011) 44:243–277 sectors to the energy sector. The result is a tightening in the levels of employment and output in the two sectors, and an increase of workers and production in the energy sector (the resource movement effect). The shrinkage in the manufacturing sector is known as ‘‘direct de-industrialisation.’’ The contraction of supply in the service sector pushes the price of services up and causes an appreciation of the exchange rate. Likewise, a boom in the natural resource sector leads to increased income for the country which, in turn, increases imports and domestic absorption for both tradables and non-tradables (the spending effect). Inasmuch as the prices of tradables are set internationally, this effect results in higher prices and wages for non-tradables relative to tradables (i.e. a further real appreciation of the exchange rate). Consequently, labour and capital shift from the manufacturing and energy sector to the non-tradable sector. The rise in wage levels in the non-tradable sector forces the manufacturing and energy sectors to make a corresponding increase in their wages. However, since prices–with the exception of non-tradables–are determined internationally, the manufacturing and energy sectors have to downsize, in terms of production and employment, to avoid profit shortfalls. This narrowing of the manufacturing sector is called ‘‘indirect de-industrialisation.’’ By combining the resource movement effect and the spending effect, it is possible to obtain the following outcomes: a real exchange rate appreciation; a reduction in manufacturing exports; and a contraction of manufacturing output and employment (non-booming tradables). Ambiguous results are observed for the non-tradable and the booming resource sectors because they can, de facto, register an increase or decrease in employment and production according to which of the two effects tends to prevail. In this context, if the spending effect overcomes the resource movement effect, the economy undergoes a reduction of manufacturing relative to services (relative de- industrialisation). If the resource effect tends to be stronger, then the levels of both manufacturing and services will drop (absolute de-industrialisation). This implies that the ratio between the sectors can increase or decrease according to the intensity of the contractions in the two sectors. If the factors of production (capital and technology) used in the booming sector are very sector-specific, and the booming sector also does not require any significant amount of labour from other sectors (i.e. the booming sector is an enclave), then the resource movement effect will not occur. Booming sectors with these characteristics are not uncommon in the real world, and the oil sector is a typical example (Migara and De Silva 1994; Kuralbayeva et al. 2001). Consequently, since there is only a spending effect in operation, an oil boom would produce a reduction in manufacturing and an expansion in services. The resource movement effect, the spending effect, and their combined effects are summarised in the following Table 1. In the presence of a resource boom, caused by an increase in the resource price or the discovery of a new vein, the economy tends to experience significant GDP growth. This is due to the higher value of export revenues in the case of soaring prices, and/or the rise in the quantities/volumes of resource exports in the case of new veins. In the future, when prices go down or the resource is depleted, GDP growth is prone to fizzle out. From another perspective, the country experiences significant economic improvements in the short-to-medium term due to better terms of trade. However, in the long-run, it faces the risk of slowing down the ‘‘cultural, 123 Econ Change Restruct (2011) 44:243–277 247

Table 1 Effects induced by the Dutch Disease Prices Wages Exports Production Employment

Resource movement effect Resource sector Internationally given Increase Increase Increase Increase Manufacturing Internationally given Increase Reduction Reduction Reduction Non-tradable sector Increase Increase – Reduction Reduction Spending effect Resource sector Internationally given Increase Increase Reduction Reduction Manufacturing Internationally given Increase Reduction Reduction Reduction Non-tradable sector Increase Increase – Increase Increase Net effects Resource sector Internationally given Increase Increase Ambiguous Ambiguous Manufacturing Internationally given Increase Reduction Reduction Reduction Non-tradable sector Increase Increase – Ambiguous Ambiguous technical and intellectual development which only a strong, healthy manufacturing industry… can provide’’ (Kaldor 1981). To be more explicit, in trade models, countries should specialise in industries in which they have a comparative advantage. This means that, theoretically, a country rich in natural resources would be better off specialising in the production and export of natural resources. In reality, however, the shift away from manufacturing can be detrimental. If the natural resources begin to run out,3 or if there is a downturn in prices, competitive manufacturing industries do not recover as quickly or as easily as they declined. This is because manufacturing is based on ‘learning-by-doing’ processes; long periods of inactivity create a comparative disadvantage in the sector because knowledge and human capital are eroded. Thus, when the country can no longer rely on natural resources, the manufacturing sector is not able to compete internationally and cannot replace the resource sector in leading the economy. Hence, the long term effect may be to erode the country’s competitive position in manufacturing.

2.2 Literature review

There is a sizable literature on Dutch Disease. Until the mid-1990s, most of the empirical works corroborate the presence of Dutch Disease in a host of countries. In particular, during the 1970s and 1980s the poor economic performance of Latin America and African countries, despite their abundance in natural resources, was compared to the economic success of the Asian Tigers, which are poor in terms of natural resources. Sachs and Warner (1995) authored the classical and most comprehensive empirical work on the Dutch Disease. The authors use an extensive empirical cross country analysis to prove how, on average, countries with a high ratio of resource-based exports to GDP have a tendency to show lower growth rates.

3 According to a recent survey by British Petroleum, at the current rate of production Russia is supposed to run out of its known reserves in about 22 years. 123 248 Econ Change Restruct (2011) 44:243–277

Resource-scarce economies such as Japan and Switzerland were often more successful than resource-rich economies in terms of economic growth. Valuable earlier findings regarding the failures of resource-driven development encompass studies by Gelb (1988) and Auty (1990). Van Wijnbergen (1984) postulates that a boom in the exports of primary goods, in addition to its detrimental effects on the manufacturing sector, can also affect economic growth through ‘‘forward and backward linkages’’. If most economic growth is attributed to learning-by-doing processes, which chiefly shape and affect the manufacturing tradable sector, a decline in that sector may reduce productivity and, hence, lower future national income. In the second half of the 1990s, the general validity of the Dutch Disease was questioned by a consistent number of empirical studies (Davis 1995; Spilimbergo 1999; Altamirano 1999). The latter elucidated that Dutch Disease hypotheses are particularly strict and hold only under specific assumptions. Therefore, the economic consequences and the policy implications of the findings by Sachs and Warner (1995) were consistently reduced. Lane and Tornell (1996), Auty (1999) and Gylfason et al. (1999) argue that, in the long term, natural resource-abundant countries may register a slowdown in their growth rates not only because of Dutch Disease but, above all, as a result of lacking and/or ill-defined property rights, weak rule of law and imperfect, missing or moonlighting markets in many developing and emerging market economies; extensive rent-seeking which can reinforce corruption in business and government; and low incentives for human capital accumulation. As a result, many people end up stuck in low skill-intensive natural resource-based industries (Stijns 2000). Matsen and Torvik (2003) affirm that some Dutch Disease is always optimal, because lower growth in resource abundant countries might be part of an optimal growth path. The empirical analysis of the Dutch Disease is mostly focused on the experience in resource-abundant developing countries (e.g. Venezuela, Ecuador, Nigeria, Indonesia) and in several industrial countries (e.g. the United Kingdom, the Netherlands, and Norway). There are, however, only a few studies regarding the effects of a booming resource sector in a post-Soviet transition economy. Rosenberg and Saavalainen (1998) evaluate the economic risks correlated to the extensive use of natural resources in Azerbaijan and suggest a policy strategy to deal with such risks. The authors revise the standard three-sector Dutch Disease model to take into account some peculiarities of transition economies, specifically: (1) depreciation of the national currency; (2) weakness of the financial system; and (3) increases in capital inflows. ‘‘Transition factors’’ turn out to magnify the speed of real appreciation. Non-oil sectors may be worse off, but mainly as a result of transition-specific structural and institutional problems than due to a real appreci- ation. The authors argue that Azerbaijan can avoid the Dutch Disease problem if the country ‘‘promotes savings and open trade and strengthens the supply side through structural policies’’. The Azerbaijan case is also analysed by Singh and Laurila (1999). According to them, Dutch Disease syndrome is supposed to become a policy concern in the medium to long term, particularly after 2005. Kutan and Wyzan (2005) and E´ gert and Leonard (2006) examine whether Kazakhstan is vulnerable to the Dutch Disease. The authors find evidence that changes in the terms of trade have a significant effect on the real exchange rate after 1996, providing evidence of Dutch Disease. 123 Econ Change Restruct (2011) 44:243–277 249

In spite of the common opinion that the Russian economy is extremely dependent on oil, there are contrasting views as to whether Russia ‘‘has contracted’’ the Dutch Disease. In particular, Westin (2005), Roland (2005), Oomes and Kalcheva (2007) argue that Russia appears to have only some symptoms, but it is premature to speak about full-blown disease. Ahrend et al. (2007) and Beck et al. (2007) find mixed evidences regarding manufacturing sector contraction in Russia. Conversely, Barisitz and Ollus (2007) ascertain an incipient de-industrialisation process that affects large parts of manufacturing. Finally, Latsis (2005), the World Bank (2005) and ICEG (2006) outline that Russia has been hit by all the classical symptoms of the Dutch Disease. A summary table of the literature for Eastern countries is presented in Appendix 1.

