Oil Prices, Effective Exchange Rate and Export Performance: Evidence from Russia

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Oil Prices, Effective Exchange Rate and Export

Performance:

Evidence from Russia

University of Groningen

Faculty of Economics and Business

Master Thesis International Economics and Business

July 2016

Student: Leo Korolev

Student Number: s2734095

Student Email: l.korolev@student.rug.nl

Supervisor: Dr. A.A. Erumban

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1. Introduction

Abstract

This master thesis examines the relationship between oil price, real effective exchange rate and export performance in the Russian Federation. The concept of Dutch disease is used as a theoretical link between oil price and non-oil exports in order to evaluate the influence of oil on the real economy of Russia. Our empirical findings reveal that there is a causal relationship that runs from the oil price to exchange rate and that exchange rate appreciation affects non-oil exports negatively. At the same time, we find that foreign direct investment and patents have positive effects on non-oil exports. We do not find that R&D expenditure, total factor productivity growth, labor force with tertiary education and manufacturing share affect non-oil export significantly. Policy implications and limitations are discussed in the last section of this paper.

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1. Introduction

Fossil fuels have been a source of wealth for countries and especially their elites in the resource-rich countries for a long time. With the onset of Industrial Revolution and the rapid economic growth in the developed core starting in the 19th century, the demand for the natural resources was consistently growing. Those trends made it profitable for resource-rich countries to extract and sell their oil, coal, certain valuable metals and minerals on the international market. It further led to terms of trade improvements and resource booms worldwide (Bénétrix, O’Rourke & Williamson, 2012).

The commodity booms have not directly transmitted into a faster economic growth and national income increase for the vast majority of the resource-rich countries. In fact, the reality was totally the opposite. A term “resource curse” was later coined by Richard Auty in 1993 to depict a phenomena of slower economic growth in the resource exporting countries. Inefficient government policies for a transition to non-commodity industries, or total lack of those policies, are among the reasons for slow growth (Sachs & Warner, 1995).

In today’s world, a significant number of countries tend to have natural resource-oriented economy, especially those that export crude oil and gas. Even large and populous countries rely on commodities as a primary part of their exports of goods (table 1.1).

Table 1.1. Fuel as a share of merchandise exports in 2013

Country Percentage Iraq 99,79 Venezuela 97,67 Algeria 96,72 Kuwait 94,22 Azerbaijan 92,99 Qatar 88,68 Nigeria 87,62 Saudi Arabia 87,38 Kazakhstan 76,70 Russian Federation 71,25 Colombia 69,43 Norway 67,69

Source: World Bank, 2016.

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1. Introduction

Crude oil is the most widely used source of energy in the world, it has arguably the most important internationally quoted price. In addition, the income of all major oil exporters, the OPEC members especially, is directly proportional to their oil rent, i.e., the difference between the cost of oil production and the international oil price (Bakhtiari, 1999).

Crude oil is an essential part of business and daily life as it affects both public and private sectors. A simple look at the Fortune 500 consolidated list of largest companies in terms of total revenues, profits, assets and market value, reveals that 6 out of 10 companies occupying the first positions in the ranking are primarily conducting crude oil and other commodity-related business (Fortune, 2015).

Besides the major impact on the business life and thus the employment in the oil producing companies, one should also remember that crude oil and natural gas are still resources of tremendous importance for the importers. Both high income countries and rapidly growing emerging economies largely rely on the price of crude oil. The trade composition of many countries demonstrates double digit percentage of fuel imports (table 1.2).

Table 1.2. Fuel as a share of merchandise imports in 2014.

Country Percentage India 41,32 Korea, Rep. 33,15 Japan 32,27 Pakistan 31,30 Singapore 31,18 Indonesia 24,72 South Africa 23,38 Philippines 20,20 Brazil 19,67 China 17,20 United States 14,92 European Union 14,91

Source: World Bank, 2016.

Notes: Fuel comprises SITC section 3 ‘mineral fuels’.

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1. Introduction

1990s, but has reached as high as $140 in the late 2010s to finally drop to below $30 at the beginning of 2016 (Bakhtiari, 1999; Baumeister & Kilian, 2016; EIA, 2016).

That kind of enormous price volatility together with the high dependency of both importing and exporting countries (which is discussed above) have regularly influenced the global real economic activity. As the oil products serve as major sources of electricity generation, a sudden rise of the oil price for energy companies can quickly result into a full-scale recession due to an increase of the electricity costs for the consumers. We can also note that the high income countries react to those uncertainties much milder as their institutions and capabilities – e.g., government support policies, fuel substitution – are also more developed (Jo, 2014; Taghizadeh-Hesary et al., 2015).

A term of so-called “petrocurrency” has been coined to primarily describe that phenomena. Dynamics of the exchange rate of oil-exporting countries are significantly affected by changes in crude oil prices. Rise of oil prices generally leads to appreciation of the national currency of the natural resource-exporting country and contraction of manufacturing and agriculture in favor of the resource sector. The rise of an international price of natural resources or a sudden discovery of those in a country can drive away all the capital and labor from the non-booming sectors of the economy, as it is more profitable to export products from the booming resource sector while the non-oil exports become less competitive and more expensive. Thus, the rise of oil price negatively affects the non-oil exports of an oil producer (Papadamou & Markopoulos, 2012; Bouchakour & Bedrani, 2015).

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1. Introduction

Figure 1.1. Relationship between oil prices and REER of oil exporters

Source: EIA, 2016; Breugel, 2016.

Figure 1.1 shows how currencies of different major oil producers react on the changes of international oil price. Certain other oil exporters have tried to hedge those fluctuations by completely pegging their exchange rates to the US dollar, euro, which is shown by the examples of the Gulf states that are represented by Saudi Arabia on the graph (Kandil & Nandwa, 2015). Of course, other underlying causes besides the downfall of oil prices should not be disregarded.

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1. Introduction

Figure 1.2. Relationship between oil prices and Russian real exports

Source: EIA, 2016; OECD, 2016.

This study is aimed at analyzing the impact of oil price on non-oil exports of Russia through the mechanism of real effective exchange rate in the scope of the Dutch disease concept. Its objective is reached by investigating the impact of oil price on the exchange rate and the importance of the exchange rate for the non-oil exports.

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2. Background

The background section gives insights into the Russian economy in the historical perspective. It starts with the explanation of the Soviet economy and its oil industry, presents the early history of the new Russian Federation in the 1990s and the 2000s, and concludes with brief discussion of the current crisis in the Russian economy.

