Tag: exports
“New Goods” Trade in the Baltics
We analyze the role of the new goods margin—those goods that initially account for very small volumes of trade—in the Baltic states’ trade growth during the 1995-2008 period. We find that, on average, the basket of goods that in 1995 accounted for 10% of total Baltic exports and imports to their main trade partners, represented nearly 50% and 25% of total exports and imports in 2008, respectively. Moreover, we find that the share of Baltic new-goods exports outpaced that of other transition economies of Central and Eastern Europe. As the International Trade literature has recently shown, these increases in newly-traded goods could in turn have significant implications in terms of welfare and productivity gains within the Baltic economies.
New EU members, new trade opportunities
The Eastern enlargements of the European Union (EU) that have taken place since 2004 included the liberalization of trade as one of their main pillars and consequently provided new opportunities for the expansion of trade among the new and old members. Growth in trade following trade liberalization episodes such as the ones contemplated in the recent EU expansions could occur because of two reasons. First, because countries export and import more of the goods that they had already been trading. Alternatively, trade liberalization could promote the exchange of goods that had previously not been traded. The latter alternative is usually referred to as increases in the extensive margin of trade, or the new goods margin.
The new goods margin has been receiving a considerable amount of attention in the International Trade literature. For example, Broda and Weinstein (2006) estimate the value to American consumers derived from the growth in the variety of import products between 1972 and 2001 to be as large as 2.6% of GDP, while Chen and Hong (2012) find a figure of 4.9% of GDP for the Chinese case between 1997 and 2008. Similarly, Feenstra and Kee (2008) find that, in a sample of 44 countries, the total increase in export variety is associated with an average 3.3% productivity gain per year for exporters over the 1980–2000 period. This suggests that the new goods margin has significant implications in terms of both welfare and productivity.
In a forthcoming article (Cho and Díaz, in press) we study the patterns of the new goods margin for the three Baltic states: Estonia, Latvia and Lithuania. We investigate whether the period of rapid trade expansion experienced by these countries after gaining independence in 1991—average exports grew by more than 700% between 1995 and 2008 in nominal terms, and average imports by more than 800%—also coincided with increases in newly-traded goods by quantifying the relative importance of the new goods margin between 1995 and 2008. This policy brief summarizes our results.
Why focus on the Baltics?
The Baltic economies present an interesting case for a series of reasons. First, along a number of dimensions, the Baltic countries stood out as leaders among the formerly centrally-planned economies in implementing market- and trade-liberalization reforms. Indeed, those are the kind of structural changes that Kehoe and Ruhl (2013) identify as the main drivers of extensive margin increases. Second, unlike other transition economies, as part of the Soviet Union the Baltics lacked any degree of autonomy. Thus, upon independence, they faced a vast array of challenges, among them the difficult task of establishing trade relationships with the rest of the world, which prior to 1991 were determined solely from Moscow. Lastly, as former Soviet republics, the Baltic states had sizable portions of ethnic Russian-speaking population, most of which remained in the Baltics even after their independence. At least in principle, this gave the Baltic economies a unique potential to better tap into the Russian market.
Defining “new goods”
We use bilateral merchandise trade data for Estonia, Latvia and Lithuania starting in 1995 and ending in 2008, the year before the Global Financial Crisis (GFC). The data are taken from the World Bank’s World Integrated Trade Solution database. The trade data are disaggregated at the 5-digit level of the SITC Revision 2 code, which implies that our analysis deals with 1,836 different goods.
To construct a measure of the new goods margin, we follow the methodology laid out in Kehoe and Ruhl (2013). First, for each good we compute the average export and import value during the first three years in the sample (in our case, 1995 to 1997), to avoid any distortions that could arise from our choice of the initial year. Next, goods are sorted in ascending order according to the three-year average. Finally, the cumulative value of the ranked goods is grouped into 10 brackets, each containing 10% of total trade. The basket of goods in the bottom decile is labeled as the “new” goods or “least-traded” goods, since it contains goods that initially recorded zero trade, as well as goods that were traded in positive—but low—volumes. We then trace the evolution of the trade value of the goods in the bottom decile, which represents the growth of trade in least-traded goods.
Findings
For ease of exposition, we present the results for the average Baltic exports and imports of least-traded goods, rather than the trade flows for each country. Results for each individual country can be found in Cho and Díaz (in press). We report the least-traded exports and imports to and from the Baltics’ main trade partners: the EU15, composed of the 15-country bloc that constituted the EU prior to the 2004 expansion; Germany, which within the EU15 stands out as the main trade partner of Latvia and Lithuania; the “Nordics”, a group that combines Finland and Sweden, Estonia’s largest trade partners; and Russia, because of its historical ties with the Baltic states and its relative importance in their total trade.
Least-traded exports
Figure 1 shows the evolution over time of the share in total exports of the goods that were initially labeled as “new goods”, i.e., those products that accounted for 10% of total trade in 1995. We find that the Baltic states were able to increase their least-traded exports significantly, and by 2008 such exports accounted for nearly 40% of total exports to the EU15, and close to 53%, 49% and 49% of total exports to Germany, the Nordic countries, and Russia, respectively. Moreover, we find that the fastest growth in least-traded exports to the EU15 and its individual members coincided with the periods when the Association Agreements and accession to the EU took place. Finally, we discover that the rapid increase in least-traded exports to the EU15 during the late 1990s and early 2000s is accompanied by a stagnation of least-traded exports to Russia. This suggest that, as the Baltics received preferential treatment from the EU, they expanded their export variety mix in that market at the expense of the Russian. Growth in least-traded exports to Russia only resumed in the mid 2000s, when the Baltics became EU members and were granted the same preferential treatment in the Russian market that the other EU members enjoyed.
Figure 1. Baltic least-traded exports
Source: Cho and Díaz (in press).
Least-traded imports
Figure 2 plots the evolution of Baltic least-traded imports between 1995 and 2008. We find that new goods imports also grew at robust rates, but their growth is about half the magnitude of the growth in the least-traded exports—the least-traded imports nearly doubled their share, whereas the least-traded exports quadrupled it. The least-traded imports from the EU15 and its individual members exhibited consistent growth throughout. On the other hand, imports of new goods from Russia—which had also been growing since 1995—started a continuous decline starting in 2003. This change in patterns can be attributed to the Baltics joining the EU customs union. Prior to their EU accession, the average Baltic tariff was in general low. Upon EU accession, the Baltics adopted the EU’s Commercial Common Policy, which removed trade restrictions for EU goods flowing into the Baltics, but—from the perspective of the Baltic countries—raised tariffs on non-EU imports, in turn discouraging the imports of Russian new goods.
Figure 2. Baltic least-traded imports
Source: Cho and Díaz (in press).
Are the Baltics different?
Figure 1 shows that the Baltic states were able to increase their least-traded exports by a significant margin. A natural question follows: Is this a feature that is unique of the Baltic economies, or is it instead a generalized trend among the transition countries?
Table 1: Growth of the share of least-traded exports (percent, annual average)
Source: Cho and Díaz (in press).
Table 1 reveals that the new goods margin played a much larger role for the Baltic states than for other transition economies such as the Czech Republic, Hungary and Poland (which we label as “Non-Baltics”), for all the export destinations we consider. Moreover, we find that while until 2004—the year of the EU accession—both Baltic and Non-Baltic countries displayed high and comparable growth rates of least-traded exports, this trend changed after 2004. Indeed, while there is no noticeable slowdown in the Baltic growth rate, after 2004 the Non-Baltic growth of least-traded exports to the world and to the EU15 all but stops, with the only exception being the Nordic destinations.
Conclusion
The Baltic states, and in particular Estonia, are usually portrayed as exemplary models of trade liberalization among the transition economies. Our results indicate that the Baltics substantially increased both their imports and exports of least-traded goods between 1995 and 2008. Since increases in the import variety mix have been shown to entail non-negligible welfare effects, we expect large welfare gains for the Baltic consumers experienced due to the increases in the imports of previously least-traded goods. Moreover, the literature has documented that increases in export variety are associated with increases in labor productivity. Our findings reveal that the Baltics’ increases in their exports of least-traded goods were even larger than their imports of new goods, thus underscoring the importance of the new goods margin because of their contribution to labor productivity gains.
References
- Broda, Christian; and David E. Weinstein, 2006. “Globalization and the gains from variety,” Quarterly Journal of Economics, Vol. 121 (2), pp. 541–585.
- Chen, Bo; and Ma Hong, 2012. “Import variety and welfare gain in China,” Review of International Economics, Vol. 20 (4), pp. 807–820.
- Cho, Sang-Wook (Stanley); and Julián P. Díaz. “The new goods margin in new markets,” Journal of Comparative Economics, in press.
- Feenstra, Robert C.; and Hiau Looi Kee, 2008. “Export variety and country productivity: estimating the monopolistic competition model with endogenous productivity,” Journal of International Economics, Vol. 74 (2), pp. 500–518.
- Kehoe, Timothy J.; and Kim J. Ruhl, 2013. “How important is the new goods margin in international trade?” Journal of Political Economy, Vol. 121 (2), pp. 358–392.
Operating and Financial Hedging: Evidence from Trade
There is a large and growing literature that has modeled how real policies affect and interact with financial policies. It is important to consider such an interaction since a firm, just as a single value-maximizing agent, should make its strategic decisions optimally, taking into account all of its multi-dimensional facets (contracts with employees and suppliers, situation with market competitors, innovation, foreign-market operations and others – on the real side, and capital structure, dividend policy, IPO, hedging behavior – on the financial side). This policy brief introduces a new type of hedging exchange-rate risks through matching currencies of export revenues and import costs, and shows how it substitutes out financial hedging using currency derivatives.
Exchange-rate exposure and financial hedging around the world
Many firms are exposed to exchange-rate fluctuations in one way or the other. Because volatility is typically considered to be bad for a firm – either because small firms are risk-averse or because it may reduce the value of a risk-neutral firm through costly distress or agency costs – firms attempt to hedge it. Indeed many successfully do so. Bartram et al. (2009) report that about 60% of non-financial firms around the world use financial derivatives (forwards, futures, swaps, etc.), with the most popular type being currency derivatives (44%). These large numbers indicate the importance of risk management in general and hedging exchange-rate shocks in particular. There is also a considerable heterogeneity across countries. According to their investigation based on a subsample of world firms, currency derivative usage ranges from 6% in China and 15% in Malaysia, to 37% in the United States and 48% across Europe, to 80% in New Zealand and 88% in South Africa.