3 Formalisation of the three symptoms

Extending the theoretical framework on the Dutch Disease (Sect. 2.1) to the Russian case, it is plausible to expect the following symptoms consequent to an oil boom. Explicitly: a real appreciation of the Russian exchange rate due to increased price in services and export revenues (symptom 1); a burgeoning period of economic expansion (symptom 2); a sluggish growth in the manufacturing sector vis-a`-vis the service sector (relative de-industrialisation) (symptom 3), a deterioration in the non- booming sector exports (symptom 4) and an overall wage boost (symptom 5). These symptoms are expected to materialise in Russia because the ‘‘discovery’’ of resources unambigously leads to an increase in national GDP (Sachs and Larraı´n 1993; Borko´ 2007), the spending effect dominates the resource movement effect, owing to the relatively small number of employees and a narrow labour mobility in the Russian oil sector (Andrienko and Guriev 2004), and because the oil sector is considered an enclave (McKinnon 1976; van Wijnbergen 1984). The first three symptoms, evaluated in this and in the next section, are estimated in a complex framework using the Johansen methodology; symptoms 4 and 5 are assessed using an index analysis in Sect. 5.

3.1 Symptom 1: a real exchange rate appreciation: theoretical background

Before implementing the Johansen methodology, it is necessary to analytically define the equations describing each warning sign and then estimate them simultaneously. The analytical formulation of Symptom 1, i.e. the real appreciation of the domestic currency, is the result of three strands of empirical literature4 on exchange rates. A first strand elucidates the dynamics of the real exchange rate in the light of the Balassa-Samuelson effect (Halpern and Wyplosz 1996; Coricelli and Jazbec 2001; De Broeck and Sløk 2001). Changes in relative prices between tradables and non- tradables cause adjustments in the real exchange rate. Productivity growth in the

4 The initial idea is allotted to De Gregorio and Wolf (1994). 123 250 Econ Change Restruct (2011) 44:243–277 tradable sector produces a boost in real wages. If wages are the same in different sectors of the economy, then wages, and therefore prices, would increase in the non- tradable sector, hence affecting the real exchange rate. In other words, economies with a higher level of productivity in tradables will be characterised by higher wages and, since international productivity differences are wider in tradables than in non-tradables, also by higher non-tradables5 prices. A second strand of literature considers changes in the real exchange rate to be a consequence of variations in relative prices between exports and imports, i.e. changes in the terms of trade (Dornbusch 1983; Roldos 1990; Frenkel and Razin 1992; De Gregorio and Wolf 1994). For a small open economy, an increase in the export price, which improves the terms of trade, will intensify its export revenues. This leads to a surge in spending on all goods, which raises domestic prices relative to foreign prices, causing a real exchange rate appreciation. For a large open economy, a rise in export prices will provoke either a slump in revenues, if its demand for exports is elastic, or, in the case of inelastic export demand, an increase in revenues. In the first case, the real exchange rate depreciates; in the latter, it picks up. A third strand of literature stresses the importance of fiscal and monetary policy changes in determining real exchange movements (Brada 1998; Dibooglu and Kutan 2001). A fiscal deficit could produce two sorts of effects. On the one hand, if the fiscal deficit increases (i.e. there is an expansive fiscal policy), and monetary policy is left unchanged, interest rates will rise and the real exchange rate will appreciate. This is what the United States experienced between 1980 and 1985. On the other hand, when a fiscal deficit increases, it can be accompanied both by a rise in interest rates and a drop in financial credibility. This combination of factors can yield to a real exchange rate depreciation, as witnessed in Italy in 1992, Argentina in 2001–2002 and Greece in 2010 (Fig. 1). As regards monetary policy, Central Banks can influence the real exchange rate through foreign reserves. To prevent the appreciation of a currency, a Central Bank accumulates foreign reserves and to avoid a depreciation it lessens foreign reserves. This practice is more effective when foreign exchange interventions are not sterilised. Therefore, the analytical specification resulting from the combined literature can be synthesised as follows: REX ¼ f ðPR, POIL, GOV, RESÞð1Þ þ þ þ where REX, PR, POIL, GOV and RES are respectively the real effective exchange rate, productivity, oil price, a government variable and international reserves. The real effective exchange rate (REX) based on the year 2000 is a significant indicator that directly reflects Russia’s international competitiveness in terms of its foreign exchange rate. It is a more suitable indicator than a bilateral exchange rate based on the US dollar because of the oil-price sensitivity of US consumption. An increase in the trade-weighted real exchange rate implies an appreciation of the domestic currency. The productivity variable (PR), which reflects the Balassa-Samuelson

5 Perfect intersectoral factor mobility ensures factor price equalisation across tradables and non- tradables. 123 Econ Change Restruct (2011) 44:243–277 251

Fiscal Deficit

Exchange rate appreciation Interest rate Interest rate Exchange rate depreciation (USA) (Italy, Argentina, Greece) Financial credibility

Fig. 1 Effects induced by a fiscal deficit increase hypothesis,6 is constructed as an index of seasonal adjusted industrial production (2000 = 100) divided by the index of employment in that sector (2000 = 100). This practice is consistent with the evidence reported by Coricelli and Jazbec (2001), De Broeck and Sløk (2001) and E´ gert (2002) who analyse the real exchange rate dynamics in different transition economies and prove that the Balassa- Samuelson effect plays a dominant role in the later stage of transition. POIL represents the second strand of the literature as it can be considered a proxy for the terms of trade7 and, at the same time, this variable measures the Dutch Disease effect.8 This practise is in line with the works by Singh and Laurila (1999) and Kutan and Wyzan (2005). The variable government (GOV) embodies the public sector deficit and has been constructed as the ratio of the total State budget expenditures and total State budget revenues. It has been considered the ratio and not the difference, to avoid negative signs in case of deficits, which would prevent this variable from being expressed in log form. International reserves are a proxy for interventions of the Central Bank in foreign exchange markets (Taylor 1982; Neumann and von Hagen 1993; Neely 2000; Willett 2009). Detailed data descriptions and variable developments are reported in Appendix 1. According to the literature, the variables PR and POIL are expected to have a positive sign, while the variable GOV may have either a positive or a negative sign: it is positive when a

6 Before conducting the econometric analysis, the Balassa-Samuelson hypothesis—namely that wages between the tradable and non-tradable sectors tend to equalise—was verified. The analysis of sectoral nominal wages data based on ILO, Laborsta (2010), reveals that the ratio between non-tradable wage and tradable wage is mean-reverting, in the sense that, while a gap in the levels between nominal wages across sectors may exist, this gap has remained stable over time. This means that the Balassa-Samuelson hypothesis holds and the econometric analysis can therefore be carried out. For reasons of space the calculations have not been reported, but they remain available upon request. 7 An increase in oil price, in fact, causes a sudden rush of export revenues for the oil-exporting country and an improvement in the terms of trade; this drives up domestic demand. In turn, that implies an upsurge in domestic prices relative to foreign prices with a consequent real appreciation. In a previous estimation, TOT and POIL were considered separately as two exogenous variables; in particular, oil and natural gas exports were taken out of the terms of trade in order to filter out the Russian manufacturing trade price structure. Due to some monthly missing data for TOT, only POIL has been included. 8 One could wonder why the REX equation includes oil price only and excludes gas price and other commodity prices. It is worth noting that there is a strong correlation between the spot prices for natural gas and crude oil (Borko´ 2007). When the price of oil goes up, gas is soon to follow, and vice versa. Therefore, a problem of multicollinearity would have arisen if both prices were included in the estimation. As for other prices, one should take into consideration that energy exports account for the main bulk of total Russian exports, while other mineral prices have only minor importance for real exchange rate fluctuations. In addition, Rautava (2004) has shown, in a similar structure, that replacing oil price with the aggregate raw material price index reinforced, but did not significantly modify, the results. For these reasons, Eq. 1 excludes gas and other commodity prices. 123 252 Econ Change Restruct (2011) 44:243–277

Fig. 2 Effects of a resource G1 boom on the pattern of growth GDP growth

B G2

H

A

Time country enjoys a strong financial credibility and vice versa. The variable RES always shows a negative sign.

