BSE Voice

This is the second of two posts reporting on the roundtable discussion that took place on Friday, 31 March as part of the BGSE’s 10^th Anniversary Celebrations. The first post focused on the contributions of the first three speakers,  Prof. Richard Blundell, Prof. Matthew Jackson and Prof. Anne Krueger. This post discusses the contributions of the last two speakers, Prof. Alvin Roth (Nobel Laureate 2012) and Prof. Christopher A. Sims (Nobel Laureate 2011) on  “The Practical Influence of Economic Research”.

Prof. Alvin Roth, Stanford University

Prof. Roth began by talking about the practical influence of his own research on matching markets. As Prof. Roth explained, these are markets where prices do not succeed in bringing the demand side and supply side of the market together. For example, while in a commodity market the price determines who will produce the good (those firms that can make profits at the given price) and to whom they will sell it (those with a willingness to pay greater than the price), the same cannot be said for the placement of students in public schools or the placement of new doctors in their first hospital.

Prof. Roth set out how economists’ research in matching markets played a crucial role in the design of more efficient school assignment systems. One problem in assigning learners to public schools, for example, is that parents may be incentivised not to list their first choice of school at the top of the list they submit to the relevant education authority, because they believe that their children’s chances of being assigned to the first choice is slim, and so they list their second or third choice at the top. However, in order to assign children to schools efficiently, the authority needs to know what parents’ true preferences are, and in this case economists can help to design the market such that parents have an incentive to reveal their true preferences.

Similarly, the college admissions process typically involves some congestion, since prospective students typically apply to many colleges,  but only accept a place in one college. This means colleges are faced with repeated rounds of admission decisions once they are informed that students who were accepted will not take up their places. A more efficient system would have prospective students submit their preferences over all colleges in advance, and are then placed in a single round using a central assignment mechanism.

Aside from designing more efficient matching markets, Prof. Roth also believes that economists can play an important role in gathering or producing evidence for the policy changes they advocate. As an example, Prof. Roth mentioned economic experiments undertaken in the United States (US), which convinced US policymakers to adopt a new mechanism for assigning new doctors to their first hospital, whereas those policymakers used to be reluctant to accept these theoretical arguments or experimental evidence from other countries.

Finally, Prof. Roth also voiced support for Esther Duflo’s view that economists have an important role to play as “plumbers”. This involves analysing and finding solutions for the ways in which markets do not perform well, due to some unforeseen practical obstacles not accounted for in the relevant economic model. In this respect, Prof. Roth talked about economists’ design of a matching market for kidney donors. Often friends or family members are unable to donate a kidney to a patient due to incompatibility of the donor and recipient, but this problem could be solved through a cross-matching mechanism whereby a donor-recipient pair is matched with another donor-recipient-pair in order to achieve compatibility.

However, in practice doctors often end up rejecting such matches for medical reasons in unforeseen ways. Economists designing this market are therefore faced with a practical problem to solve, namely how to elicit doctors’ preferences in such a way that once a kidney is matched to a patient, the doctor does not ultimately reject the organ for the operation.

Prof. Christopher A. Sims, Princeton University

Prof. Sims’ contribution was concerned with developments in his own field of monetary policy, including the 2008/2009 financial crisis, and he began by pointing out that many central bank employees (including the heads of central banks) graduated with PhDs in economics, and therefore economics is certain to have a practical influence on monetary policy. In this respect, Prof. Sims believes that the effects of the financial crisis would have been far more serious if Ben Bernanke (and other policymakers) had not relied on the lessons of the Great Recession of the 1930s in implementing dramatically expansionary policies in response to the crisis.

In order to provide further context for his discussion of the financial crisis and the policy response in the United States, Prof. Sims noted that the policy prescriptions of monetarism had been a mistake, and that instead of aiming only at a constant growth rate of money, monetary policy should use interest rates as a tool to smooth fluctuations in economic activity. According to Prof. Sims, this shift in focus did indeed result in increased stability in the period leading up to the financial crisis.

Nevertheless, the overwhelming majority of economists, and in particular those in charge of monetary policy, did not predict the financial crisis, and the question has been asked why they were not able to do so. However, according to Prof. Sims, the role of economists in this instance can be compared to that of seismologists. In the same way that seismologists cannot predict earthquakes, but can only analyse them and provide very broad guidelines on when they might be likely to occur, recessions are inherently unpredictable, and economists should not be expected to predict them in advance.