There is also some cross-sectional variation across firms. Geczy et al. (1997) report that among U.S. firms those with greater growth opportunities, tighter financial constraints, extensive foreign exchange-rate exposure and economies of scale in hedging activities are more likely to use currency derivatives.
Operational hedging
So what are potential alternatives to hedging exchange-rate exposure through currency derivatives? The literature has suggested other ways of reducing such cash-flow volatility – through operational hedges. The examples include diversifying the company’s operations and production geographically (as in Allayannis et al., 2001). The authors provide an example of Schering-Plough (a United States-based pharmaceutical company) that in their 1995 annual report suggested that hedging using financial instruments was not considered cost-effective, since the company operated in many foreign countries where the currencies would not generally move in parallel. More recent studies (e.g. Kim et al., 2006; Hankins, 2011) also support the geographical diversification of production and acquisition of foreign subsidiaries as important channels of operational hedging, and as such they can act as substitutes for financial hedging.
These papers are also part of the larger literature on the interrelations between real and financial strategies, and in particular the literature that has modeled how real policies, aimed at lowering operational risks (or alternatively increasing operating flexibility), reflect in various financial decisions (such as e.g. capital structure). Examples of such policies include the use of flexible manufacturing systems that allow changing the level of output, the product mix, or the operating “mode” (as in Brennan and Schwartz, 1985; He and Pindyck, 1992; and Kulatilaka and Trigeorgis, 2004); employing a contingent workforce (e.g. part-time and seasonal labor, as in Hanka, 1998 or workers on temporary contracts, as in Kuzmina, 2014); adopting a defined contribution, rather than a defined benefit or pension plan (as in Petersen, 1994); and many others.
Trade-related operational hedges
In Kuzmina and Kuznetsova (2016), we explore a different type of operational hedging – the one arising from exporting final goods and importing intermediate inputs from abroad at the same time. As previous literature has suggested, firms that export their final goods are naturally more exposed to exchange-rate risks due to their foreign-denominated contract obligations that have to be translated into domestic currency when the transaction clears in the future, the so-called transaction exposure of companies (Glaum, 2005). As long as volatility is costly for firms, higher exchange-rate exposure leads to more financial hedging, so previous papers indeed find a positive correlation between exporting and currency hedging (e.g. Geczy et al., 1997; He and Ng, 1998; Allayannis and Ofek, 2001).
This argument would similarly apply to firms that import their intermediate inputs from abroad, since they are similarly exposed to exchange-rate fluctuations on the cost side. In our paper, we attempt to provide new evidence on these channels, as well as to introduce a novel explanation to why not all firms hedge using financial derivatives. We show that firms that export and import at the same time hedge less using currency derivatives, and especially when volatility of exchange rate is high. We argue that when firms both export and import at the same time, their net foreign-denominated position (and thus exchange-rate exposure) becomes lower on average, and hence there is less incentive to hedge against it. This is consistent with foreign-currency matching of costs and revenues, which is a phenomenon also observable in other data. Although in our data we cannot observe currency of individual transactions for each firm, we do so in another project based on the data from Russia. Our calculations for Russian data, based on the whole universe of import and export declarations, suggest that for the major currencies, the probability of importing in the same currency is higher than in any other currency when a firm also exports in this currency. For example, out of all firms that have exports in Euro and some imports, 82% would import in Euro. The similar number for the U.S. dollar is 71%. Such trade-related operational hedge may arise naturally for firms in the global world, thus reducing their need to use financial instruments.
Germany as an interesting laboratory
To test our hypotheses, we use hand-collected data on a sample of German public firms during 2011-2014. Germany is a particularly relevant country for testing our hypotheses for at least three reasons.
First of all, it is the world’s third largest exporter and importer and the top one in Europe. Second and most importantly, if we want to explore currency risk arising from exporting and importing, at least some (and preferably many) of the export and import transactions have to occur in a foreign currency. This means that, for example, looking at the U.S. data would not give us a lot of power in identifying our mechanism, since according to Goldberg and Tille (2008), only 5% of all U.S. export contracts are set in a currency other than the U.S. dollar. On the other hand, more than half of German exports and imports outside the euro area are denominated in a currency other than the Euro, and in particular about 30-40% of all contracts are set in U.S. dollars. This means that our measured shares of non-euro zone exports and imports will actually have a large component of non-euro-denominated contracts, and we will have more power to measure the actual exchange-rate exposure arising from exporting and importing. Finally, we analyze the largest companies in Germany – those that trade on the Prime Standard segment of the Frankfurt Stock Exchange, since they have to disclose their use of derivatives due to the highest accounting and transparency requirements of this listing. These mandatory disclosure rules enable us to collect the data on hedging from companies’ annual reports and perform the analysis.
Identification strategy and results
To start the analysis, we provide some cross-sectional correlations. We find that firms in industries with more out-of-euro-zone exporting (importing) have a higher propensity to hedge using currency derivatives. In particular, a firm in an industry with 10pp higher export (import) shares has on average a 10.5pp (28.9pp) higher probability of currency hedging.
Although many industries simultaneously export and import a lot, others have a substantial imbalance in terms of export and import shares. We are therefore interested in whether this translates into different hedging behaviors. By adding the interaction between export and import shares in our regression specifications, we find that firms that simultaneously export and import hedge less than firms that just export or import. This is consistent with our hypothesis that firms decrease their effective exchange-rate exposure by having both revenues and costs in foreign currency and implies that operational hedging through matched currencies is a substitute for financial hedging.
In order to strengthen the result, we complement our cross-sectional correlations with a difference-in-differences methodology. To do this, we compare firms in industries with higher and lower out-of-euro-zone export and import shares during times of higher and lower exchange-rate volatility. We find that the higher the exchange-rate volatility, the larger this substitution effect is. This finding is stronger than a simple cross-sectional correlation between exporting, importing and hedging (which can be driven by omitted factors), since it uses an arguably exogenous volatility shock to show that operational hedging substitutes for financial hedging precisely during times when firms have highest incentives to hedge. The results are robust to using a set of control variables and firm and year fixed effects.
Implications
From an applied perspective, the interrelation between operational and financial strategies of the firm suggests that the decisions of the CEO and CFO should be complementary to each other to achieve the value-maximization goal of the firm. From a policy perspective, they imply that exogenous changes in government policies aimed at certain organizational changes in the firm (e.g. export promotion policies) could have indirect consequences for their riskiness and financing decisions.
References
- Allayannis, G., J. Ihrig, and J. P. Weston (2001), “Exchange-rate hedging: Financial versus operational strategies”. American Economic Review 91 (2), 391-395.
- Allayannis, G. and E. Ofek (2001), “Exchange rate exposure, hedging, and the use of foreign currency derivatives”, Journal of International Money and Finance 20 (2), 273-296.
- Bartram, S. M., G. W. Brown, and F. R. Fehle (2009), “International evidence on financial derivatives usage”, Financial Management 38 (1), 185-206.
- Brennan, M. and E. S. Schwartz (1985), “Evaluating natural resource investments”, The Journal of Business 58 (2), 135-157.
- Geczy, C., B. A. Minton, and C. Schrand (1997), “Why firms use currency derivatives”, Journal of Finance 52 (4), 1323-1354.
- Glaum, M. (2005), “Foreign-Exchange-Risk Management in German Non-Financial Corporations: An Empirical Analysis”, Springer.
- Hanka, G. (1998), “Debt and the terms of employment”, Journal of Financial Economics 48 (3), 245-282.
- Hankins, K. W. (2011), “How do financial firms manage risk? Unraveling the interaction of financial and operational hedging”, Management Science 57 (12), 2197-2212.
- He, H. and R. S. Pindyck (1992), “Investments in flexible production capacity”, Journal of Economic Dynamics and Control 16 (3-4), 575-599.
- He, J. and L. K. Ng (1998), “The foreign exchange exposure of Japanese multinational corporations”, Journal of Finance 53 (2), 733-753.
- Kim, Y. S., I. Mathur, and N. Jouahn (2006), “Is operational hedging a substitute for or a complement to financial hedging?” Journal of Corporate Finance 12 (4), 834-853.
- Kulatilaka, N. and L. Trigeorgis (2004), “The general flexibility to switch: Real options revisited”, Real options and investment under uncertainty: classical readings and recent contributions, 179-198.
- Kuzmina, O. (2014), “Operating flexibility and capital structure: Evidence from a natural experiment”, American Finance Association Conference, Philadelphia.
- Kuzmina O. and O. Kuznetsova (2016), “Operating and Financial Hedging: Evidence from Trade”, CEFIR Working paper.
Petersen, M. (1994), “Cash flow variability and a firm’s pension choice: A role for operating leverage”, Journal of Financial Economics 36, 361-383.
Trade Preferences Removal – The Case of Belarus
How does the removal of trade preferences influence the exports of the affected country? We study this question on the example of Belarus’ loss of trade preferences granted by the EU to developing countries. Our brief argues that trade preferences are most important for simple non-manufactured goods. As a result, removal of trade preferences should increase the manufactured goods in the export structure. Indeed, the overall complexity of Belarusian exports was not harmed by the removal of EU preferences and the manufactured exports increased relative to non-manufactured exports.
Belarus losing trade preferences
As a developing country, Belarus used to receive trade preferences from the US and EU. These preferences grant duty-free imports or provide a discount on the import tariff under the so-called Generalized System of Preferences (GSP). The preferences are provided on a unilateral basis to developing countries and can also be removed on a unilateral basis for various reasons. Their stated objective is to support the economic development of poorer countries (Ornelas 2016). In particular, the US removed Belarus’ preferences in 2000 for worker rights violations. Later, the EU removed the preferences in 2007 for similar reasons. It is a relevant question for policy to understand how the removal of trade preferences affected exports.
This brief discusses the effect of trade preferences removal on the value of Belarus’ exports to the EU and on the structure of exports. Utilization of trade preferences might not be uniform across sectors. In fact, a preference-receiving country should satisfy the Rules of Origin (ROO) requirements and demonstrate that a large enough share of the exported product was produced in the country. This requirement might be more difficult to satisfy for complex manufactured goods with many components from several countries (Hakobyan 2015). Exporters of such products might find satisfying the ROO more costly than what they could gain from receiving an import tariff preference. Exporters of simple or raw products, on the other hand, face a lower cost of demonstrating the origin.
The remainder of the brief develops the hypothesis of a differential impact of trade preferences removal on manufactured and non-manufactured goods; and makes an event study of Belarus’ loss of EU trade preferences in 2007. Our findings suggest that GSP withdrawal affected disproportionally non-manufactured exports, leading to an increase in the manufacturing exports share. This means that harm caused by losing trade preferences was, to some extent, reduced by higher incentives to export more complex manufactured exports.