3.2 Symptom 2: a GDP growth fuelled by oil price increases

The analytical representation of Symptom 2, i.e. an economic growth fuelled by oil prices increase, derives from the theoretical structure implemented by IMF (2002, p. 8), Rautava (2004) and Beck et al. (2007). It is interesting to mention that in recent years Russia has experienced an ‘‘economic miracle’’: the country has recorded strong macroeconomic performance, marked by protracted GDP growth and rising income. Per capita GDP has increased from less than $2,000 to $9,000 in 2008 at the current rate of exchange, and salaries have grown by up to 16% (the Economist, 1/3/2008). In 2009, Russia’s economy saw a reversal from brisk average growth of 7% per year due to the global financial crisis. All these trends could mask the risk hidden in the DD in the near future. The addiction to oil can be considered a ‘‘double-edge weapon’’ because it can increase economic growth in the short-to- medium run, but it can also reverse GDP growth in the future if the natural resource runs out. This idea is illustrated in Fig. 2. The magnitude of benefits and losses in utility, in present value terms, that result from being on growth path G2 vs. G1 depends on various factors, such as price shock and other disturbances such as domestic policies. For a given size of A and B, the net present value is influenced by the discount rate. The bigger this rate is, the more relevant are the augmented oil gains (A is wider) and the less valuable the future divide between the welfare along the two patterns of growth G1 and G2. To evaluate how the Russian ‘‘economic miracle’’ has been mainly triggered by high oil prices, a GDP equation has been included in the VECM framework. Explicitly, the specification includes the Russian GDP index at 2,000 price (GDP),9 the real effective exchange rate of the country (REX) and the international oil prices (POIL):

9 Quarterly data on GDP were transformed into monthly series through interpolation using the moving average filter. 123 Econ Change Restruct (2011) 44:243–277 253

GDP ¼ f ðPOIL; REXÞð2Þ þ The variables POIL and REX are expected to have a positive and a negative sign respectively, because an increase in oil prices would push export revenues and therefore stimulate growth, while a decrease in price competitiveness would slow down GDP.

3.3 Symptom 3: relative de-industrialisation

Before formulating a specific equation for Symptom 3, the process of de- industrialisation is investigated through a descriptive analysis. When examining the contributions of the sectors to GDP formation, it should be noticed that post-Soviet Russia experienced a significant structural change: the shares of agriculture and industry recorded major falls in favour of the service sector’s share. The contribution of the industrial sector to GDP fell from 48% in 1990 (with a peak of 57% in 1992) to about 36% in 2006. That of agriculture dropped by 69.4% between the same periods. In contrast, the share of the service sector increased over time. According to the official statistics issued by Rosstat, starting in 1995, services have contributed between 55 and 60% of the national GDP compared to 35% in 1990 (Table 2). Considering the year-by-year percentage change in GDP growth by sector (Table 3), it is worthwhile noting that manufacturing growth has slowed down, while the growth of the non-tradable sector, in particular construction, financial intermediation, retail and wholesale trade, and more recently hotels and restaurants, outstripped the growth of other branches of the economy. This means that while there has been not an absolute de-industrialisation, i.e. negative manufacturing growth rates, there has been indeed a relative de-industrialisation, i.e. a reduction in the ratio of manufac- turing vis-a`-vis the service output. This feature tends to confirm symptom 3 of the Dutch Disease, which predicts a relative de-industrialisation when the spending effect prevails over the resource movement effect. There are three caveats. One is the fact that services, such as transportation, computer and financial, and hotel and restaurants, are no longer necessarily non-tradable because Russia also exports these services (Beck et al. 2007). Secondly, before 1994, the compression of the service sector was the result of the Soviet development policy that focused on fast growth through intensive industrialisa- tion, while the service sector was considered to be non-productive. This led to the over-industrialisation of the former Soviet Union. Thirdly, the growth of the service sector might reflect the ‘‘tertiarisation process’’ which characterises all of the most advanced economies. The shift from an industrial to a service economy is mainly linked to the higher productivity of manufacturing compared to services, and consequently, to the higher number of employees in services, which increases the sector share in total GDP. In order to isolate the effect of the oil price changes on Russian production, and to evaluate the relative de-industrialisation in a more robust way, the following specification has been included in the VECM system:

123 5 cnCag etut(01 44:243–277 (2011) Restruct Change Econ 254 123

Table 2 Gross domestic product by sector % 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Agriculture 17.00 13.00 10.00 9.00 7.00 8.00 7.24 6.56 5.68 7.36 6.48 6.65 5.78 5.44 5.10 5.20 5.20 Industry 48.00 50.00 57.00 45.00 39.00 37.00 39.00 38.30 37.67 37.46 38.18 35.97 34.38 34.28 35.46 35.60 36.00 Services 35.00 37.00 33.00 46.00 54.00 55.00 53.76 55.14 56.65 55.18 55.34 57.38 59.83 60.28 59.44 59.20 58.80

Sources: Own calculations on Rosstat (2007) and Economist Intelligence Unit Econ Change Restruct (2011) 44:243–277 255

Table 3 GDP growth by sector (year-on-year % change) 2003 2004 2005 2006 2007

GDP at market prices 7.3 7.2 6.4 7.4 8.1 Agriculture, hunting, forestry 5.5 3.0 1.1 3.6 3.1 Fishing 3.4 1.2 2.8 4.9 2.9 Natural resource extraction 10.8 7.9 0.5 1.6 0.3 Manufacturing 9.5 6.7 6.0 4.8 4.1 Electricity, gas and water supply 1.6 2.0 1.2 4.7 -1.9 Construction 13.0 10.3 10.5 11.6 16.4 Retail and wholesale trade; repair of vehicles and household goods 13.2 9.2 9.4 14.6 12.9 Hotels and restaurants 1.3 5.9 9.7 11.2 12.1 Transport and communication 7.2 10.9 6.2 9.6 7.7 Financial intermediation 9.6 9.9 10.9 10.3 11.4 Real estate and leasing 3.0 2.8 12.5 10 10.3 Public administration and defence -0.5 4.5 -3.1 2.6 7.7 Education 0.9 0.4 0.4 0.8 1.0 Health and social work -3.9 1.1 1.7 1.7 2.8

Source: Own calculations on Rosstat (2007)

Yman=Yserv ¼ f ðPOIL; PRÞð3Þ

Expressly, Yman/Yserv is the ratio of manufacturing production to service production. The productivity variable (PR) reflects the tertiarisation process, since countries where productivity increases over time usually show a tendency towards tertiarisation. The sign for productivity is expected to be negative, because when productivity grows, the contribution of service to the economy increases, while the contribution of manufacturing decreases. The expected sign for oil is negative too, since the Dutch Disease hypothesis postulates a reduction in manufacturing production vis-a`-vis service whenever a resource boom materializes.

4 Empirical evidence

4.1 Preliminary unit root test

To examine whether there is a long-run equilibrium relationship among the variables in Eqs. 1, 2 and 3, a VECM-based cointegration analysis using the methodology developed by Johansen (1991, 1995) and Johansen and Juselius (1990) has been carried out. The period of analysis stretches from November 1993 to December 2009 and the data, expressed in log form (ln), have a monthly frequency. The basic idea of cointegration is that two or more variables may be regarded as defining a long-run equilibrium relationship if they move close together in the long- run, even though they may drift apart in the short-run. Prior to testing for cointegration, the time series properties have been investigated. Specifically, the

123 256 Econ Change Restruct (2011) 44:243–277

Augmented Dickey-Fuller (ADF) and the Philips Perron (P–P) tests have been conducted for each variable to formally test for the presence of unit roots in the series. The critical values for the rejection of the null hypothesis of a unit root are those computed according to the McKinnon criterion (1991). The lag length for the ADF test is based on the Schwarz Information criterion. The lag structure for the P–P is selected using the Bartlett Kernel with automatic Newey-West bandwidth. The two tests have been carried out in three settings: with a constant, without a constant and with a constant plus a linear trend. The ADF and P–P tests show that all the independent and dependent variables are integrated of order one I(1). The only exception is for the GOV variable, which, according to the P–P test with a constant and with a constant and linear trend, is stationary at each critical value. Even though the two tests show different results, it is acceptable to follow the ADF technique which, according to the literature, is more accurate. The outcomes of the tests are reported in Appendix 2 (Table 12). The presence of non-stationarity implies that standard time-series methods are no longer suitable and that, consequently, a cointegration analysis is required (Enders 1995 p. 374).

