In this brief, we report the results of a quantitative assessment of the consequences of counter-sanctions introduced by the Russian government in 2014 – Russian food embargo. We consider several affected commodity groups: meat, fish, dairy products, fruit and vegetables. Applying a partial equilibrium analysis to the data from several sources, including Rosstat, Euromonitor, UN Comtrade, industry reviews etc. as of 2018, we obtain that consumers’ total loss amounts to 445 bn Rub, or 3000 Rub per year for each Russian citizen. This is equivalent to a 4.8% increase in food expenditure for those who are close to the poverty line. Out of this amount, 84% is distributed towards producer gains, 3% to importers, while the deadweight loss amounts to 13%. Based on industry dynamics, we identify industries where import substitution policies led to positive developments, industries where these policies failed and group of industries where partial success of import substitution was very costly for consumers.
The full text of the underlying paper is forthcoming in the Journal of the New Economic Association in October 2019.
In August 2014, in response to sectoral sanctions against Russia, the national government issued resolution No. 778, which prohibited import of processed and raw agricultural products from the United States, the EU, Ukraine and a number of other countries (Norway, Canada, Australia, etc.). The goal was to limit market access for countries, which supported sectoral sanctions. The other rhetoric of the counter-sanctions was to support domestic producers via trade restrictions, or by other words – import substitution.
This brief provides an update of welfare analysis of counter-sanctions based on partial equilibrium model of domestic market. The initial estimations based on 2016 data can be found in another FREE Policy Brief here. This time we compare the consumption, outputs and prices of the counter sanctioned goods as of 2018 relative to 2013. The estimated consumer surplus changes, producer gains and prices are reported in Table 1.
Table 1. Welfare effects of counter-sanctions in 2018 relative to 2013.
Data sources: Rosstat, Euromonitor, UN COMTRADE
* Negative losses correspond to gains
** Negative gains correspond to losses
Green color was used to mark the commodity groups with a noticeable consumption growth in 2013-2018 and red color those with consumption decrease.
Effect on production
From the point of view of price dynamics, on the one hand, and consumption and output, on the other, the studied products can be divided into three groups.
The first group which we call “Success of import substitution” includes goods for which real prices (in 2013 level) increased by 2016 but afterwards, the growing domestic production ensured that by 2018 prices fell below the level of 2013 with a corresponding increase in consumption. This group includes tomatoes, pork, poultry and, with some reservation, beef. For beef, growing domestic production pushed prices down after 2016, but the level of consumption and prices have not yet reached the pre-sanction level.
For the second group, import substitution has not resulted in a price decrease, we call this group “Failure of import substitution”. For products in this group, the initial increase in prices by 2016 was not reverted afterwards. Their consumption decreased significantly compared to 2013, and domestic production either continued to fall after 2016, or its growth turned out to be fragile. This group includes apples, cheese, fish, as well as condensed milk and processed meat.
We call the third group “Very expensive import substitution”. It includes fromage, sour milk, milk and (to a lesser extent) butter. This group is characterized by increase in consumption and output in the period 2016–2018, but real prices over this period still remain very high.
Effect on consumers
By comparing the losses and gains of consumers in different categories of goods due to changes in real prices and real consumption, our analysis provides the following monetary equivalents. For all considered counter-sanctioned product groups, with the exception of poultry, pork and tomatoes, consumer losses are around 520 billion rubles per year (in 2013 prices). In three product groups (poultry, pork, tomatoes), in which there was a decrease in prices and a significant increase in consumption, the consumer gains are equivalent to 75 billion rubles per year. Thus, the total negative effect from counter-sanctions for the consumers amounted to 445 billion rubles a year, or about 3000 rubles for a person per year.
Given the cost of the minimum food basket, defined in Russia as 50% of the subsistence level, the impact of counter-sanctions on the budgets of Russian consumers can be estimated as follows. 3000 rubles account for approximately 4.8% of the annual cost of the minimum food basket. The minimum food basket is a set of food products necessary to maintain human health and ensure its vital functions that is established by law. In other words, one can say that 3000 rubles a year are equivalent to a 4.8% increase in food expenditure for those who are close to the poverty line.
Consumer surplus losses were significantly redistributed in favor of domestic production, totaling 374 billion, or 2500 rubles per year per person. Another 56 billion rubles (or 390 rubles per person) correspond to the deadweight loss, i.e., reflect the inefficiency increase of the Russian economy, and 16 billion rubles (110 rubles per person) is the equivalent of redistribution in favor of foreign producers, who get access to Russian market with higher priced products than before counter-sanctions.
Effect on foreign partners
As a result of the selective embargo, the geography of Russian imports of the affected goods has changed. Traditional suppliers of these goods, primarily from Europe, were replaced by suppliers from other countries due to trade diversion. Given the changes in the composition of importers after the imposition of sanctions, we single out countries that have lost and countries that have gained access to the Russian market. We use the change in trade volumes from the respective countries as indicators of growth and decrease in share of these importers in the Russian market. Below we consider in detail the three groups of goods with the largest gains for importers in 2018 compared with 2013: cheese, apples, butter.
Cheese imports decreased significantly after the imposition of counter-sanctions, in 2018 accounting for only 42% of their dollar value in 2013. The total gain of importers due to the growth of domestic prices in 2013-2018 amounted to 17.3 billion rubles (Table 1) and was distributed among following importing countries: Belarus (78%), Argentina (6%), Switzerland (4%), Uruguay (3%), Chile (3%), other countries (6%). Countries that lost their shares of the Russian cheese market included Ukraine, Holland, Germany, Finland, Poland, Lithuania, France, Denmark, Italy and Estonia. As mentioned earlier, domestic production and Belarusian imports were not able to fully compensate for imports from countries on the counter-sanctions list, and in 2016-2018 cheese consumption in Russia decreased significantly.
Apple imports after the initial drop in 2016 partially recovered in 2018, amounting to 66% of their dollar volume in 2013. The total gain of importers in 2018 compared to 2013 amounted to 15.0 billion rubles (Table 1); it was distributed between Serbia (22%), Moldova (19%), China (13%), Turkey (10%), Iran (10%), Azerbaijan (7%), South Africa (4%), Chile (3%), Brazil (3%) and other countries (9%). Poland suffered the most from the ban on apple imports; it accounted for about 80% of all losses. Other losers from counter-sanctions include Italy, Belgium and France. The reorientation of trade flows did not completely replace Polish imports, so apple consumption in 2016-2018 was significantly lower than in 2013.
Imports of butter in 2018 was also below the level of 2013 (67% of dollar value). The gain of importers in 2018 compared to 2013 amounted to 11.2 billion rubles and was distributed among the following trading partners: Belarus (90%), Kazakhstan (4%), Kyrgyzstan (3%) and other countries (3%). Among the countries bearing most of the negative burden of the diversion of trade, one should mention Finland and Australia.
Five year after counter-sanctions were put in place Russian consumers continue paying for them out of their pockets. While few industries have demonstrated a positive effect of import substitution policies, most are not effective enough to revert the price dynamics.