The complexity of Belarusian exports
To understand the overall structure of Belarusian exports, we first look at the complexity of Belarusian exports over time. Figure 1 presents the economic complexity index (ECI), developed by Hausmann et al. (2014), of exports of Belarus relative to Russia from 1995 to 2014. The ECI measures the diversity and ubiquity of a country’s exports. It considers the number of products a country exports with revealed comparative advantages and how complex these products are. In turn, the complexity of the products is accessed by a so-called product complexity index, PCI. It is determined in an analogous fashion: if few countries are able to export a good and these countries have diversified exports, this product is complex. For example, fertilizers and oil (important exports of Belarus) have low complexity scores, as countries that export these products tend to not have diversified exports.
Figure 1 shows that the difference between the economic complexity of Belarus and Russia increased following the two incidents of Belarus losing trade preferences; first from the US and then from the EU. The incidents of removal of trade preferences are associated with an increase in economic complexity of Belarusian exports relative to Russia. That is, the export of more complex manufactured goods became more important in the export basket of Belarus when it lost the trade preferences. This is consistent with the hypothesis that trade preferences are more important for simpler goods, and following a preference removal their share will go down. Russia is chosen for comparison due to its similarity in economic perspective (economies in transition, similar complexity, GDP trends, dependence on oil and fertilizer prices) and because it also received trade preferences from both the US and EU throughout the considered period.
Figure 1. GSP withdrawal and Export Complexity in Belarus relative to Russia
Note: the figure presents the ECI of Belarus over ECI of Russia in logarithmic form. Source: Authors’ calculations using the ECI data from the Observatory of Economic Complexity.
Export structure of Belarus
To make a first pass at understanding how GSP withdrawal affects the composition of exports, we conduct an event study centered on the year of 2007, when the EU withdrew its GSP preferences for Belarus. We consider the three years before and after the revocation, and benchmark the share of manufacturing exports from Belarus to the EU with its share of manufacturing exports to the US. Since the US had already withdrawn its preferences earlier, its trade regime with Belarus stayed unchanged throughout the period. This makes the US a natural point of comparison to understand the effect of GSP withdrawal.
Findings
As Figure 2 shows, the average share of manufactured products in Belarusian exports to the EU increased slightly after the GSP withdrawal, increasing to 40.4% from its earlier level of 37.9%. At the same time, mineral and fuel exports, though falling slightly, remain the backbone of Belarusian exports accounting for 50% of total exports to Europe. Interestingly, the share of non-fuel exports to the EU remained approximately unchanged at 9%. In other words, the composition of exports to Europe did not drastically change after the GSP withdrawal, as had been anticipated by some ex-ante studies (e.g. BISS 2007).
This comparison alone does not address the question of what might have happened to Belarusian manufacturing exports had the GSP preference not been removed. One possible counterfactual is that the trends in the European export market would have been the same as in the US, where Belarusian manufacturing exports massively lost ground. Their share decreased from 53.4% to 19.3%. Hence, a difference-in-difference estimator would suggest that perhaps the withdrawal of the GSP reduced non-manufacturing export growth to Europe. In turn, the Belarusian manufacturing export share is estimated to be 36.5% higher than it might have been if the GSP had not been withdrawn (statistically significant at the 1% level). This estimate may be a result of trade diversion of non-manufactured goods from the EU to the US. To the extent that non-manufacturing products benefit more from the GSP preferences, these should be stronger affected by trade diversion and would therefore reduce the manufacturing share of Belarus’ exports to the US.
Figure 2. Share of Manufacturing Exports
Note: Manufacturing includes sectors 5, 6, 7 and 8 according to the SITC classification. Source: Authors’ calculations using data from the UN COMTRADE.
Alternatively, one could consider the Belarusian manufacturing export share in relation to Russia, within the European market. For Russia, there is a pattern of declining manufacturing shares. Before 2007, manufacturing accounted for 17.7% of exports to the EU, but afterwards it declined to 14.2%, a 2.5% fall. If Belarus had experienced the same trend, its manufacturing share would have fallen from 37.9% to 34.4%. Instead, Belarusian manufacturing share grew from 37.9 to 40.4%, which suggests that due to the GSP removal, the Belarusian manufacturing export increased by 6%. Given the smaller effect size and the short sample period, this increase is not statistically significant. However, in economic terms, it would still be an important shift.
Conclusion
Although development is one of the main goals of the GSP, there is little evidence that the EU’s Generalized Scheme of Preferences supported the development of advanced industries in Belarus. To the contrary, after the GSP withdrawal the export complexity of Belarus increased relative to that of Russia. There is also some suggestive evidence that the GSP may have encouraged an export profile more focused on non-manufactured products, for which rules of origin are easier to satisfy in practice. More research is clearly needed, not least to analyze other cases of GSP withdrawal for external validity.
Our preliminary findings suggest that GSP in its current form might have created incentives for exporting relatively simple goods, thus creating a risk of “middle-income trap”. Policy implications are twofold: First, the goal of preference programmes like the GSP is development, i.e. more advanced economy with more complex production, and if the preferences in fact foster simple exports, it could create a barrier to development; Second, removal of preferences might have a large negative impact overall but the observation that it removes the previous incentive of producing simple non-manufacturing goods can be seen as positive and thus cushion the negative impact.
References
- Belarusian Institute for Strategic Studies (BISS), 2007. “Belarus exclusion from the GSP: possible economic repercussions”, at: http://www.belinstitute.eu.
- Hakobyan, Shushanik, 2015. “Accounting for underutilization of trade preference programs: The US generalized system of preferences.” Canadian Journal of Economics/Revue canadienne d’économique, 48.2, 408-436.
- Hausmann, Ricardo; Hidalgo, Cesar A., Bustos, Sebastian; Coscia, Michele, Simoes, Alexander, & Yildirim, Muhammed A. (2014). The atlas of economic complexity: Mapping paths to prosperity. Mit Press.
- Ornelas, Emanuell, 2016. “Special and differential treatment for developing countries.” Handbook of Commercial Policy 1, 369-432.ilable online, please hyperlink the title.
Russia and Oil — Out of Control
Russia’s dependence on oil and other natural resources is well known, but what does it actually mean for policy makers’ ability to control the economic fate of the country? This brief provides a more precise analysis of the depth of Russia’s oil dependence. This is based on a careful statistical analysis of the immediate correlation between international oil prices — that Russia does not control — and Russian GDP, which policy makers would like to control. I then look at how IMF’s forecast errors in oil prices spillover to forecast errors of Russian GDP. These numerical exercises are striking; over the last 25 years oil price changes explain on average two thirds of the variation in Russian GDP growth and in the last 15 years up to 80 percent of the one-year ahead forecast errors. Instead of controlling the economic fate of the country, the best policy makers can hope for is to dampen the short-run impact of oil price shocks. A flexible exchange rate and fiscal reserves are key volatility dampers, but not sufficient to protect long-term growth. The latter will always require serious structural reforms and the question is what needs to happen for policy makers to take action to get control over the long-term fate of the economy.
In a recent working paper (Becker, 2016), I take a careful look at the statistical relationship between Russian GDP and international oil prices. This brief summarizes this analysis and its policy conclusions.
Russia and oil, the basics
Although Russia’s oil dependence is discussed every time international oil prices drop, it is not uncommon to hear that oil is not really so important for the Russian economy. The argument is that the oil and natural resource sector only accounts for some 10 percent of Russian production. This is indeed consistent with the official sectoral breakdown of GDP that is shown in Figure 1 where the minerals sector indeed only has a 10 percent share.
Figure 1. Structure of GDP in 2015
Source: Federal State Statistics Service, 2016
However, this static picture of production shares does not translate into a dynamic macro economic model that allows us to understand what is driving Russian growth. Instead a careful analysis of the time series of Russian GDP is required to understand how important oil is for growth.
Russian GDP can be measured in many different ways: nominal rubles, real rubles, U.S. dollars, or in purchasing power parity (PPP) terms to mention the most common. Here we focus on GDP measured in real rubles and U.S. dollars since we want to get rid of Russian inflation, which has been quite high for most of the studied time period. The PPP measure generates figures and numerical estimates that are in between the real ruble and U.S. dollar measures and are not included here to conserve space.
The first evidence of the importance of international oil prices as a major determinant of Russian income at the macro level is presented in Figures 2 and 3 where the first figure shows dollar income and the second real ruble income. In both cases it is obvious that there is a strong correlation and that the correlation is higher for income measured in dollars.
Figure 2. U.S. dollar GDP and the oil price
Source: IMF, 2016
Figure 3. Real ruble GDP and the oil price
Source: IMF, 2016
However, it is also clear that all the time series have some type of trends or in econometric language, are non-stationary. This means that simple correlations of the time series shown in Figure 2 and 3 may not be statistically valid (or “spurious” as it is called in the literature). This is not a critical issue but can be handled by regular econometric methods.
Russia and oil, the econometrics
When time series are non-stationary they need to be transformed to some stationary form before we can do regular regressions (in Becker, 2016 I also address the issue of using a framework that allows for co-integration).
Two transformations that make the variables stationary are to use first differences or percent growth rates. Both are used before we run simple regressions of growth or first differences of GDP on growth or first difference in international oil prices. The full sample starts in 1993, but since the early years of transition were subject to many different shocks at the same time, a shorter sample starting in 2000 is also used.
A number of observations come from the estimates that are presented in Table 1: Oil prices are always statistically significant; the adjusted R-squared is higher for dollar income than real rubles (with one exception due to a large outlier in 1993); overall the explanatory power of these simple regressions are very high (42-92 percent) and the explanatory power increases in all specifications when going from the full sample (1993-2015) to the more recent sample (2000-2015). Note that the latter sample perfectly overlaps with the current political leadership so contrary to some wishes; the oil dependence has not been reduced under Putin/Medvedev.
Table 1. Russian macro “models”
Source: Becker 2016
Russia and oil, the forecasts
The strong correlation between international oil prices and Russian GDP provides a very simple econometric model for explaining past variations in Russian GDP. Unfortunately it does not imply that it is easy to forecast Russian GDP since international oil prices are very hard to predict. There are many models that have been used to forecast oil prices, but the IMF and many others now use the market for oil futures to generate its central forecast of oil prices.
The IMF also provides confidence intervals around the central forecast, and the uncertainty surrounding the forecast is substantial: In the latest forecast the 68 percent confidence interval goes from around 20 dollars per barrel to 60 one year ahead, while the 98 percent interval ranges from 10 dollar per barrel to around 85. With oil currently around 45 dollars per barrel, these variations imply that oil prices could either halve or double in the next year, not a very precise prediction to base economic policy on for Russia since the estimates for real ruble growth in the later sample in Table 1 imply that Russian GDP growth in real ruble terms could be anywhere from minus 5 to plus 10 percent, or a fifteen percentage point difference!