4.2 Johansen analysis

The Johansen methodology, based on maximum likelihood estimation, permits us to simultaneously evaluate equations involving two or more variables and to determine whether the series are cointegrated. Furthermore, this technique is independent from the choice of the endogenous variable, and it enables us to assess and test for the presence of more than one cointegrating vector. Specifically, a VAR system of seven variables has been constructed to test whether real oil prices are cointegrated with the real effective exchange rate, GDP and the ratio of manufacturing

Table 4 Johansen cointegration tests: Sample period 1993:11–2009:12

Eigenvalue k Trace stat 5% Critical value Prob** Hypothesized no. of CE(s)

0.445 239.45 125.62 0.0000 None* 0.223 128.19 95.75 0.0001 At most 1* 0.188 80.419 69.82 0.0056 At most 2* 0.082 42.318 47.86 0.1380 At most 3 0.076 26.175 29.79 0.1235 At most 4

Eigenvalue k Maximum Eigen stat 5% Critical value Prob** Hypothesized no. of CE(s)

0.445 111.26 46.23 0.0000 None* 0.223 47.77 40.08 0.0056 At most 1* 0.188 39.36 33.88 0.0089 At most 2* 0.082 16.17 27.58 0.7755 At most 3 0.076 15.01 21.13 0.2883 At most 4

Estimations include two dummies, one relative to September 1998 to control for the Russian financial crisis, and one referring to April 2008 * Denotes rejection of the hypothesis at 5% level ** MacKinnon et al. (1999) P-values 123 Econ Change Restruct (2011) 44:243–277 257 production to service production, while controlling for productivity, government expenditure and reserves. To identify the proper model, the five possibilities considered by Johansen (1995) were tested, specifically: (1) the series have no deterministic trends and the cointegrating equations do not have intercepts, (2) the series have no deterministic trends and the cointegrating equations have intercepts, (3) the series have linear trends but the cointegrating equations only have intercepts, (4) both series and the cointegrating equations have linear trends, and (5) the series have quadratic trends and the cointegrating equations have linear trends. The results indicate that the third model is the most appropriate. To identify the lag length, the Aikaike Information and the Schwarz Criteria have been implemented. The chosen lag structure is three (the smallest value) following the AIK criterion. Two dummies relative to 1998 and 2008 have been included in the cointegration test to take into account the rouble devaluation in 1998 and the social and economic instability in 2008. The results of Johansen’s test for cointegration are reported in Table 4. The first row of the trace statistic tests the hypothesis of no cointegration, the second row tests the hypothesis of one cointegrating relation, the third row tests the hypothesis of two cointegrating relations, and so on, all against the alternative hypothesis of full rank, i.e. all series in the model are stationary. The ktrace test indicates the presence of three cointegrating equations at the 5% level. The kmax statistic confirms this result: the null hypotheses of no cointegrating vector (r = 0), of one contegrating vector (r = 1) and two cointegrating vectors (r = 2) can be rejected at the 5% level. Conversely, the null of r = 3 cannot be rejected. So, it can be concluded that there are three-cointegrating vectors in the system, hence confirming the hypotheses formulated about the first three symptoms. To extract the cointegrating vectors, a VEC representation has been adopted. A number of restrictions were imposed in order to indentify them. Convergence was reached after 145 iterations. The restricted cointegrating vectors and the speed of adjustment coefficients are reported in the following Table 5.

Table 5 Restricted cointegration estimations: Sample period 1993:11–2009:12 Cointegrating vector b Equation 1 Equation 2 Equation 3 ln rex 1.000 -0.366 0.000 ln gdp 0.000 1.000 0.000 ln yratio 0.000 0.000 1.000 ln pr 0.990 0.000 -0.313 ln poil 0.372 0.311 -0.310 ln gov 0.227 0.000 0.000 ln res -0.450 0.000 0.000 Speed of adjustment a ln rex -0.057 (0.030) -0.099 (0.047) -0.049 (0.028) ln gdp -0.010 (0.002) -0.039 (0.019) -0.013 (0.003) ln yratio -0.052 (0.009) -0.113 (0.014) -0.053 (0.008)

Standard error in brackets 123 258 Econ Change Restruct (2011) 44:243–277

The columns of b are interpreted as long-run equilibrium relationships between variables, and the matrix a determines the speed of adjustment towards this equilibrium. In particular, the cointegration analysis suggests that, oil prices (ln poil) are cointegrated with real exchange rate (ln rex), Russian GDP (ln gdp) and the ratio of manufacturing production to service production (ln yman/yserv) and the normalised log-linear cointegrating relationships can be formalised as:

ln rext ¼6:93 þ 0:372 ln poilt þ 0:990 ln prt þ 0:227 ln govt ð5:89Þ ð6:50Þ ð3:96Þ 0:450 ln rest ð4Þ ð11:79Þ

ln gdpt ¼2:01 0:366 ln rext þ 0:311 ln poilt ð5Þ ð7:11Þ ð8:08Þ

ln ymant =yservt ¼0:51 0:310 ln poilt 0:313 ln prt ð6Þ ð4:78Þ ð3:05Þ log likelihood = 2,950.336. where the numbers in brackets are t-statistics. The estimated speed of adjustment coefficients carry the expected signs and are statistically significant different from zero. This means that cointegrating vectors converge towards their long-run equilibrium in the presence of a shock to the system. Expressly, about 5% of the disequilibrium is eliminated in 1 month, i.e. it takes 20 months to restore the equilibrium after a shock. The short-run dynamics are reported in Table 6. It is clear that in the short period, the variable gov does not have any significant influence on changes in real exchange rate, while other variables do affect exchange rate to a different extent and depending on their lags. At the same time, gdp and the manufacturing-service ratio are influnced by oil price variations. The properties of the residuals of the estimated model have been carefully analysed (Tables 13, 14, 15, Appendix 2). The system residual Lagrange-Multiplier test for autocorrelation shows that the null of no residual correlation up to lag 12 cannot be rejected. The Doornik-Hansen test gives evidence that residuals are multivariate normal, and hence serial correlation is deemed to be absent in the residuals. The Chow Breakpoint Test reveals that there are not relevant structural changes.

4.3 Analysis of results

The existence of three-cointegrating vectors in the system corroborates the presence of three symptoms of Dutch Disease. More specifically, Eq. 4 provides suggestive evidence that higher oil prices lead to a rouble appreciation as predicted by the Dutch Disease hypothesis. An increase in international oil prices by 10% brings about a Russian real exchange rate appreciation by about 4%. The real exchange rate is an indicator of competitiveness. A stronger currency deteriorates the competitive position of a country by making exports dearer. It also encourages domestic consumers to switch from domestic goods to cheap imports.

123 Econ Change Restruct (2011) 44:243–277 259

Table 6 VECM system short-run coefficients Variables Equation 1 Equation 2 Equation 3

D ln rext-1 -0.180 (-2.33) -0.002 (-0.80)

D ln rext-2 -0.042 (-0.61) -0.006 (-1.95)

D ln rext-3 -0.093 (-1.43) -0.002 (-0.80)

D ln gdpt-1 0.818 (10.5)

D ln gdpt-2 0.028 (0.27)

D ln gdpt-3 0.189 (1.96)

D ln yratiot-1 -0.131 (-1.44)

D ln yratiot-2 -0.369 (-5.35)

D ln yratiot-3 -0.142 (-2.00)

D ln prt-1 0.203 (2.22) 0.009 (0.36)

D ln prt-2 0.248 (2.96) 0.121 (4.87)

D ln prt-3 0.203 (2.24) 0.016 (0.61)

D ln poilt-1 0.121 (2.01) 0.003 (3.17) -0.009 (-3.24)

D ln poilt-2 0.026 (1.05) 0.001 (0.86) -0.008 (-2.69)

D ln poilt-3 0.017 (0.69) 0.002 (2.16) -0.003 (-1.41)

D ln govt-1 -0.009 (-0.93)

D ln govt-2 -0.007 (-0.78)

D ln govt-3 -0.005 (-0.66)

