Belarus has faced unprecedented sanctions during the last year and the new economic conditions have led to a GDP decline and inflation growth. At the same time, the situation on the currency market has been stable since April 2022. The Belarusian Ruble demonstrated a gradual appreciation to the US Dollar and the Euro and a decline to the Russian Ruble. The appreciation of the Belarusian Ruble against the US Dollar has given households the illusion that the economic situation is not that bad. This brief analyses the main factors of the current situation on the currency market as well as describes the challenges which might destabilise the market. The importance of changing selected currencies in the currency basket and the start of a reorientation of the Belarusian economy from Western to Eastern partnerships, are also described.
The National Bank of the Republic of Belarus’ Policy on the Currency Market
In Belarus, currency has always played an important role as an indicator of economic stability. Household’s reactions to sharp fluctuations of the Belarusian Ruble have been expressed in an immediate demand growth for foreign currency (US Dollar and Euro mostly). After the war in Ukraine started and the exchange rate of the Belarusian Ruble began declining, people tried to make currency deposits from banks and buy foreign currency. In contrast to the Central Bank of Russia, the National Bank of the Republic of Belarus (NBRB) introduced no restrictions on the currency market. However, Belarusian financial institutions imposed their own limits on carrying out non-cash exchange operations, cash withdrawals from ATMs and from bank accounts. Financial institutions also limited the availability of currencies in exchange offices and imposed limits on payment transactions by credit card outside of Belarus. All these processes took place under the condition of a sharp devaluation of the Russian Ruble.
The dynamics in the Russian Ruble have affected the Belarusian Ruble fluctuation (see Figure 1). The correlation between the currencies was strong even before the war, given that the Russian Federation is a dominant economic partner for Belarus, and has since become stronger.
The share of Russian Ruble in the Belarusian currency basket is at 50 percent. Moreover, in Q1-Q3 2022 the Belarusian dependency on the Russian economy increased in the aftermath of losing the Ukrainian market and facing European export shortages. Between January and August 2022, the share of export of goods to CIS countries (where the main share of exports goes to Russia) was 65,7 percent, as compared to 58,4 percent for the corresponding months in 2021. The same tendencies are apparent when considering the import of goods. The share of import from CIS countries reached 64,7 percent between January and August in 2022, as compared to 61,3 percent for January-August in 2021 (BSCBR, 2022).
Figure 1. The weighted average exchange rate of the Belarusian Ruble, in Belarusian Rubles.
Sanctions and the Russian Central Bank’s policy have led to a stabilisation on the Russian currency market. The Central Bank of Russia has introduced restrictions on capital outflow from the country, limited cash withdrawals from bank accounts and foreign currency purchases in exchange offices (Tinkoff, 2022). The cancelation of budget rule has further supported the Russian Ruble exchange rate. But the main reason for the Russian currency exchange rate reversal post March 2022, relates to the situation regarding foreign trade. Due to sanctions, imports had significantly decreased. At the same time, high energy prices allowed for export growth. Between January and June 2022 Russia displayed a high positive trade balance (169,62 billion USD), the largest in the last 7 years (CBR, 2022). As a result of sanctions, the Central Bank of Russia started to prepare the market to work with currencies of friendly countries.
Similar tendencies can be seen in Belarus. NBRB has changed the composition of the foreign currency trade to turn the Belarusian economy from a Western to an Eastern direction regarding economic cooperation. In July 2022 the Chinese Yen was included in the currency basket. At the same time the share of Russian Ruble was at 50 percent, the US Dollar at 30 percent, the Euro at 10 percent and the Chinese Yen at 10 percent. In August 2022, the NBRB began to define daily exchange rates for the Vietnamese Dong, Brazilian Real, Indian Rupee and UAE Dirham. Finally, since October 2022, the exchange rate for the Qatari Riyal has been defined on a monthly basis (The National Bank of Belarus, 2022). These changes are indicators of ongoing and planned structural changes to the economy to accommodate increased cooperation with the Eastern economies.
Currency Market Stabilisation and Current Risks
The Belarusian Ruble has not repeated the fluctuation of the Russian currency. It did however copy its tendency to appreciate to the US Dollar and the Euro, as of April 2022. Besides the appreciation of the Russian Ruble and personal bank’s restrictions on national currency markets, the stabilisation of the Belarusian Ruble can be explained by the positive trade balance. In contrast to Russia, the growth of net export in Belarus was due to a faster decline of imports than exports. There are several reasons why this can be a problem for currency market stabilisation in the future.
First, Belarus’ foreign trade has become more and more oriented toward the Russian market. If the main trade partner experiences difficulties (for example, oil price caps) this could lead to a devaluation of the Russian Ruble and, as a result, declining competitiveness of Belarusian goods on the Russian market.
Second, reorientation of Belarusian exports from Western to Eastern countries require time and additional financial resources and exports are not always profitable due to high logistical costs. Any additional sanctions may further limit such opportunities.
Third, main export-oriented services, such as the Transport and ICT sectors, are affected by sanctions and their consequences. In Q3 2022, the transport turnover was equal to 68,3 percent, as compared to the same period 2021. The ICT sector is still having a positive impact on GDP growth. However, in January-September 2021 the positive contribution from this sector to the Belarusian GDP was 0,9 percent, while it between January and September 2022 was only 0,2 percent.
Recent success in foreign trade is mostly due to the continuation of selling potash, nitrogen fertilisers and other products on the global market, a strong Russian Ruble and Russian market openness towards Belarusian companies, low levels of Belarusian imports, and cheap Russian gas (the special price for Belarus is 128 US Dollars for 1000 cubic meters). If the terms of trade with Russia worsen and key export-oriented industries suffer from sanctions and reputational risks, the currency market could however be destabilised.
Another problem for the Belarusian Ruble stability in the middle and long term is related to household behaviour. In January-August 2022 Belarusians sold more foreign currency than they bought. Despite the Ruble fluctuation, the high levels of net sales in March was due to bank restrictions. In June, the net purchase was related to seasonal factors (see Figure 2). For the other months of the period the net selling can be explained by a stable situation on the currency market and real incomes declining. People sold currency in an attempt to maintain their previous standards of living.