If we look at past IMF forecasts of oil prices and Russian GDP and see how much they deviate from actual values a year later we can compute one year ahead forecast errors. We can do this calculation for the last 16 years for which the IMF data is available. Figures 4 and 5 show how the forecast errors in oil prices correlate with the forecast errors for dollar income and real ruble income, respectively. Similar to the regressions presented in Table 1, the correlations are very high for both measures of GDP: 82 percent for dollar GDP, and 65 percent for real ruble GDP.
In other words, a very large share of the uncertainty surrounding Russian GDP forecasts can be directly attributed to variations in international oil prices, a variable that (again) Russia does not control. The fact that the variations in oil prices explain somewhat more of the variation in dollar income compared to real ruble income is a result of a policy change that in later years allowed the exchange rate to depreciate much more rapidly when oil prices fall.
Figure 4. Forecast errors
Source: Becker 2016
Figure 5. Forecast errors
Source: Becker 2016
Policy conclusions
The depth of Russia’s oil dependence is much greater than what casual observers of the mineral sectors share of GDP would suggest. At the macro level, variations in international oil prices explain at least two thirds of actual Russian growth and even more of the one-year ahead forecasts errors.
The experience of the 2008/09 global financial crisis provided an important lesson to Russian policy makers, which is that exchange rate flexibility is required to dampen the real impact of falling oil prices and to protect both international reserves and the fiscal position. In the more recent years, the currency has been allowed to depreciate in tandem with falling oil prices and the drop in real ruble income was therefore less severe in 2015 than in 2009. Income in dollar terms, instead, took a greater hit, but this was a necessary corollary to protecting reserves and the budget. A flexible exchange rate and gradual move to inflation targeting in combination with accumulating fiscal reserves in times of high oil prices are key to Russia’s macro economic stability.
Nevertheless, these policies are not sufficient to remove the long-run impact that low or declining oil prices will have on growth, measured both in real ruble terms or dollar terms. It is nice to have fire insurance when your house burns down, but when you rebuild the house you may want to consider not building another straw house. For Russia to build a strong economy that is not completely hostage to variations in international oil prices, fundamental reforms that encourage the development of alternative, internationally competitive, companies are needed. This includes reforms that initially will reduce policy makers control over the economy and legal system, but over time it will provide the much needed diversification away from exporting oil that puts the fate of the Russian economy squarely in the hands of international oil traders. Losing some control today may provide a lot more control in the future for the country as a whole, but perhaps at the expense of less control for the ruling elite.
References
- Becker, T, 2016, “Russia’s oil dependence and the EU”, SITE Working paper 38, August.
- Federal State Statistics Service (or Goskomstat), 2016, data http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/en/figures/domestic/
- IMF, 2016, World Economic Outlook, April data from http://www.imf.org/external/pubs/ft/weo/2016/01/weodata/index.aspx
Intermediate and Capital Goods Import and Economic Growth in Belarus
This policy brief presents estimation results of the influence of intermediate and capital goods (ICGs) imports on GDP growth taking into account changes in the exchange rate. The Belarusian economy substantially relies on ICGs imports, and my research indicates that imports of intermediate inputs negatively contribute to Belarus’ economic growth. The findings suggest that a devaluation of national currency can negatively influence both GDP growth and imports of intermediate goods. The negative influence on GDP growth is caused by a lower price competitiveness of the export sector, and the negative influence on imports of intermediate goods is due to a significant increase in the costs of imports.
According to endogenous growth theory technological progress is a key factor that enhances long-run economic growth (Grossman and Helpman, 1994). However, in developing countries scarce commercial activities in R&D limit technological progress (Grossman and Helpman, 1991). From this point of view, imports of ICGs play the same role in the development of the Belarusian economy (taking into account the nature of Belarusian manufacturing, which is mostly to assemble finished goods) as R&D activities in developed countries by transferring foreign technology and innovations (Coe et al., 1997; Mazumdar, 2001). In turn, Belarusian economic policy related to imports of ICGs is seriously conditioned by the foreign exchange constraint.
Imports of ICGs and GDP Growth
Imported ICGs (excluding energy goods) account for approximately 55% of all Belarus’ imports. Starting from 2001 up to 2010 high levels of GDP growth (7-8% on average) were associated with even higher growth levels of ICGs imports (see Figure 1).
Figure 1. Imports of ICGs in 2001-2014
Source: Belstat.
However, from 2011, average growth rate of GDP has decreased significantly from 7% in 2006-2010 to 2% in 2011-2014. This was coupled with a substantial drop in the average growth rates of ICGs imports. All these may indicate an insolvency of the current import-led growth (ILG) strategy of Belarus.
Moreover, using an Autoregressive-Distributed Lag (ARDL) approach (Pesaran et al., 2001) to study the long-run relationship between ICGs imports and GDP growth, it was found that a 1% growth in imports of intermediate goods caused a 2.7% decrease in real GDP (Mazol, 2015). The effect of capital goods imports is statistically insignificant.
The Toda-Yamamoto (TY) causality test (Toda and Yamamoto, 1995) clarifies this result, indicating unidirectional causality running from economic growth to imports of intermediate goods, and further to imports of capital goods (see Figure 2).
Figure 2. TY Causality Test
Note: * 10% level of significance; ** 5% level of significance; *** 1% level of significance. Source: Author’s own estimations.
Thus, instead of an ILG hypothesis, the findings establish presence of a GLI hypothesis for Belarus, supporting the view that for developing countries, trade is more a consequence of the rapid economic growth than a cause (Rodrik, 1995).
What is the intuition behind these results? The ILG strategy aims to improve efficiency and productivity, and can be appropriate only under two crucial conditions: first, it is necessary to acquire preferably advanced technology from abroad; and, second, there have to exist enough domestic technological capabilities and skilled human capital in order to successfully adapt new technologies from R&D intensive countries.
In Belarus, a violation of the first condition was caused by an ineffective industrial policy aimed to modernize state-owned enterprises (SOEs) (Kruk, 2014). In many cases, capital accumulation was accomplished without appropriate investment appraisal and efficient marketing strategies.
Furthermore, there is serious evidence against the second condition being fulfilled: the share of innovative goods of all shipped goods in the past 4 years have dropped by 5.5 percentage points – from 17.8% to 12.3% (Belstat); and the «brain drain» is still a big problem (mostly due to low salary levels in research areas).
Influence of Exchange Rate Policies
Through the cost of imported intermediates, the exchange rate has an important influence on the price competitiveness of the Belarusian economy. However, the Belarusian exchange rate has fluctuated widely since 2000s (see Figure 3). For example, between 2000 and 2014, the annual percentage change in the nominal effective exchange rate (NEER) has varied from approximately 135% to -2%, and the real effective exchange rate (REER) fluctuated between 23% and 11% annually.
Figure 3. The Exchange Rate 2000-2014
Source: Belstat, IFS.
The results from estimated ARDL models (Mazol, 2015) show that while a depreciation of the Belarusian currency negatively influences both the imports of intermediate goods and GDP growth, it does not have a statistically significant effect on the imports of capital goods.
Concerning the influence on intermediate inputs, the explanation is that there are two effects of exchange rate policy on trade. On the one hand, depreciation of national currency leads to growth in the domestic currency price of exports, which motivates national companies to expand production of exports – the derived demand effect. On the other hand, it increases the domestic currency price of imported intermediate inputs, decreasing the quantity of intermediate imports domestics companies can buy – the direct cost effect. The direct cost effect and the derived demand effect have opposite signs (Landon and Smith, 2007).
Additionally, devaluations in Belarus occur in most cases both to import source and export destination countries (first of all Russia). Thus, in the case of imports of intermediate goods, the impact of the direct cost effect is greater than the impact of the derived demand effect, leading to a negative effect on imports of intermediate goods.
Furthermore, the substantial reliance of the Belarusian export sector on imported inputs, combined with above-presented side effects, cause cost-push inflation in the export sector, which decreases its price competitiveness and, overly, the economic growth. This statement is confirmed by the fact that in the period 2002-2011, intermediate inputs were imported both under the permanent expansionary monetary policy and the fixed exchange rate policy (see Figure 3). As a result of such twin strategies, intermediate imports have become more and more expensive, while the price competiveness of Belarusian export goods have steadily declined (taking into account that most of its industrial part is shipped to Russia).
The reason why the exchange rate policy do not seem to have had an effect on capital goods imports is that machinery and equipment were typically imported in accordance with the government’s modernization plans. The realization of these plans often disregarded the current macroeconomic situation in Belarus, and the imports were made just for the sake of importing (to accomplish the plan).
Finally, starting in 2012, depreciation of the Belarusian ruble coincided with the economic recession caused primarily by structural problems that hit the country (Kruk and Bornukova, 2013). Therefore, the increase in flexibility of exchange rate policy had no additional effect on ICGs imports and economic growth in Belarus.
Conclusion
The findings presented here indicate that trade (in terms of ICGs imports) is more a consequence of the rapid economic growth in Belarus rather than a cause. The influence of imports of intermediate goods on GDP growth in the long run is negative. Additionally, the depreciation of the national currency has had a large negative effect on both intermediate imports and economic growth, while its effect on capital goods imports was statistically insignificant.
Thus, Belarusian economic policy based on imported technologies seems ineffective especially in recent years, most probably due to decreasing skills and the ability to imitate and innovate using foreign inputs. Therefore, policy should focus on abolishing the directive industrial management, which has led to a negative influence of ICGs imports on economic growth in Belarus.
Additionally, the country’s export strategy should be refined so that export destinations are different from import sources of intermediate goods that are used for export production. Moreover, the imports of capital goods should contribute to the development of new export markets, and monetary and fiscal policies should be refined in order to promote positive effects of currency valuation changes.
References
- Kruk D., Bornukova K. 2013. Decomposition of economic growth in Belarus. FREE Policy Brief Series, October 2013.
- Coe D., Helpman E., Hoffmaister A. 1997. North-south R&D spillovers. The Economic Journal 107(440): 134-149.
- Grossman G., Helpman E. 1991. Innovation and growth in the global economy. The MIT Press, Cambridge MA.
- Grossman G., Helpman G. 1994. Endogenous innovation in the theory of growth. Journal of Economic Perspectives 8: 23–44.
- Kruk, D. 2014. Stimulating growth in Belarus: Selecting the right priorities. FREE Policy Brief Series, November 2014.