D ln rest-1 -0.087 (-3.82)

D ln rest-2 -0.047 (-2.06)

D ln rest-3 -0.046 (-1.98) Summary statistics Adj. R2 0.62 0.95 0.71 DW 1.98 2.01 1.97 S.E. equation 0.002 0.001 0.008

The symbol D is the difference operator. Figures in brackets are t-statistics. DW tests first order residuals autocorrelation

The productivity variable is positively linked to the real effective exchange rate, thus confirming the presence of the Balassa-Samuelson effect. A real appreciation in fact implies that the productivity level in a certain country increases more than abroad. For the Russian economy, a boost in productivity of 10% would lead to a real appreciation of 9.9%. The government variable shows a positive relationship with the real exchange rate. That is, an expansive fiscal policy of 10% triggers a real appreciation of 2.3%. This means that the increase in Russian interest rates has been bigger than the loss in Russia’s financial credibility (paragraph 3.1). An increase of foreign reserves by 10% produces a depreciation of about 5%. The estimated coefficients testify that oil price have a significant effect on real exchange rate, although more limited than that played by productivity. Besides, the results indicate that oil price increases,

123 260 Econ Change Restruct (2011) 44:243–277 producing a real appreciation, give statistically significant evidence of symptom 1. In order to offset real appreciation pressures from upsurging oil price, the Russian government could adopt restrictive fiscal policies or the Central Bank could purchase foreign exchange so as to increase foreign reserves. Equation 5 shows the sensitivity of the Russian GDP to changes in international oil prices and the real effective exchange rate. In the long-run, the oil price elasticity is 0.31 and the real effective exchange rate elasticity is -0.37. The oil price and the exchange rate have the expected signs. The long term estimates point out that a boost in international oil prices by 10% will have an impact on national GDP by 3.1%. Indeed, an upturn in international oil price will produce more revenues for Russian oil producers, since the oil demand is quite inelastic to price changes. On the other hand, a real exchange rate appreciation of 10% will cause a reduction of Russian GDP by 3.7% because Russian products will be less competitive in the international arena, and their demand will break down, thereby negatively impacting on the Russian domestic product. In other words, a real exchange rate appreciation can be considered as an increase of national labour costs, which dampen the international competitiveness of a country. The results of this estimation indicate that the impact of oil price changes on output could be balanced by respective changes in the real effective exchange rate. However, this counterbalancing effect also has to take into account the influence of oil price changes on the real effective exchange rate as estimated under Eq. 4.Asa consequence, an increase in oil price by 10% produces two effects: a rise in GDP by 3.1% (Eq. 5) and an appreciation of the real exchange rate by 3.7% (Eq. 4). An appreciation of real exchange rate of 3.7% (Eq. 4) leads to a drop in GDP growth by 1.35% (Eq. 5). The total long-run GDP growth is thus 1.75%. The combination of long-run effects are summed up in Table 7. In a nutshell, Eq. 5 demonstrates that oil contributes to driving the growth momentum. This result can be considered as a confirmation of symptom 2, because the flourishing economic situation can be empirically ascribed to the oil price boom. To make a complete assessment of the effect of the full disease, on the other hand, and test for an eventual GDP decline due to the perverse effects of a resource addiction, one still has to wait for prolonged oil price falls or the vanishing of oil reserves. Equation 6 testifies that the oil price variable is significant in explaining output movements and it has the correct sign. An upturn in oil prices of 1% will lead to a reduction in the output ratio of the non-booming sector of 0.310%. The coefficient of productivity is also negatively linked to the ratio of manufacturing/service. An increase in productivity by 1% generates a ratio decline of 0.313%. This highlights the fact that relative production is very sensitive to changes in productivity. The

Table 7 Joined long-run effects 10% change in oil price =[ 3.7% appreciation of REX =[ 1.75% GDP growth 10% change in oil price =[ 3.1% GDP growth 3.7% appreciation of RER =[ (-) 1.35% GDP fall

123 Econ Change Restruct (2011) 44:243–277 261 coefficient of the oil price variable should be interpreted as an indicator of symptom 3 of the Dutch Disease, such that oil price increases contribute to the Russian relative de-industrialisation; likewise the productivity variable, which mirrors the tertiarisation process, also has a role in lessening the ratio.

5 Analysis of symptom 4: Russian export loss and symptom 5: increase in real wages

To complete the Dutch Disease investigation, I have analysed which Russian manufacturing sectors seem to have been hampered by changes in oil prices and whether wages have risen over time, i.e. symptom 4 and 5 respectively. Some statistical indices have been computed for the period 1996–2005 to emphasise the loss in export levels registered by some manufacturing sectors. It is evident from these figures (Table 8) that losses have been recorded in the high-tech sectors (e.g. television receivers, office machines, sounds recorders, automatic data processing), in the textile industry (e.g. women and men’s clothes, clothing accessories, tulle, lace, leather, wool hair) and in the car and ship sectors. This confirms to a certain extent the presence of symptom 4. As regards symptom 5, the evolution of real wages has been examined computing the percentage changes in real wage growth rate for selected sectors (Table 9). Since 2000, all sectors of the Russian economy have experienced a rapid real wage growth, with the exception of 2009. This is in line with the movement and spending effects. In detail, significant increases in real wages occurred in the fuel sector, likely as a consequence of rocketing oil prices at the beginning of the new millennium. In the same way, wage growth has affected other sectors of the economy, as predicted by the Dutch Disease hypothesis. Over time, wages in manufacturing and services rose even faster than in the fuel sector. Naturally, not all the results relative to export losses or increases in real growth wages can be ascribed to the oil price and its related exports burst. To show the exact values of the oil effect on each manufacturing industry and wages one should carry out a more comprehensive analysis which takes into consideration other explanatory variables. All in all, an oil bonanza causes a sudden rush of foreign earning which drives up the value of the rouble and wages. That, in turn, makes domestically produced goods less competitive at home and abroad. Over time, the domestic manufacturing vis-a`-vis non-tradable goods production fade, and after a prosperous phase, growth could face glum prospects.

6 Conclusions

This paper has examined Russia’s vulnerability to the Dutch Disease, and has provided empirical evidence for its typical symptoms, specifically: a real exchange rate appreciation; a GDP growth fuelled by oil price increases; a reduction in the ratio of manufacturing to service output (relative de-industrialisation), a crowding- out effect of manufacturing exports and a sharp rise in real wages. The first three 123 6 cnCag etut(01 44:243–277 (2011) Restruct Change Econ 262 123 Table 8 Loosing export sectors 1996–2005: Values in US $ constant price (xt-xt-1 /xt-1 ) Product group % % % % % % % % % change change change change change change change change change 96–97 96–98 96–99 96–00 96–01 96–02 96–03 96–04 96–05