Figure 2. Balance of purchase and sale of foreign currency by households (+ “net purchase”, – “net sale”), mln. USD.
In September-October 2022 Belarusian households bought more than (an equivalent of) 300 mln. USD on net basis, primarily in USD or Euro, which is very unusual for the Belarusian market situation. There are several possible explanations for such behaviour:
- Despite difficulties with obtaining visas Belarusians are going to Poland and other European countries to shop. Because of sanctions, retaliatory sanctions as well as a high price control on the domestic market, the range of goods has shrunk, and prices have risen. In European countries Belarusians can purchase much cheaper goods both for personal use and for resale.
- Partial mobilisation in Russia has increased the uncertainty of further political steps in Belarus. Households thus purchase foreign currency to establish an extra safety cushion.
- In Q3 2022 there was a net cash outflow on international remittances, for the first time since 2017. Traditionally, Belarus has seen a net inflow of foreign remittances. In 2022 Belarusian banks were switched off from the SWIFT system which incurred problems with operations in foreign currencies for banks under sanctions. As a result, cash inflow has declined (see Figure 3). Cash outflows however remained on the same level as in previous years. This can be explained by high-level specialists and people employed within ICT leaving the country. During relocation people have sold apartments and cars and exchanged accumulated incomes from Belarusian Rubles to US Dollars or Euros and sent to foreign bank accounts (even under the conditions of facing difficulties with conducting money transfers).
Figure 3. Net cash inflow (+)/ outflow (-) for international remittances, USD mln.
Maintaining the trend of net currency purchase together with possible trade balance deterioration may exacerbate the situation on the domestic currency market. Another risk to the currency market stability is posed by the insufficient size of FX reserves (in the amount of less than 3 months of import). Moreover, the 900 mln. US Dollars in reserves, given by the IMF in 2021 as support to fight Covid-19, can’t be used as this financial support is given in the form of SDR (Special Drawing Rights), and the exchange of SDR to US Dollars or other currencies is challenging due to sanctions (Congress, 2022).
At the same time, the Government’s decision to make external debt payments in Belarusian Rubles supports the FX reserves level. It has also been decided that payments on Eurobonds to the Nordic Investment Bank, the European Bank of Reconstruction and Development and the International Bank of Reconstruction and Development are to be paid in Rubles. These decisions have decreased the country’s long-term rating on foreign liabilities to the Restricted Default level. In that sense, short-term gains can lead to significant financial losses in the long term. In the future it will be necessary not only to pay outstanding debts but also to improve Belarus’ reputation on the international financial market. Today, the Russian Federation is the main investor in the Belarusian economy. But since its support is limited, it is likely to be insufficient for the safe functioning of the Belarusian economy.
The stability of the Belarusian currency market is not the result of economic success, but rather a reflection of the tightening of the economy. The appreciation of the Belarusian Ruble to the US Dollar and Euro has taken place during an accelerated reduction in Belarusian imports. At the same time the weakness of the Belarusian currency to the Russian Ruble entails competitiveness of Belarusian products on the Russian market. Foreign exchange reserves, although insufficient, have maintained in size due to the low demand for foreign currency and foreign debt payments in Belarusian Rubles. Disruptions to economic and political relations with Western countries stimulates the Belarusian authorities to reorient the economy towards Eastern partners, which has led to a modification of the currency basket composition. In the long run, the current stability of the Belarusian currency can quickly disappear in case one or several risks are realised. If the Russian Ruble devaluates or trade balance deteriorates and demand for foreign currency increases, the stability of the Belarusian Ruble exchange rate can be ruined.
- Belarusian State Committee of the Republic of Belarus (BSCRB). (2022). Socio- Economic Situation of the Republic of Belarus in January- September 2022. https://www.belstat.gov.by/ofitsialnaya-statistika/publications/izdania/public_bulletin/index_58794/
- The National Bank of the Republic of Belarus. (2022). Statistical Bulletin #9 (279) 2022.
- The National Bank of the Republic of Belarus. (2022). https:// www.nbrb.by
- Tinkoff. (2022). The Central Bank has extended the currency restrictions for six months. https://secrets.tinkoff.ru/novosti/czentrobank-prodlil-valyutnye-ogranicheniya-na-polgoda/
- CBR. (2022). Balance of payments, international investment position and external debt of the Russian Federation in the first half of 2022. http://www.cbr.ru/statistics/macro_itm/svs/p_balance/
- Congress. (2022). H.R. 6899- Russia and Belarus SDR Exchange Prohibition Act of 2022. Public Law No: 117-185 (10/04/2022). https://www.congress.gov/bill/117th-congress/house-bill/6899.
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.
Georgia has an 8000-year-old winemaking tradition, making the country the first known location of grape winemaking in the world. In this policy brief we analyze and discuss major characteristics of the wine sector in Georgia, government policies regarding the sector and major outcomes of such policies. The brief provides recommendations on how to ensure sustainable development of the sector in a competitive, dynamic environment.
The Georgian winemaking tradition is 8000 years old, making Georgia the world’s first known location of grape winemaking. There are many traditions associated with Georgian winemaking. One of them is ‘Rtveli’ – the grape harvest that usually starts in September and continues throughout the autumn season, accompanied with feasts and celebrations. According to data from the National Wine Agency, the annual production of grapes in Georgia is on average 223.6 thousand tones (for the last ten-years), with most grapes being processed into wine (see Figure 1).
Figure 1. Grape Processing (2013-2021)
Wine is one of the top export commodities for Georgia. It constituted 21 percent of the total Georgian agricultural export value in 2021 (Geostat, 2022). Since 2012 wine exports have, on average, grown 21 percent in quantitative terms, and by 22 percent in value (Figure 2). The average price per ton varies from 3 thousand USD to 3.9 thousand USD (Figure 2). Exports of still wine in containers holding 2 liters or less constitute, on average, 96 percent of the total export value.