2.1. Soviet economy and the role of oil

Before moving to the economic performance of the present day Russian Federation, it is essential to discuss the economic peculiarities of its predecessor – the Soviet Union, a country that had a centrally-planned command economy. The Soviet Union was created shortly after the Bolshevik Revolution of 1917. The Russian Civil War, that followed the revolution, had devastating effects on the country (Encyclopædia Britannica, 2016).

The new communist government enacted a set of novel economic measures: the nationalization of all the means of production and transportation, the imposition of a single plan on the national economy, the introduction of compulsory labor, and – for a certain period of time – the abolition of money and its replacement by barter tokens as well as free goods and services (Encyclopædia Britannica, 2016). British economist John Maynard Keynes in his work on the USSR analyzed the Soviet Leninist variant of socialism as a combination of business in a subordination to religion. According to Keynes, it was religious in terms of the fanatical zeal of its converts, and businesslike in its claim to provide a superior technical instrument – central economic planning – that would generate improvements in welfare. At the same time, Keynes was very skeptical of the prospects of potential success of this system (Keynes, 1919; Barnett, 2009).

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2. Background

(Guriev & Ickes, 2000). The rapid industrialization program needed large surpluses of food for cities and raw materials for manufacturing plants. It led to the embark of collectivization, a nationwide program to consolidate individual peasant households into collective farms. The human and social costs were dreadful (Davies, 1980). The industrialization and collectivization were coordinated by Gosplan – the State Planning Committee that was responsible for central economic planning. Gosplan replaced the market capitalism that existed in pre-revolutionary Russia. The fundamental document for implementing an administrative allocation of resources was the material-balance plan, which was issued by the government officials and specified flow of inputs and outputs, and allocations of government investments in the economy. The plans were drafted for five-year periods and had a status of law, thus an fulfillment of the plan was considered a violation of law (Litwack, 1991).

However, the rapid economic growth of the Soviet economy began to steadily decline. Growth rates of output per worker decreased from 5,8 percent in the 1950s to 2,1 percent in the 1970s and then to 1,4 percent in the early 1990s (Easterly & Fisher, 1994). The Soviet economic model fell to the extensive growth trap as it became more difficult to mobilize resources over time. Extensive growth required high input growth, which was previously achieved by shifting the labor from the traditional sector to the modern sector via the ambitious industrialization program. Though, this source of growth was exhaustible, as this reserve was used up, the labor participation has reached its upper boundary. After it happened, the Soviet economic growth was constrained by the growth of population. Due to the aforementioned reasons, the growth of capital eventually outpaced the growth of labor, the capital-labor ratio started to rise at a higher rate. Consequently, the marginal product of capital began to fall, thus causing the lagging of output (Guriev & Ickes, 2000).

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2. Background

an oil exporter before the Second World War, the oil exports grew in the 1950s. Finland was the first non-communist country that started importing Soviet oil at large, later re-exporting some of it to Western Europe (Jensen-Eriksen, 2007).

The 1973-74 oil crisis that ignited by the Yom Kippur War between Israel and Arab nations led to soaring oil prices worldwide In response to the US aid to Israel, the OPEC countries raised the posted price of their oil, cut production and agreed to place an embargo on exports to the United States. It caused a severe oil supply shock, the crude oil prices rose fourfold from $3 to almost $12 per barrel. The following 1979-80 crisis that commenced with the Iranian Revolution led to further increase of the oil price from $15 in September 1978 to around $40 in April 1980 (Baumeister & Kilian, 2016). At the same moment, those crises positively affected the Soviet oil exports, making it even more attractive to actively engage in trade with the Western countries. The Soviet government massively invested in exploration and extraction projects. The crude oil and natural gas fields in Siberia and Far East became sources of enormous supply of natural resources. The oil exploration in inhospitable regions required vast technological inputs that were purchased in the Western countries. Overall, the Soviet economy at that period of time has been already heavily dependent on its natural resources as well as their international prices, and this dependency continued to rise (Yergin, 2012).

The Soviet economy performed relatively well until the mid-1980s, in spite of the disastrous War in Afghanistan. Financial assistance to the Afghan resistance from the United States, Pakistan, Saudi Arabia and other countries led to heavier casualties and increased military spending. Starting in 1985, Saudi Arabia tripled its oil production from 2 to 6 million barrels a day. Oil prices collapsed from around $30 per barrel in November 1985 to $12 in March 1986. The adverse effects on the Soviet economy were immediate, resulting into a current account deficit and reduced foreign earnings, making it more difficult to import Western technology (Reynolds & Kolodziej, 2008) as even $1 decrease in oil price led to $1 billion decrease in the Soviet foreign currency earnings (Schweizer, 1996).

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2. Background

liberalization without lifting price controls led to shortages (Gregory, 2008; Osband, 1992). At the end, the actions of Mikhail Gorbachev further led to the dissolution of the Soviet Union in 1991 and establishment of 15 newly independent states.

2.2. Russia from the 1990s to the 2010s

Yegor Gaidar’s liberal reforms under the administration of the first Russia’s president Boris Yeltsin introduced the basic principles of market economy to the newly created Russian Federation. Despite the “shock therapy” and rapid transition to capitalism, Russia showed weak economic performance and negative growth rates in the 1990s. Many other post-communist economies had the same problem. Russia, Ukraine, Georgia, Kyrgyzstan and Moldova – that represented a combined population of 76 percent of the former Soviet Union – all had lower GDP per capita levels in the early 2000s than before the transition. Even in 2005, Russia’s GDP per capita was 94 percent of the 1990 level (Altman, 2009).

During the 1990s, the oil price was fluctuating between $10 and $25 per barrel (EIA, 2016). The oil prices gradually plunged in 1998 in a response to the outbreak of the Asian financial crisis a year earlier. Brent spot price hit records and has plunged below $10 in December 1998, falling from over $24 two years ago. Russian government still had vast natural gas and oil supply capabilities, though export of those resources did not possess the previous price premiums that existed from the 1970s to mid-1980s.

Despite political reforms – e.g., the adoption of the 1993 Constitution of Russia – under Yeltsin administration, the Russian institutions apparatus were still ineffective and lacked ability to be defender of the public interest, the state was infected by immense corruption (Ellman, 2000). For instance, Transparency International ranked Russia the 47th out of 54 countries in their 1996 Corruption Perception Index, while in 1999 Russia ended up on the 82nd place out of 99 states (Transparency International, 2016).

