THE RELATIONSHIP BETWEEN TRADE POLICIES AND MACROECONOMIC ADJUSTMENTS IN THE RUSSIAN CHEESE MARKET INTEGRATION

Alisher Tleubayev; Jaghdani Tinoush Jamali; Götz Linde; Svanidze Miranda

А. Тлеубаев

Университет им. Сулеймана Демиреля, г. Алматы, Казахстан; Галле-Виттен-бергский университет им. Мартина Лютера, г. Галле, Германия

Т. Д. Джагдани

Институт аграрного развития в странах с переходной экономикой им. Лейбница, г. Галле, Германия

Л. Гёц

Институт аграрного развития в странах с переходной экономикой им. Лейбница, г. Галле, Германия

М. Сванидзе

Институт аграрного развития в странах с переходной экономикой им. Лейбница, г. Галле, Германия

Интеграция внутреннего рынка сыров в России: влияние торговой политики на макроэкономические изменения

Аннотация. События, происшедшие в России в 2014 г., оказали заметное влияние на развитие внутреннего рынка, в том числе продовольственного. В сфере молочной продукции наиболее значимый рост отечественного производства и падение импортных закупок отмечены в отношении такого продукта, как сыр. В статье исследуется влияние торговой политики России и макроэкономических сдвигов 2014 г. на динамику цен на сыры внутри страны. Методологической основой анализа стоимостных показателей, демонстрируемых регионами - производителями и потребителями сыров, послужили концепции единой цены, ценовой коинтеграции и дисперсии. В качестве методов применялись тест Йохан-сена на коинтеграцию, векторная модель коррекции ошибок и динамическая модель панельных данных. Информационную базу для анализа ценовой коинтеграции и дисперсии составили 39 ценовых пар для субъектов РФ - лидеров по производству и потреблению сыров в 2008-2016 гг. Результаты исследования показали, что с лета 2014 г. в этих регионах наблюдается существенное усиление коинтеграции цен. Доказано, что российские ответные санкции, снижение объемов импорта и падение курса рубля привели к существенному увеличению дисперсии цен на сыры в Москве и регионах-производителях. Согласно выводу авторов, рост внутренней торговли не оказал существенного положительного влияния на эффективность рынка сыров в России.

Ключевые слова: торговая политика; рыночная эффективность; контрсанкции; коин-теграция; ценовая трансмиссия; ценовая дисперсия; рынок сыров; Россия.

Благодарности: Исследование выполнено при финансовой поддержке Федерального Министерства продовольствия и сельского хозяйства Германии, проект № 2816HS005 «Анализ стратегии Российской Федерации по расширению сельскохозяйственного производства (STARLAP)».

Для цитирования: Tleubayev A., Jaghdani T. J., Gotz L., Svanidze M. (2021). The relationship between trade policies and macroeconomic adjustments in the Russian cheese market integration // Journal of New Economy. Т. 22, № 3. С. 44-68. DOI: 10.29141/2658-5081-202122-3-3

Дата поступления: 23 июня 2021 г.

Introduction

Russia plays an important role in global food security. It is already one of the largest producers

and exporters of key crops, including wheat, barley and sunflower seeds worldwide, and the country has huge potential to further increase its list of exported agri-food products. Russia's enormous agricultural land of more than 200 million ha1 and its favourable climate with high levels of rainfall, provides good potential for the production of agri-food products. Moreover, Russia alone accounts for more than 40 % of all chernozem (black earth) soil around the globe2 [Kuznetsov, Isaeva, Pavlushkina, 2017]. However, despite the country's food growing potential and its excellent natural conditions, Russia was one of the world's largest food importing countries up until mid-2014, importing the major share of its dairy, meat and fish products, as well as fruits and vegetables consumed domestically. In the case of total food products, the share of imports in domestic consumption reached over 40 % in 2013 [Liefert et al., 2019].