- Kuznetsova, Polina; and Natalya Volchkova, 2019. “How Much Do Counter-Sanctions Cost: Welfare Analysis”, Journal of New Economic Association, N3(43), pp 173-183. (in Russian)
Recent studies have highlighted the role of human capital and good economic institutions in establishing a comparative advantage in trade in complex institutions-intensive goods. We show that the effect of institutions on comparative advantage in services trade is quite different: in fact, countries with bad institutions rely significantly more on services exports. More specifically, as the quality of institutions deteriorates, information technology sector (ICT) services exports as a share of total ICT exports increase significantly and countries with worse institutions get a substantial comparative advantage in the provision of ICT services. This is especially applicable to transitional economies characterized by high, arguably exogenous, human capital at the level of most advanced countries.
Recent research in international trade has demonstrated that institutions influence the determination of comparative advantage in the trade of goods. Countries with strong domestic institutions have a significant comparative advantage in producing complex, institutions-intensive goods while countries with weak institutions tend to specialize in less complex goods. Through this channel, weak institutions can hinder growth and development (Nunn and Trefler, 2014).
We argue that the role of institutions in services trade can differ significantly from the one in trade in goods. The intuition behind it is that services provision often relies less on institution-driven factors, such as public infrastructure, availability of large number of inputs, property rights and capital investments than the production of complex goods.
We show, in the case of the information technology sector (ICT), that countries with bad institutions rely significantly more on services exports even after controlling for human capital input requirements and availability. We focus on the ICT sector to isolate the differences in the role of institutions in determining comparative advantage in goods and services. Both ICT goods and services provision are equally intensive in human capital and thus present a good opportunity to study differences between goods and services provision.
Our study is motivated by high ICT services exports (e.g. software development) and low ICT goods exports (e.g. computers, phones, etc.) of transition countries which are known to have high human capital and low institutional indicators.
Institutions and ICT Services Exports
Figure illustrates the high human capital availability of transitions economies and weak domestic institutions relative to other countries. Specifically, we categorize countries into four groups: 23 most developed economies (e.g. USA, Canada, Japan and Western European economies); new members of the European Union (a group of 13 countries including Poland, Slovakia, and Baltic countries); transition economies group consists of 17 mostly post-Soviet countries including Russia, Ukraine, Belarus; the most numerous fourth group includes more than hundred other developing countries.
Figure 1. Institutions quality and schooling by country groups
Source: Authors’ calculations, schooling data from Barro and Lee (2013)
Source: Authors’ calculations, institutional indicators data from the World Bank World Governance Indicators
Figure 1a presents an average number of years of schooling, our measure of human capital, for each country group in 2000 and 2010 (the years are chosen based on data availability). The human capital is at a similar level in the most developed economies, EU-13 and transition economies, but significantly lower in other developing countries. Figure 1b illustrates the average institutional quality for each group in 2000 and 2010. Institutional quality for each country is calculated as an average of six indicators, distributed approximately from -2.5 to 2.5: control of corruption, government effectiveness, political stability, rule of law, regulatory quality, voice and accountability, with a lower value corresponding to worse institutional quality. In contrast to education, the average institutional quality of transition economies, although improving from 2000, remains on average lower than the institutional quality of other developing countries.
Consistent with the literature on institutions and comparative advantage in relationship and investment-intensive goods production, ICT goods export from transition economies is significantly lower than in other countries. In contrast, ICT services exports is at a higher level and faster growth in transition economies than in other countries.
Belarus presents a good motivating example. On the one hand, fundamental education in Belarus is at a level of the most advanced countries, which allows 21 universities in the country to educate about 7,000 graduates in IT industry in a year. On the other hand, ICT services exports in Belarus is thriving: over the last 10 years, the growth of ICT services is an eightfold increase (it was 150M USD in 2008 and 1.2B USD in 2017). Nowadays, Belarus is one of the world leaders in ICT services exports per capita. At the same time, ICT goods export is not growing even close to the level of ICT services exports. Over the same time period, it has grown only by about 30 percent: in 2008 ICT goods export was 105M USD, in 2016 – 140M USD (BELARUS.BY, 2019).
The importance of ICT services exports in transition economies is seen in Figure 2. The figure presents ICT services exports as a share of total exports of ICT goods and services. To obtain values for each country group, we average ICT services shares across countries within each group.
Figure 2. ICT services exports as share of total ICT exports
Source: Authors’ calculations, ICT services export data from Trademap, ICT goods export data from WDI
As Figure 2 shows, the average share of ICT services exports in transition economies is higher than the share of ICT services exports in all other groups of countries. Transition economies, characterized by high human capital and weak institutional quality, specialize in exports of services over goods in their ICT exports. This descriptive evidence suggests that abundant human capital, inherited from the USSR and arguably exogenous, shifts to services within the human capital intensive ICT sector when facing weak institutions.
Empirical panel analysis confirms the descriptive evidence. To test our hypothesis, we use the share of ICT services in total ICT exports as a dependent variable and we show that quality of institutions is a significant determinant. Our regressions show that the higher the quality of institutions is, the lower will the share of ICT services in total ICT exports be. Moreover, regression analysis allows us to quantify this dependence: as the quality of institutions increases by 1, which is approximately the difference between Belarus and Georgia (as can be seen in figure 3 below), the share of ICT goods in total ICT services increases by about 20%.
Institutions as a source of comparative advantage in services
To explore the role of institutions in the relative services provision within a sector further, we look at comparative advantage in exporting ICT services. We incorporate a measure similar to Relative Share measure used in Levchenko (2007) for the analysis of comparative advantage in goods export. The measure effectively compares the share of ICT services export for a given country with the world average. The index of revealed comparative advantage in ICT services over ICT goods is computed for country in the following way:
where is share of ICT services exports in total ICT exports for country, is the export of ICT services for all countries, and is the total ICT export (goods plus services) for all countries.
We look at the revealed comparative advantage index across our group of transition economies in figure 3 and see that even within this group, there is a negative correlation between institutions quality and revealed comparative advantage in ICT services.
Figure 3. Revealed Comparative Advantage and Institutions Quality
Source: Authors’ calculations
Countries with high institutional quality, like Georgia, export relatively more goods compared to services. Countries with low institutional quality, like Ukraine and Belarus, have a comparative advantage in ICT services exports.
We hypothesize that the main mechanism responsible for this is as follows. Poor institutional quality, resulting in, for example, corruption and the impossibility to create binding contracts does not allow the countries to produce complex goods in the ICT industry, while the presence of high human capital in these countries allows them to produce ICT services that much less depend on corruption and contracting inefficiencies but are as intensive in human capital as ICT goods.
For a better understanding of the relationship between institutions and comparative advantage determination, we run panel regressions analysing the probability of having a comparative advantage in ICT services in exports of ICT goods and services as a function of institutional quality. Following Balassa (1965), a country has a comparative advantage in ICT services if the share of services in overall ICT exports is higher than the world average, in other words, revealed comparative advantage index is greater than 1. We find that one unit increase in institutional quality reduces the probability of having a comparative advantage in services by about 25%, which means that a country with institutional quality similar to Georgia is about 25% less likely to have comparative advantage than a country with institutional quality similar to Belarus.