112–Alcoholic beverages -50.12 -81.47 -79.76 -70.19 -62.63 -44.64 -30.40 -33.82 -3.49 121–Tobacco, unmanufactured; tobacco refuse 51.23 -32.62 -61.87 -63.15 -35.30 -56.64 16.11 44.18 -29.48 122–Tobacco, manufactured -44.91 -82.68 -83.79 21.47 171.91 320.50 571.94 635.40 1,278.12 211–Hides and skins (except fur skins), raw 25.33 17.04 -48.79 -51.56 -65.01 -81.71 -88.86 -91.71 -97.52 268–Wool and other animal hair (including wool tops) 29.09 -52.46 -92.38 -92.68 -92.92 -69.70 -53.19 -75.97 -76.98 541–Medicinal and pharmaceutical products -10.71 -18.53 -24.03 -43.52 -44.17 -26.26 23.07 -35.39 -36.28 612–Manufactures of leather or of composition leather -19.12 -9.16 -62.45 -70.92 98.21 300.40 782.07 1,160.46 2,023.31 629–Articles of rubber, n.e.s. -9.94 -15.12 -47.42 -40.98 -30.99 -39.70 -27.94 -7.51 -19.77 652–Cotton fabrics, woven (without narrow/special fabrics) -4.97 -26.94 -40.30 -13.27 -17.08 -16.09 0.60 9.65 -6.75 656–Tulles, lace, embroidery, ribbons and other small wares -64.70 -80.31 -80.52 -85.76 -84.67 -76.98 -76.43 -78.12 -73.65 658–Made-up articles, wholly or chiefly of textile materials -13.19 -23.56 -24.25 -15.19 -0.53 14.88 4.61 22.30 29.93 663–Mineral manufactures, n.e.s. -1.84 -55.97 -55.20 -21.30 -49.88 -51.25 -44.82 -21.79 -5.05 676–Iron and steel bars, rods, angles, shapes and sections -14.58 -46.88 -63.12 -53.46 -50.70 -53.56 -32.48 2.23 21.27 685–Lead 1.43 -59.87 -39.75 -83.57 -12.20 -53.60 -58.39 184.65 123.54 713–Internal combustion piston engines, parts thereof, n.e.s. -7.29 -24.12 -44.27 -28.49 -29.73 -41.90 -27.37 4.81 28.87 724–Textile and leather machinery, and parts thereof, n.e.s. -31.14 -48.30 -23.60 -19.60 -39.85 -43.20 -46.90 -43.41 -59.69 725–Paper and pulp mill machinery, paper-cutting machines -37.58 -63.46 -56.56 -40.46 -52.39 -64.25 -64.20 -34.02 –35.72 751–Office machines -17.79 -54.82 -59.40 -66.98 -58.94 -43.81 -16.53 -8.12 -15.75 752–Automatic data-processing machines and units thereof -34.83 -29.84 -4.02 -40.34 -57.48 6.83 -34.20 -5.23 -5.23 759–Parts and accessories for machines within group 751 -37.85 -57.18 -62.92 -39.43 -82.62 -49.75 -59.33 -46.75 -46.75 761–Television receivers (with video monitors and projectors) 121.87 -67.74 -87.94 -96.24 -82.42 -98.03 -95.65 -87.36 -87.36 762–Radio-broadcast receivers 582.65 -6.37 -77.79 -61.74 -69.75 -68.25 -59.62 -69.58 -65.62 763–Sound recorders or reproducers; television image 112.98 -67.48 -91.97 -92.85 -94.50 -94.45 -92.49 -89.23 -86.89 cnCag etut(01 42327263 44:243–277 (2011) Restruct Change Econ Table 8 continued Product group % % % % % % % % % change change change change change change change change change 96–97 96–98 96–99 96–00 96–01 96–02 96–03 96–04 96–05

764–Telecommunications equipment 14.36 -42.28 -27.93 -56.77 -42.45 -33.29 -6.86 32.22 49.85 774–Electro diagnostic devices for medical, dental, veterinary aims -19.40 -12.07 -21.43 365.85 51.22 62.75 52.91 240.08 287.34 775–Household-type electrical and non-electrical equipment 30.97 -66.03 -43.09 -56.98 -64.30 -51.19 -34.18 -5.67 31.07 781–Motor cars and other motor vehicles -35.75 -50.11 -67.38 -44.82 -52.85 -44.44 -37.50 -11.85 242.35 782–Motor vehicles for good and special-purpose transports -35.26 -42.28 -56.42 -47.01 14.73 84.43 57.62 106.70 278.38 784–Parts and accessories of the motor vehicles -26.91 -39.24 -46.67 -24.00 -31.87 -24.81 3.69 93.91 123.74 785–Motor cycles and cycles, motorized and non-motorized -12.32 -30.02 -25.78 -29.61 -52.67 -30.63 -42.72 13.26 -7.01 793–Ships, boats (including hovercraft) and floating structures -3.44 -9.84 -35.26 -11.74 -65.36 -60.45 -68.77 -51.43 -49.44 842–Women’s/girls’ coats, jackets, trousers, shirts, dresses and skirts, -15.70 -10.68 -23.77 -39.25 -37.36 -46.96 -45.01 -50.97 -47.99 underwear, nightwear 843–Men’s/boys’ coat, jackets, suits, blazers, trousers, shorts, shirts, -71.28 -71.32 -64.76 -76.08 -81.47 -76.16 -80.23 -85.03 -85.28 underwear, nightwear 846–Clothing accessories, of textile fabrics -29.53 -57.90 -63.14 -61.04 -68.10 -66.32 -62.93 -52.70 -44.77 851–Footwear 9.36 -26.69 -53.42 -66.56 -71.30 -73.55 -67.42 -64.08 -63.87 871–Optical instruments and apparatus, n.e.s. 37.04 53.07 27.32 23.39 13.17 49.09 112.78 79.77 146.76 872–Instruments/appliances, surgical, dental, veterinary aims -36.68 -42.64 -37.14 -32.53 -15.99 -13.84 23.01 38.10 61.12 881–Photographic apparatus and equipment, n.e.s. -12.93 -23.86 -48.40 -70.41 -65.53 -66.93 -75.96 -71.43 -82.20 885–Watches and clocks -38.13 -53.44 -44.54 -56.12 -51.91 -51.82 -44.43 -47.59 -36.27

Source: Own calculations on International Trade Center Statistics on Russia (2009) 123 6 cnCag etut(01 44:243–277 (2011) Restruct Change Econ 264 123 Table 9 Real wage growth rate for selected sectors (percentage change) 1995–2000 2000–2004 2004–2003 2005–2004 2006–2005 2007–2008 2008–2009

Total -3.25 71.25 12.59 13.31 17.24 11.53 -2.64 Agriculture, hunting and forestry -21.93 72.89 7.26 14.23 23.34 20.95 0.06 Fishing, fish farms -21.59 40.61 28.12 9.66 10.24 15.55 7.10 Mining and quarrying 14.43 60.15 3.89 6.95 11.39 3.59 -5.13 Mining and quarrying of energy producing materials 18.49 60.94 4.53 7.32 10.53 2.90 -5.00 Mining and quarrying, except of energy 9.27 53.59 7.45 6.30 13.98 5.34 -7.33 Manufacturing 7.14 63.57 9.06 10.40 15.82 9.27 -7.33 Manufacture of food products, including beverages and tobacco -8.89 56.95 6.80 9.92 15.28 10.35 1.59 Manufacture of textile and textile products 3.73 56.06 5.32 13.52 21.77 12.48 -4.85 Manufacture of leather and, leather products 0.01 58.18 10.32 9.68 22.38 10.78 -6.23 Manufacture of wood and wood products -8.54 49.89 13.30 7.47 16.35 12.40 -13.10 Manufacture of pulp, paper and paper products; publishing -1.23 62.86 5.86 5.72 15.80 12.10 -9.58 and printing Manufacture of coke, refined petroleum products 24.64 57.74 25.32 4.89 17.39 7.17 -3.92 Manufacture of chemicals, chemical products and man-made fibres 9.43 57.52 14.62 6.50 15.58 9.30 -3.26 Manufacture of rubber and plastic products 3.62 57.23 2.43 16.19 15.94 6.52 -3.50 Manufacture of other non-metallic mineral products -8.39 66.24 9.42 14.88 21.20 8.81 -15.88 Manufacture of basic metals and fabricated metal products 15.21 34.75 -1.04 6.62 14.57 6.28 -12.62 Manufacture of machinery and equipment 7.45 86.29 14.11 13.33 18.68 10.19 -11.00 Manufacture of electrical, electronic and optical equipment 11.14 81.29 13.34 14.13 16.89 11.05 -3.65 Manufacture of transport equipment 2.21 80.17 6.25 11.13 12.45 8.44 -10.39 Manufacturing n.e.c. 12.91 42.57 9.33 18.15 12.07 9.17 -7.96 Electricity, gas and water supply -17.52 54.62 9.18 9.93 11.45 7.20 1.80 Construction -7.58 56.29 9.80 9.56 20.95 13.63 -11.65 cnCag etut(01 42327265 44:243–277 (2011) Restruct Change Econ Table 9 continued

1995–2000 2000–2004 2004–2003 2005–2004 2006–2005 2007–2008 2008–2009

Wholesale and retail trade; repair of motor vehicles; -8.88 74.83 18.46 14.57 27.82 14.05 -4.38 household goods Hotels and restaurants 3.65 63.14 12.97 13.66 13.88 8.31 -1.36 Transport and communication -5.82 63.48 8.03 7.53 12.70 10.65 -2.92 of which communication 0.97 76.06 12.57 5.81 11.31 8.86 -5.44 Financial activities 42.43 87.67 14.62 13.16 14.73 5.26 -9.28 Real estate, renting and business activities 21.37 79.19 16.49 13.65 19.60 12.09 -0.42 Public administration and defence; social security 7.84 64.51 23.06 12.10 14.99 10.77 0.52 Education -17.58 91.45 14.59 17.23 15.30 13.05 5.16 Health and social work -20.57 95.42 13.59 24.40 14.22 14.00 2.07 Other community, social and personal service -32.39 75.98 15.70 15.86 19.21 14.24 3.71