A further question addressed by Prof. Sims is why the Fed chose to keep interests rates so low in the years leading up to the financial crisis. According to Prof. Sims, the Fed did have a rationale in adopting this policy, namely that it recognised the risks of falling into the kind of trap experienced by Japan since the 1990s, where the economy experiences low growth and the central bank is unable to provide monetary stimulus due to the zero lower bound on the interest rate. Nevertheless, it remains an open question whether the Fed should have better weighed these risks against the risk of creating a crisis through an extended period of low interest rates.

A further interesting possibility raised by Prof. Sims, although he did not address it conclusively,  is whether the post-monetarist stability leading up to the crisis paradoxically served to increase its severity. Here Prof. Sims again used an earthquake metaphor: if engineers design structures that are more resistant to earthquakes, this could allow a society both to construct more buildings, and to construct taller buildings that hold more people. In this way the original technology designed to protect the society against earthquakes could increase the expected damage of a very serious earthquake. In the same way, increased economic stability could have contributed to the deregulation and expansion of the financial sector which amplified the effects of the financial crisis.

Ultimately Prof. Sims expressed the view that economics as a field cannot carry the blame for an event such as the financial crisis. Which monetary policy is appropriate at a given moment is a hard problem, and economists should not carry the blame for attempting to tackle such problems, which are important to analyse, even if they are not completely successful in solving them.

On Friday, 31 March, the BGSE played host to a number of Nobel laureates and other leading academics from around the world as part of its 10^th Anniversary Celebrations. The first event of the weekend was a roundtable discussion with five eminent academic guests about “The Practical Influence of Economic Research”. This post highlights some of the main points to come out of the contributions of the first three speakers: Prof. Richard Blundell, Prof. Matthew Jackson and Prof. Anne Krueger.

Prof. Richard Blundell, University College London

With the help of attendant BGSE staff, Prof. Blundell overcame a minor hiccup with his microphone to speak on the practical influence of his research in the microeconomics of public policy and tax reform, and argued that the evidence economists present can have an important impact on government policy. As an example, he referred to the Mirrlees Review, which was produced under the auspices of the UK’s Institute for Fiscal Studies (IFS), and published in 2011, with the aim to  “identify the characteristics of a good tax system for any open developed economy in the 21^st century, assess the extent to which the UK tax system conforms to these ideals, and recommend how it might realistically be reformed in that direction.”

According to Prof. Blundell, the Mirrlees Review has been successful in identifying flaws in the UK tax system (and those of other countries), such as effective marginal tax rates that decrease over certain ranges of income levels, and that differ across different sources of income, such as earned income, self-employment income and dividend income.  Tax benefits for low-income members of the population also tend to be unnecessarily complex and difficult to understand. These aspects of developed economies’ tax systems carry particular weight in the context of increased inequality and decreasing incomes at the lower end of the income distribution.

Prof. Blundell also argued that the Mirrlees Review has had some success in addressing these flaws, referring to the fact that a number of UK lawmakers have accepted some of its core proposals, and that the Review has been widely translated and distributed around the world.

Prof. Matthew O. Jackson, Stanford University

Prof. Jackson started his presentation with a question that would be referred to a number of times by other speakers in the contributions that followed: what is (and what should be) the role of economists in society? Prominent economists have offered different definitions of their role since the inception of the field, variously likening the profession to those of artists, ethicists, story-tellers, scientists, engineers and, most recently, plumbers. Prof. Jackson focused mainly on the contrasting characterisation of economics as story-telling (as propounded by Robert Lucas) or as a science.

According to the story-telling view, economists deliberately work in an “unrealistic”, simplified world in order to tell us useful things about the real world using the power of imagination and ideas. In contrast, seeing economists as scientists doing the same kind of work as, for example, physicists, would imply that economists are engaged in discovering universally applicable laws of how markets work, and how firms and consumers make decisions. Ultimately Prof. Jackson highlighted how many of the most exciting recent advances in economics appear to fit better with the characterisation of economists as engineers or plumbers, such as recent developments in market design and development policy.

Prof. Jackson concluded by pointing out the potential practical implications of his own research on economic and social networks, and how modern technological tools can help us to better understand such networks. As an example, he referred to a figure produced using Python, showing how the US Senate had become more partisan over time, by drawing connections between senators that voted for the same legislation across party lines, and illustrating how the number of connections between Democrats and Republicans had declined over time.