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2. Background

the state politics. An important implication of this system was a negative impact on the democratic development, i.e., a political competition between parties is more likely to occur in case of an existence of financial competition between the firms (Guriev & Rachinsky, 2005). Besides this “oligarchy” or “clan capitalism” Russia’s economy under Yeltsin was characterized by enormous importance of subsistence sector in agriculture, huge and increasing importance of barter transactions, non-payment of wages and pensions, criminalization of economy, and virtual absence of private bank accounts (Ellman, 2000).

Most of the Yeltsin’s presidency, the economy was in a deep depression in 1992-1998. The inflation was high, distortions in trade were massive, unemployment was growing, living standards plunged (Ellman, 2000). Mass impoverishment of the population also took place. The precise figures are not determined, yet between the 1987 and 1995 the poverty rose from 2 percent to as high as 37 percent of the population, or from 2,2 million to 37,8 million. The lower estimates provide a figure of 15 million, however the upward trend is beyond any doubt (Milanovic, 1998; Clarke, 1999; Ellman, 2000). Furthermore, based on official Goskomstat statistics, Russian real GDP per capita fell by about 24 percent between 1991 and 2001, (Shleifer & Treisman, 2005; Gibson, Stillman & Le, 2008). GDP measured by purchasing power parity declined by 42 percent, from $2,87 trillion in 1990 to $1,65 trillion in 1999 (World Bank, 2016).

In 1998, Russia defaulted on its sovereign debt due to both internal and external factors. The government had large and chronic budget deficit and difficulties serving the external debt throughout the 1990s. The 1997 Asian financial crisis led to the decline of oil prices to $10 per barrel and investors’ flight from the emerging markets. The Russian Central Bank had no other choice but to devaluate the ruble exchange rate from 6 rubles per US dollar in January 1998 to 22 rubles per dollar one year later (Sinyagina-Woodruff, 2003; OECD, 2016).

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2. Background

Vladimir Putin was elected as president in 2000. The oil prices were on the rise, the international competitiveness of Russian-made goods increased, the investment was going up. All those effects led to robust real GDP growth averaging almost 6 percent from 1999 to 2004, with a remarkable hike to 7,5 percent in 2004 (Desai, 2006).

In October 2003, Mikhail Khodorkovsky – an ex-CEO and chairman of then-largest and most successful Russian Yukos Oil Company – was arrested and charged with fraud and tax evasion. He was sentenced to nine years in prison for economic crimes in 2005, while Yukos was forcibly broken up for later nationalization to form a state-run oil company Rosneft. It was a turning point in new Russia’s government policy to gain a full control of the energy sector, which president Putin declared crucial (Puffer & McCarthy, 2007).

Under Vladimir Putin, the economic system of Russian Federation was changed from Yeltsin-era oligarchic capitalism to a state-managed network capitalism with a leading role of the resources sector. The role of natural resources in the economy increased considerably – the total rents of natural resources rose from 16 percent of GDP in 1998 to 33 percent in 2008, the fuel exports as a percentage of merchandise exports went up from 39 percent in 1998 to 67 percent in 2008. That success was achieved not only through the nationalizations, as the increase of international oil price played a major role in bringing more revenues and growth. The Russian government launched multiple ambitious projects to expand its presence and market share in both European and Asian gas markets, e.g., the Nord Stream pipeline, liquefied natural gas (LNG) plant in Sakhalin Island (Puffer & McCarthy, 2007; Boussena & Locatelli, 2013; Shadrina, 2014; World Bank, 2016).

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2. Background

US dollar was generally following the price of crude oil during this period (see figure 2.1).

Figure 2.1. Oil price and Russian ruble exchange rate.

The figure 2.1 shows the fluctuation of the Russian ruble exchange rate in response to change in Brent oil price. One can infer that there is a visible positive relationship between the ruble and the spot prices, i.e., the higher the prices, the less rubles are needed to buy one US dollar. Nevertheless, oil price cannot be the only factor as the discussion above showed us, e.g., the transition challenges during the 1990s.

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2. Background

Figure 2.2. Oil price and Russian GDP growth.

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3. Literature review

The problem of connecting oil to exchange rates and exchange rates to trade and exports in particular has been widely discussed in recent academic literature as well as in the past. In this section we provide overview of work of various scholars and their contribution to that discussion. The section is divided into three subsections. First, we discuss the Dutch disease that we later use as our main theoretical link. Second, we describe different export determinants besides oil. Third, we come up with our hypotheses.

3.1. Dutch disease and oil price

Dutch disease is an important concept of relationship between oil prices, exchange rate and export performance. In broad terms it means a causal relationship between an increase of economic development in natural resources sector and a decline in the other sectors, as the appreciation of exchange rate hurts non-resource exports of a country. Thus, the Dutch disease provides an explanation why the competitiveness of non-oil exports can suffer in an oil-exporting country.

The original model of the Dutch disease looks at a small open economy with three sectors: manufacturing and energy sectors, which are tradable, and a non-tradable services sector (Corden & Neary, 1982). According to the model, a natural resource boom can happen either due to a discovery of new resources or to an increase of the resource prices, while the prices of goods from manufacturing and energy sector are traded at world price. Another assumption is that the price of services is determined by domestic supply and demand, where exchange rate is simply a ratio of price of manufacturing goods over services, and income is equal to expenditures. Thus, the model establishes a theoretical link between the appreciation of exchange rate and decrease in non-oil exports.

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3. Literature review

exchange rate appreciates even more. Two effects summed up result into the overall Dutch disease effect.

An illustration of Dutch disease model is provided below (see figure 3.1 below).

Figure 3.1. Dutch disease illustration.

Source: own work

There is plenty of evidence from the literature on different cases of Dutch disease. Different researchers conduct case studies and find the effects of Dutch disease – exchange rate appreciation and fall in non-resource exports – in oil-rich nations, such as Algeria (Bouchakour & Bedrani, 2015), Nigeria (Ezeala-Harrison, 1993), Iran (Atashbar, 2013), Egypt, Jordan, Lebanon, Syria (Saab and Ayoub, 2010), Russia (Kalcheva and Oomes, 2007).

In particular, Kalcheva and Oomes (2007) and Mironov and Petronevich (2015) find that Russia has the problem of Dutch disease in Russia, especially during the crude oil boom of the 2000s when the oil prices soared. Ito (2010) finds that a 1 percent of oil price yields a 0,44 percent real GDP growth in Russia in the long run. The author explains it with a massive foreign currency inflow into Russia, the imports become cheaper, the domestic prices fall. Thus, the population income rises for the period of oil boom, the consumption drives GDP upwards. Rautava (2004) also finds that Russian GDP is influenced by oil price. The researcher finds that a 10 percent

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3. Literature review

permanent increase (decrease) in oil prices is associated with a 2,2 percent growth (decline) in Russian GDP. Dreger et al. (2016) look at the 2014-2015 ruble depreciation while controlling for the Western sanctions. The research concludes that the bulk of the decrease of ruble value was due to the crude oil downfall rather than sanctions and foreign policy.