Source: Own calculations on Federal State Statistics Service via Datastream 2010. Real wages are calculated using CPI deflator 123 266 Econ Change Restruct (2011) 44:243–277 symptoms have been estimated simultaneously in a VECM framework. The analysis has shown the existence of three cointegrating vectors, hence confirming the presence of these first three warning signs. Specifically, the first cointegrating vector shows that oil prices (which mirror the Dutch Disease), productivity changes (which reflect the Balassa-Samuelson effect), government deficit and international reserves are highly significant determinants of real effective exchange rate movements. In particular, a 10% rise in the international oil price brings about a real effective exchange rate appreciation of about 4%; an upturn in Russian productivity leads to a real appreciation of about 10%; an increase in budget deficit produces a real appreciation of about 2% and an expansion in foreign reserves causes a real depreciation of about 5%. All variables show the expected signs. The second cointegrating vector suggests that, an increase in international oil prices of 10% implies a GDP growth of about 2%, when the real appreciation effect is included. Oil therefore contributes to creating a growth momentum that might vanish in the future. The third cointegrating vector indicates that the ratio between the Russian manufacturing production and service production is influenced by productivity and oil. Specifically, there is a drop of 3.1% in the output ratio of the non-booming sector when oil prices increase by 10%. This testifies that oil contributes to relative de-industrialisation–as predicted by the Dutch Disease hypothesis– although to a lesser extent with respect to the tertiarisation process. The evaluation of symptom 3 has been also examined from a statistical perspective. The analysis of symptom 4, carried out through an index analysis, suggests that some manufactures have despecialised throughout the 90s, while other sectors (mostly linked to the resource areas) have strengthened their competitive position. The examination of symptom 5 highlights that Russia has recorded relevant real wage growth. Subject to the cautions concerning the reliability of Russian figures, the recurrent adjustments that occurred throughout the transition phase and some hidden factors behind the last two symptoms, this empirical analysis suggests that Russia is showing the signs of Dutch Disease. Even though the economy has picked up, easy money from oil and other natural resources is keeping the exchange rate and wages high. This process is beginning to delineate a pattern of relative de-industrialisation and to strangle some sectors of the economy, for instance: the automobile and service vehicle sectors; the aircraft and spacecraft sectors; and the photo apparatus and optical goods sectors. The Dutch Disease could become a serious problem for Russia if oil wells go dry, international oil prices fall abruptly for a prolonged time or other alternative energy sources are discovered. From a political point of view, it is known from the literature that natural resource rents create a stagnant response to reforms, thereby increasing the risk of policy corruption.10 Therefore it is crucial that policy makers design appropriate macroeconomic policies to successfully deal with such issues.

10 According to International Transparency, Russia’s corruption is currently on a similar level with Togo. 123 Econ Change Restruct (2011) 44:243–277 267

More specifically, there are two routes to forestalling the full disease: slow the appreciation of the real exchange rate and diversify the economy. Particularly, the econometric results of this study suggest that restrictive fiscal policy or specific monetary manoeuvres (e.g. the Central Bank could buy foreign exchange) would slow the real appreciation. In this way, tradable sectors that are hampered by higher prices could become more competitive. To hold back the real exchange rate, Russia created a Stabilization Fund. For a fruitful sterilisation it is important that the revenues channelled in the Fund would be used to diversify the economy and stimulate productivity improvements in those non-booming sectors that show some sign of vivacity (in particular in the knowledge-based and high tech sectors11). Generally speaking, this would help to broaden the production structure and the tax collection base of the Russian economy, and make it less vulnerable to exogenous shocks, such as significant and non-temporary declines in international oil prices.

7 Appendix 1

See Tables 10, 11 and Fig. 3.

8 Appendix 2

See Tables 12, 13, 14, 15.

11 For instance it could be stimulated by the so-called New Silicon Valley, specialising in programming offshore, which includes the Moscow district, St. Petersburg district and Novosibirsk district. 123 6 cnCag etut(01 44:243–277 (2011) Restruct Change Econ 268 123 Table 10 Literature summary Authors Appreciation of REX Lost in manufacturing Wage growth Service sector growth competitiveness (de-industrialisation)

Country and period Evidences Methodology Evidences Methodology Evidences Methodology Evidences Methodology of analysis

Ahrend et al. Russia and Ukraine Appreciation Descriptive No evidence of Revealed Yes Descriptive –– (2007) (1995–2004) analysis deindustrialisation Comparative analysis Advantage index Barisitz and Russia Appreciation Descriptive Incipient Descriptive –– – – Ollus (2007) (2002–2006) analysis deindustrialisation analysis Beck et al. Russia Appreciation, Descriptive No lost in Descriptive Yes, but Descriptive Yes, but may Descriptive (2007) (2000–2005) but may be analysis competitiveness analysis may be analysis be due to analysis due to due to several several several factors e.g. factors e.g. factors economic economic restructuring restructuring and catching and catching up up Dobrynskaya Russia Appreciation Descriptive No evidence of Descriptive Yes Descriptive Yes, but it can Descriptive and (1999–2007) analysis deindustrialisation analysis analysis be due to analysis Turkisch several (2010) factors E´ gert and Kazakhstan Appreciation Engle and –––––– Leonard (1994–2005) Granger (2006) (1987)Dynamic OLS Bounds testing approach cnCag etut(01 42327269 44:243–277 (2011) Restruct Change Econ Table 10 continued

Authors Appreciation of REX Lost in manufacturing Wage growth Service sector growth competitiveness (de-industrialisation)

Country and period Evidences Methodology Evidences Methodology Evidences Methodology Evidences Methodology of analysis

Kutan and Kazakhstan Appreciation Arch model – – – – – – Wyzan (1996:1–2003:11) (2005) Oomes and Russia Appreciation Cointegration Signs of relative Descriptive Yes Descriptive Yes Descriptive Kalcheva (1997:4–2005:12) analysis deindustrialisation analysis and analysis analysis (2007) OLS estimation Rosenberg Azerbaijan Appreciation Descriptive No lost in Descriptive –– – – and (1994–1997) analysis competitiveness analysis Saavalainen (1998) Singh and Azerbaijan Expected Descriptive –––––– Laurila (1992–1997) future analysis (1999) appreciation 123 7 cnCag etut(01 44:243–277 (2011) Restruct Change Econ 270 123 Table 11 Data sources and definitions Series going from Source and construction 1993:11 to 2009:12

Real effective exchange rate A real effective exchange rate index represents a nominal effective exchange rate index adjusted for relative movements in national (2000 = 100) price or cost indicators of the home country and selected countries. This index is adjusted for relative changes in consumer prices indices of the reference and partner-competitor countries. Data have been taken from the IMF’s International Financial Statistics via Datastream Productivity index Productivity was constructed as the ratio between the index of industrial production and industrial employment, both based on the (2000 = 100) year 2000. Figures were extracted respectively from OECD Main Economic Indicators and IMF, International Financial Statistics through Datastream Oil price Oil prices are extracted from the Energy Information Administrator (2010), explicitly Europe Brent Spot price FOB has been considered (http://tonto.eia.doe.gov/dnav/pet/hist/rbrteM.htm). Note that the Urals oil price has not been selected due to lack of data: the Energy Information Administrator supplies weekly data starting from 1997 onwards. In any case, the two oil prices have a very strong correlation (0.998) Government index The government variable was constructed as the ratio of the total federal expenditure to the total revenue based on the year 2000. (2000 = 100) Data were collected from the Economic Expert Group International reserves A country’s international reserves refer to ‘‘those external assets that are readily available to and controlled by monetary authorities (2000 = 100) for direct financing of payments imbalances, for indirectly regulating the magnitudes of such imbalances through intervention in exchange markets to affect the currency exchange rate, and/or for other purposes.’’ As defined, the concept of international reserves is based on the balance-sheet framework, with ‘‘reserve assets’’ being a gross concept. It does not include external liabilities of the monetary authorities. Data were collected from the IMF International Financial Statistics via Datastream Real GDP (2000 = 100) GDP data have been taken from OECD Main Economic Indicators via Datastream. To control for seasonal changes data were seasonally adjusted Ratio between manufacturing Manufacturing production, based on the year 2000, is extracted from the OECD Main Economic Indicators. Service production was and service production calculated as the difference between Russia’s monthly total output on the one hand, and monthly industrial and agricultural (2000 = 100) production on the other, using the same source Econ Change Restruct (2011) 44:243–277 271