Prof. Anne Krueger, The Johns Hopkins University

Prof. Krueger highlighted two ways in which economists exercise practical influence, namely by providing evidence that influences policy, and by providing blueprints to follow when change happens too fast for appropriate evidence to be gathered.

Regarding the former avenue of influence, Prof. Krueger’s points were in line with those made by Prof. Blundell.  Her most important example was India’s use of a small scale industry (SSI) reservation policy for more than 60 years, through which the Indian government reserved the production of certain goods to firms that employed fewer than a specific threshold of employees. Economists ultimately produced convincing evidence that this policy was not allowing firms in the reservation industries to exploit economies of scale, thereby rendering them uncompetitive relative to producers of the same goods in other countries. According to Prof. Krueger, this economic evidence helped to convince the Indian government to scale back and ultimately do away with its SSI reservation policy, to the benefit of Indian businesses in the affected industries.

Prof. Krueger made a similar argument concerning the cost of protecting employment through the use of import constraints, and referred to an example in the US where the costs of higher prices to consumers of import protection were many multiples greater than the employment income saved through that protection.  Prof. Krueger argued that by attaching figures to these costs in dollar terms, economists could influence lawmakers to adopt better policies.

Finally, Prof. Krueger referred to the 2008 financial crisis as an instance where economists had formulated a blueprint for responding to a rapidly changing situation, partly based on research on the Great Depression. Prof Krueger argued that this blueprint, which among other things called for large monetary and fiscal stimulus, had probably prevented a more serious recession following the crisis. As a further example, she mentioned the Mexican sovereign debt crisis of 1982, and argued that the structural reforms proposed by economists as a blueprint following the crisis have helped Mexico to achieve a better economic position than it otherwise may have done.

Continue with Part 2, which covers presentations by Prof. Alan Roth and Prof. Christopher Sims

Does exchange rate volatility negatively affect exports? This question is of great value to policymakers, especially in small open economies, which often rely heavily on exports and often face a choice of exchange rate regimes. If volatility is found to constrain exports, that could provide an argument in favor of an exchange rate regime in which volatility may be subdued, i.e. a currency peg. If volatility does not negatively affect exports, such arguments are less valid. Another, equally important question, turns the causal relationship on its head: To what extent is exchange rate volatility caused by changes in exports?

In this article, I contribute to the discussion by studying the relationship between exchange rate volatility and goods exports in Iceland. The recent economic history of Iceland has been characterized by different exchange rate regimes and several episodes of turmoil in the currency market. Another interesting aspect of this case study is that the supply of Iceland‘s goods exports industries is by nature relatively inelastic. I focus on short-run effects using high-frequency data.

The effect of exhange rate volatility on exports has been extensively studied. Various estimation methods have been employed in the literature, but error correction models seem to be the most popular. Researchers are now increasingly addressing the issue using sector-level and firm-level data (Héricourt and Poncet, 2013; Serenis and Tsounis, 2015). Estimates of the effect of exchange rate volatility on exports range from being significantly negative (e.g. Asserry and Peel, 1991) to small (e.g. Bahmani-Oskooee, Harvey and Hegerty, 2013).

In this article, I propose a relatively straight-forward method to test for the short-run effect of exchange rate volatility on exports. Daily nominal exchange rates and monthly exports are de-trended using a Hodrick-Prescott filter. Within each month, the standard deviation of the cyclical, or residual, component of the exchange rate is calculated. This is used as a measure of exchange rate volatility, and regressed on detrended monthly exports along with control variables which pick up the annual cyclical component of exports and the short-run effect of exchange rate appreciation or depreciation.

Crucially, I achieve identification by using variation in Iceland‘s exchange rate regime as a source of exogenous variation in exhange rate volatility. Finally, I ask the other question: whether exports affect exchange rate volatility.

Exchange rate volatility in Iceland is found to be positively and significantly associated with the cyclical value of goods exports within a month in the period 1999-2015. When instrumental variables are used in order to address endogeneity, I do not find a significant short-run effect of exchange rate volatility on goods exports. This finding is not surprising given the nature of the Icelandic economy. Furthermore, I find no evidence that exports negatively affect short-run exchange rate volatility.