To mobilise the production potential in the agri-food sector, the Russian government introduced an agricultural import substitution policy, which is comprised of two elements. On the one hand, the country's government has launched comprehensive agricultural supporting programs to attract investments in agricultural production. On the other hand, the government has gradually changed its agricultural import policies to favour a boosting of domestic agricultural production. Ultimately, this policy aims to increase self-sufficiency levels for most of the food products and, furthermore, to become one of the largest exporters of these products worldwide [Götz, Djuric, 2016]. Agricultural producers were supported within the "2006 National Project" for the development of an agro-industrial complex, which was later transformed into the "Agricultural Development Program 2008-2012", then to the "Food Security Doctrine of 2010" and eventually the "Agricultural Development Program of 2013-2020"3 [Vassilieva, 2012; Vassilieva, Smith, 2010].

The year 2014 held many events that make it a special and stand-out year for the Russian economy. Between June 2014 and January 2015, oil prices dropped by more than 50 US dollars per barrel, which resulted in a huge drop in revenues, real exchange rate depreciation and even a negative GDP growth rate in Russia [Polbin, Skrobotov, Zubarev, 2020]. Furthermore, in 2014, a set of sanctions and counter-sanctions were also implemented between Russia and certain western countries due to regional disputes [Gould-Davies, 2020]. As a result, imports were restricted by import taxes, non-tariff barriers and even an import ban, which was implemented in August 2014. The import of dairy products was particularly affected by Russia's counter-sanctions, as up to around 80 % of all dairy imports were previously imported from the sanctioned countries [Boulanger et al., 2016].

In the study, we aim to assess how efficient the Russian dairy market is with a focus on the cheese market's functioning, a question which has not been addressed in the literature before. Following a price transmission and price dispersion modelling approach, we attempt to identify and measure the effects of these post-August 2014 changes in Russian trade policies and macroeconomic adjustments on the Russian dairy market. Our hypothesis is that Russia's counter-sanctions on the import of food and agricultural commodities both positively affected the domestic regional price integration and increased the regional price dispersion within the Russian dairy sector. Because of the implementation of the counter-sanctions on food imports, importing regions of Russia substituted dairy imports from international markets with imports from domestic dairy producing regions. Therefore, the domestic

1 FAO. (2021). FAOSTAT, Russian Federation Country Indicators. http://www.fao.org/faostat/en/#country/185.

2 FAO. (2001). Mineral Soils conditioned by a Steppic Climate. FAO.

3 DONLAND. (2010). National Project "Development of Agro-Industrial Complex". http://www.donland.ru/ Deyatelnost/Nacproekt/APK/?pageid=81498. (in Russ.)

dairy trade increased with the implementation of Russia's counter-sanctions and import restrictions, strengthening regional integration in the Russian dairy market. We measure the spatial price integration during free trade and restricted trade (imports) by employing the Johansen cointegration and vector error correction model (VECM) approach to weekly consumer price series from January 2008 to December 2016. Furthermore, following the price integration, the bivariate price dispersion between all market pairs in the first part are estimated before and after August 2014. Finally, possible factors that can affect the bivariate price differences between Moscow, a major urban centre, and cheese producing regions is tested with a dynamic panel model.

The paper is structured as follows. First, we provide an overview of the Russian dairy sector and its external trade of dairy products. Following, we present a review of the fundamental literature on regional price integration and price dispersion. Then, the paper gives an overview of the methodology and data used in the study and provides the empirical results. Finally, the discussion and conclusion are presented.

Specifics of the Russian dairy sector Production. During transformation period after the collapse of the Soviet Union, milk production in Russia decreased (Figure 1), mainly due to a significant drop in the number of cows (Figure 2).

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While the country's annual milk production reached 55 million tonnes in 1990, production levels amounted to only 30 million tonnes in 2016. However, the structure of milk production did not notably change during the import restrictions. Milk producers are categorised into households, large agricultural enterprises3 and farms, and individual entrepreneurs. At the beginning of the 1990s, Russia's milk production was highly dominated by agricultural enterprises. However, their share steadily decreased until 1999, with farm households becoming the largest producers of milk since then (Figure 3). Concurrently, butter production dropped dramatically between 1990 and 2000 (Figure 4), and did not recover during this period. Moreover, an increase in butter production was not observed after the implementation of import restrictions in 2014.