Figure 2. Georgian Wine Exports (2012-2021)
The main destination market for exporting Georgian wine is the Commonwealth of Independent States (CIS) countries which account for, on average, 78 percent of the export value (2012-2021). The corresponding share for EU countries is 10 percent. As of 2021, the top export destinations are Russia (55 percent), Ukraine (11 percent), China (7 percent), Belarus (5 percent), Poland (6 percent), and Kazakhstan (4 percent). While Russia is still a top market for Georgian wine, Russia’s share of Georgian wine exports declined after Russia imposed an embargo on Georgian wines in 2006. The embargo forced market diversification and even after the reopening of the Russian market and Georgian wine exports shifting back towards Russia, its share declined from 87 percent in 2005 to 55 percent in 2021.
While there are more than 400 indigenous grape varieties in Georgia, only a few grape varieties are well commercialized as most of the exported wines are made of Rkatsiteli, Mtsvane, Kisi, and Saperavi grape varieties (Granik, 2019).
Government Policy in the Wine Sector
The Government of Georgia (GoG) actively supports the wine sector through the National Wine Agency, established in 2012 under the Ministry of Environmental Protection and Agriculture (MEPA). The National Wine Agency implements Georgia’s viticulture support programs through: i) control of wine production quality and certification procedures; ii) promotion and spread of knowledge of Georgian wine; iii) promotion of export potential growth; iv) research and development of Georgian wine and wine culture; v) creation of a national registry of vineyards; and vi) promotion of organized vintage (Rtveli) conduction (National Wine Agency, 2022).
During 2014-2016, the GoG’s spending on the wine sector (including grape subsidies, promotion of Georgian wine, and awareness increasing campaigns) amounted to 63 million GEL, or 22.8 million USD (As of November 1, 2022, 1 USD = 2.76 GEL according to the National Bank of Georgia). Out of the spending, illustrated in Figure 3, around 40-50 percent was allocated to grape subsidies implemented under the activities of iv) (as mentioned above).
There are two types of subsidies used by the GoG– direct and indirect. Direct subsidies imply cash payments to producers per kilogram of grapes. As for indirect subsidies, they entail state owned companies purchase grapes from farmers.
Starting from 2017, the GoG decided to abandon the subsidiary scheme and decrease its spending on of the wine sector. The corresponding figure reached a minimum of 9.2 million GEL (3.3 million USD) in 2018. Meanwhile, the grape production has been increasing, reaching its highest level in 2020 (317 thousand tons). In 2020, the GoG resumed subsidizing grape harvests to support the wine sector as part of the crisis plan aimed at tackling economic challenges following the Covid-19 pandemic. The corresponding spending in the wine sector increased from 16.7 million GEL (around 6 million USD) in 2019 to 113.4 million GEL (41 million USD) in 2020, out of which the largest share (91 percent) went to grape subsidies. In 2021, the GoG continued its extensive support to the wine sector and the corresponding spending increased by 44 percent, compared to 2020. The largest share again went to grape subsidies (90 percent).
Figure 3. Grape Production and Government Spending on the Wine Sector (2014-2021)
In 2022, the GoG have continued subsidizing the grape harvest to help farmers and wine producers sell their products. During Rtveli 2022, wine companies are receiving a subsidy if they purchase and process at least 100 tons of green Rkatsiteli or Kakhuri grape varieties grown in the Kakheti region, and if the company pays at least 0.90 GEL per kilogram for the fruit. If these two conditions are satisfied, 0.35 GEL is subsidized from a total of 0.9 GEL per kilogram of grapes purchased (ISET Policy Institute, 2022). Moreover, the GoG provides a subsidy of 4 GEL per kilogram for Alksandrouli and Mujuretuli grapes (unique grape varieties from the Khvanchkara “micro-zone” of the north-western Racha-Lechkhumi and Kvemo Svaneti regions), if the buying company pays at least 7 GEL per kilogram for those varieties (Administration of the Government of Georgia, 2022). Overall, about 150 million GEL (54.2 million USD), has been allocated to grape subsidies in 2022.
Although the National Wine Agency is supposed to implement support programs in various areas like quality control, market diversification, promotion and R&D, these areas lack funding, as most of the Agency’s funds are spent on subsidies. Given that the production and processing of grapes have increased over the years, subsidies have been playing a significant role in reviving the wine sector after the collapse of the Soviet Union (Mamardashvili et al., 2020). However, since the sector is subsidized as of 2008, the grape market in Georgia is heavily distorted. Prices are formed, not on the bases of supply and demand but on subsidies, which help industries survive in critical moments, but overall prevent increases in quality and fair competition. They further lead to overproduction, inefficient distribution of state support and preferential treatment of industries (Desadze, Gelashvili, and Katsia, 2020). After years of subsidizing the sector, it is hard to remove the subsidy and face the social and political consequences of such action.
Nonetheless, in order to support the sustainable development of the sector, it is recommended to:
- Replace the direct state subsidy with a different type of support (if any), directed towards overcoming systemic challenges in the sector related to the research and development of indigenous grape varieties and their commercialization level.
- Further promote Georgian wine on international markets to diversify export destination markets and ensure low dependence on unstable markets like the Russian market. Although wine exporters have in recent years entered new markets, to further strengthen their positions at those markets, it is vital to:
- ensure high quality production through producers’ adherence to food safety standards.
- promote digitalization – e-certification for trade and distribution, block chain technology for easier traceability and contracting, e-labels providing extensive information about wine etc. – enabling producers to competitively operate in the dynamic environment (Tach, 2021)
- identify niche markets (e.g. biodynamic wine) and support innovation within these sectors to ensure competitiveness of the wine sector in the long-term (Deisadze and Livny, 2016).
- Administration of the Government of Georgia. (2022). “Gov’t releases updated conditions for vineries in grape harvest subsidies”
- Deisadze, S., Gelashvili, S. and Katsia, I. (2020). ”To Subsidize or Not to Subsidize Georgia’s Wine Sector?”, ISET Economist Blog.
- Deisadze, S. and Livny, E.(2016). “Back to the Future: Will an Old Farming Practice Provide a Market Niche for Georgian Farmers?”, ISET Economist Blog.
- GeoStat. (2020). Statistics of food balance sheets, retrieved from: https://www.geostat.ge/en/modules/categories/297/food-security
- Mamardashvili, P., Gelashvili, S., Katsia, I., Deisadze, S., Ghvanidze, S., Bitsch, L., Hanf, J. H., Svanidze, M. and Götz, L. (2020). “The Cradle of Wine Civilization”—Current Developments in the Wine Industry of the Caucasus”. Caucasus Analytical Digest (CAD), Vol 117.