Background, data and hypotheses

Iceland is a very small, open economy. It has an independent currency, the Icelandic króna (ISK), whose value in terms of a trade-weighted basket of foreign currencies is calculated daily by the Central Bank of Iceland (CBI). For a large part of the 20th century, the market for the ISK was distorted due to capital controls and government interventions. This changed in the 90s and early 2000s and from March 2001 to September 2008, the ISK was free floating. In November 2008, in response to a severe banking and currency crisis, the CBI instituted capital controls which significantly affected the ISK market. These restrictions were in place until 2016-2017, when they were partly lifted.

The natural logarithm of the trade-weighted index of the ISK exchange rate, retrieved from CBI and taken on a daily basis, is shown in Figure 1, along with the trend component of the exchange rate as captured by a Hodrick-Prescott filter with a smoothing parameter of 10 million. As is evident from the graph, the trend component picks up all trends that last more than a couple of months. In Figure 1, a rise in the exchange rate indicates depreciation of the ISK.

The standard deviation of the residual component is shown in Figure 2. Note the large heterogeneity in exchange rate stability over the 15 year period. In the very beginning and towards the end of the period, exchange rates were basically stable, while during some months in 2008 the standard deviation of the residual component of the exchange rate was above 0.05 on the log scale, or about 5 percentage points in terms of the exchange rate itself.

Note also that that my definition of exchange rate volatility is somewhat unorthodox. It pertains to the heterogeneity in deviations from a medium-run trend within a month. This means that during a period in which the exchange rate is appreciating or depreciating fast in the medium to long-run, a stable exchange rate in a given month is interpreted as being more volatile than if the exchange rate would follow the trend. This may raise some eyebrows, but casual observation of the data does not indicate to me that this method is critical to the measure of volatility throughout the sample period.

The natural logarithm of monthly goods exports from Iceland, retrieved from Statistics Iceland, is shown in figure 3 along with the trend component as captured by an HP filter with a smoothing parameter of 14.400 (the standard value in the literature for monthly data). Iceland‘s goods exports are very homogeneous. In both 1999 and 2015, around 75% of the country‘s goods exports were marine products and metals, mostly aluminum. Both industries arguably have relatively inelastic short-run supply. The marine industry is mostly constrained by natural factors such as the size of fish stocks, and for technical reasons aluminum production has to be maintained at a very stable level.

In this article, I will not provide additional empirical support for my claim that the supply of Icelandic exports are inelastic, but simply use the above anecdotal evidence to motivate the following hypothesis:

Hypothesis 1: Exchange rate volatility does not have a significant short-run effect on goods exports from Iceland.

Analysis of the data indicates that exchange rate volatility is higher during periods of currency depreciation than appreciation. This makes some intuitive sense, if one believes that due to risk aversion or an endowment effect financial markets are more volatile during stress than during an upswing. If this story is true, then it would also be true that during periods when exports rise relatively more than can be expected based on secular trends and cyclical factors, the currency market is more calm. This story can be formalized in the following hypothesis:

Hypothesis 2: In the short run, goods exports have a negative effect on exchange rate volatility in Iceland.

At first, the two results can seem contradictory. However, one has to keep in mind that both exchange rate volatility and exports are endogenously determined along with a variety of other variables. To circumvent this issue, I use different instrumental variables to test each hypothesis.

For Hypothesis 1, I use that Iceland has recently undergone periods of dramatically different exchange rate regimes, ranging from a free floating ISK with huge capital movements to a capital controls regime with little activity in the currency market. These regimes provide a source of variation in exchange rate volatility which is completely exogenous to the cyclical component of exports.

For Hypothesis 2, I use that exports are quite cyclical in nature and use monthly dummies as instruments. It is more debatable whether these dummies are valid instruments, but at the very least they do not appear to be correlated with exchange rate volatility.

Results

Table 1 shows the results from OLS regressions on the monthly cyclical component of the log of goods exports. Exchange rate volatility as defined above is the main independent variable of interest. I control for the effect of the overall movement in the exchange rate within the given month and in specification 2, two lagged values of these variables are included as well. Also included, but not reported, are dummies for every month of the year. The standard errors used to compute p-values correct for autocorrelation and heteroskedasticity using the Newey-West method with 6 lags.

The most significant result is that in specification 1, exchange rate volatility is positively related with exports within a given month (p=0.002). The point estimate suggests that an increase in exchange rate volatility within a month by 0.01 on the log scale (≈1% in terms of the exchange rate itself) is associated with 3,4% higher exports within a month. Beyond this, however, these regressions do not tell us a big story since we expect widespread endogeneity and reverse causality issues.