It should be noted that while most cases of Dutch disease refer to natural resources, similar effect can be caused by any large inflow of foreign currency, including international aid, workers’ remittances and foreign direct investment. Such cases are found in Bangladesh (Chowdhury & and Rabbi, 2013).

There are numerous expansions of the original model by Corden and Neary (1982). Some researchers abstain from the non-tradable services sector, but rather look at countries’ tight monetary policies in 1970s as a measure to fight increasing inflation (Buiter & Purvis, 1980). A recent model takes into account input-output linkages between energy and manufacturing sector (Beverelli, Dell’Erba & Rocha, 2011). The authors link an increased output of natural resources sector to an increasing output in energy-intensive industries and to a decreasing output in labor-intensive industries. It is an illustration of the Rybczynski theorem, which states that an increase in factor endowment increases the output of the industry that uses this factor intensively more than proportionally and decreases the output of the one that does not (Rybczynski, 1955). According to the theorem, a country with rich oil reserves will export oil, it is in line with the model by Beverelli, Dell’Erba & Rocha (2011).

Overall, the Dutch disease is a well-researched phenomenon that has many theoretical expansions as well as case studies on particular countries. Its effects have dangerous consequences for economic development of resource-endowed countries.

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3. Literature review

more as the oil price increases, importers’ currencies depreciate. That mechanism is extensively described by Krugman (1980, 1983), Golub (1983), and summarized by Pershin, Molero and de Gracia (2016) in their study on African countries.

Amano and van Norden (1998) examine a link between real price of oil and real US dollar exchange rate, they find that oil prices are the source of persistent real exchange rate shocks in the Post-Bretton Woods era. They also find that causality runs from oil price to exchange rate and not vice versa. Another paper by Amano and van Noden (1998) describes a robust relationship between the domestic price of oil and real effective exchange rate for Japan, Germany and the United States. The research finds that the oil price Granger-causes the real exchange rate in case of those countries.

On the empirical side, 1973-1974 US dollar appreciated in a response to crude oil price increase, while the value of pound sterling also increased from the benefits of the North Sea oil, as it is found by Golub (1983). Brahmasrene, Huang and Sissoko (2014) study the US response to imported oil price and use Granger causality test, variance decomposition and impulse response (only the first technique will be in the scope of this research). They find that the US dollar exchange rates Granger-cause oil prices in the short run and the oil prices Granger-cause exchange rates in the long run, while oil price shocks have a significant negative impact on the exchange rates.

Chinn (2000) finds that oil prices effect on exchange rate is limited in case of East Asian countries. He controls for several factors that might influence exchange rate such as productivity and government expenditure, the results show that the oil price is an important determinant in cases of Indonesia, Japan and Korea, but not for the majority of other countries in the region, e.g., Taiwan, China. The oil price increase depreciates Indonesian rupiah and depreciates the Japanese yen and Korean won against the US dollar. A study by Alquist and Chinn (2002) finds that oil prices are not a significant determinant in case of the dollar-euro real effective exchange rate.

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3. Literature review

assets of emerging economies, which increased capital flows to those economies and therefore the exchange rates.

Another study by Turhan et al. (2014) has analyzes co-movements of oil price and currencies of the G20 members. The study finds that an increase of the oil price led to the US dollar depreciation, which is in line with the findings of a previous 2013 study by Turhan et al. In addition, Turhan et al. (2014) finds that there were two events that made the negative correlation stronger: the 2003 Iraq invasion and the 2008 financial crisis. Authors associate the former with a stronger global integration in the early 2000s that made the G20 countries major players in the global economy, while the latter can be explained by an increase of use of oil as a financial asset. The authors believe that the financialization of the crude oil and its usage in other innovative financial instruments can cause the oil prices to react to the other asset prices, thus the oil prices immediately respond to information provided by the other assets.

A study by Basher et al. (2016) analyzes the impact of oil shocks on real exchange rates for a sample of oil exporting and oil importing countries. The researchers have detected significant REER appreciation pressures in oil exporting countries after oil demand shocks, while there is only a limited evidence that exchange rates are affected by oil supply shocks. The study uses a Markov-switching model, a financial econometric technique. A study on the OPEC oil exporters by Al-mulali and Che Sab (2012) has found the similar evidence of the real effective exchange rate appreciation after surge in oil price. A study on the Venezuela has found the same strong positive effect of oil price on REER (Zalduendo, 2006).

A study by Beckmann and Czudaj (2013) that also uses Markov-switching model finds that causality patterns run from nominal US dollar exchange rates to nominal oil prices. The authors attribute their findings to price reactions as a response to changes in exchange rates as well as to changes in the demand for crude oil, they once again note that the United States is the largest crude oil consumer.

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3. Literature review

Jahan-Parvar and Mohammadi (2011) perform find evidence of short-run causality from oil prices to exchange rates in four countries – Angola, Colombia, Norway, and Venezuela, from exchange rates to oil prices in two countries – Bolivia and Russia, and bidirectional causality in four countries – Gabon, Indonesia, Nigeria and Saudi Arabia.

As we can see, those studies on Dutch disease and role of oil agree on the fact that an increase of oil price tends to appreciate the currencies of oil exporters. The concept of Dutch disease also explains how non-oil export (e.g., manufacturing) is affected by oil price increase through the mechanics of exchange rate: a country’s currency appreciates and the non-oil export becomes less competitive. We employ that model to create our proposed framework (see figure 3.2 below). The further evidence on the influence of exchange rate on exports will be provided in the next subsection.

Figure 3.2. Proposed framework.

Source: own work

3.2. Other export determinants

Export performance is a relative success or failure of efforts of a country to sell its goods or services in the foreign markets. This subsection has an overview of various factors that can affect export performance.