In this brief we have discussed the role of institutions in determining comparative advantage in services. Our study argues that, given high human capital, low quality institutions create comparative advantage in services provision. Since low quality institutions act as an implicit tax on the production of complex goods, rational agents reallocate most resources to the production of services that are less sensitive to the institutional quality, while still requiring high level of human capital. We showed that transition economies are characterized by low institutions quality and high human capital. At the same time, transition economies have the highest share of ICT services export in total ICT export. We also showed that institutions negatively affect comparative advantage in ICT services export. Our results suggest that services exports can be a novel development channel for countries with weak institutional, capital investments and infrastructure. Specialization in high-value added services exports provides opportunity for fostering high human capital.
- Arshavskiy, Victor, Arevik Gnutzmann-Mkrtchyan and Aleh Mazol, 2019. “Institutions and Comparative Advantage in Service Trade”, Working paper
- Balassa, B. (1965). Trade liberalisation and “revealed” comparative advantage 1. The Manchester School of Economics and Social Studies, 33(2), 99-123.
- Barro, Robert J. and Jong Wha Lee, 2013. “A new data set of educational attainment in the world, 1950–2010”, Journal of Development Economics, vol. 104, pp 184-198
- Levchenko, Andrei A., 2007. “Institutional Quality and International Trade”, Review of Economic Studies, vol. 74, pp 791-819.
- Nunn, Nathan and Daniel Trefler, 2014. “Domestic Institutions as a Source of Comparative Advantage”, Handbook of International Economics, Volume 4, Chapter 5, pp 263-315.
- BELARUS.BY, 2019. “ИТ в Беларуси”, it-belarus, accessed on May 19, 2019
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
On June 27, 2014, Georgia and the EU signed an Association Agreement (AA) and its integral part – the Deep and Comprehensive Free Trade Area (DCFTA). In this policy brief, we discuss the changes and analyze the agricultural exports statistics of Georgia since 2014. Furthermore, we will provide the recommendations to capitalize on the opportunities that the DCFTA offers to Georgia.
Georgia is a traditional agrarian country, where agriculture constitutes an important part of the economy. 36.6% of the country’s territory are agricultural lands and 48.2% of the Georgian population live in villages. Although 55% of population are employed in agriculture, Georgia’s agriculture accounts for only 15.8% of its GDP (Geostat, 2019). Agricultural exports constitute an important part of Georgia’s economy, accounting for about 25-30% of total exports.
On June 27, 2014, Georgia and the EU signed an Association Agreement (AA) and its integral part, the Deep and Comprehensive Free Trade Area (DCFTA). On July 1st, 2016, the DCFTA fully entered into force. The DCFTA aims to create a stable and growth-oriented policy framework that will enhance competitiveness and facilitate new opportunities for trade. The DCFTA widens the list of products covered by the Generalized System of Preferences+ (GSP+) and sets zero tariffs on all food categories (only garlic is under quota), including potentially interesting products for Georgian exports – wine, cheese, berries, hazelnuts, etc. (Economic Policy Research Center, 2014).
As July 2018 marked only two years since the implementation of the DCFTA between Georgia and EU, valuable conclusions on its impact cannot be formulated yet. In this policy brief, we will give an overview of Georgia’s agricultural trade statistics, particularly, we will focus on agricultural exports and provide recommendations for capitalizing on opportunities offered by the DCFTA.
Georgia’s agricultural trade
Despite its potential and natural resources, Georgia is a net importer of agricultural products. In 2018, Georgia’s agricultural exports increased by 23.2% (181 million USD), while the respective imports grew by only 15.5% (179 million USD) compared to 2017. Therefore, the trade balance (the difference between exports and imports) remained almost unchanged at (-394) million USD (Figure 1).
Figure 1: Georgia’s Agricultural Trade (2014-2018)
Source: Geostat, 2019
Out of the sharp increase in agricultural exports, 100 million USD are attributed to tobacco and cigars. Since Georgia cultivates very little tobacco, the growth was instigated mostly from the import, slight processing and re-export of tobacco products. Consequently, the export of tobacco and cigars increased by 240% in 2018, and it currently holds second place (after wine) in Georgia’s total food and agricultural exports. It should be mentioned that wine exports contributed to 26 million USD in export growth.
Over the last five-year period, the top export countries for Georgia were mainly neighboring counties (Azerbaijan, Russia, Armenia, Turkey); for imports, we see the same neighboring countries as well as China and Ukraine. Observing the trade statistics over the years, 45% of Georgia’s agricultural exports were destined for markets in countries of the former Soviet Union, so-called Commonwealth of Independent States (CIS), while the EU’s share in Georgia’s total agricultural exports was 24%.
Trade relationships between Georgia and the EU
The EU is one of Georgia’s largest trade partners. The EU’s share of total Georgian imports was 28% in 2018, and for exports, 24%. Total exports have been more or less stable since 2014, except for 2016, when an 11% decrease was observed (Figure 2). Specifically, for agriculture, in 2017, the EU’s share of Georgian imports was 22%, and its share of exports was 19%. During the same period, the top export products were hazelnuts (shelled), spirits obtained by distilling grape wine or grape marc, wine, mineral and aerated waters and jams, jellies, marmalades, purées or pastes of fruit.
Figure 2: Total and Agricultural Exports to the EU (2014-2018)
Source: Geostat, MoF, 2019
In 2015 (before the full enforcement of the DCFTA), Georgia’s agricultural exports to EU countries (including the United Kingdom) increased by 20% compared to the previous year. This positive trend remained in 2016, when the same indicator increased by 5%. In 2017, which was quite a bad year in terms of harvest in Georgia, we observed a 38% decrease in the country’s agricultural export to the EU (Figure 2). This decrease was mainly caused by a significant decrease (64%) in hazelnut exports during the same period. The reason for such a large decrease is that hazelnut production suffered from various fungal diseases due to unfavorable weather conditions in 2017. The Asian Stink Bug invasion worsened the situation, and in the end, hazelnut exports dropped dramatically in both value and quantity. In 2018, Georgia’s agricultural export in EU slightly increased by 6% compared to 2017.
Trade relationships between Georgia and CIS countries
It is interesting to observe agricultural trade within the same time period with CIS countries. In 2018, the CIS’ share of Georgian imports was 51%, and its share of exports was 60%. The top export products to CIS countries were wine, mineral and aerated waters, spirits obtained by distilling grape wine or grape marc, hazelnuts (shelled), and waters, including mineral and aerated, with added sugar, sweetener or flavor, for direct consumption as a beverage. As we can see in both EU and CIS countries, the top export products are more or less the same. However, the main export destination market for Georgian hazelnuts are EU countries, but wine is mostly exported to the CIS countries.
Figure 3: Agricultural Exports to CIS Countries (2014-2018)
Source: Geostat, MoF, 2019
Due to the worsened economic situation in CIS countries, Georgia’s agricultural exports to these countries decreased by 37% in 2015. Such a sharp decrease was mainly driven by a significant decrease in the export of alcoholic and non-alcoholic beverages, hazelnut, and live cattle. However, since 2015, Georgia’s agricultural exports to CIS countries have been increasing; we observed a slight 2% increase in the value of agricultural exports in 2016, while the same indicator was 37% in 2017 (Figure 3). That was mainly caused by the increased exports of alcoholic and non-alcoholic beverages (wine by 61%, spirits by 28%, mineral and aerated waters by 22%). In 2018, Georgia’s agricultural export in CIS countries increased by 12% compared to 2017.