Real effective exchange rate index 2000= 100 cpi based 130

115

100

85

70

55

40 2008M11 1993M11 1994M04 1994M09 1995M02 1995M07 1995M12 1996M05 1996M10 1997M03 1997M08 1998M01 1998M06 1998M11 1999M04 1999M09 2000M02 2000M07 2000M12 2001M05 2001M10 2002M03 2002M08 2003M01 2003M06 2003M11 2004M04 2004M09 2005M02 2005M07 2005M12 2006M05 2006M10 2007M03 2007M08 2008M01 2008M06 2009M04 2009M09 Europe Brent Spot price FOB ($ per barrel) 140

120

100

80

60

40

20

0 1994M04 1995M07 1995M12 1996M05 1997M03 2001M05 2002M03 2002M08 2004M09 2006M05 2007M08 2008M01 1993M11 1994M09 1995M02 1996M10 1997M08 1998M01 1998M06 1998M11 1999M04 1999M09 2000M02 2000M07 2000M12 2001M10 2003M01 2003M06 2003M11 2004M04 2005M02 2005M07 2005M12 2006M10 2007M03 2008M06 2008M11 2009M04 2009M09 Index of industrial productivity 160

150 140

130 120

110

100

90 80

70 60 000M07 001M10 003M01 008M01 009M04 000M02 000M12 001M05 002M03 002M08 003M06 003M11 004M04 004M09 005M02 005M07 005M12 006M05 006M10 007M03 007M08 008M06 008M11 009M09 2 2 2 2 2 1994M09 1995M02 1996M05 1996M10 1997M08 1998M11 1993M11 1994M04 1995M07 1995M12 1997M03 1998M01 1998M06 1999M04 1999M09 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

Fig. 3 Variable developments 123 272 Econ Change Restruct (2011) 44:243–277

Government Index 2000=100 500 450

400

350

300

250

200

150

100 50

0 2000M12 2001M05 2001M10 2002M03 2003M06 2004M04 2005M02 2005M07 2005M12 2006M10 2007M08 2008M06 1994M04 1995M02 1995M07 1995M12 1996M05 1996M10 1997M03 1997M08 1998M01 1998M11 1999M04 1999M09 2000M02 2002M08 2003M01 2003M11 2004M09 2006M05 2007M03 2008M01 2008M11 2009M04 2009M09 1993M11 1994M09 1998M06 2000M07 Real GDP s.a. 2000=100 180

160

140

120

100

80

60

40 1996M10 2003M01 2004M09 1997M08 1998M06 1998M11 1999M04 2005M07 2006M05 2007M03 2007M08 2008M01 2008M06 2009M09 1993M11 1994M09 1995M02 1996M05 2001M10 2002M03 2002M08 2003M06 2003M11 2004M04 1997M03 1998M01 1999M09 2000M02 2000M12 2005M02 2005M12 2008M11 2009M04 1994M04 1995M07 1995M12 2006M10 2000M07 2001M05 Index of International Reserves 2000=100 3000.00

2500.00

2000.00

1500.00

1000.00

500.00

0.00 1994M09 1995M12 1996M05 1996M10 1997M08 1998M11 1999M09 2000M02 2001M05 2001M10 2002M08 2003M01 2004M04 2004M09 2005M07 2005M12 2006M05 2006M10 2007M08 2008M01 2009M04 2009M09 1993M11 1994M04 1995M02 1995M07 1997M03 1998M01 1998M06 1999M04 2000M07 2000M12 2002M03 2003M06 2003M11 2005M02 2007M03 2008M06 2008M11

Fig. 3 continued

123 cnCag etut(01 42327273 44:243–277 (2011) Restruct Change Econ Table 12 Unit root tests Constant Augmented Dickey-Fuller test statistic** Constant Phillip-Perron test statistic***

ADF Level ADF 1st difference PP Level ADF 1st difference

t-Statistic Prob.* t-Statistic Prob.* t-Statistic Prob.* t-Statistic Prob.*

LREX -1.804166 0.3777 -10.17590 0.0000 LREX -1.532786 0.5150 -10.22086 0.0000 LPOIL -1.181255 0.6825 -11.57130 0.0000 LPOIL -1.060197 0.7311 -11.57567 0.0000 LPR -0.752954 0.8294 -15.39957 0.0000 LPROD -0.462654 0.8943 -16.31850 0.0000 LRES -0.417132 0.9025 -15.24734 0.0000 LRES -0.484364 0.8903 -15.23571 0.0000 LGDP -1.126311 0.7052 -2.634431 0.0879 LGDP -0.271384 0.9763 -3.78979 0.0619 LGOV -2.093670 0.2476 -13.67078 0.0000 LGOV -8.152581 0.0000 LMANSERV -0.727068 0.8362 -14.95089 0.0071 LMANSERV -0.736053 0.8339 -14.91131 0.0000 Constant and linear trend Constant and linear trend LREX -2.411870 0.3722 -10.14780 0.0000 LREX -2.227012 0.4715 -10.19318 0.0000 LPOIL -2.787077 0.2039 -11.53944 0.0000 LPOIL -2.872747 0.1738 -11.54395 0.0000 LPR -2.792119 0.1990 -15.35901 0.0768 LPROD -3.723528 0.0230 -16.25714 0.0000 LRES -2.137905 0.5210 -15.21360 0.0000 LRES -2.432339 0.3617 -15.20723 0.0000 LGDP -2.303206 0.4298 -3.159922 0.0963 LGDP -2.826433 0.1900 -3.54825 0.0377 LGOV -2.329602 0.4155 -7.746941 0.0001 LGOV -10.19073 0.0000 LMANSERV -3.084053 0.0907 -14.98189 0.0007 LMANSERV -3.984053 0.0107 -14.94051 0.0000 No constant No constant LREX 0.728233 0.8713 -10.15256 0.0000 LREX 0.702505 0.8663 -10.09460 0.0000 LPOIL 0.874700 0.8972 -11.51209 0.0000 LPOIL 0.837502 0.8910 -11.51838 0.0000

123 LPR 1.578842 0.9720 -15.20182 0.0000 LPROD 2.390299 0.9961 -15.25184 0.0000 LRES 2.385287 0.9960 -5.909948 0.0000 LRES 2.134104 0.9923 -14.96962 0.0000 LGDP 0.927598 0.9055 -2.455116 0.0140 LGDP 1.232726 0.9444 -2.603304 0.0093 LGOV -0.061363 0.6611 -13.70685 0.0000 LGOV -0.071504 0.6577 -46.30474 0.0001 7 cnCag etut(01 44:243–277 (2011) Restruct Change Econ 274 123 Table 12 continued

Constant Augmented Dickey-Fuller test statistic** Constant Phillip-Perron test statistic***

ADF Level ADF 1st difference PP Level ADF 1st difference

t-Statistic Prob.* t-Statistic Prob.* t-Statistic Prob.* t-Statistic Prob.*

LMANSERV 0.515316 0.8262 -14.96177 0.0000 LMANSERV 0.523341 0.8280 -14.92092 0.0000

Null hypothesis: there is a unit root * McKinnon (1991) one-sided P-values ** Lag Length: Automatic based Schwarz Information Criterion *** Lag Length: Bandwidth Newey-West using Bartlett kernel Econ Change Restruct (2011) 44:243–277 275

Table 13 Residual multivariate serial correlation analysis: Lagrange Multiplier (LM) Test Lags LM-stat Prob Lags LM-stat Prob

1 50.55 0.4123 7 46.40 0.5789 2 49.90 0.4373 8 46.89 0.5589 3 61.74 0.1046 9 52.50 0.3398 4 59.22 0.1504 10 54.75 0.2653 5 53.72 0.2983 11 39.45 0.8331 6 52.62 0.3354 12 50.77 0.4035

Null Hypothesis: no serial correlation at lag order h. Probs from chi-square with 49 df

Table 14 Residual multivariate Doornik-Hansen normality tests: Null Hypothesis: residuals are multi- variate normal Component Jarque–Bera Prob

Joint 2.62 0.1266

Table 15 Residual multivariate Heteroskedasticity: No cross terms Test Chi-sq Prob

Joint 20.67 0.7016

Chow Breakpoint Test 1998:9 F-statistic 0.778 (0.357)

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