As we have already noted earlier, exchange rate is an important export determinant. Findings of study on the United States exports performance shows evidence of exchange rate importance for a large developed economy (Alenezy, 2014). Research on top 10 trading partners suggests that a 10 percent US dollar depreciation leads to a 3 percent increase of exports. A Federal Bank of New York staff report finds that a 30-35 percent dollar depreciation is needed in order to rebalance the US current account deficit and increase cost-competitiveness of the US-made goods (Cavallo & Tille, 2006). A study on the US exports to Canada and Japan confirms that depreciation of the US dollar makes American exports more

Crude oil

prices

exchange rate

Real effective

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3. Literature review

internationally competitive and helps to reduce the trade deficit (Sukar & Zoubi, 2011). Rodrik (2008) agrees that exchange rate depreciation increases economic output and export growth. Though it should be kept in mind that it cannot be the only mechanism since a continuous currency depreciation drives down the national income.

A similar study on China’s trade with the US and Japan reveals the same results (Chen, Rau & Chiu, 2011). The authors find that Chinese renminbi real appreciation leads to adverse effects on exports. The Sino-US export is affected much severely than the Sino-Japanese export since China depends on Japan-made intermediate goods. Another research on China also finds that the renminbi appreciation would increase imports (Thorbecke & Smith, 2012). The authors provide evidence that a 10 percent Chinese renminbi appreciation yields an increase of imports for processing and ordinary imports by 3-4 percent. The authors underline that an appreciation of the renminbi also reduces imports that are used to produce goods for re-export, thus there is a close link between the imports for processing, e.g., intermediates, and processed exports. The study generally supports conclusions of earlier research on the role of exchange rate movements in Chinese trade with industrialized countries conducted by Hua (2008) that finds that renminbi appreciation decreases Chinese trade surplus with the United States. The findings of Pham and Nguyen (2013) illustrate that exchange rate depreciation can promote exports of Vietnam through a better cost competitiveness.

Sato et al. (2013) analyzed export performance of China, Japan and South Korea.The study has revealed that as the Japanese yen appreciated against the Korean won, Japanese companies were exposed to increased competition and during a period of won appreciation the Korean firms have managed to stay competitive by lowering their production costs, which again stresses importance of cost-competitiveness. The authors of the paper also emphasize the importance of stabilizing regional exchange rate volatility to avoid regional trade imbalances.

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3. Literature review

A study by Bailey, Tavlas, and Ulan (1986) evaluates effects of exchange rate volatility on trade flows of seven OECD countries - Canada, France, Germany, Italy, Japan, the UK, and the US. The authors analyze whether the volatility of exchange rate adversely impacts trade in the Post-Bretton Woods period. However, their results indicate that exchange rate volatility does not adversely affect exports of any of the seven OECD countries.

Aghion, Bacchetta, Rancière, and Rogoff (2009) test how the exchange rate volatility affects productivity growth. The authors look at the interaction between volatility and the level of financial development of a country and the nature of macroeconomic shocks. Their results suggest that exchange rate volatility adversely affects productivity growth, the effect is stronger in the countries with lower level of financial development.

A study by United Nations Conference on Trade and Development finds that both demand and supply factors of a country are important for its export performance, while relative weight of those factors depend on the level of development (UNCTAD, 2005). The study confirms that exchange rates significantly affect export performance of developing countries and overvalued currency directly transits in a loss of cost competitiveness. At the same time, countries with focus on capital-intensive exports depend less on their exchange rate movements. Besides the exchange rate, the study shows significance of foreign direct investment, foreign market access and transport costs, i.e., infrastructure.

Exchange rate regime and its effects on exports are studied by Mao and Yao (2016). They have found that countries with fixed exchange rate regime have lower share of exports in GDP as well as industrial employment and investments. Those findings are in line with studies of Kandil and Nandwa (2015), who conclude that fixed exchange rate regimes of Arab oil producers impede their export competitiveness and diversification. Moreover, they note that oil producers are particularly prone to have a fixed exchange rate and currency overvaluation, e.g., Arab countries.

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3. Literature review

performance of service industries of 17 OECD member states illustrates that non-cost factors play a major role (Seo, Lee & Kim, 2012). Both technological (IT capital, R&D investments, fixed capital formation) and institutional variables (human capital, free trade, English language) are found to be significant determinants. Chuang (2000) finds that human capital accumulation stipulates exports in Taiwan.

Braunerhjelm and Thulin (2008) find evidence that a one percent increase in country’s R&D expenditure boosts the share of high technology exports by three percent. Fagerberg (1995) comes to the similar results regarding the relationship between R&D and exports. Some other authors do not find R&D investments a significant factor that determines export intensity. Huang, Zhang, Zhao and Varum, (2008) find that Chinese manufacturing export growth is not determined by the R&D investments. Liu and Shu (2003) reach the same conclusion and find R&D intensity to be an insignificant factor for Chinese export performance. Overall, we can say that the role of R&D is inconclusive when we look at the empirical evidence.

Relationship between exports and productivity also has been studied intensively. Máñez, Rochina-Barrachina and Sanchis-Llopis (2014) find that productivity is an important determinant of export in case of Spain. Sharma and Mishra (2011) study the effects of productivity on Indian exporting industries and do not have conclusive results. Only in some sectors the effects of productivity is positive, while the others do not have the same relationship. Kim, Lim and Park (2009) perform causality analysis on Korean trade in 1980-2003 and do not find any causal relationship between TFP and exports.

Amable and Verspagen (1995) provide evidence of the importance of patents, investments – as non-cost factors, as well as wage rates – as cost factors, for export success in industrialized countries. Boring (2015) finds that patent protection in developing countries increase the US pharmaceutical exports to those countries. Ivus (2010) agrees that strengthening patent rights in the developing countries – in compliance with the TRIPS agreement on the intellectual property – increases the high-technology exports of the developed countries. On the other hand, developing countries receive few benefits from patent protection.

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3. Literature review

relationship between FDI and exports in African countries, the results show that FDI promotes export only in 3 countries out of 12.

It is safe to assume that various determinants of export performance are relevant depending on the level of development and export composition of a country. Specifically, the relative weight of the exchange rate depends gradually on whether it is labor intensive, or capital intensive trade. At the same moment, the examples mentioned above illustrate that right exchange rate policies can enhance international competitiveness even in the highly developed economies. We summarize all export determinants from this subsection in table 3.1.

Table 3.1. Determinants of export performance.