Despite its potential and comparative advantage in agriculture, Georgia is still a net importer of agricultural products and has negative trade balance (-394 mn USD). Two years after the DCFTA came into force, it is challenging to know its impact on Georgia’s agricultural trade due to the insufficient passage of time since. Notwithstanding, we can formulate some conclusions from trade statistics. The diversity of the destinations for Georgia’s agricultural exports has not changed through the years. Georgia’s agricultural exports has increased to the EU, but at a quicker pace to CIS too. Furthermore, Georgia’s share of agricultural exports to CIS countries is still significant (60%).
While it is obvious that Georgia needs to diversify its agricultural export destination markets, there are several challenges facing small and medium size farmers and agricultural cooperatives in Georgia that are not specific to implementation of the DCFTA. As the previous regime (GSP+) with the EU already covered most products, the DCFTA did not represent a significant breakthrough. On the path to European integration, the biggest challenge for Georgia is to comply to non-tariff requirements such as food safety standards and SPS measures. The attention should be paid on providing consultations to farmers regarding certification processes and standards and better information sharing (e.g. developing online platforms).
In Georgia, agri-food value chains are not well-developed and lack coordination among different actors. In order to capitalize on opportunities offered by the DCFTA, government and private sector should work together to improve logistics infrastructure. There is a need for upgrading at every stage of export logistics: warehousing, processing, labeling, regional consolidation, final customer services. In this regard, there are high approximation costs for business that should be considered as long-term investment to modernize agriculture and improve food the safety system in the country. This would boost the export potential not only to the EU, but to other countries with similar requirements as well.
- ISET Policy Institute, 2016. “DCFTA Risks and Opportunities for Georgia”
- Economic Policy Research Center, 2014. “Agreement on the Deep and Comprehensive Free Trade Area and Georgia”. Available only in Georgian
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
The trade war between the United States and China has become one of the main events in the global economy this year. What could be its consequences for the US and China, and how might it affect other countries – for example, Russia?
Chronology of the trade war
Donald Trump started the war, raising import tariffs on solar panels in January 2018, of which the main supplier is China. In response, on April 2nd, China raised import duties on 128 commodities originating from the United States. On July 6th, the US increased tariffs on Chinese goods by 25 pp., imports worth $34 billion. China responded symmetrically. In August, the United States increased the tariffs on another $16 billion of imported goods from China, to which a symmetrical response again followed. In September, the United States again applied higher tariffs for $200 billion of Chinese exports, and China for $60 billion of US exports. At each stage of the conflict escalation, China appealed to the WTO with complaints about the actions of the United States, pointing to the inconsistency of their actions with the obligations and principles of the WTO. There were several meetings of official representatives from the United States and China – without any significant results.
What are the main reasons for this unprecedented escalation?
Imbalance and intellectual property
The economies of the US and China today are by far the largest in the world, and the trade turnover between the two countries is one of the most important. A remarkable feature of these trade flows over last decades is their imbalance. In 2017, the United States imported $526 billion worth of goods from China, while China’s imports from the United States amounted to $154 billion. Part of this imbalance is offset by trade in services, but it is not enough to even it out: in the same the year the United States delivered $57 billion worth of services to China while importing services of $17 billion from China.
Experts have different views on this imbalance. On the one hand, there is a perception that it is a source of world economy vulnerability, a source of potential crisis. Therefore, it is necessary to reduce the trade deficit. Another point of view is that this imbalance merely reflects the fact that the US economy and its assets are very attractive to investors from all over the world, including Chinese – and that, in turn, requires that the surplus of capital flows biased to US side, was compensated by the corresponding deficit of trade in goods and services. One such investor is the Chinese state itself, which for many years has been pursuing a policy of exchange rate undervaluation in order to promote foreign trade. It led to an enormous accumulation of foreign exchange reserves and as of January 2018, China held $1.17 trillion of US bonds and was the largest creditor of US government.
US President Donald Trump referred to this trade imbalance as one of the reasons for the outbreak of this trade war against China. Trump aims at reducing the deficit by $100 billion from the current $375 billion. The unilateral increase in import tariffs applied to Chinese goods was the first action of the US administration in this direction.
The second, no less important, formal reason for the trade war is the inadequate protection of intellectual property rights in China. China’s production of counterfeit products, the lack of adequate practices and laws to protect foreign technologies from illegal dissemination in the country, is not news to anyone. And although the almost two decades since China’s WTO accession have meant a largely modernized legal framework in this regard, a number of important provisions are still inconsistent with international practices, and the implementation of existing intellectual property rights leaves much to be desired. Established in 2012, The Commission on the Theft of American Intellectual Property identifies China as the most malicious violator of US rights. The exact damage is not known, but the commission assessment of the losses to the American economy due to the forced transfer of technology to Chinese partners – which is an unspoken condition of foreign manufacturers access to the Chinese market – industrial espionage, contradictions in legislation, requirements for the storage of sensitive data in China are in the range from $225 to $600 billion per year (Office of US Trade Representative, 2018).
While both the trade deficit and the intellectual property rights issue were recognized for many years, it was in 2018 that Trump started acting on them. Therefore, in order to discuss the potential impact of the conflict between the world’s largest economies on themselves and other economies, such as Russia, it is important to understand what drives the actions undertaken by Trump’s administration.
Trump won the elections in 2016 with a minimum margin against the Democratic rival. To provide support for his decisions and to increase the chances of being reelected for the next term in 2020, it is crucial to maximize the pool of his supporters. Trade policy measures aimed at import substitution are very effective populist policies in any country. One of the first steps made by the US toward trade war was the increase in import tariffs on steel and aluminum – for all countries. Metallurgy and coal industries are among the most organized and strong lobbyists in any country. The European Union as an economic organization started with the European Coal and Steel Association. By aligning interests with these sectors much can be achieved in relation to trade liberalization, and vice versa – by increasing the level of protectionism, a significant popularity increase can be among voters whose incomes depend on the success of companies in these industries.
China works hard raising the technological level of its economy. In recent years the Chinese government and Communist party launched a number of ambitious programs aimed at achieving a technological breakthrough, lessening the dependence on imported technologies by substituting them with ones produced by domestic innovation centers. These programs specify the priority sectors, in which state subsidies are provided for the acquisition of foreign technologies by Chinese companies and their adaptation. One of the common arguments was that the United States believes that powerful state support for technology sectors in China, along with the existing problems in protecting intellectual property rights, increases the risks and potential losses of American companies.
However, while these concerns seem reasonable at first, they should not be taken at the face value.
China’s ability to push out American companies in the high-tech sector on the world market seems rather limited. So far, China has only succeeded in increasing its share in the middle and low technology segments. Instead, in recent years, China is rapidly increasing its defense spending, which in 2017, for the first time, reached a level of 1 trillion yuan (about $150 billion). China’s defense spending is the second highest in the world after the United States. Moreover, it’s growing very fast. While in 2005 the Chinese nominal defense expenses were only 10% of American expenses, in 2018 they are already around 40%. The dominance of state enterprises in the defense industry in China implies that the real purchasing value of these expenditures is quite comparable. New and existing Chinese industrial policy programs target military and dual-use industries among others. Therefore whilst addressing the intellectual property rights problem in China now, Trump’s administration also aims at preserving US leadership position in the military sector, which finds widespread support in Trump’s main voter groups among Republicans.