- Landon S., Smith C.E. 2007. The exchange rate and machinery and equipment imports: Identifying the impact of import source and export destination country currency valuation changes. North American Journal of Economics and Finance 18: 3–21
- Mazumdar J. 2001. Imported machinery and growth in LDCs. Journal of Development Economics 65: 209-224.
- Mazol, A. 2015. Exchange Rate, imports of intermediate and capital goods and GDP growth in Belarus, BEROC Working Paper Series, WP no. 32.
- Pesaran M.H., Shin Y, Smith R.J. 2001. Bounds testing approaches to the analysis of level relationships. Applied Econometrics 16: 289–326.
- Rodrik, D. 1995. Getting interventions right how South Korea and Taiwan grew rich. Economic Policy 10: 53-107.
- Toda H.Y., Yamamoto, T. 1995. Statistical inference in vector auto regressions with possibly integrated processes. Econometrics 66: 225–50.
The Economic Complexity of Transition Economies
‘Diversification’ is a constant concern of policy-makers in resource rich economies, but measurement of diversification can be hard. The recently formulated Economic Complexity Index (ECI) is a promising predictor of economic development characterizing the overall complexity and diversity of the economy as a system. The ECI is based on the diversity and ubiquity of a country’s exports. This brief uses ECI to discuss the economic diversity of transition economies in the post-Soviet decades, and the relationship between economic diversification and per capita income.
The search for and construction of appropriate predictors of economic development are among the main goals of economists and policy-makers. Education, infrastructure, rule of law, and quality of governance are all among the commonly used indicators based on inputs. The recently formulated Economic Complexity Index (Hidalgo and Hausmann, 2009) is a new promising predictor of economic development characterizing the overall complexity and diversity of the economy as a system.
Indeed, the importance of production and trade diversification for economic development has been highlighted by the economic literature. Numerous studies have found a positive relationship between diversified and complex export structure, income per capita and growth (Cadot et al., 2011; Hesse, 2006; Hausmann et al., 2007). In line with this, Hausmann et al. (2014) demonstrate the predictive properties of the ECI for economic development and GDP per capita, which implies that the ECI can serve as a useful complement to the input-based measures for policy analysis by reasoning from current outputs to future outputs.
This brief uses the ECI to discuss the evolution of economic diversification, its relationship to per capita income in transition economies in the post-Soviet decades, and its policy implications.
How is economic complexity measured?
The economic complexity index (ECI) is a novel measure that reflects the diversity and ubiquity of a country’s exports. The index considers the number of products a country exports with revealed comparative advantage and how many other countries in the world export such goods. If a country exports a high number of goods and few other countries export these products, then its economy is diversified (a wide range of exports products) and sophisticated (only a few other countries are able to export these goods). Thus, the measure tries to capture not a specific aspect of the economy, but rather its overall sophistication.
For example, Japan, Switzerland, Germany and Sweden have been in a varying order at the top of the ranking of the Economic Complexity Index from 2008 until 2013. This means that these countries export a large number of highly sophisticated products.
In contrast, Tajikistan is among the countries at the bottom of the world ranking by the ECI with raw aluminum, raw cotton and ores making up 85% of all Tajikistan’s exports in 2013. However, not only are Tajikistan’s exports concentrated among very few narrow products, these products are also ubiquitous and the ability to export them does not require knowledge and skills that can be used in the production and exports of many other products.
As the index for each country is constructed relative to other countries’ exports, it is comparable over time.
What can we learn from the economic complexity of transition economies?
The economic complexity index can serve as a useful indicator for understanding transition economies in the post-Soviet period. A strong relationship between GDP per capita and economic complexity is found in the sample of transition economies in Figure 1. This figure presents the relationship for the last year for which data is available for the sample of 13 post-Soviet states and Poland. As can be seen in Figure 1, the economic complexity is positively related to income per capita. This is especially true for Poland, Estonia, Lithuania, Latvia and Russia, who all have higher than average economic complexity and high levels of per capita income. While Belarus and Ukraine also have diverse and complex economies, they have somewhat lower income per capita than the first group.
Figure 1. Economic Complexity and GDP per capita
Source: Data on GDP per capita is from the World Bank, and the data on the Economic Complexity Index is from the Observatory of Economic Complexity.
Natural resource-rich, or rather, oil-rich countries are the exception from the abovementioned correlation. Most transition countries with below than average economic complexity are characterized by low income per capita levels, except for Kazakhstan and Azerbaijan, which are oil-rich countries. Still, the overall picture is straightforward: countries with a complex export structure have a higher level of income.
One of the advantages of a systemic measure like export complexity is its straightforward policy application. The overall diversity and sophistication of the economy can thus be a complementary measure for the assessment of economic progress and development to GDP and GDP per capita, which are more susceptible to the volatile factors such as commodity prices.
Figure 2 shows the development of economic complexity for 14 post-Soviet countries and Poland between 1994 and 2013 (due to data availability issues, only one year is available for Armenia).
First, we see that the economic complexity has diverged over time, although there is some similarity in the rankings among countries over time. The initial closeness is likely related to the planned nature of the Soviet economy that aimed to distribute production among Soviet Republics. In the post-Soviet context, however, the more complex economies (Estonia, Belarus, Lithuania, Ukraine, Latvia, Russia) kept or increased their sophistication and diversity of exports. Poland is the leading economy in terms of complexity, both in the beginning and towards the end of the sample period. Belarus, the second most complex economy in 2013 and the most complex economy in several years prior, shows an increasing trend in its sophistication of exports. Although its GDP per capita is noticeably lower than what would be expected from such a sophisticated economy, the complex production structure may explain its ability to withstand a permanent high inflation and external macroeconomic shocks. Some others, e.g., Tajikistan and Azerbaijan, saw a decreasing trend in economic complexity; Georgia and Kazakhstan, notably, lost in economic complexity but also in their ranking among their peers.
Figure 2. Economic Complexity of Transition Economies
Source: Data on GDP per capita is from the World Bank, and the data on the Economic Complexity Index is from the Observatory of Economic Complexity.
Conclusion
This brief revisited the economic complexity of transition economies and its evolution since the 1990s. The post-Soviet and other transition countries have had diverging economic development paths: Some have managed to build complex production economies, while others’ comparative advantage remains in raw materials. These differences are also reflected in their income levels.
Across the world, economic diversification is associated with higher per-capita income. As the brief showed, this relationship also holds for the post-Soviet countries; policy-makers should take economic diversification seriously. Increasing economic complexity may well pave the path to higher income levels.
References
- Cadot, O., Carrère, C., & Strauss-Kahn, V. (2011). Export diversification: What’s behind the hump?. Review of Economics and Statistics, 93(2), 590-605.
- Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Simoes, A., & Yildirim, M. A. (2014). The atlas of economic complexity: Mapping paths to prosperity. Mit Press.
- Hausmann, R., Hwang, J., & Rodrik, D. (2007). What you export matters. Journal of economic growth, 12(1), 1-25.
- Hesse, H. (2006). Export diversification and economic growth. World Bank, Washington, DC.
- Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. proceedings of the national academy of sciences, 106(26), 10570-10575.
Non-Tariff Measures in the Context of Export Promotion Policies
This brief focuses on the role of non-tariff measures (NTMs) in international trade. While multilateral and bilateral trade negotiations have resulted in worldwide reductions in tariffs, we observe an increasing trend in the application of non-tariff measures. In this brief, we will discuss the evidence of the effect of such measures on exports. The brief also contributes to the discussion of export promotion policies: whether governments, especially in developing countries, should concentrate their efforts to remove only external barriers since there is empirical evidence that internal barriers are no less important for exports.
Economists, policy makers and international organizations are increasingly recognizing the importance of non-tariff measures (NTMs) as substantial impediments to international trade. A survey conducted by UNCTAD among exporters in several developing countries ranks SPS and TBT measures the top trade barriers with on average 73 percent of the respondents viewing them as the primary trade barrier (UNCTAD 2010). The World Bank published a book on NTBs where different authors contributed chapters addressing many aspects of the NTMs (World Bank, 2012). The World Trade Organization (WTO) itself devoted its entire 2012 World Trade Report to such measures with a particular focus on technical barriers to trade (TBT) and sanitary and phytosanitary (SPS) measures. Availability of the new datasets on NTBs allowed researchers to study the effect of these measures on intensive (changes for existing exports) and extensive margins (changes due to entry and exit into exporting) of trade.
Even though trade theory does not specifically address the question of non-tariff barriers that include (but are not limited to) technical regulations, sanitary and phytosanitary measures, the logic of traditional models can easily be extended to these measures. In particular, they can be thought of as part of the fixed/additive costs for exporting firms as they impose compliance costs on exporters. These compliance costs are related to potential adjustments of production processes, and certification procedures needed to meet the requirements of countries imposing such regulations and standards (Schlueter et al., 2009). In a Melitz-type model, these costs are expected to have a negative impact on volumes of trade, number of exporters and number of goods exported. At the same time, average exports per firm may actually increase as the export market-shares are reallocated towards firms that are more efficient.
The existing empirical evidence of the impact of NTMs is mixed; researchers have found both positive and negative effects. The differences in results depend largely on the sector, country and type of NTM imposed. While the effect may overall be negative or null, for some sectors the effect is found to be positive (Moenius, 2004; Fontagné et al., 2005; Chen et al., 2006; Disdier et al., 2008; Medin and Melchior, 2015).
In a recent working paper, Besedina (2015) investigates the effect of introducing an NTM (either SPS or TBT) on export dynamics (in particular, exports concentration and entry and exit into exporting) using the World Bank Exporters database, with a special focus on trade in foodstuff. In particular, we examine how TBT and SPS measures affect export concentration and diversification (both at product and destination level) as well as entry and exit of firms into exporting. If introduction of an NTM increases costs of exporting, the ‘new’ trade theory started by Melitz (2003) predicts that some exporters will stop to export and thus the number of exported product varieties will fall as well (change in extensive margin).
The most important result from our analysis is that the introduction of a TBT or an SPS measure does not seem to affect sectoral export dynamics. Given the above discussion, this result may appear surprising at first. What can possibly explain this zero effect?
First, one may argue that the sector dynamic variables we use in our analysis may not capture changes in the behavior of economic agents (firms) well: while marginal firms may be affected by technical barriers and SPS, averaging across firms may actually conceal this. However, in our analysis we investigate exports at a relatively disaggregated level (4-digit product lines). So while averaging might be a concern, we believe it is not likely to be driving the zero effect.