- Granik, L. (2019). “Understanding the Georgian Wine Boom”. SevenFiftyDaily.
- ISET Policy Institute, 2022. “Agri Review October 2022“
- Ministry of Finance of Georgia. (2022). Statistics of State Budget, retrieved from: https://www.mof.ge/en/4537
- National Wine Agency (NWA). (2022). Main activities the agency, retrieved from: https://wine.gov.ge/En/Page/mainactivities
- Tach, L. (2021). “What Are The Future Digital Technology Trends In Wine? New OIV Study Reveals Answers”. Forbes.
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.
This brief summarizes the results of recent work on the effects of the COVID-19 pandemic on Russian exporting companies (Volchkova, 2021). We use data from the CEFIR NES survey of exporters conducted in 2020. 72% of respondents reported that they were affected by the crisis. We scrutinize this impact. Contrary to popular wisdom, we observe little difference in delays of inputs by domestic and foreign suppliers. On the other hand, exporters experienced more disruptions in their sales in foreign destinations than in the domestic market. Possible reasons for this may be due to restrictions on international travel.
According to experts at the Gaidar Institute (Knobel, Firanchuk, 2021), in 2020, Russia’s non-resource non-energy exports, decreased by 4.3%, while export prices fell by 4.1 % on average. The export of high-tech goods decreased by 14% due to a reduction in the physical volume of export. These changes in export intensity are mainly associated with the COVID-19 pandemic crisis. But are exporting firms more affected by the crisis than firms only active in the domestic market? What are the main channels through which the crisis influenced exporters? And how do exporters adjust to the COVID-19 related shocks?
The analysis in this brief is based on forthcoming publication in the Journal of New Economic Association (Volchkova, 2021). We use data from a survey of Russian non-resource exporters conducted in 2020. We show that involvement in international trade did not affect the company’s vulnerability to the crisis on the production side: supply delays were equally likely to occur from domestic and foreign suppliers. These findings are consistent with Bonadio et al. (2021) who consider a numerical multi-sectoral model for 64 countries around the world linked by supply chains. They show that, in the face of the employment shocks associated with quarantine measures and switching to a remote work format, the contribution of global chains to the decline of real GDP is about one quarter. Importantly, the authors show that the “re-nationalization” of supply chains does not make countries more resilient to shocks associated with quarantine measures on the labor market because these shocks are also bad for domestic industries.
At the same time, our results indicate that exporting companies are exposed to additional risks associated with the need to adjust to shocks in the sales markets. According to the data, exporters find it more difficult to adjust their sales in foreign markets than in the domestic one. This is consistent with the fact that, during the pandemic, all countries introduced a strict ban on international travel, reducing the possibility of establishing new business ties through personal contacts. Similarly, Benzi et al. (2020) show a significant negative effect of international travel restrictions on the export of services.
Survey of Non-resource Exporters
The survey of exporters was carried out in June – November 2020 by CEFIR NES. The primary purpose of the survey was to identify and estimate barriers to the export of non-primary non-energy products. In the context of the developing economic crisis caused by the COVID-19 pandemic, we have added several questions to identify how the crisis influenced companies’ operations and how the respondent firms adjusted to the new conditions.
The survey was conducted using a representative sample of Russian exporting firms. As a control group, we interviewed non-exporting firms with (observable) characteristics (region, industry, labor productivity) similar to those of the surveyed exporters. Altogether, 928 exporting companies and 344 non-exporting companies were interviewed during the field stage of the study.
Most exporting companies that took part in the survey produce food products, chemicals, machinery and equipment, electrical equipment, metal products, and timber. On average, a surveyed exporter had 827 full-time employees; 25% of the firms had fewer than 26 employees. More than half of the surveyed exporting firms (53%) are also importers: 81% import raw materials and other inputs, 66% import equipment, and 22% import technology. Most interviewed exporters sell their products both abroad and on the domestic market. On average, an enterprise supplies 67% of its output to the domestic market and 32% abroad.
Impact of the COVID-19 Crisis on Firms’ Performance
Among exporters that participated in the survey, 25% reported that their business was not affected by the COVID-19 crisis, while 72% of respondents stated that the crisis did have an impact. Like any crisis, the COVID-19 pandemic created problems for some enterprises and provided new beneficial opportunities for others. According to the data, exporting businesses were significantly more likely to be negatively affected by the crisis than their non-exporting counterparts, and the impact of the crisis was not correlated with the size of the enterprise. Figure 1 presents the exporters’ answers to the question of how their sales in the domestic and foreign markets have changed with the COVID-19 pandemic.
The distribution of changes in sales volume in domestic and foreign markets significantly differ from each other. Estimates of the mean values of changes in sales volumes also differ significantly: the average drop in sales in the domestic market was 5%, while for the external market, it reached 17%. Hence, in times of the COVID-19 crisis, opportunities for growth were less prominent in foreign markets than in the domestic one, while significant market losses were more frequent.
Figure 1. Change in sales of export companies associated with the COVID-19 pandemic
Adjustment to the Crisis
The most frequently used crisis adjustment measure was employees transition to remote work – it was reported by 70% of the surveyed companies. 25% of exporters were forced to suspend their work during the crisis, while 72% were not. 14% of respondents stated they had to cut their payroll expenditures and other non-monetary benefits for employees (food, insurance, etc.), 12% of companies sent workers on unpaid leave. Only 6.5% of export firms had to lay off workers, while 91% handled the crisis without layoffs.
Comparing exporters’ answers with those of non-exporters while controlling for enterprise size, we conclude that exporting firms were more rigid in their adjustment to the crisis. They were significantly more likely to suspend enterprise activities, dismiss of employees, send workers on unpaid leave, and reduce of wages. Also, these events were more likely to occur for smaller companies than for larger ones.
At the same time, flexible adjustment measures such as remote work were equally likely to be used by exporters and non-exporters, as well as by firms of different sizes. In general, Russian exporters of non-primary goods maintained their efficiency mainly by adjusting the labor relations to the new epidemiological conditions rather than by reducing employee-related expenses.