Historically, trade wars implied tariff escalations to protect domestic industries from foreign competition. Today, the Trump administration behaves in a similar manner. However, the circumstances now are fundamentally different from those in the first half of 20th century and earlier. Firms not only trade in final goods, but more and more they trade in intermediate products and within firms themselves (Baldwin, 2012). The distribution of the production process to many companies across different countries of the world leads to two important effects, which were not observed in previous trade wars.
First, it is the effect of the escalation of tariff protection in the framework of the value chains. The import tariff is applied to the gross value of the product crossing the customs border. However, the exporting firm’s contribution to the gross value might be quite small. So the effective level of the tariff will be higher than the nominal level of the tariff, known as a so called amplification effect (World Bank, 2017, page 98). It means that the effective growth of the tariff by 25 percentage points in relation to Chinese imports will significantly exceed 25 % and in some cases can even become prohibitive. So, the tariff warfare will result in significantly greater losses for the sectors involved in the value chains, compared to the sectors less exposed to them. It means that foreign investors and multinational companies in China will suffer bigger losses compared to purely domestic Chinese companies. The Peterson Institute for International Economics made an assessment and confirmed these observations (Lovely and Yang, 2018).
Second, China’s participation in international multinational companies most often occurs in the assembly segments, while developed countries’ companies contribute at other stages, such as with innovation, design, financial and consulting services, marketing, and after-sales services. Then, the protectionist measures against goods produced in China by multinational companies will hit an American economy, generating losses in the service segments. A similar episode happened, for example, in 2006, when the European Union introduced anti-dumping duties on imported footwear from China and Vietnam, which in turn lead to a decline in the services sector in Europe – imported footwear contained a significant share of the value added created by European designers and distributors (World Bank, 2017). Obviously, we will observe the same consequences in the United States now, since the role of the American services sector in creating and promoting Chinese goods on the American market is significant and according to World Bank estimates in 2011, the contribution of value added generated by foreign services in China’s gross exports amounted to about 15% (World Bank, 2017).
Thus, not only the economy of China, but also the US economy itself will suffer from the growth of import tariffs in the USA. The USA is not an exception here – the governments of most countries continue to live in the paradigm of trade policy, which suits the structure of the world trade as at the beginning of the 20th century, while trade has gone far ahead and requires much more elaborate effective regulatory tools than tariffs on imported goods.
Consequences for Russia
The consequences of the US trade war with China for the Russian economy depend on what the main goals of the war are. If the motive is primarily electoral – to secure enough support in 2020, one can expect that the protective measures will be short-lived, and the geographical distribution of investment flows will remain almost intact and that China will remain an important location for global value chains transactions. The trade war will in this case lead to some economic slowdown in the short term. The main effects will be related to the redistribution of income within economies, where protected sectors will benefit on the expense of all other sectors. In these circumstances, Russia would suffer direct losses from the growth of tariffs on their exports to US (now it is predominantly steel and aluminum), but for the economy as a whole, the losses will not be significant, especially relative to the losses Russia bears because of sanctions.
However, if the main reason for the trade war has a long-term perspective, the investors will be forced to adjust the geography of their investment plans and China will face a significant outflow of foreign investments, which will significantly affect Chinese – and global – economic growth. In this case, both for Russia and for the whole world, the indirect effect of the US-Chinese trade conflict will be quite noticeable and it will take years to create new trade links and restore world trade and global value chains.
- Baldwin, Richard, 2012. “Global supply chains: why they emerged, why they matter, and where they are going”, CTEI Working papers 2012-13, The Graduate Institute, Geneve
- Lovely, Mary E., and Liang Yang, 2018. “Revised Tariffs Against China Hit Chinese Non-Supply Chains Even Harder.” PIIE Policy brief, Peterson Institute
- Office of the US Trade Representative. March 22, 2018. “Executive office of the President findings of the investigation into China’s acts, policies, and practices related to technology transfer, intellectual property, and innovation under section 301 of the trade act of 1974.” https://ustr.gov/sites/default/files/Section%20301%20FINAL.PDF
- World Bank, 2017. “Measuring and analyzing the impact of GVCs on economic development”. World Bank, Washington DC.
A longer version of this brief has been published in Russian by Republic: https://republic.ru/posts/92217
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
In this brief we provide a quantitative assessment of the consequences of countersanctions introduced by the Russian government in 2014 in response to sectoral restrictive measures initiated by a number of developed countries. Commodity groups that fell under countersanctions included meat, fish, dairy products, fruit and vegetables. By applying a basic partial equilibrium analysis to data from several sources, including Rosstat, Euromonitor, UN Comtrade, industry reviews etc., we obtain that total consumers’ loss due to countersanctions amounts to 288 bn Rub or 2000 rubles per year for each Russian citizen. Producers capture 63% of this amount, importers 26%, while deadweight loss amounts to 10%. 30% of the transfer from Russian consumers toward importers was acquired by Belarus. The gain of Belarusian importers of cheese is especially impressive – 83% of total importer’s gains on the cheese market.
In August 2014, in response to sectoral sanctions initiated by some countries against Russia, the national government issued resolution No. 778, which prohibited import of processed and raw agricultural products from the United States, the EU, Ukraine and a number of other countries (Norway, Canada, Australia, etc.).
Russian countersanctions were, in particular, imposed on meat, fish, dairy products, fruit and vegetables. Later the list of counter sanctioned goods was edited: inputs for the production of baby food and medicines have been deleted from the ban list, while new items were added. Salt was added to the list in November 2016 and animal fats in October 2017.
The popular idea behind the countersanctions was to limit market access for countries, which supported sectoral sanctions. The other rhetoric of the countersanctions was to support domestic producers via trade restrictions, or by other words – import substitution.
We apply a basic partial equilibrium analysis in order to evaluate the effect of countersanctions on the welfare of main stakeholders – consumers, producers and importers. The overall results are in line with general microeconomic consequences of trade restrictions in a small open economy, that is, we observe a decline in consumer surplus, increase in producer surplus and redistribution across importers. Perhaps, even more interestingly, we are able to provide a numerical assessment of redistribution effects between Russian consumers and producers, on the one hand, and among importers from different countries, on the other.
Partial equilibrium welfare analysis
We apply a framework of the classical analysis of import tariff increases to Russian countersanctions. Countersanctions resulted in increased domestic prices, declining consumption and increased domestic production. Given the increase in prices and declined volumes of consumption, we evaluate the losses by consumers as a decline in consumer surplus. Respectively, given the increase in prices and increase in domestic output we identify the producers gains as an increase in producer surplus. The only difference with a classical analysis is the lack of increase in government revenues. In this case increases in domestic prices were driven by restrictions on trade with historical partners which were substituted by more costly producers. Given the changes in the composition of importers after sanctions, we identify countries which lost and gained access to the Russian market. We use changes in volumes of trade as a measure of respective gains and losses. Figure 1 presents all relevant concepts.