Second, the concern is that the effect of introducing an NTM measure may not be felt immediately (within one year). In order to verify this, we include lagged trade-barrier variables two periods, but the results were unchanged. Third, it may be the case that it is the number of NTMs rather than the introduction of them that matters. In order to address this point, we performed the same type of analysis using the change in the number of measures introduced. The results were again not affected, and we still do not find any statistically significant relationship between NTMs and exports dynamics.
Despite the absence of an effect of NTMs, this paper reveals an important and policy-relevant finding: the home country’s business environment and institutional factors are important determinants of export performance. It is rather the monetary costs and more complicated exporting procedures imposed by the NTM measures that hamper product and market diversification of the country’s exporters. Hence, policy makers, especially in developing countries, should not only be concerned with removing external barriers to exports (like NTMs) but should also aim to reduce internal barriers and costs imposed on exporting firms by corrupt practices and burdensome regulatory procedures.
Another important dimension for domestic policies towards exporters stems from the work by Melchior (2015, forthcoming) who studies Norwegian exports to BRICS countries overtime and shows that export growth largely depends on the intensive margin (it explains 93 percent of the export growth). Using firm-level data for seafood exports, he finds that only 54% of “trades” – measured as firm/importing country/product combinations – survive from one year to the next. Hence, there is massive “churning” (entry and exit at the same time), and churning is relatively more important in small and in growing export markets. In other words, exporting companies constantly enter and exit foreign markets, add new products, or discontinue exporting some products. A policy implication from this finding is that export-promotion offices should help firms stay in export markets rather than focus on entering these markets. Hence, while it is important to enable domestic firms to enter foreign markets, it seems equally important to ensure their survival in foreign markets, which can be facilitated by a removal of both external and internal barriers.
▪
References
- Disdier, A-S, L. Fontagné and M. Mimouni (2008), “The Impact of Regulations on Agricultural Trade: Evidence from the SPS and TBT Agreements”, American Journal of Agricultural Economics 90(2): 336-350.
- Fontagné, L., F. von Kirchbach, and M. Mimouni (2005). “An Assessment of Environmentally-related Non-tariff Measures”, The World Economy 28(10): 1417-1439.
- Medin H. and A. Melchior (2015) ”Trade barriers or trade facilitators? On the heterogeneous impact of food standards in international trade”, NUPI mimeo
- Melchior (2015) ” Non-tariff barriers, firm heterogeneity and trade: A study of seafood exports, with a particular focus on BRICs”, NUPI mimeo
- Melitz, M. J. (2003), “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity,” Econometrica, 71(6): 1695–1725.
- Moenius, J. (2004), “Information versus Product Adaptation: The Role of Standards in Trade”, Working Paper, International Business & Markets Research Center, Northwestern University mimeo.
- UNCTAD (2010), Non-Tariff Measures: Evidence from Selected Developing Countries and Future Research Agenda (UNCTAD/DITC/TAB/2009/3). New York and Geneva.
- World Bank (2012), Non-Tariff Measures – A Fresh Look at Trade Policy’s New Frontier, ed. O. Cadot and M. Malouche, The World Bank, Washington D.C.
Is Cutting Russian Gas Imports Too Costly For The EU?
This brief addresses the economic costs of a potential Russian gas sanction considered by the EU. We discuss different replacement alternatives for Russian gas, and argue that complete banning is currently unrealistic. In turn, a partial reduction of Russian gas imports may lead to a loss of the EU bargaining power vis-à-vis Russia. We conclude that instead of cutting Russian gas imports, the EU should put an increasing effort towards building a unified EU-wide energy policy.
Soon after Russia stepped in Crimea, the question of whether and how the European Union could react to this event has been in the focus of political discussions. So far, the EU has mostly implemented sanctions on selected Russian and Ukrainian politicians, freezing their European assets and prohibiting their entry into the EU, but broader economic sanctions are intensively debated.
One such sanction high on the political agenda is an EU-wide ban on imports of Russian gas. Such a ban is often seen as one of the potentially most effective economic sanctions. Indeed the EU buys more than half of total Russian gas exports (BP 2013), and gas export revenues constitute around one fifth of the Russian federal budget (RossBusinessConsulting,2012 and our calculations). Thus, by banning Russian gas the EU may indeed be able to exert strong economics pressure on Russia.
However, the feasibility of such sanction is questionable. Indeed, in 2012 Russia supplied around 110 bcm of natural gas to EU-28 (Eurostat), which constitutes 22.5% of total EU gas consumption. There are a number of alternatives to replace Russian gas, such as an increase in domestic production by investing in shale gas, or switching to other energy sources, such as nuclear, coal or renewables. However, many of the above alternatives, e.g. shale gas or nuclear power, involve large and time-consuming investments, and thus cannot be used in the short run (say, within a year). Others, such as wind energy, are subject to intermittency problem, which again requires investments into a backup technology. The list of alternatives implementable within a short horizon is effectively down to replacing Russian gas by gas from other sources and/or switching to coal for electricity generation. Below, we argue that even if such a replacement is feasible, it is likely to be very costly for the EU, both economically and environmentally.
Notice that any replacement option will be automatically associated with a significant increase in economic costs. This is due to the fact that a substantial part of Russian gas exports to Europe (e.g., according to Financial Times, 2014 – up to 75%) are done under long-term “take-or-pay” contracts. These contracts assume that the customer shall pay for the gas even if it does not consume it. In other words, by switching away from Russian gas, the EU would not only incur the costs of replacing it, but also incur high financial or legal (or both) costs of terminating the existing contracts with Russia, with the latter estimated to be around USD 50 billion (Chazan and Crooks, Financial Times, 2014).
Due to this contract clause, own costs of replacement alternatives become of crucial importance. The coal alternative is currently relatively cheap. However, a massive use of coal for power generation is associated with a strong environmental damage and is definitely not in line with the EU green policy.
What about the cost of reverting to alternative sources of gas? First, in utilizing this option, the EU is bound to rely on external and potentially new gas suppliers. Indeed, the estimates of potential contribution within the EU – by its largest gas producer, the Netherlands – are in the range of additional 20 bcm (here and below see Zachmann 2014 and Economist 2014). Another 15-25 bcm can be supplied by current external gas suppliers: some 10-20 bcm from Norway, and 5 bcm from Algeria and Libya. This volume is not sufficient for replacement, and is not likely to be cheaper than Russian gas.
This implies that the majority of the missing gas would need to be replaced through purchases of Liquefied Natural Gas (LNG) on the world market, in particular, from the US. This option may first look very appealing. Indeed, the current gas price at Henry Hub, the main US natural gas distribution hub, is 4.68 USD/mmBTU (IMF Commodity Statistics, 2014). Even with the costs of liquefaction, transport and gasification – which are estimated to be around 4.7 USD/mmBTU (Henderson 2012) – this is way lower than the current price of Russian gas at the German border (10.79 USD/mmBTU, IMF).
However, this option is not going to be cheap. A substantial increase in the demand for LNG is likely to lead to an LNG price hike. Notice that, at the abovementioned prices, US LNG starts losing its competitive edge in Europe already at a 15% price increase. Just for a very rough comparison, the 2011 Fukushima disaster lead to 18% LNG price increase in Japan in one month after disaster. Some experts are expecting the price of LNG in Europe to rise as much as two times in these circumstances (Shiryaevskaya and Strzelecki, Bloomberg, 2014).
Moreover, it is not very likely that there will be sufficient supply of LNG, even at increased prices. For example, in the US, which is the main ”hope” provider of LNG replacement for Russian gas, only one out of more than 20 liquefaction projects currently has full regulatory approval for imports to the EU. This project, Cheniere Energy’s Sabine Pass LNG terminal, is planned to start export operations no earlier than in the 4th quarter of 2015 with a capacity of just above 12bcma (World LNG Report, 2013). Of course, there are other US and Canada gas liquefaction projects currently undergoing regulatory approval process, but none of them is going to be exporting in the next year or two. Another potential complication is that two thirds of the world LNG trade is covered by long-term oil-linked contracts (World LNG Report, 2014), which significantly restricts the flexibility of short-term supply reaction, contributing to a price increase. All in all, LNG is unlikely to be a magical solution for Russian gas replacement.
All of the above discussion suggests that it may be prohibitively expensive for the EU to do completely without Russian gas. Maybe the adequate solution is partial? That is, shall the EU cut down on its imports of natural gas from Russia, by, say, a half, instead of completely eliminating it?
On one hand, this may indeed lower the costs outlined above, such as part of take-or-pay contract fines, or costs associated with an LNG price increase. On the other hand, cutting down on Russian gas imports may lead to an important additional problem, loss of buyer power by the EU.
Indeed, the dependence on the gas deal is currently mutual – as outlined above, not only Russian gas is important for the EU energy portfolio; the EU also represents the largest (external) consumer of Russian gas, with its 55% share of the total Russian gas exports. In other words, the EU as a whole possesses a substantial market power in gas trade between Russia and the EU, and this buyer power could be and should be exercised to achieve certain concessions, such as advantageous terms of trade from the seller etc.
However, the ability to have buyer power and to exercise it depends crucially on whether the EU acts as a whole to exercise a credible pressure on Russia. That is, the EU Member States may be much better off by coordinating their energy policies rather than diluting the EU buyer power by diversifying gas supply away from Russia. This coordination may be a challenge given the Member States’ different energy profiles and environmental concerns. Also, such coordination requires a stronger internal energy market that will allow for better flow of the gas between the Member States. While demanding any of these measures would be double beneficial: they will improve the internal gas market’s efficiency, and at the same time reinforce the EU’s buyer power vis-à-vis Russia.
To sum up, the EU completely banning Russian gas imports does not seem a feasible option in the short run. In turn, half-measures are not necessarily better due to the loss of the EU’s buyer power. Thereby, the best short-term reaction by the EU may be to put the effort into working up a strong unified energy policy, and to place “gas at the very back end of the sanctions list” for Russia as suggested by the EU energy chief Gunther Oettinger (quoted by Shiryaevskaya and Almeida, Bloomberg, 2014).