Table 2 shows the results from more interesting GMM regressions in which the log of cyclical goods exports is again the dependent variable. There is a single instrumental variable: a dummy indicating a period spanning roughly 2004-2008 during which the Icelandic economy experienced large capital flows and significant exchange rate volatility. The Kleibergen-Paap F statistics also reported allow us to reject overidentification, but the LM statistics raise some concern about weak identification in the regressions. Ignoring these concerns for the moment, we find that goods exports are not significantly affected by exchange rate volatility, neither in the current month nor in the month before.

Table 3 reports the results from GMM regressions where exports are regressed on exchange rate volatility. The instrumental variables are five monthly dummies which are chosen as they are most strongly associated with cyclical trends in exports. The Kleibergen-Paap statistics raise no concern about identification. Exports do not have a significant effect on exchange rate volatility, neither in the current month nor with a one month lag.

Some robustness checks were performed. The results in Table 2 are not sensitive to the choice of currency regime used as an instrument, although regressions using other regimes exhibit more identification problems. The same goes with the results in Table 3; including all monthly dummies as instruments weakens identification but does not otherwise affect the results. When I exclude March 2008 to February 2009 from the sample – a period of extreme volatility and uncertainty in the Icelandic economy – the effect of volatility in the regressions reported in Table 2 become more statistically significant, attributing a negative effect of volatility on exports with p-values of 0.098 and 0.111, respectively. However, these regressions are weakly identified and highly sensitive to the choice of regime as instrument. The results in Table 3 are not significantly affected by excluding this period. Controlling for quarterly movements in world prices of export products also does not qualitively affect the results. I did not specifically check whether the choice of smoothing parameter in the HP filter would affect the results. Checking this seems like a logical next step.

Discussion

I have studied the very short-run relationship between exports and exchange rate volatility in Iceland. I hypothesized that due to the inelastic nature of Iceland‘s goods exports, exchange rate volatility would not significantly affect goods exports in the very short run, i.e. within 1-2 months. The above results are consistent with this hypothesis. I also hypothesized that exports would negatively affect exchange rate volatility in the short run. The analysis above does not serve to support this claim.

The analysis in this article contributes to a large discussion about the relationship between exchange rate volatility and exports. I have added yet another case study to this discussion, and demonstrated how the use of HP filters can be useful in identifying short-run fluctuations in exchange rates and exports. For data availability reasons, I only looked at Iceland‘s goods exports industries. With a surge in tourism in recent years, goods industries are a declining share in Iceland‘s total exports profile. One would expect tourism and other service industries to be more sensitive to short-run exchange rate volatility.

The study is an ongoing project and ideally, I would need to perform more robustness checks. In particular, I stress that one has to take the GMM regressions with a grain of salt, as some of them are not strongly identified and results seem to depend somewhat on the sample period and choice of instruments.

Ólafur Heiðar Helgason

Barcelona GSE, Master’s Program in Economics, 2016-2017

References

Asseery, A. & Peel, D.A. (1991). The effects of exchange rate volatility on exports: Some new estimates. Economics Letters, 37(2), 173-177.

Bahmani-Oskooee, M., Harvey, H. & Hegerty, S.W. (2013). The effects of exchange-rate volatility on commodity trade between the U.S. and Brazil. The North American Journal of Economics and Finance, 25, 70-93.

Héricourt, J. & Poncet, S. (2013). Exchange Rate Volatility, Financial Constraints, and Trade: Empirical Evidence from Chinese Firms. The World Bank Economic Review, 29(3), 550-578.

Serenis, D., & Tsounis, N. (2014). The effects of exchange rate volatility on sectoral exports evidence from Sweden, UK, and Germany. International Journal of Computational Economics and Econometrics, 5(1), 71-107.

This research paper explores the Northern Sea Route (henceforth NSR), which is a shipping lane located along the Russian Arctic coast, and its predicted impacts on the relevant economies, especially Russia, in 2013, the time when the NSR was about to play an instrumental role in future shipping. This paper will also examine the economic implications.

Russia is one of the largest economies in the world by nominal value and even ranks among the top ten by purchasing power parity (CIA factbook). Nevertheless, it is still classified as a developing country.