Dealing with Counterparties
Delays in the supply of components and raw materials were reported by 36% of the surveyed companies, and such delays were equally likely for shipments from abroad and domestic shipments. There is a perception that international supply chains in the context of the pandemic crisis are an additional risk factor. Our results indicate that domestic and international supply chains were equally challenged in 2020. Nevertheless, non-exporting companies faced the problem of delayed deliveries significantly less often than exporters did, and about 60% of companies experienced no problems at all on the input supply side.
27% of surveyed exporters stated that they delayed payments to counterparties. Non-exporting companies reported these reactions much less frequently regardless of firm size.
On the sales side, half of the surveyed exporters experienced delays in payments from their customers during the pandemic crisis. Non-exporting enterprises encountered the problems with the same frequency, and companies of all sizes were affected by this obstacle equally.
The cases of planned purchases cancellation on behalf of buyers were reported by 34% of exporting companies. Exporters experienced these problems significantly more often than non-exporters, and smaller companies experienced them much more often than larger ones.
Crossing international borders presented a certain problem for Russian exporters when it concerns product delivery. Just over half of the respondents indicated that they had to delay deliveries due to difficulties with border crossing. However, about the same share of companies (48%) reported that they delayed products delivery due to the introduction of lockdowns. Thus, during the COVID-19 pandemic, exporters’ operations were complicated to the same extent by problems related to border crossings as by those associated with lockdown regimes.
It is widely believed that international exposure of companies in the context of the COVID-19 pandemic crisis creates additional risks. Our study shows that, regarding existing inputs supply, international relations pose problems for Russian companies just as often as relations with domestic partners. As far as sales are concerned, adjustment to the crisis was better on the domestic market than on foreign markets. A possible explanation of this phenomenon is that, in addition to the shocks associated with quarantine measures in the labor market, access to foreign markets was hampered by restrictions on international travel, which is essential for readjusting contractual relations to explore new opportunities brought by crises (Cristea, 2011). Without personal interaction, new contracts were more difficult to launch. Thus firms’ opportunities to adjust foreign sales were more restricted than the ones in the domestic market.
- Benzi, S., F. Gonzalesi and A. Mourouganei, 2020, “The Impact of COVID-19 international travel restrictions on services-trade costs“, OECD Trade Policy Papers, No. 237, OECD Publishing, Paris
- Bonadio, B, Z. Huo, A. Levchenko and N. Pandalai-Nayar, 2021, “Global Supply Chains in the Pandemic“, NBER WP 27224
- Cristea A.D. (2011). “Buyer-seller relationships in international trade: Evidence from U.S. States’ exports and business-class travel“. Journal of International Economics, 84, 2, 207–220.
- Knobel A.Yu., A. Firanchuk, 2021, “International trade in 2020: overcoming decline”, Economic development of Russia, V. 28, № 3, pp. 12–17 (in Russian).
- Volchkova, 2021. Russian exporters during economic crisis caused by COVID-19 pandemic. Journal of New Economic Association, forthcoming.
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.
Media mentions: Key takeaways from this policy brief have been published by one of the most influential media outlets in Russia Kommersant – Коммерсант: «Ковид сильнее ударил по экспортерам». Исследование ЦЭФИР РЭШ.
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
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.
How does the removal of trade preferences influence the exports of the affected country? We study this question on the example of Belarus’ loss of trade preferences granted by the EU to developing countries. Our brief argues that trade preferences are most important for simple non-manufactured goods. As a result, removal of trade preferences should increase the manufactured goods in the export structure. Indeed, the overall complexity of Belarusian exports was not harmed by the removal of EU preferences and the manufactured exports increased relative to non-manufactured exports.
Belarus losing trade preferences
As a developing country, Belarus used to receive trade preferences from the US and EU. These preferences grant duty-free imports or provide a discount on the import tariff under the so-called Generalized System of Preferences (GSP). The preferences are provided on a unilateral basis to developing countries and can also be removed on a unilateral basis for various reasons. Their stated objective is to support the economic development of poorer countries (Ornelas 2016). In particular, the US removed Belarus’ preferences in 2000 for worker rights violations. Later, the EU removed the preferences in 2007 for similar reasons. It is a relevant question for policy to understand how the removal of trade preferences affected exports.
This brief discusses the effect of trade preferences removal on the value of Belarus’ exports to the EU and on the structure of exports. Utilization of trade preferences might not be uniform across sectors. In fact, a preference-receiving country should satisfy the Rules of Origin (ROO) requirements and demonstrate that a large enough share of the exported product was produced in the country. This requirement might be more difficult to satisfy for complex manufactured goods with many components from several countries (Hakobyan 2015). Exporters of such products might find satisfying the ROO more costly than what they could gain from receiving an import tariff preference. Exporters of simple or raw products, on the other hand, face a lower cost of demonstrating the origin.
The remainder of the brief develops the hypothesis of a differential impact of trade preferences removal on manufactured and non-manufactured goods; and makes an event study of Belarus’ loss of EU trade preferences in 2007. Our findings suggest that GSP withdrawal affected disproportionally non-manufactured exports, leading to an increase in the manufacturing exports share. This means that harm caused by losing trade preferences was, to some extent, reduced by higher incentives to export more complex manufactured exports.
The complexity of Belarusian exports
To understand the overall structure of Belarusian exports, we first look at the complexity of Belarusian exports over time. Figure 1 presents the economic complexity index (ECI), developed by Hausmann et al. (2014), of exports of Belarus relative to Russia from 1995 to 2014. The ECI measures the diversity and ubiquity of a country’s exports. It considers the number of products a country exports with revealed comparative advantages and how complex these products are. In turn, the complexity of the products is accessed by a so-called product complexity index, PCI. It is determined in an analogous fashion: if few countries are able to export a good and these countries have diversified exports, this product is complex. For example, fertilizers and oil (important exports of Belarus) have low complexity scores, as countries that export these products tend to not have diversified exports.