References
- BP, 2013, Statistical review of the world energy
- Chazan, Guy and Ed Crooks, Financial Times, April 3, 2014, Europe’s dangerous addiction to Russian gas needs radical cure
- Economist, April 6, 2014, Conscious uncoupling
- Eurostat energy statistics
- Henderson, James, 2012, “The potential impact of North American LNG exports”, Oxford Institute for Energy Studies, Working Paper NG 68, Oxford,
- IMF commodity statistics, April 2014
- Le Coq, Chloé and Elena Paltseva, 2013, “EU-Russia Gas Relationship at a Crossroads”, in “Russian Energy and Security up to 2030”, edited by Susanne Oxenstierna and Veli-Pekka Tynkkynen, Roothledge
- RossBusinessConsulting,Feb 6, 2012, “Доходы РФ от экспорта нефти и газа выросли в 2011 г. на треть» (The revenues of Russia from oil and gas export have growth by a third in 2011)
- Shiryaevskaya and Strzelecki, Bloomberg, Mar 28, 2014, Europe Seen Paying Twice as Much to Replace Russian Gas
- Shiryaevskaya and Almeida, Bloomberg, May 7, 2014, Europe Gas Options Seen Limited by Costs at $200 Billion
- World LNG Report, 2013, International Gas Union (IGU)
- World LNG Report, 2014, International Gas Union (IGU)
- Zachmann, Georg, Bruegel, March 21, 2014, Can Europe survive without Russian gas?
The crisis in Ukraine and the Georgian economy
We analyze how the crisis in Ukraine will likely impact the Georgian economy and distinguish between short-run and long-run effects. We argue that the short-run effects are transmitted through trade and capital flows and that they are rather negative for Georgia and can hardly be bolstered. In the long-run, however, the crisis could improve the competitiveness of the Caucasus Transit Corridor, an important trading route between Europe and Central Asia Georgia participates in. We give recommendations how political decision makers could support such a development in the wake of an impairment of the northern Ukrainian transit routes.
Introduction
When Ukrainian President Victor Yanukovich decided not to sign the association agreement with the European Union and instead opted for a Russian package of long-term economic support, many Ukrainians perceived this not to be a purely economic decision. Rather, they feared this to be a renunciation of Western cultural and political values, and – to put it mildly – were not happy about this development.
The Russian political system, characterized by a prepotent president, constrained civil rights, and a government controlling important parts of the economy through its secret service, is not exactly the dream of young Ukrainians. Russia can offer economic carrots, but these do not count much against the soft power of Europe that comes in the form of political freedom, good governance, and economic development to the benefit of not just a small group of oligarchs.
Hence, it was all but surprising when many young Ukrainians took their anger about Yanukovich to the streets. After protests that lasted for nearly three months, President Yanukovich fled the country, a temporary government took over, and chaos broke out on the Crimean peninsula.
The dispute about the Crimea has the potential to impede the relations between Russia and the West for a long time to come, in particular if Russia enforces an annexation of the territory. Moreover, the tensions could quickly turn into a military conflict. The aircraft carrier USS George H.W. Bush was moved into an operational distance to the Crimea, accompanied by 20 smaller U.S. warships, and 12 additional fighter planes will be stationed in Poland. Yet even if there will be no direct confrontation between official Russian and U.S. forces, Ukraine could become the battleground of a proxy war, a kind of conflict that was common in the Cold War era. In this respect, one can already read the writing on the wall: the new Ukrainian government begs the U.S. for supplying arms and ammunition, and while the Obama administration is still reluctant to give in to such requests, the call is supported by hawkish U.S. congressmen who might finally prevail.
Ukraine is a country that is geographically close to Georgia and, like Georgia, has vital economic stakes in the Black Sea area. Georgia will not be unaffected by whatever happens in Kiev and Simferopol. In this policy brief, we will inform policy makers about the likely short-run and long-run economic consequences of the turmoil in Ukraine, discuss the challenges and opportunities that may arise, and derive some policy recommendations.
Short-run economic consequences
The crisis in Ukraine will almost instantaneously affect trade and capital flows between Georgia, Ukraine, and Russia. The effects will likely be negative and hit Georgia in a situation of economic recovery.
The Georgian real GDP growth rates were 6.3% in 2010, 7.2% in 2011, and 6.2% in 2012, and the real GDP per capita evolved from about 2,600 USD to about 3,500 USD in this time, but the upsurge discontinued in 2013 (if no other source is mentioned, figures presented in this policy brief (including those in the graphs) come from the Georgian statistical office GeoStat). ISET-PI, in its February 2014 report on the leading GDP indicators for Georgia, estimates the GDP in 2013 to be 2.6%, while GeoStat, the statistical office of Georgia, believes it to be 3.1%.
The unsatisfactory performance of the Georgian economy in 2013 was arguably caused by political uncertainties resulting from the government change that took place in late 2012, and as these uncertainties are largely overcome, most economists believe that Georgia will get back to its remarkable growth trajectory in 2014. The IMF, in its Economic Outlook, predicts a real GDP Growth of 6% in 2014, and the government of Georgia expects this number to be 5%. With an escalating crisis in Ukraine, it is questionable whether these rosy forecasts are still realistic.
Effects on imports
In 2013, Ukraine and Russia were the 3rd and the 4th largest importers to Georgia, respectively. Graph 1 shows the top five importers to Georgia, which together make up about 50% of total imports. The imports from Ukraine and Russia are mainly comprised of consumption goods: of all goods that were imported between 2009 and 2013 from Ukraine and Russia, about 30% were foodstuff. The ten main import goods in this time (in order of monetary volume) were cigarettes, sunflower oil, chocolate, bread, cakes, meat other than poultry, poultry, and sugar.
If the supply of these goods would be reduced through a breakdown of production and logistics, roadblocks, damaged infrastructure etc., the consequences for Georgia would not be utterly severe. From Ukraine and Russia, Georgia receives few goods that are (1) needed for investment projects and (2) cannot be produced domestically (an example of sophisticated investment goods that need to be imported would be ski lifts for tourism projects). Moreover, as Ukraine and Russia supply primarily standard goods that are produced almost everywhere, it is unlikely that a cutback in their imports would lead to sharp price rises in Georgia. Very quickly, increased imports from other countries would close any supply gaps. In addition, many imported consumption goods, like Ukrainian orange juice, are but luxury for ordinary Georgians, who buy their food in cheap domestic markets that sell almost exclusively local products.
Effects on exports
A small anecdote may illustrate the status of Georgian products in the Russian market. In the late 1940s and early 1950s, Stalin used to invite his comrades to his Kuntsevo dacha almost every night. At these occasions, he drank only semi-sweet Georgian red wine. His clique, usually preferring Russian vodka, adopted this habit out of fear to displease the dictator. Yet the real highlight of these nightly gatherings took place after midnight, when an opulent feast began, featuring all the delicacies of the Georgian cuisine. Through Stalin (and the fact that Georgia was a preferred destination of Soviet tourism), Georgian food obtained an excellent reputation in most countries of the former Soviet Union, and, to the dismay of Georgians, some younger Russians even do not know that Khinkali is not an originally Russian dish.
As can be seen in Graph 2, Russia and Ukraine are among the top 5 destinations for Georgian produce, together absorbing about 14% of total Georgian exports in 2013. In 2006, two Georgian products that are traditionally highly popular in Russia, namely wine and mineral water (the famous “Borjomi” brand), were banned from the Russian market. Yet in the wake of the diplomatic thaw that set in after the new government assumed power last year, this ban was lifted, and in 2013, the export of these goods regained momentum. In 2013, 68% of all wine exported from Georgia was sold in Russia and Ukraine (44 and 24 percentage points, respectively). In both countries, Georgian wines are sold at the higher end of the price range and are typically consumed by people with middle and high income. It is likely that these exports, in particular those to Ukraine, will be affected considerably by the crisis. This may happen through decreased demand for luxury foods and through a possible depreciation of the Ukrainian hryvna and the ruble vis-à-vis the Georgian lari.
Another sector that may be affected by the situation in Ukraine is the car re-export business. Georgia imports huge numbers of used cars from the U.S., Europe, and Japan, and passes them on to countries in the region. While this business hardly yields potential for real economic progress, it accounts for roughly 25% of Georgian exports! Of these 25%, about 7 percentage points go to Russia and Ukraine. Moreover, many cars are imported to Georgia on the land route from Europe through Ukraine and Russia (often driven by private, small-scale importers). If it will become more difficult to cross the border between Russia and Ukraine, this business, providing income to many low-skilled Georgians, may be at risk.
It should also be noted that Ukrainians and Russians make up an ever-increasing share of the tourists coming to Georgia (though the biggest group of tourists are Israelis). Also through this channel, an economic downturn in Ukraine and Russia will have unpleasant consequences for Georgia.
Effects on capital flows
According to the National Bank of Georgia, in 2013 a total of 801 mln USD was flowing in from Russia (see Graph 3). Ukraine contributed 45 mln USD to the money inflows, still significant for an economy as small as Georgia’s. An economic downturn in Russia and Ukraine would hit many Georgian citizens, often pensioners and elderly people, who depend on remittances of their children and other family members sent from these countries. This may aggravate a trend that already exists: in January 2014, money inflows decreased by 4% from Russia and by 5% from Ukraine (compared to January 2013).
Long-run economic consequences
Most of the economic dynamics Georgia experienced since 2003 was “catch up growth”. A country permeated by corruption, with a dysfunctional police and judicial system, without protection of property rights and contract enforcement, will grow almost automatically when the government restarts to fulfill its basic functions. Yet once this phase of returning to normal economic circumstances is over (Georgia probably is already in this situation), high growth rates can hardly be achieved without a strong export orientation of the economy, in particular when an economy is as small as Georgia’s. Most economists concerned with Georgia are therefore struggling to identify economic sectors where Georgia is in a good position to develop export potential. The National Competitiveness Report for Georgia, written in 2013 by the ISET Policy Institute on behalf of USAID, therefore extensively discusses the question what Georgia can deliver to the world. Though not related to export in a classical sense, the report points out that one of the advantages Georgia has is its geographical location, providing for possibilities to transform Georgia into a logistics hub.
There are three main routes to transport goods from Europe to the Central Asian countries (e.g. from Hamburg to Taraz in Kazakhstan). One route goes via the Baltic ports of Klaipeda or Riga, and then through Ukraine and Russia, and another route goes overland through Ukraine. A third one, the so called Caucasian Transit Corridor, has the Georgian port city of Poti and Turkey as its Western connection points, then goes through Georgia, Azerbaijan, and the Caspian Sea, and further east it splits up into a Kazakhstan and a Turkmenistan branch.
According to the Almaty based company Comprehensive Logistics Solutions, the fastest and cheapest route is the one through the Baltic ports. The transport from Hamburg to Taraz takes around 33 days and costs 6,220 USD per standard container. The overland transport via Ukraine takes around 34 days and costs 7,474 USD. Finally, transport through the CTC currently takes the longest time, namely around 40 days, and costs 6,896 USD.