Study Export determinant

UNCTAD (2005) Foreign market access

UNCTAD (2005); Alenezy (2014); Sukar and Zoubi (2011); Chen, Rau and Chiu (2011); Hua (2008); Cavallo and Tille (n.d.); Thorbecke and Smith (2012); Sato et al. (2013); Pham and Nguyen (2013)

Exchange rate

Seo, Lee and Kim (2012); Chuang (2000) Human capital

Kandil and Nandwa (2015), Mao and Yao (2016) Exchange rate regime

Kandilov (2008), Rey (2006) Exchange rate

volatility UNCTAD (2005); Amable and Verspagen (1995); Pham and

Nguyen (2013), Akoto (2012); Seo, Lee and Kim (2012); Keho (2015)

Foreign direct investment Seo, Lee and Kim (2012); Braunerhjelm and Thulin (2008);

Amable and Verspagen (1995); Fagerberg (1995) R&D expenditure Máñez, Rochina-Barrachina and Sanchis-Llopis (2014);

Sharma and Mishra (2011); Kim, Lim and Park (2009) Productivity Boring (2015); Ivus (2010); Amable and Verspagen (1995) Patents

Source: own work, literature review, subsection 3.2.

3.3. Hypotheses

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3. Literature review

In order to reach our objective and perform the analysis, we propose two hypotheses. First, we start with causality analysis of exchange rate. Hence, our first hypothesis assumes a Granger causality that runs from oil price to Russian REER.

Hypothesis 1: Oil price Granger-causes REER

Based on the existing literature that underlines importance of cost factors in exports and the ULC framework, which explains how REER interacts with cost effectiveness, we can assume that an increase of REER adversely affects export performance of Russia. It leads us to the second hypothesis.

Hypothesis 2: REER negatively affects non-oil exports

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4. Methodology

To test our hypotheses, we use several models that are described in details in this section. Specifically, we first talk about the Granger causality to test for our H1.

Second, we discuss the regression that is used to analyze the influence of REER on export performance (H2). Two econometric models are presented in different

subsections.

The intention of this chapter is in presenting our data analysis and methods that are to be employed. At the beginning, we provide the description of our statistical model. Next, we discuss the dependent and independent variables for our models. At the end, we give an overview of our sample and data.

4.1. Granger causality

Granger causality is prediction-based causality concept developed by Professor Clive Granger (1969). Usually it is tested on the time series linear regression models. The interpretation is straightforward: variable X Granger-causes variable Y if variable Y is better predicted by using the histories of both variables than it by using the histories of variable Y alone. Overall, Granger test allows to test for the direction of causality as well as for its presence, it has been empirically found more powerful than both Sims or Modified Sims tests (Berzuini, Dawid & Bernardinelli, 2012).

If in a linear regression of variable Yt on lagged values of both variables Xt

and Yt, the coefficients of variable Xt are zero, then the series Xt fails to

Granger-cause Yt. In our case, Yt is m_REERt, and Xt is m_OIL+t. Also, to avoid unnecessary

confusions of a reader, a short prefix m_ is used to denote our monthly variables in the dataset, the same variables without prefixes indicate annual variables.

Hence we consider the following model to test for our first hypothesis:

𝑚_𝑅𝐸𝐸𝑅𝑡 = ∑ 𝑎𝑗 𝑚_𝑅𝐸𝐸𝑅𝑡−𝑗+ 𝑚

𝑗=1 ∑ 𝛽𝑖 𝑚_𝑂𝐼𝐿𝑡−𝑖 + 𝐷𝑡 + 𝜀𝑡

𝑛 𝑖=1

where the m_REERt is variable that shows a monthly index of a weighted

value of Russian ruble against a basket of 138 other currencies, 2007 is a reference year. m_OILt is variable that shows monthly mean spot oil price, Brent oil is taken as

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4. Methodology

We use the Akaike Information Criterion (AIC) for the purpose of optimal lag length selection (Ivanov & Kilian, 2005).

4.2. Export performance regression analysis

In that subsection, we discuss the regression model that is constructed in order to test our hypothesis 2 on export performance.

Ordinary least squares regression (OLS) is statistical method to estimate the unknown parameters in a linear regression model. In our research, we employ OLS method to test our H2.

To test our hypothesis 2 on export performance, we use 19-year time series data on Russia for the period of 1996-2014. Hence, we present the following baseline regression on export performance:

Baseline model:

EXP

t

= β

0

+ β

1

REER

t

+

controls

+

𝜀

t

where EXP is the dependent variable that shows yearly non-oil exports in real terms (Russian rubles), expressed in natural logarithms. REER is the independent variable that shows an annual index of a weighted value of Russian ruble against a basket of 172 other currencies, 2007 is a reference year. Controls are different control variables that we use in our regression model in order to check for other possible determinants of export performance. We use the four models for each control group, while the model 1 is a basic regression without controls, Models are selected in a way to avoid multicollinearity (see the quality of data subsection for details).

Model 1:

EXP

t

= β

0

+ β

1

REER

t

+

𝜀

t

Model 2:

EXP

t

= β

0

+ β

1

REER

t

+ β

2

L.RND

t

+ β

3

L.FDI

t

+ β

4

L.TFP

t

+

𝜀

t

Model 3:

EXP

t

= β

0

+ β

1

REER

t

+ β

2

L.TFP

t

+ β

3

L.PAT

t

+ β

4

TERT

t

+

𝜀

t

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4. Methodology

where L.RND is a control variable that shows a one-year lag of expenditures on research and development as a percentage of GDP. L.FDI is a control variable that shows a one-year lag of annual foreign investment inflows in Russian economy as percentage of GDP. L.TFP is a control variable that shows a one-year lag of percentage change in total factor productivity. L.PAT is a control variable that shows a one-year lag of annual number of patent applications in Russia by her nationals, expressed in natural logarithms to correct for large numbers. L.TERT is a control variable that shows an annual share of labor force with attained tertiary education as a share of total labor force. MAN is a control variable that shows an annual share of manufacturing output as a percentage of GDP.

We should note that the choice of the variables is not spontaneous and gradually based on the literature background, which is discussed in the previous section.

We also note that we use lags since we assume that technological progress, FDI inflows, patent applications and R&D expenditures take at least one year to affect export performance significantly.

4.3. Sample design

We employ two separate – annual and monthly – samples of time series data for Russian Federation. Our monthly sample covers the period from January 1995 and March 2016, the sample contains 255 observations. Our yearly sample starts in 1996 and ends in 2014, it contains 19 observations. The monthly sample is used for H1, the

yearly sample is used for testing H2.

When compiling the data, we used various sources. The main bulk of the information is obtained from the World Bank World Development Indicators, several variables are taken from the Organization for Economic Co-operation and Development, Conference Board, Breugel think tank, and the United Nations.

4.4. Variables

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4. Methodology

4.4.1. Crude oil price

We use oil prices in our Granger causality model. By crude oil price we denote a mean monthly spot oil price provided by Energy Information Administration of the United States (2016). We use Brent Crude as a reference benchmark oil brand as it is the most prominent benchmark in the world. The oil prices in our dataset are expressed in current United States dollars.