1 Own illustration based on data from Rosstat.

2 Own illustration based on data from Rosstat.

3 The Russian word for this organisation is "Сельскохозяйственные организации", which used to be called "Selkhoz".

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— Butter production, thousand tonnes

— Cheese production, 150 ' thousand tonnes

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The opposite is true in the case of cheese production. In contrast, it nearly halved between 1990 and 1995, and has steadily recovered since then. In 2006, cheese production reached the levels of 1990. Since 2013 cheese production has risen dramatically and continued to grow even during and after the import restrictions of 2014 (Figure 4). The production of skim milk powder (SMP) and whole milk powder (WMP) has been decreasing significantly over the last decade. In 2010 alone, the production of both milk powders saw a more than twofold drop. While the production of WMP has not changed substantially since 2010, the production of SMP increased by around 200 % during the period of 2010-2014, but has constantly decreased since then. In 2016, the production levels of WMP and SMP was 42 and 63 thousand tonnes, respectively3.

Milk processing is highly concentrated and dominated by a few large enterprises. While the two largest companies, Danone and PepsiCo, jointly accounted for about 18.1 % of total milk processing in 2015, that same year, the Top 10 companies jointly processed 30.7 % of all milk around Russia4. Figures 5 and 6 present the largest domestic cheese exporting and importing regions, respectively5.

1 Source: own illustration based on data from Rosstat.

2 Source: own illustration based on data from Rosstat.

3 USDA. (2017). Data & Analysis, USDA Foreign Agricultural Service. Online Database. https://www.fas.usda. gov/data/

4 RussianDairy. (2016). Rating top-50 dairy companies. Russian Dairy Research Centre. http://www.dairynews. ru/news-image/20161230/TOn-50. (in Russ.)

5 Internal regional trade data is used for these figures.

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Fig. 5. Top regional cheese exporters, tonnes1 Рис. 5. Субъекты РФ, лидирующие по экспорту сыров, т

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city krai of Saint oblast oblast oblast

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Fig. 6. Top regional cheese importers, tonnes2 Рис. 6. Субъекты РФ, лидирующие по импорту сыров, т

While exports from almost all regions have risen since 2014, the opposite is true for the case of Moscow oblast3 (Figure 5). Similarly, cheese importing regions have increased their imports from domestic markets since 2014 (Figure 6). The highest growth in domestic imports is observed for Smolensk (78 %), Saint Petersburg (22 %) and Samara (18 %). This is presumably because of their higher dependence on European cheeses and relatively lower levels of local production.

Trade patterns. Russia's importation of dairy products has faced two major challenges recently: the Eurasian Customs Union between Russia, Belarus and Kazakhstan in 2010 and the import restrictions of August 2014 and afterwards. The food import restrictions seem to have had different effects on different types of dairy products, as were observed in the production data. The import of raw milk products and butter has more or less been replaced by imports

1 Source: own illustration based on data from Rosstat.

2 Source: own illustration based on data from Rosstat.

3 Administrative subdivision of Imperial Russia or a republic of the former Soviet Union or of the Russian Federation which can be translated to "area", "zone", "province" or "region".

from Belarus after Russia's counter-sanction of August 2014 (Figures 7 and 8). As a result, total imports of butter and milk since the introduction of the import restriction have not changed much.

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However, in the case of cheese, the picture is completely different (Figure 9). Total imports of cheese dropped dramatically since the import restrictions of 2014 and were at a level nearly two times lower in 2017 than before August 2014. The share of European countries in the total imports of cheese was extremely high and imports from Belarus were not enough to compensate for such a huge drop in imports.

1 Source: own illustration based on data from TradeMap.

2 Source: own illustration based on data from TradeMap.

45

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Fig. 9. Monthly cheese imports, thousand tonnes1 Рис. 9. Ежемесячные объемы импорта сыров, тыс. т

Imports of both SMP and WMP have increased dramatically since 2007, nearly 2.5 times and almost twofold, respectively. While imports of SMP and WMP from the European Union have been decreasing since 2010 and even totally stopped after 2014, imports of both products from Belarus have skyrocketed, with even higher rates. The share of Belarus in the imported quantity of SMP and WMP is huge. However, this share decreased significantly in 2016 by nearly 18 % and about half for SMP and WMP, respectively2. It must be added that a minor increase in the import of dairy products by Belarus from the EU can be observed in the trade data. However, the import of fluid milk products (HS3 Code 401) from the EU to Belarus increased dramatically since 2014 (Figure 10). In 2016, Belarus exported more than 300 thousand tonnes of raw milk products (not including cheese, butter and milk powder) to Russia and imported more than 100 thousand tonnes of the same commodity from the EU. This trend was not there before 2014. It seems that Belarus covers part of its raw milk shortages with milk imports from the EU to export the dairy products to Russia.