In order to measure all relevant welfare changes, we rely on consumption, production and price data from Rosstat and Euromonitor, trade data from the UN Comtrade database. We use data for 2013 as a benchmark before countersanctions and compare it to 2016. The measures of own price elasticities of Russian demand and supply were taken from the literature. We use real price (in terms of 2013 prices) and volume information for consumption and supply in 2016 as the resulting points on the supply (point C) and demand (point A) curves as shown on Figure 1. Then we restore the consumption and production points on these curves (points F and B) as they would have been in 2013 given the own price elasticities of demand and supply and price level as of 2013.
Figure 1. Visualization of deadweight losses, consumer and producer surplus changes
We consider 12 commodity groups that were included in 2014 in the countersanctions list: pork, cheese, poultry, apples, beef, tomatoes, processed meat, fromage frais, butter, oranges, condensed milk, grapes, cream, sour milk products, milk, and bananas.
Prices and volumes information are taken from Rosstat official statistics, which in a few cases were adjusted by data from Euromonitor. Import values were obtained from the UN Comtrade database. The summary of the original data and results of welfare analyses are reported in table 1. Below we discuss in details the situation in three markets – beef, apples and cheese.
Table 1. Summary table of the welfare effects of countersanctions
|Group||Price (RUR per kg, 2013)||Production (thous. tons)||Consumption (thous. tons)||Elasticity||Consumer losses, RUR mn||Producer surplus, RUR mn||Deadweight loss, RUR mn||Importer gains, RUR mn|
Data sources: Rosstat, Euromonitor, UN COMTRADE
Bold figures were used to mark the commodity groups with a noticeable consumption growth in 2013-2016, italic figures – for those with consumption decrease, and underlined – for groups where consumption changed insignificantly during the period.
The Russian beef market experienced a drastic decrease in consumption during two years under countersanctions. In 2013 constant prices, the average real of 1 kg of beef increased by 5.3% from 357 Rub/kg in 2013 up to 376 Rub/kg in 2016. Domestic output decreased by 0.8% and to 238 thousand tons in 2016 from 240 in 2013. Domestic consumption decreased by 33.1% to 600 thousand tons in 2016 from 897 in 2013. Our estimations indicate that consumer losses amount to 11.3 bn Rub or 3.5% of beef consumption in 2013; producers’ gains are 4.4 bn Rub or 1.4%; deadweight losses are estimated at 0.2 bn Rub or 0.07%; and importers’ gains equal 6.7 bn Rub or 2.1%.
Out of total 6.7 bn Rub of importers’ gains, importers from Belarus acquire the major share (88%) – 5.9 bn Rub. Importers of beef from India and Colombia gained 0.4 bn Rub (6% of total) and 0.3 bn Rub (5%) respectively. Beef importers from Mongolia gained 0.03 bn Rub, from Kazakhstan – 0.01 bn Rub. Importers of beef from Brazil, Paraguay, Australia, Uruguay, Ukraine, Lithuania, Poland, and Argentina lost market shares in over the period 2013-2016.
Average real price for 1 kg of cheese increased by 26.5% up to 358 Rub/kg in 2016 from 283 Rub/kg in 2013, both in constant 2013 prices. Domestic output increased by 39.1% to 605 thousand tons in 2016 from 435 thous. tons in 2013. Domestic consumption decreased by 2.1% to 748 thous. tons in 2016 from 764 thous. tons in 2013. Our results indicate the following effects of countersanctions on cheese market: consumers’ losses amounted to 63.5 bn Rub or 29.4% of cheese consumption in 2013; producer’s gain is 44.3 bn Rub or 20.5%; deadweight loss is estimated at 8.4 bn Rub or 3.9%; importers’ gains equal 10.8 bn Rub or 5.0%.
Out of a total 10.8 bn Rub of importer’s gains on the cheese market, importers of cheese from Belarus acquired the major share (82.9%) – 9.0 bln Rub, importers of cheese from Argentina gained 0.5 bn Rub (4.8% of total importers’ gain), importers from Uruguay gained 0.4 bn Rub (3.9%), Swiss cheese importers gained 0.2 bn Rub, importers from Armenia – 0.2 bn Rub (1.8%). While importers of cheese from Ukraine, the Netherlands, Germany, Finland, Poland, Lithuania, France, Denmark, Italy, and Estonia lost market access over 2013-2016.
In 2013 constant prices, average real price for 1 kg of apples increased by 20.0% up to 84 Rub/kg in 2016 from 70 Rub/kg in 2013. Domestic output increased by 3.5% to 324 thous. tons in 2016 from 313 thous. tons in 2013. Domestic consumption decreased by 40.8% to 986 thous. tons in 2016 from 1665 thous. tons in 2013. According to our analysis, the effects of countersanctions on the apple market are the following: consumers’ losses amounted to 15.2 bn Rub or 13.1 of apple consumption in 2013; producer’s gain is 4.6 bn Rub or 3.0%; deadweight loss is estimated at 1.2 bn Rub or 1.1%; importers’ gains equal 9.4 bln Rub or 8.1%.
Out of a total 9.4 bn Rub of importer’s gains, importers from Serbia acquired the major share (49.7%) – 4,7 bn Rub, importers of apples from China gained 1.6 bn Rub (16.7% of total importers’ gains), those importing from Macedonia gained 0.8 bn Rub (8.4%), from Azerbaijan 0.6 bn Rub (6.0%), and from South Africa 0.4 bn Rub (4.5% of total importers’ gains). While importers of apples from Poland, Italy, Belgium, and France lost market access.
Overall effects for 12 commodity groups
We calculated the welfare effects for 12 commodity groups: beef, poultry, milk, cheese, cottage cheese, ton butter, dairy products, apples, bananas, oranges, grapes and tomatoes.
Total consumers’ loss due to countersanctions amounts to 288 bn Rub, producers gain 63% out of this amount (182 bn Rub), 26% of total consumers’ loss is redistributed to importers (75 bn Rub), deadweight losses amount to 10% (31 bn Rub).
Distribution of importers’ gains
Belarus is the major beneficiary of Russians countersanctions: its exporters gain 29.4 bn Rub (38%), Ecuador’s exporters are in the second place with 16.4 bln Rub (21). Exporters from Serbia gained 5.1 bn Rub (7%).
There is no doubt that countersanctions were paid out of the pockets of Russian consumers: our estimation of total consumer losses amounts to 288 billion rubles, i.e. each Russian citizen paid 2000 rubles per year. Out of this sum, Russian producers received 144 billion rubles, i.e. transfer from Russian consumers to producers equals 1260 rubles per person per year. Among Russian sectors, major gains and associated increases in production happened in pork industries (50%), poultry (20%), dairy products (10-30%), fruit and vegetables (10-50%).
The transfer from Russian consumers toward importers from non-sanctioned countries equals 75 billion rubles a year (520 rubles per person per year), out of which 30% was acquired by Belarusian importers. Countersanctions lead to deadweight losses in the efficiency of Russian economy equal to 31 billion rubles or 215 rubles per person per year.