4.4.2. Export performance: total non-oil exports of goods

Annual export performance is the dependent variable in the second hypothesis of this research. We measure export performance as the real value of all non-oil exported goods in constant local currency (Russian rubles), then taken into a natural logarithm. First, we take the gross merchandise exports in current Russian rubles and manually calculate the non-oil exports by using the share of non-oil exports. Afterwards, we use the GDP deflator to calculate the total non-oil exports in real terms. We employ the GDP deflator as long as trade deflator is not available for corresponding time period. A natural logarithm is used in order to make a correction for large numbers. We take data for calculation from the OECD (2016) and the World Bank (2016).

4.4.3. Real effective exchange rate

Real effective exchange rate (REER) is a measure of real value of Russian ruble against the basket of Russia’s trading partners. REER is given as an annual index, where ruble value in the year 2007 corresponds to 100. The list of countries is available in the Appendix, there are 138 and 172 countries and political entities in the monthly and annual datasets, respectively.The data are taken from Breugel (2016). 4.4.4. Research and developments expenditure

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4. Methodology

4.4.5. Total factor productivity growth

Annual total factor productivity (TFP) growth shows total output growth relative to the growth in inputs of capital and labor per year. TFP is measured by the most common approach, which is the Tornqvist index (Tornqvist, 1936). TFP growth is used as a proxy for technological change. We collect the data on TFP growth from the Conference Board (2015). TFP growth is an important variable in the model as long as the literature suggests that non-cost determinants play a major role in the export performance (Carlin, Glyn & Van Reenen, 2001; Bournakis, 2013).

4.4.6. Foreign direct investment share

Annual foreign direct investment (FDI) share corresponds to net investment inflows. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments (World Bank, 2016).

4.4.7. Share of labor force with tertiary education

Annual share of labor force with tertiary education is the percentage of the total labor force that attained or completed tertiary education as the highest level of education, i.e., bachelor’s degree or higher including its Russian or Soviet equivalents. That variable acts as an indicator for the level of human capital in the country. The data are taken from the World Bank (2016). Several values in the dataset were missing due to the lack of data. A cascade approach was used to deal with the missing variables (StataCorp, 2015).

4.4.8. Patent applications by residents

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4. Methodology

4.4.9. Manufacturing share

Annual manufacturing share shows manufacturing output as a share GDP. It is taken from the United Nations Statistics Division (2014). Manufacturing is an integral part of the Dutch disease model by Corden and Neary (1982), we use that to examine whether the industrial production in Russia plays a major role in export performance.

4.5. Expected signs

The expected signs for variables of our regression model to test our H2 are

provided below (table 4.1). The table presents the big picture and offer a basic overview of the considered variables. The pluses and minuses indicate accordingly expected positive and negative relationships. Existing research provided in the literature review was employed in order to determine the expected signs and create the conceptual model.

Table 4.1. Expected signs for considered variables.

Variable Expected sign

REER Negative

R&D expenditures Positive

FDI inflows Positive

TFP growth Positive

Patent applications Negative

Manufacturing share Positive

Tertiary education share Positive

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5. Results

This section is dedicated to our results. We begin with discussing the dataset quality, then we move to reports from our econometric models, and we have a discussion subsection at the end.

5.1 Quality of data

Before testing our hypotheses, we perform various tests on the quality of data in order to find outliers in both samples and to check the assumptions of our OLS in the sample for the regression model. Specifically, we test for heteroscedasticity, endogeneity, multicollinearity and normality.

To check the data for the outliers, we employ Studentized residual method with ±2,5 cutoff points. We only find outliers in the monthly sample. The outliers correspond for the period of 2008 financial crisis, thus we decide to keep them in the sample (Chen et., 2003).

To check for heteroscedasticity, we perform the Breusch-Pagan test on our annual data, the tables with the results are available in the Appendix. The results indicate no presence of heteroscedasticity (Hill, Griffiths & Lim, 2012).

Correlation table is available in the Appendix. Based on the correlation between the variables, we select four regression models to run the analysis. We have selected separate models for each group of controls in such a manner that we can avoid multicollinearity between them.

We also calculate the variance inflation factor (VIF) for every regression model, the results are in the Appendix. We conclude that models are not affected by the multicollinearity as their VIF values are below 5 (Hill, Griffiths & Lim, 2012).

To test for normality, we employ test techniques described by Shapiro and Francia (1972). Based on the results, we conclude that four variables in the dataset are not distributed normally. We continue without any furthers data adjustments, but we should keep in mind that the data may be slightly biased.

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5. Results

5.2. Granger causality results

In that subsection, we present our results from the Granger causality test employed for testing Hypothesis 1. Before running the corresponding test, we select the appropriate lag using the Akaike Information Criterion (AIC). We assume that 12 months can be the maximum lag for our model. The output is given in the Appendix.

In accordance with the literature (Ivanov & Kilian, 2005), we choose the optimal lag that minimum the AIC value. In our case, it is the lag of three months. Thus, we proceed with results for Granger causality test that are given below.

Table 5.1. Results of Granger causality test, three-month lags.

Equation Excluded chi2 df Prob > chi2

m_REER m_OIL 8.959 3 0.030

m_REER ALL 8.959 3 0.030

m_OIL m_REER 5.1331 3 0.162

m_OIL ALL 5.1331 3 0.162

Source: own results.

The null hypothesis of Granger causality test is that lagged oil price does not cause REER. Probability value is much smaller than 5 percent. Based on the results of the Granger causality test, we overwhelmingly reject the null hypothesis.

Now, we can select a two month-long lag instead. That lag length is based on the Bayesian information criterion (BIC).

Table 5.2. Results of Granger causality test, two-month lags.

Equation Excluded chi2 df Prob > chi2

m_REER m_OIL 10.225 2 0.006

m_REER ALL 10.225 2 0.006

m_OIL m_REER 2.8594 2 0.239

m_OIL ALL 2.8594 2 0.239

Source: own results.

With the two-month lag, the results do not change considerably We are still able to safely reject the null hypothesis of no-causality since the probability value is significantly lower than 5 percent.

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5. Results

5.3. Export performance regression results

The results of the export performance model are revealed below (table 5.3). Each column represents a model of our regression analysis.

Table 5.3. Results of regression on non-oil export performance.