Unlike many other economic activities, competition for transportation is more or less a zero-sum game played by nations. If transport through Ukraine and Russia will be restrained due to closed borders and political and economic instability, the total transport volume will not change substantially. Rather, instead of going through the northern routes, the goods will flow through the CTC. A similar development could be observed when the embargo against Iran was tightened and shipping goods through Iranian ports became increasingly difficult for Armenia and Azerbaijan. As a result, Azerbaijan, traditionally importing through Iran and exporting through Poti, now facilitates both its imports and exports through Poti.
This is a great chance for Georgia if it wants to become serious about transforming into a logistics hub. In our policy recommendations, we will speak about how to utilize on this opportunity.
Policy recommendations
Georgia can do little to bolster the short-run effects that are transmitted through the trade and capital flow channels. Political decision makers should be aware of problems that might arise for particularly vulnerable groups in the population, like pensioners who lose income in case remittances from Russia and Ukraine run dry, and help out with social support if necessary.
Regarding the long-run impact, Georgia should use this opportunity for gaining ground in the competition with northern transit routes. The Caucasus Transit Corridor can become much faster and cheaper if (a) a deepwater port and modern port facilities with warehouses will be built in Poti, (b) the road and train infrastructure will be improved, and (c) it will be easier to bring cargo over the Caspian Sea. Regarding the latter point, it would be important to assist Azerbaijan in improving the port management at Baku (in particular reducing corruption), and in reforming the monopolistic Azerbaijani State Caspian Sea Shipping Company.
Azerbaijan invests 775 mln USD into the Georgian part of the Baku-Tbilisi-Kars railway, proving their serious interest to upgrade CTC. Given this impressive commitment of Azerbaijan, Georgia should not stand back.
Conclusion
The crisis in Ukraine yields short-run risks and long-run opportunities for the Georgian economy. While there is little that can be done about the risks, the opportunities call for courageous steps to improve the Caucasus Transit Corridor. If the countries that hold stakes in the CTC are now further reducing the cost of transportation and make the route faster and more customer-friendly, the CTC may establish itself as the main trading route connecting Europe and Central Asia. Once critical investments have taken place, CTC’s advantage could be sustained beyond the current crisis. It is a competitive route that simply needs upgrading, which can happen now as a fallout of the conflict between Ukraine and Russia.
References
- Leading GDP Indicators for Georgia, The ISET Policy Institute, February 2014, http://www.iset-pi.ge/index.php?article_id=711
- The National Competitiveness Report for Georgia, The ISET Policy Institute, 2013, http://www.iset-pi.ge/index.php?article_id=713
- World Economic Outlook, The International Monetary Fund, October 2013, http://www.imf.org/external/pubs/ft/weo/2013/02/
Trade Policy Uncertainty and External Trade: Potential Gains of Ukraine Joining the CU vs. the Signing Free Trade Agreement with the EU
This policy brief summarizes the results of recent research which predicts gains in Ukrainian exports from signing a deep and comprehensive free trade agreement with EU, and compares these gains with predicted gains from joining the Customs Union of Belarus, Kazakhstan, and Russia. We argue that the gains would be mostly due to elimination of uncertainty in trade policy of Ukraine with the CU and the EU countries. We find that European integration brings higher potential for export growth, and that it also shifts the structure of Ukrainian exports towards capital goods, reducing the share of raw materials in total export.
Trade Policy Uncertainty and Export
Trade policy uncertainty (TPU) is a powerful negative factor that prevents economy from the realization of its export potential. In a recent paper, Handley and Limao (2012) argue that since the exporting decision involves substantial fixed costs, TPU significantly affects investment and entry decisions in international trade. In particular, they show that preferential trade agreements (PTAs) are important even when the pre-PTA tariff barriers are low. Comparing pre- and post-EU accession patterns of Portuguese exports, they find that Portuguese trade increased dramatically after 1985. The increase was the largest towards the EU partners, suggesting that it was caused by the accession. Export expanded through considerable entry of Portuguese firms into EU markets, even in industries where applied tariffs did not change. Handley and Limao estimated that the tariff reduction, which averaged 0.66 percentage points, has been responsible for only 20 percent of the increase in exports to EU10 after the EU accession, while 80 percent of the increase was due to resolving TPU.
Handley and Limao further argue that the Portuguese example should be highly relevant for any small open economy, facing important trade policy choices. In this regard, Ukraine is facing a very hard choice of selecting its regional integration strategy – towards the EU or the Customs Union (CU) with Belarus, Kazakhstan and Russia, resulting in severe TPU. The options are mutually exclusive since the CU trade policy is not compatible with neither the WTO commitments of Ukraine, or with the parameters of the deep and comprehensive free trade agreement (FTA) between Ukraine and the EU, finalized in 2012. Average tariff protection within the CU in 2012 was 10 percent (Shepotylo and Tarr, 2012), while the average WTO binding tariff rates in Ukraine were only 5 percent; the parameters of the FTA with the EU are even less protective, which would cause even stronger disagreements regarding the tariff schedules. Moreover, technical and phyto-sanitary standards in the EU and the CU are different; therefore, it would be extremely hard to harmonize the Ukrainian standards with both of them.
Despite low tariff protection, uncertainty on the parameters of the long run trade policy of Ukraine with the CU and EU countries is extremely high. It is crucial for both foreign and domestic investors to understand in what direction the regional integration will proceed before making decisions on investing or exporting, since these decisions can incur substantial sunk costs. Suppose that a large European multinational firm were interested in including Ukrainian companies in its production chains only if Ukraine signs the FTA with the EU (integrate vertically). If Ukraine instead joined the CU, this presumed European company would rather be interested in horizontal integration and invest by building a plant for final assembly of products to serve the Ukrainian and CIS markets. For Russian companies the situation would be the reversed. They would be interested to integrate vertically if Ukraine is a member of the CU and integrate horizontally if Ukraine signed FTA with EU. However, since vertical and horizontal integration are quite different strategies, neither European nor Russian companies invest in Ukraine before the uncertainty is resolved. The same holds true for domestic companies which would like to extend their export activities to new markets. Since entrance to new markets is costly and requires some irreversible investment, it is optimal to wait until the policy uncertainty is resolved.
Modeling Trade Policy Options of Ukraine
In Shepotylo (2013), we investigate which integration scenario is more preferable for Ukraine under the assumption that TPU is fully resolved and Ukraine trades up to its potential. Based on export data in 2001-2011, we estimate the gravity model by Helpman, Melitz, and Rubinstein (2008) method, adjusted for panel data case and endogeneity of a decision to sign a PTA. Using this model, we predict bilateral exports of Ukraine under three counterfactual scenarios: a) Ukraine joined the Customs Union in 2009 (CU); b) Ukraine signed the FTA with the EU in 2009 (EU FTA); c) Ukraine joined the EU in 2009 (EU). The model predictions take into account the level of economic development, geographical location, industrial structure, and quality of government and regulatory agencies. It also accounts for macro trends, including the global trade collapse of 2008-2009.
The results are not intended for a short-term forecast, but should be rather used as indicators of the long-run effects. Their interpretation is as follows. Suppose that Ukraine has signed the FTA with the EU in 2009. Taking into account all observable characteristics of Ukraine, what would be the level of Ukrainian export of product k to country j, if Ukraine, in all other respects, would behave as a typical country-member of the FTA EU? That would involve removal of the trade policy uncertainty, stronger integration of domestic companies into the global supply chains, and increase in foreign direct investments from the EU countries.
Unlike the studies based on the Computable General Equilibrium (CGE) method, which assumes that the policy choice affects the economy only marginally through reduced tariff barriers, and that the underlying economic structure and expectations of the economic agents remain intact, the gravity model captures all changes that occur in the economy over the investigated period and extract the differences in export flows between any two counterfactual scenarios, given all background economic changes.
Results
Our main results are as follows. First, the actual exports of Ukraine are far below their potential, compared with performance of both the CU countries and the FTA EU countries. The expected long run gains in Ukrainian exports to all countries under the CU scenario are equal to 17.9 percent above the export level in 2009-2011. The corresponding number for the FTA EU scenario is 36 percent, and for the full EU scenario, 46.1 percent. Based on 2011, the export of Ukraine would have been 98 billion US dollars under the EU scenario, 91 billion US dollars under the FTA EU scenario, and 72 billion US dollars under the CU scenario. All these numbers should be compared with the actual 68 billion US dollars of Ukrainian export in 2011.
Figure 1. Ukrainian Export under the Different Scenarios
Second, any scenario predicts that Ukraine severely underperforms in its trade with both CIS and EU countries, while its export to the rest of the world is in line with the predictions of the model. These results are consistent with the theory that unresolved TPU in relationships with the CIS and EU countries severely hurts the Ukrainian export potential to these countries.
Table 1. Ukrainian Export under the Different Scenarios
Note: CIS – Commonwealth of Independent States; EU12 – countries that joined EU after 2003; EU15 – countries that joined EU before 2004; RoW – rest of the World
Third, CU integration would be more beneficial for the Ukrainian agriculture and food industry, while FTA EU or full EU integration would be more beneficial for textiles, metals, machinery and electrical goods, and transportation. Conditional on not worsening its market access to Russia, Ukraine would expand its trade in these sectors to all countries, including Russia and other members of CU.
Figure 2. Expected Increase of Ukrainian Export under the Different Scenarios 
Finally, the CU integration would lead to a small increase in the share of capital goods from 17 percent to 20 percent of total exports. FTA EU would increase the share of capital goods to 28 percent, while full EU integration would increase it to 29 percent. In all scenarios, the share of raw materials would decline from 16 percent to 10-12 percent. The share of intermediate goods would decline from 48 percent to around 40 percent under the two EU scenarios and would only marginally decrease under the CU scenario. The share of consumer goods would remain stable around 20 percent.
Conclusions
Ukraine would be better off by signing a deep and comprehensive trade agreement with the EU and integrate into its production chains than joining the CU. Right now, Ukraine severely underperforms by exporting far below its potential. Evidence shows that high trade policy uncertainty plays a large role in Ukraine’s poor performance, since the gap between actual and potential exports are mainly due to low levels of export to the EU and CIS countries. Moreover, Ukraine should be interested in moving the integration process even further, because EU accession would bring even better results.
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References
- Handley, K., & Limão, N. (2012). Trade and investment under policy uncertainty: theory and firm evidence (No. w17790). National Bureau of Economic Research.
- Helpman, E., Melitz, M., & Rubinstein, Y. (2008). Estimating trade flows: Trading partners and trading volumes. The Quarterly Journal of Economics,123(2), 441-487.
- Shepotylo, O., & Tarr, D. (2012). Impact of WTO accession and the customs union on the bound and applied tariff rates of the Russian federation. World Bank Policy Research Working Paper, (6161).