Figure 1 shows that the difference between the economic complexity of Belarus and Russia increased following the two incidents of Belarus losing trade preferences; first from the US and then from the EU. The incidents of removal of trade preferences are associated with an increase in economic complexity of Belarusian exports relative to Russia. That is, the export of more complex manufactured goods became more important in the export basket of Belarus when it lost the trade preferences. This is consistent with the hypothesis that trade preferences are more important for simpler goods, and following a preference removal their share will go down. Russia is chosen for comparison due to its similarity in economic perspective (economies in transition, similar complexity, GDP trends, dependence on oil and fertilizer prices) and because it also received trade preferences from both the US and EU throughout the considered period.
Figure 1. GSP withdrawal and Export Complexity in Belarus relative to Russia
Export structure of Belarus
To make a first pass at understanding how GSP withdrawal affects the composition of exports, we conduct an event study centered on the year of 2007, when the EU withdrew its GSP preferences for Belarus. We consider the three years before and after the revocation, and benchmark the share of manufacturing exports from Belarus to the EU with its share of manufacturing exports to the US. Since the US had already withdrawn its preferences earlier, its trade regime with Belarus stayed unchanged throughout the period. This makes the US a natural point of comparison to understand the effect of GSP withdrawal.
As Figure 2 shows, the average share of manufactured products in Belarusian exports to the EU increased slightly after the GSP withdrawal, increasing to 40.4% from its earlier level of 37.9%. At the same time, mineral and fuel exports, though falling slightly, remain the backbone of Belarusian exports accounting for 50% of total exports to Europe. Interestingly, the share of non-fuel exports to the EU remained approximately unchanged at 9%. In other words, the composition of exports to Europe did not drastically change after the GSP withdrawal, as had been anticipated by some ex-ante studies (e.g. BISS 2007).
This comparison alone does not address the question of what might have happened to Belarusian manufacturing exports had the GSP preference not been removed. One possible counterfactual is that the trends in the European export market would have been the same as in the US, where Belarusian manufacturing exports massively lost ground. Their share decreased from 53.4% to 19.3%. Hence, a difference-in-difference estimator would suggest that perhaps the withdrawal of the GSP reduced non-manufacturing export growth to Europe. In turn, the Belarusian manufacturing export share is estimated to be 36.5% higher than it might have been if the GSP had not been withdrawn (statistically significant at the 1% level). This estimate may be a result of trade diversion of non-manufactured goods from the EU to the US. To the extent that non-manufacturing products benefit more from the GSP preferences, these should be stronger affected by trade diversion and would therefore reduce the manufacturing share of Belarus’ exports to the US.
Figure 2. Share of Manufacturing Exports
Alternatively, one could consider the Belarusian manufacturing export share in relation to Russia, within the European market. For Russia, there is a pattern of declining manufacturing shares. Before 2007, manufacturing accounted for 17.7% of exports to the EU, but afterwards it declined to 14.2%, a 2.5% fall. If Belarus had experienced the same trend, its manufacturing share would have fallen from 37.9% to 34.4%. Instead, Belarusian manufacturing share grew from 37.9 to 40.4%, which suggests that due to the GSP removal, the Belarusian manufacturing export increased by 6%. Given the smaller effect size and the short sample period, this increase is not statistically significant. However, in economic terms, it would still be an important shift.
Although development is one of the main goals of the GSP, there is little evidence that the EU’s Generalized Scheme of Preferences supported the development of advanced industries in Belarus. To the contrary, after the GSP withdrawal the export complexity of Belarus increased relative to that of Russia. There is also some suggestive evidence that the GSP may have encouraged an export profile more focused on non-manufactured products, for which rules of origin are easier to satisfy in practice. More research is clearly needed, not least to analyze other cases of GSP withdrawal for external validity.
Our preliminary findings suggest that GSP in its current form might have created incentives for exporting relatively simple goods, thus creating a risk of “middle-income trap”. Policy implications are twofold: First, the goal of preference programmes like the GSP is development, i.e. more advanced economy with more complex production, and if the preferences in fact foster simple exports, it could create a barrier to development; Second, removal of preferences might have a large negative impact overall but the observation that it removes the previous incentive of producing simple non-manufacturing goods can be seen as positive and thus cushion the negative impact.
- Belarusian Institute for Strategic Studies (BISS), 2007. “Belarus exclusion from the GSP: possible economic repercussions”, at: http://www.belinstitute.eu.
- Hakobyan, Shushanik, 2015. “Accounting for underutilization of trade preference programs: The US generalized system of preferences.” Canadian Journal of Economics/Revue canadienne d’économique, 48.2, 408-436.
- Hausmann, Ricardo; Hidalgo, Cesar A., Bustos, Sebastian; Coscia, Michele, Simoes, Alexander, & Yildirim, Muhammed A. (2014). The atlas of economic complexity: Mapping paths to prosperity. Mit Press.
- Ornelas, Emanuell, 2016. “Special and differential treatment for developing countries.” Handbook of Commercial Policy 1, 369-432.ilable online, please hyperlink the title.
Russia’s dependence on oil and other natural resources is well known, but what does it actually mean for policy makers’ ability to control the economic fate of the country? This brief provides a more precise analysis of the depth of Russia’s oil dependence. This is based on a careful statistical analysis of the immediate correlation between international oil prices — that Russia does not control — and Russian GDP, which policy makers would like to control. I then look at how IMF’s forecast errors in oil prices spillover to forecast errors of Russian GDP. These numerical exercises are striking; over the last 25 years oil price changes explain on average two thirds of the variation in Russian GDP growth and in the last 15 years up to 80 percent of the one-year ahead forecast errors. Instead of controlling the economic fate of the country, the best policy makers can hope for is to dampen the short-run impact of oil price shocks. A flexible exchange rate and fiscal reserves are key volatility dampers, but not sufficient to protect long-term growth. The latter will always require serious structural reforms and the question is what needs to happen for policy makers to take action to get control over the long-term fate of the economy.
In a recent working paper (Becker, 2016), I take a careful look at the statistical relationship between Russian GDP and international oil prices. This brief summarizes this analysis and its policy conclusions.
Russia and oil, the basics
Although Russia’s oil dependence is discussed every time international oil prices drop, it is not uncommon to hear that oil is not really so important for the Russian economy. The argument is that the oil and natural resource sector only accounts for some 10 percent of Russian production. This is indeed consistent with the official sectoral breakdown of GDP that is shown in Figure 1 where the minerals sector indeed only has a 10 percent share.