Do ethnic networks facilitate international trade when formal institutions are weak? Using data collected by ethnologists on the share of ethnic groups across countries, this study assesses the effect of ethnic networks on bilateral trade across the sphere of the former Soviet Union. This region provides a perfect setting to test for this effect as both forced re-settlement of entire ethnic groups during the Stalin era and artificially drawn borders in Central Asia led to an exogenous ethnic composition within countries. While ethnic networks do not seem to have played a role in inter-republic trade during the Soviet Union, they did facilitate trade in the years following the collapse of the Soviet Union, a transitional period when formal institutions were weak. This effect, however, eroded steadily from the early 2000s.
Economists and historians alike study the role of ethnic networks in international trade. Some prominent examples are the Greek commercial diaspora of the Black Sea in the 19th century (Loannides and Minoglou, 2005), the Maghribi traders in 11th-century North Africa (Greif, 1993), or the overseas Chinese all around the world in the last decades (Rauch and Trindade, 2002). Such networks facilitate trade by building trust relationships, enforcing contractual agreements in weak legal environments, matching buyers with faraway sellers that speak different languages, and by exchanging information on arbitrage opportunities.
In “Ethnic Minorities and Trade: The Soviet Union as a Natural Experiment”, forthcoming in The World Economy, we study the Soviet Union (USSR) to assess the role of ethnic networks in international trade. We argue that ex-USSR countries are particularly well suited for such a study. Indeed, the ethnic diversity of ex-USSR countries is exogenous, partly due to the creation of artificial borders cutting through ethnic homelands, and partly due to forced relocations (deportations) during the Stalin era, which brought ethnic groups to various remote regions of the USSR. This exogeneity adds power to our empirical strategy.
Ethnic Networks in the USSR
We first build a measure of ethnic networks based on the size of common ethnic groups using ethnologists’ data from the Ethnic Power Relations Dataset on the resulting ethnic groups across ex-USSR countries (Vogt et al., 2015; Bormann et al., Forthcoming). It covers all ethnic groups in every country of the world from 1946 to 2013. While there is some yearly variation in the data, we focus on the cross-section average for the pre-1991 period as per our identification strategy based on exogenous distributions.
Figure 1 gives an overview of the spatial distribution of ethnic groups, such as Russian, Kazakh, or Uzbek.
Figure 1. Ethnic Groups in the USSR
Russians are ubiquitous across the Soviet sphere. Countries with the largest ethnic Russian populations are Kazakhstan, Estonia, Latvia and Moldova. At the same time, Russia is very diverse. Almost all of the 60 ex-USSR ethnic groups are present in Russia, and ethnic Russians account for only 62% of the population. Most countries are ethnically diverse. Kazakhstan for example is home to Russians as well as Germans, Tatars, Ukrainians, Uzbeks and Uighurs.
From the information on ethnic populations within each country, we create an ethnic network index as the sum of products of common ethnic groups as a share of the country’s population. Figure 2 presents a matrix overview of the ethnic network index among country pairs with darker shades corresponding to higher scores. Some high scoring country pairs are Russia—Kazakhstan, Ukraine—Russia, Uzbekistan—Tajikistan, Kyrgyzstan—Uzbekistan, Latvia—Kazakhstan, and Ukraine—Kazakhstan.
Figure 2. Ethnic Networks Index
Effect of Ethnic Networks on Bilateral Trade in the USSR
Next, we evaluate the impact of ethnic networks on aggregate trade between the countries of the former Soviet sphere. We use trade data from two sources. First, the data on internal trade between Soviet republics from 1987 to 1991 are from the input-output tables of each Soviet Union republic as compiled by the World Bank mission to the Commonwealth of Independent States (Belkindas and Ivanova, 1995). Second, the Post-1991 to 2009 trade data are from the Correlates of War Project (Barbieri et al., 2009, 2016), which offers the best coverage of the trade in the region.
We follow the migrant network and trade literature and estimate a standard log-linear gravity equation controlling for importer-year and exporter-year fixed effects (Anderson and van Wincoop, 2003).
Figure 3 presents the results on the effect of ethnic networks on trade over time. We observe that there is no effect in the period before the end of the USSR, a positive effect after the breakup of the Soviet Union, and an erosion of this effect from 2000s on (omitting Russia from the sample does not alter the results).
These results can be explained with the fact that in the Soviet Union ethnic ties did not matter as official production and trade were centrally planned by the State Planning Committee, Gosplan, and by State Supplies of the USSR, or Gossnab, which was in charge of allocating producer goods to enterprises. Free trade was forbidden. However, once the Soviet system collapsed and before countries could establish more formal trade ties, the first reaction and fallback option for many people was to reach out to their co-ethnics (in the 1990s) to substitute for the broken chains of the centrally planned trade (Gokmen, 2017). The other reason is that the institutional framework was at its weakest in this transitional period, and hence, reliance on informal institutions such as ethnic networks may have been especially strong (Greif, 1993). Once systematic and formal trade ties could be established, more and more traders no longer had to rely on their ethnic networks and this could explain the decline in the effect in the 2000s.
Figure 3. The Effect of Ethnic Networks on Trade over Time
This study shows that ethnic minorities played a role in shaping trade patterns across ex-USSR countries, but only in the early years following the collapse of the Soviet Union. Thus, we argue that reliance on informal institutions, such as ethnic networks, in forming trade relations is especially strong when the institutional framework is at its weakest in the transition period. This message may hold, not only for transition countries, but also for other developing countries with poor institutions.
- Anderson, J. E. and E. van Wincoop, 2003. “Gravity with Gravitas: A Solution to the Border Puzzle,” American Economic Review, 93, 170-192.
- Barbieri, K., M. G. Omar, and O. Keshk, 2016. “Correlates of War Project Trade Data Set Codebook, Version 4.0.”
- Barbieri, K., M. G. Omar, O. Keshk, and B. Pollins, 2009. “TRADING DATA: Evaluating our Assumptions and Coding Rules,” Conflict Management and Peace Science, 26, 471-491.
- Belkindas, M. and O. Ivanova, 1995. “Foreign Trade Statistics in the USSR and Successor States,” Tech. rep., The World Bank, Washington, DC.
- Bormann, N. C., L. E. Cederman, and M. Vogt, Forthcoming. “Language, Religion, and Ethnic Civil War,” Journal of Conflict Resolution.
- Gokmen, G., 2017. “Clash of civilizations and the impact of cultural differences on trade,” Journal of Development Economics, 127, 449-458.
- Gokmen, Gunes; Elena Nickishina; and Pierre-Louis Vezina, forthcoming. “Ethnic Minorities and Trade: The Soviet Union as a Natural Experiment”, The World Economy.
- Greif, A., 1993. “Contract enforceability and economic institutions in early trade: The Maghribi traders’ coalition”, The American Economic Review, 525-548.
- Loannides, S.; and I. P. Minoglou, 2005. “Diaspora Entrepreneurship between History and Theory”, London: Palgrave Macmillan UK, 163-189.
- Rauch, J. E. and V. Trindade, 2002. “Ethnic Chinese networks in international trade”, Review of Economics and Statistics, 84, 116-130.
- Vogt, M., N. C. Bormann, S. Regger, L. E. Cederman, P. Hunziker, and L. Girardin, 2015. “Integrating Data on Ethnicity, Geography, and Conflict: The Ethnic Power Relations Dataset Family,” Journal of Conflict Resolution, 1327-1342.
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.
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.
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
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
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)
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.
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.
- 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.
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.