Figure 1: Russia’s GDP growth from 2010 to 2013 (Kolyandr and Ostroukh, 2013)

In 2012, Russia’s economic growth was solid, at a comfortable 3.4%, while the global economy was stuck in recession. The Russian rate of growth was faster than that of many other developing countries.  Moreover, unemployment fell to 5.4%, which was a record low for the past 20 years. Not only that, wages grew considerably fast (see Figure 2). In 2013, Russia’s economy started to display signs of weakness. Its economic growth dropped to half the level of what it was in the decade leading up to the 2008 crisis, falling short of economists’ consistent expectation of a 1.9 percent rise. Some analysts and officials even started comparing the situation to a recession. In early 2013, the industrial output dropped for the first time since 2009. Furthermore, inflation increased in the second half of 2012 and was set to remain high in early 2013 (Kolyandr and Ostroukh, 2013).

Figure 2: Real wage growth in Russia in 2012 (trading economics)

One needs to adopt an international perspective when examining this case. The increase in inflation in Russia was related to three factors. First, overall inflation increase was due, in part, to the increase in food inflation triggered by the drought in Russia, exacerbated by a rise in prices among international grain producers, as well as higher consumption taxes on alcohol. Second, the rise in administrative prices in July and September 2012 and January 2013 led to inflated prices for services. Finally, there was an upward bend in core inflation, which excludes food and energy. After it had stabilized at approximately 5.7 percent for a few months, it increased from 5.1 percent in May 2012 to 5.8 percent in October 2012 (World Bank, 2013). Countries with high inflation face high government borrowing costs as a result, since lenders need to be compensated for the loss of their investments’ purchasing power. Low demand for industrial goods remained the main challenge for Russian businesses and, as a consequence, enterprises were not stimulated to invest and expand their production.

Russian official rates are known to have been high ever since the Perestroika. With an official interest rate of 8.25%, money and investment became very expensive relative to Russia’s main competitors. Banks would often ask for two-digit interest rates when lending. Consequently, this resulted in higher costs for loans and borrowed capital, thereby giving Russian stakeholders a competitive disadvantage when making investments. However, interest rates decreased from 15.2% in 2009 to 9.1% in 2012 (see Figure 3).

Figure 3: Interest rates for corporate loans on average (World Bank, 2013)

With regard to Russian shippers and ports, capital costs for investment decisions decreased by 40.13% on average. This certainly played in favor of the Russian shipping and port industry. On the other hand, other countries experienced a record low in interest rates. The EU, which harbors the competing ports as well as the most competitive shippers interested in the Northern Sea Route, will lend money at 0.5% through the European Central Bank. As a reference average lending rates to prime customers would therefore be at 3% (European Central Bank, 2013). Bearing these facts in mind, it is clear that pressure from finance-related costs had eased slightly. At the same time, however, competing companies from the EU would be able to acquire considerably cheaper capital than the companies located in or around St. Petersburg.

As shipping rates are almost exclusively quoted in European Euros and American Dollars, foreign exchange rates against those currencies played a major role in every internal and external calculation for shippers, ports and other stakeholders. In September 2013, 43-45 Russian Ruble could be exchanged for a Euro. Moreover, the Russian currency stood at around 32 RUB against one Dollar. However, the most important factor in foreign exchange related to long-term investment decisions (e.g. financing port infrastructure or new ships) as well as for daily money inflows (i.e. port handling fees, shipping revenue) is / was (present tense if it is a fact in general) volatility. The lower it is, the more predictable and, hence, the easier it becomes to calculate an investment decision for parties involved, i.e. the investor and the lender. Consequently, analyzing the Ruble’s performance and its volatility with Bollinger bands appears to be a suitable approach (Murphy, 1999).

As we see from Figure 4, volatility between the US Dollar and Russian Ruble clearly decreased compared to its trend at the beginning of the economic crisis in 2008 and 2009. However, volatility in this currency pair was still high enough to keep most American companies away from the Northeast Passage and its route. The volatility of the Ruble against the Euro was smaller. This was mainly due to steady, high volume of trade balance based on oil, gas and other raw materials, which offset the effects of an unstable Russian economy. Therefore, banks and especially forex finance were more optimistic about investments. For instance, they committed themselves to in- and outflows of the Russian Ruble.

Figure 4: Rates of US Dollar and Russian Ruble with Bollinger Bands (Yahoo Finance 2013)

To sum up, the NSR was meant to have a huge impact on the involved economies, especially Russia. But its neighboring countries, or countries otherwise depending on shipping, were likely to also be affected. Nevertheless, there will be more time needed to judge whether the predictions were correct.