Figure 1. Structure of GDP in 2015
However, this static picture of production shares does not translate into a dynamic macro economic model that allows us to understand what is driving Russian growth. Instead a careful analysis of the time series of Russian GDP is required to understand how important oil is for growth.
Russian GDP can be measured in many different ways: nominal rubles, real rubles, U.S. dollars, or in purchasing power parity (PPP) terms to mention the most common. Here we focus on GDP measured in real rubles and U.S. dollars since we want to get rid of Russian inflation, which has been quite high for most of the studied time period. The PPP measure generates figures and numerical estimates that are in between the real ruble and U.S. dollar measures and are not included here to conserve space.
The first evidence of the importance of international oil prices as a major determinant of Russian income at the macro level is presented in Figures 2 and 3 where the first figure shows dollar income and the second real ruble income. In both cases it is obvious that there is a strong correlation and that the correlation is higher for income measured in dollars.
Figure 2. U.S. dollar GDP and the oil price
Figure 3. Real ruble GDP and the oil price
However, it is also clear that all the time series have some type of trends or in econometric language, are non-stationary. This means that simple correlations of the time series shown in Figure 2 and 3 may not be statistically valid (or “spurious” as it is called in the literature). This is not a critical issue but can be handled by regular econometric methods.
Russia and oil, the econometrics
When time series are non-stationary they need to be transformed to some stationary form before we can do regular regressions (in Becker, 2016 I also address the issue of using a framework that allows for co-integration).
Two transformations that make the variables stationary are to use first differences or percent growth rates. Both are used before we run simple regressions of growth or first differences of GDP on growth or first difference in international oil prices. The full sample starts in 1993, but since the early years of transition were subject to many different shocks at the same time, a shorter sample starting in 2000 is also used.
A number of observations come from the estimates that are presented in Table 1: Oil prices are always statistically significant; the adjusted R-squared is higher for dollar income than real rubles (with one exception due to a large outlier in 1993); overall the explanatory power of these simple regressions are very high (42-92 percent) and the explanatory power increases in all specifications when going from the full sample (1993-2015) to the more recent sample (2000-2015). Note that the latter sample perfectly overlaps with the current political leadership so contrary to some wishes; the oil dependence has not been reduced under Putin/Medvedev.
Table 1. Russian macro “models”
Russia and oil, the forecasts
The strong correlation between international oil prices and Russian GDP provides a very simple econometric model for explaining past variations in Russian GDP. Unfortunately it does not imply that it is easy to forecast Russian GDP since international oil prices are very hard to predict. There are many models that have been used to forecast oil prices, but the IMF and many others now use the market for oil futures to generate its central forecast of oil prices.
The IMF also provides confidence intervals around the central forecast, and the uncertainty surrounding the forecast is substantial: In the latest forecast the 68 percent confidence interval goes from around 20 dollars per barrel to 60 one year ahead, while the 98 percent interval ranges from 10 dollar per barrel to around 85. With oil currently around 45 dollars per barrel, these variations imply that oil prices could either halve or double in the next year, not a very precise prediction to base economic policy on for Russia since the estimates for real ruble growth in the later sample in Table 1 imply that Russian GDP growth in real ruble terms could be anywhere from minus 5 to plus 10 percent, or a fifteen percentage point difference!
If we look at past IMF forecasts of oil prices and Russian GDP and see how much they deviate from actual values a year later we can compute one year ahead forecast errors. We can do this calculation for the last 16 years for which the IMF data is available. Figures 4 and 5 show how the forecast errors in oil prices correlate with the forecast errors for dollar income and real ruble income, respectively. Similar to the regressions presented in Table 1, the correlations are very high for both measures of GDP: 82 percent for dollar GDP, and 65 percent for real ruble GDP.
In other words, a very large share of the uncertainty surrounding Russian GDP forecasts can be directly attributed to variations in international oil prices, a variable that (again) Russia does not control. The fact that the variations in oil prices explain somewhat more of the variation in dollar income compared to real ruble income is a result of a policy change that in later years allowed the exchange rate to depreciate much more rapidly when oil prices fall.
Figure 4. Forecast errors
Figure 5. Forecast errors
The depth of Russia’s oil dependence is much greater than what casual observers of the mineral sectors share of GDP would suggest. At the macro level, variations in international oil prices explain at least two thirds of actual Russian growth and even more of the one-year ahead forecasts errors.
The experience of the 2008/09 global financial crisis provided an important lesson to Russian policy makers, which is that exchange rate flexibility is required to dampen the real impact of falling oil prices and to protect both international reserves and the fiscal position. In the more recent years, the currency has been allowed to depreciate in tandem with falling oil prices and the drop in real ruble income was therefore less severe in 2015 than in 2009. Income in dollar terms, instead, took a greater hit, but this was a necessary corollary to protecting reserves and the budget. A flexible exchange rate and gradual move to inflation targeting in combination with accumulating fiscal reserves in times of high oil prices are key to Russia’s macro economic stability.
Nevertheless, these policies are not sufficient to remove the long-run impact that low or declining oil prices will have on growth, measured both in real ruble terms or dollar terms. It is nice to have fire insurance when your house burns down, but when you rebuild the house you may want to consider not building another straw house. For Russia to build a strong economy that is not completely hostage to variations in international oil prices, fundamental reforms that encourage the development of alternative, internationally competitive, companies are needed. This includes reforms that initially will reduce policy makers control over the economy and legal system, but over time it will provide the much needed diversification away from exporting oil that puts the fate of the Russian economy squarely in the hands of international oil traders. Losing some control today may provide a lot more control in the future for the country as a whole, but perhaps at the expense of less control for the ruling elite.
- Becker, T, 2016, “Russia’s oil dependence and the EU”, SITE Working paper 38, August.
- Federal State Statistics Service (or Goskomstat), 2016, data http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/en/figures/domestic/
- IMF, 2016, World Economic Outlook, April data from http://www.imf.org/external/pubs/ft/weo/2016/01/weodata/index.aspx
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.