(1) (2) (3) (4)

VARIABLES Model 1 Model 2 Model 3 Model 4

REER -0.00527*** -0.00970*** -0.0131*** -0.00858** (0.00155) (0.00232) (0.00321) (0.00308) L.RND 0.565 (0.354) L.FDI 0.0812** (0.0373) L.TFP 0.00731 0.00237 (0.00822) (0.00892) L.PAT 0.827** (0.308) TERT 0.00587 (0.00357) MAN -0.0305 (0.0228) Constant 6.281*** 5.862*** -1.636 7.121*** (0.155) (0.364) (2.931) (0.648) Observations 19 18 18 19 R-squared 0.315 0.631 0.648 0.384

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source: own results

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5. Results

5.4. Discussion of the results

The results of two econometric models provide us with enough information to test the hypotheses of our research. The results are robust as they hold in different versions of our models – for different lengths of lags for Granger test and for different sets of controls for OLS.

Results of our Granger causality test for H1 suggest that we can safely reject

the null hypothesis that the oil price does not cause the REER. Based on the available evidence, we can conclude that oil price is a cause of Russian ruble exchange rate. Furthermore, it is possible to infer that we can observe the first component of the Dutch disease – namely, changes in national currency exchange rate are caused by the changes in the international oil price (Corden & Neary, 1982). That finding contrasts with the results of Jahan-Parvar and Mohammadi (2011) who find that in case of Russia the causality runs in the opposite direction, i.e., from REER to oil price. However, our finding is supported by other authors who confirm the presence of Dutch disease in the Russian economy (Kalcheva & Oomes, 2007; Mironov & Petronevich, 2015).

Results of our regression model for H2 provide us with enough evidence to

draw conclusions on the determinants of export performance of Russia. Based on the results, we can reject the null hypothesis that REER does not negatively affect the export performance. Instead, we can conclude that REER is a key factor that influences real non-oil exports. We should note that the findings do not show whether there is a causal relationship between REER and non-oil exports. Since REER is negative and significant in all four versions of the model, we can conclude that the linkage is strong.

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5. Results

The R&D expenditure is not found to be a statistically significant determinant for non-oil exports. That finding is in line with the evidence provided by Huang et al. (2008) and Liu and Shu (2003) who reach the same conclusions in case of China. The finding contrasts with the conclusions by Braunerhjelm and Thulin (2008) as well as Seo, Lee and Kim (2012), who do find that R&D is an important determinant of export performance. Previously in our literature review section, we indicated that there was no consensus on the role of R&D in exports in the economic literature, however now we can state that our research does not find the R&D expenditure to be an important factor for exporting. A possible explanation lies with the lack of necessary linkages between the institutions that perform the R&D and the exporting industries. One can also infer that the high level of corruption in Russia (Transparency International, 2016) leads to a low efficiency of R&D investments.

The foreign direct investment is a statistically significant factor that affects the non-oil exports. As it was predicted, the impact is positive, which is gradually backed by the existing body of empirical studies on different countries and regions. Our findings are supported by the similar conclusions for other emerging economies – South Africa (Akoto, 2012) and Vietnam (Pham & Nguyen, 2013), but also by the studies on the developed OECD economies (Seo, Lee & Kim, 2012). Our conclusion is not surprising as long as MNEs and other large companies comprise a major part of the Russian economy (Puffer & McCarthy, 2007), it is safe to assume that they are also the net receivers of the relatively big part of the FDI that they use to expand their exports and operations abroad. At the same moment, one can note that FDI in Russia is not always related to trade, i.e., Russia-based projects involving international investors also include those that do not add any export value to the economy, e.g., 2014 Sochi Olympic Games, 2018 FIFA World Cup. However, our findings illustrate that in general FDI positively influences Russian non-oil exports.

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5. Results

subsides, thus productivity might not be the reason for them to start exporting due to the Russia’s economic structure (Puffer & McCarthy, 2007).

Patent applications have positive and significant impact on non-oil exports. The finding is surprising since we predicted a negative linkage between patents and export performance. Russia is an emerging economy while the existing literature suggests that a major part of benefits from patent enforcement in developing economies goes to developed countries and promotes export from the North to the South but not vice-versa. Furthermore, the Western companies were large lobbyists of implementation of the TRIPS agreement, e.g., the US pharmaceutical companies (Ivus, 2010; Boring, 2015). Our findings could be possibly explained by the presence of the large firms that may indeed benefit from the patent protection in Russia, though we do not have the firm-level data in order to confirm that explanation. We can also note the state-run nature of the Russian economy; the government can engage into protectionist measures that may compensate for the negative effects of patent enforcement and enhance the positive effects.

Tertiary education share is not found to be a significant determinant of non-oil export performance. The outcome is not predicted since the literature suggests otherwise, Seo, Lee and Kim (2012) and Chuang (2000) find that human capital is an important determinant of export performance. We have to note though that those studies look at the developed countries, Chuang (2000) looks exclusively at Taiwanese economy while Seo, Lee and Kim (2012) examines 17 member states in the OECD. Russia still falls almost 50 percent lower than an average OECD country in terms of GDP per capita, and we have already mentioned the small percentage of high-tech exports (OECD, 2016; World Bank, 2016). Thus, the structure of trade and the level of overall economic development may be the key differences that explain our findings on tertiary education share. We can also consider the lack of the job opportunities for university graduates in those industries that engage into exporting. Finally, we can again take into account high corruption level in Russia (Transparency International, 2016), i.e., high education does not translate into better jobs and thus the businesses cannot utilize the human capital properly.

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5. Results

relatively low. Then the deindustrialization effect coincided with the export growth in the late 1990s and early 2000s, when the oil prices were growing (Altman, 2009; Berkowitz. & DeJong, 2011). In general, the non-significance of manufacturing share for exports might be in line with the Dutch disease model as the non-oil sector of economy was in decline was in decline even during the last decades of the Soviet period (Reynolds & Kolodziej, 2008). Based on that and our findings, that might indeed point to another sign of the Dutch disease in the country.

Taking all those results together, we can conclude that Russian export performance gradually depends on oil. The Granger test reveals a causality that runs from international oil price to the ruble exchange rate, and regression analysis provides conclusive evidence that appreciation of REER has a negative impact on real non-oil exports. In other words, our research shows that Russia has symptoms of the Dutch disease.

The results summary for the whole analysis of each hypotheses is presented in a table below.

Table 5.4. List of hypotheses and outcomes.

Hypothesis Outcome

H1: Oil price Granger-causes REER Null hypothesis is rejected

H2: REER negatively affects non-oil exports Null hypothesis is rejected

Oil Prices, Effective Exchange Rate and Export Performance: Evidence from Russia