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.
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.
- 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.
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
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
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
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.
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.
- 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.
A recent study of capital flows between Sweden and Russia provides many policy lessons that are highly relevant for the current economic situation in Russia. In line with studies on other countries, bilateral FDI flows were more stable than portfolio flows, which is important for a country looking for predictable external sources of funding. However, much of the FDI flows came with trade and growth of the Russian market. The sharp decline in imports and fall in GDP is therefore bad news also when it comes to attracting FDI. The conclusion is (again) that institutional reforms and reengaging with the West are crucial policies to stimulate both the domestic economy and encourage much-needed FDI.
In a recent paper (Becker 2016), I take a detailed look at the trends and nature of bilateral capital flows between Sweden and Russia over that last 15 years. Although the paper focuses on the capital flows of a relatively small country like Sweden with Russia, it sheds some light on more general theoretical and empirical issues associated with FDI and portfolio flows that are highly relevant for Russia today.
Measuring Bilateral FDI
One general qualifier for studies of bilateral capital flows is however the reliability of data; Not only is a significant share of international capital flows routed through offshore tax havens which makes identifying the true country of origin and investment difficult, but also many investing companies are multinationals (MNEs) with operations and shareholders in many countries so it is hard to have a clear definition of what is a “Swedish” or a “Russian” company. In addition, when different official data providers, in this case Statistics Sweden (SCB) and the Central Bank of Russia (CBR), report capital flows on the macro level, there are large discrepancies.
Private companies also gather company level data on FDI that can be aggregated and compared with the macro level FDI data. This data is on gross FDI flows and should not be expected to be the same as the net macro level FDI flows data but is a bit of a “reality check” of the macro data.
Figure 1. Average annual FDI flows
Sources: SCB, CBR, fDi Market, MergerMarkets
The reported annual average flow of FDI from Sweden to Russia varies from around USD500 million to USD1.2 billion depending on the data source. Russian flows to Sweden are rather insignificant regardless of the source but the different sources do not agree on the sign of the net flows (Figure 1).
The differences between data sources suggest that some caution is warranted when analyzing bilateral FDI flows. With this caveat in mind, there are still some clear patterns in the capital flows data from Sweden to Russia that emerge and carries important policy lessons in the current Russian economic environment.
FDI vs. Portfolio Investments
There is a large literature discussing the distinguishing features of FDI and portfolio flows (see Becker 2016 for a summary). Some of the key macro economic questions include which type of flows provides most international risk sharing; are most stable over time; or most likely to contribute to balance of payments crises when the flows go in reverse. In addition, there are potential differences in terms of the amount of international knowledge transfers and how different types of capital flows respond to institutional factors.
Figure 2. FDI and portfolio investments
Figure 2 shows that FDI has been much more stable than portfolio flows in the years prior to and after the global financial crisis as well as in more recent years. Although all types of capital flows respond negatively to poor macroeconomic performance, and the stock of portfolio investments swing around much faster than FDI investments, i.e., portfolio flows go in reverse more easily and can contribute to external crises. This makes FDI a more preferable type of capital flow for Russia.
FDI and Trade Go Together
Since FDI is a desired type of capital flow, it is important to understand its driving forces. The first question to address is whether FDI and trade are substitutes or complements. Since the bulk of FDI comes from MNEs that operate in many countries, we can imagine cases both when FDI supports existing trade and cases when it is aimed at replacing trade by moving production to the country where the demand for the goods is high.
In the case of Sweden and Russia, the macro picture is clear; FDI has increased very much in line with Swedish exports to Russia (Figure 3). Both of these variables are of course closely correlated with the general economic development in Russia, but even so, the very close correlation between FDI and trade over the last 15 years suggests that they are compliments rather than substitutes.
Figure 3. Swedish Exports and FDI to Russia
Most FDI is Horizontal
FDI flows are often categorized in terms of the main motivating force for MNEs to engage in cross-border investment: vertical (basically looking for cheaper inputs), horizontal (expanding the customer base), export-platform (producing abroad for export to third countries) or complex (a mix of the other reasons) FDI.
Looking at the sectoral composition of FDI from Sweden to Russia (Figure 4), most investments have come in sectors where it is clear that MNEs are looking to expand their customer base. Even in the case of real estate investments, a large share is IKEA developing new shopping centers that host their own outlets together with other shops. Communication and financial services are also mostly related to service providers looking for new customer. Only a small share is in natural resource sectors that would be more in line with vertical FDI, while there are very few (if any) examples of MNEs moving production to Russia to export to third countries.
Figure 4. Sectors of Swedish FDI to Russia
The above figures on bilateral capital flows from Sweden to Russia carry three important policy messages: 1) FDI is more stable than portfolio flows; 2) Trade goes hand in hand with FDI; and 3) FDI to Russia has mostly been horizontal and driven by an expanding customer base.
In the current situation where Russia should focus on policies to attract private capital inflows, the goal should be to attract FDI. Instead, the government is now looking for portfolio inflows in the form of a USD3 billion bond issue. But FDI is a more stable type of international capital than portfolio flows and also come with the potential of important knowledge transfers both in terms of new technologies and management practices.
However, as we have seen above, FDI inflows have in the past been correlated with increased trade and an expanding Russian market. In the current environment, where imports with the West declined by 30-40 percent in the last year, GDP fell by around 4 percent, and the drop in consumers’ real incomes have reached double digits in recent months, it is hard to see any macro factors that will drive FDI inflows.
Instead, attracting FDI in this macro environment requires policy changes that remove political and institutional barriers to investments. The first step is to fulfill the Minsk agreement and contribute to a peaceful solution in Ukraine that is consistent with international laws. This would not only remove official sanctions but also provide a very serious signal to foreign investors that Russia plays by the international rulebook and is a safe place for investments from any country.
The second part of an FDI-friendly reform package should address the institutional weaknesses that in the past have reduced both foreign and domestic investments. It is telling that many papers that look at the determinants of FDI flows to transition countries include a ‘Russia dummy’ that is estimated to be negative and both statistically and economically significant (see e.g. Bevan, Estrin and Meyer, 2004 and Frenkel, Funke, and Stadtmann, 2004). One factor that reduces the significance of the ‘Russia dummy’ is related to how laws are implemented. Other studies point to the negative effect corruption has on FDI.
Reducing corruption and improving the rule of law are some of the key reforms that would have benefits far beyond attracting FDI and has been part of the Russian reform discussion for a very long time. It was also part of the reform program that then-President Medvedev presented to deal with the situation in 2009 together with a long list of other structural reforms that would help modernize the Russian economy and society more generally.
As the saying goes, don’t waste a good crisis! It is time that Russia implements these long-overdue reforms and creates the prospering economy that the people of Russia would benefit from for many generations.
- Becker, T, 2016, “The Nature of Swedish-Russian Capital Flows”, SITE Working paper 35, March.
- Bevan, A, Estrin, S & Meyer, K 2004, “Foreign investment location and institutional development in transition economies”, International Business Review, vol. 13, no. 1, pp.43-64.
- Frenkel, M, Funke, K & Stadtmann, G 2004, “A panel analysis of bilateral FDI flows to emerging economies”, Economic Systems, vol. 28, no. 3, pp. 281-300.