‘Diversification’ is a constant concern of policy-makers in resource rich economies, but measurement of diversification can be hard. The recently formulated Economic Complexity Index (ECI) is a promising predictor of economic development characterizing the overall complexity and diversity of the economy as a system. The ECI is based on the diversity and ubiquity of a country’s exports. This brief uses ECI to discuss the economic diversity of transition economies in the post-Soviet decades, and the relationship between economic diversification and per capita income.
The search for and construction of appropriate predictors of economic development are among the main goals of economists and policy-makers. Education, infrastructure, rule of law, and quality of governance are all among the commonly used indicators based on inputs. The recently formulated Economic Complexity Index (Hidalgo and Hausmann, 2009) is a new promising predictor of economic development characterizing the overall complexity and diversity of the economy as a system.
Indeed, the importance of production and trade diversification for economic development has been highlighted by the economic literature. Numerous studies have found a positive relationship between diversified and complex export structure, income per capita and growth (Cadot et al., 2011; Hesse, 2006; Hausmann et al., 2007). In line with this, Hausmann et al. (2014) demonstrate the predictive properties of the ECI for economic development and GDP per capita, which implies that the ECI can serve as a useful complement to the input-based measures for policy analysis by reasoning from current outputs to future outputs.
This brief uses the ECI to discuss the evolution of economic diversification, its relationship to per capita income in transition economies in the post-Soviet decades, and its policy implications.
How is economic complexity measured?
The economic complexity index (ECI) is a novel measure that reflects the diversity and ubiquity of a country’s exports. The index considers the number of products a country exports with revealed comparative advantage and how many other countries in the world export such goods. If a country exports a high number of goods and few other countries export these products, then its economy is diversified (a wide range of exports products) and sophisticated (only a few other countries are able to export these goods). Thus, the measure tries to capture not a specific aspect of the economy, but rather its overall sophistication.
For example, Japan, Switzerland, Germany and Sweden have been in a varying order at the top of the ranking of the Economic Complexity Index from 2008 until 2013. This means that these countries export a large number of highly sophisticated products.
In contrast, Tajikistan is among the countries at the bottom of the world ranking by the ECI with raw aluminum, raw cotton and ores making up 85% of all Tajikistan’s exports in 2013. However, not only are Tajikistan’s exports concentrated among very few narrow products, these products are also ubiquitous and the ability to export them does not require knowledge and skills that can be used in the production and exports of many other products.
As the index for each country is constructed relative to other countries’ exports, it is comparable over time.
What can we learn from the economic complexity of transition economies?
The economic complexity index can serve as a useful indicator for understanding transition economies in the post-Soviet period. A strong relationship between GDP per capita and economic complexity is found in the sample of transition economies in Figure 1. This figure presents the relationship for the last year for which data is available for the sample of 13 post-Soviet states and Poland. As can be seen in Figure 1, the economic complexity is positively related to income per capita. This is especially true for Poland, Estonia, Lithuania, Latvia and Russia, who all have higher than average economic complexity and high levels of per capita income. While Belarus and Ukraine also have diverse and complex economies, they have somewhat lower income per capita than the first group.
Figure 1. Economic Complexity and GDP per capita
Source: Data on GDP per capita is from the World Bank, and the data on the Economic Complexity Index is from the Observatory of Economic Complexity.
Natural resource-rich, or rather, oil-rich countries are the exception from the abovementioned correlation. Most transition countries with below than average economic complexity are characterized by low income per capita levels, except for Kazakhstan and Azerbaijan, which are oil-rich countries. Still, the overall picture is straightforward: countries with a complex export structure have a higher level of income.
One of the advantages of a systemic measure like export complexity is its straightforward policy application. The overall diversity and sophistication of the economy can thus be a complementary measure for the assessment of economic progress and development to GDP and GDP per capita, which are more susceptible to the volatile factors such as commodity prices.
Figure 2 shows the development of economic complexity for 14 post-Soviet countries and Poland between 1994 and 2013 (due to data availability issues, only one year is available for Armenia).
First, we see that the economic complexity has diverged over time, although there is some similarity in the rankings among countries over time. The initial closeness is likely related to the planned nature of the Soviet economy that aimed to distribute production among Soviet Republics. In the post-Soviet context, however, the more complex economies (Estonia, Belarus, Lithuania, Ukraine, Latvia, Russia) kept or increased their sophistication and diversity of exports. Poland is the leading economy in terms of complexity, both in the beginning and towards the end of the sample period. Belarus, the second most complex economy in 2013 and the most complex economy in several years prior, shows an increasing trend in its sophistication of exports. Although its GDP per capita is noticeably lower than what would be expected from such a sophisticated economy, the complex production structure may explain its ability to withstand a permanent high inflation and external macroeconomic shocks. Some others, e.g., Tajikistan and Azerbaijan, saw a decreasing trend in economic complexity; Georgia and Kazakhstan, notably, lost in economic complexity but also in their ranking among their peers.
Figure 2. Economic Complexity of Transition Economies
Source: Data on GDP per capita is from the World Bank, and the data on the Economic Complexity Index is from the Observatory of Economic Complexity.
This brief revisited the economic complexity of transition economies and its evolution since the 1990s. The post-Soviet and other transition countries have had diverging economic development paths: Some have managed to build complex production economies, while others’ comparative advantage remains in raw materials. These differences are also reflected in their income levels.
Across the world, economic diversification is associated with higher per-capita income. As the brief showed, this relationship also holds for the post-Soviet countries; policy-makers should take economic diversification seriously. Increasing economic complexity may well pave the path to higher income levels.
- Cadot, O., Carrère, C., & Strauss-Kahn, V. (2011). Export diversification: What’s behind the hump?. Review of Economics and Statistics, 93(2), 590-605.
- Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Simoes, A., & Yildirim, M. A. (2014). The atlas of economic complexity: Mapping paths to prosperity. Mit Press.
- Hausmann, R., Hwang, J., & Rodrik, D. (2007). What you export matters. Journal of economic growth, 12(1), 1-25.
- Hesse, H. (2006). Export diversification and economic growth. World Bank, Washington, DC.
- Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. proceedings of the national academy of sciences, 106(26), 10570-10575.