Belarus milk export to Russia

Belarus milk import from EU

Fig. 10. Belarus fresh milk product imports and exports, million tonnes4 Рис. 10. Экспорт и импорт молока в Республике Беларусь, млн т

1 Source: own illustration based on data from TradeMap.

2 USDA. (2017). Data & Analysis, USDA Foreign Agricultural Service. Online Database. https://www.fas.usda. gov/data/

3 Harmonized Commodity Description and Coding Systems (HS).

4 Source: own illustration based on data from TradeMap and Eurostat.

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To sum up, the import substitution strategy of Russia did not have any significant impacts for the case of milk and butter, as production and self-sufficiency have not improved since 2014. However, the opposite is true for the case of cheese, where self-sufficiency levels increased by almost 20 %. Therefore, in our further analysis, we concentrate only on cheese, as it is by far the major dairy product for which the import substitution strategy seems to have had a remarkable effect.

Literature review

Price transmission. Our paper contributes to the literature on the degree of market integration in spatially separated markets [Alexander, Wyeth, 1994; Goodwin, Piggott, 2001]. The degree of market integration is one of the common ways for indicating market efficiency [Faminow, Benson, 1990; Goodwin, Grennes, McCurdy, 1999; Goodwin, Schroeder, 1991]. Study techniques are mainly built on the law of one price [Sanogo, Amadou, 2010], which is an important component of almost all of the international trade models [Officer, 1986]. In an efficient market with well-developed transportation and storage infrastructure, regional price differences should at most be equal to the costs of trade between the trading regions. The prices in two trading regions tend to co-move in the long run, with price shocks transmitting from one region to the other [Asche, Gordon, Hannesson, 2004; Goodwin, 1992; Van Campenhout, 2007].

One of the pioneering cointegration models was introduced by Engle and Granger [1987]. Despite its popularity among researchers, Engle & Granger (E&G) approach is subject to a number of serious limitations, such as: (i) cointegration considerations are restricted to pair-wise comparisons; (ii) one of the two prices must be designated as exogenous; (iii) the potential for small sample biases in parameter estimates; and lastly, (iv) the E&G approach does not have well defined limiting distributions and therefore the direct testing of a hypothesis is not possible [Asche, Gordon, Hannesson, 2004; Banerjee et al., 1986; Goodwin, 1992; Hall, 1986].

A more powerful approach for the cointegration test, introduced by Johansen [1988], can be a good alternative in this regard [Alexander, Wyeth, 1994]. Johansen's approach can be used for the multivariate cointegration analysis [Goodwin, 1992]. It also allows the generation of test statistics with exact limiting distributions, which makes the straightforward hypothesis testing possible [Asche, Gordon, Hannesson, 2004; Asche et al., 2012; Johansen, Juselius, 1990]. Moreover, the restriction for one price to be designated as exogenous can be relaxed, which is especially important when the two prices illustrate two-way causality, as is the case with the data used in this study. Therefore, the Johansen cointegration approach is used in the current work to analyse the regional price integration of dairy products before and after the implementation of counter-sanctions.

Studies on the effects of trade policies on domestic prices are rare (see, for example, [Diao, Kennedy, 2016; Djuric, Götz, 2016; Nogues, 2014; Wong, 2014]), especially for transition countries like Russia (see, for example, [Götz, Glauben, Brümmer, 2013; Götz et al., 2016; Svanidze, Götz, Serebrennikov, 2021; Svanidze, Götz, 2019a, 2019b]). One such study, which analysed the effects of wheat export restrictions in Russia and Ukraine during the world food crisis was conducted by Götz, Glauben and Brümmer [2013]. They found that export restrictions reduced the degree of price transmission with the world market during the world food crisis. A similar finding was observed by Nogues [2014], who did not observe any significant impacts of export restrictions on lowering domestic consumer prices in the case of Argentina. Furthermore, Svanidze, Götz and Serebrennikov [2021], focusing exclusively on Russia's domestic wheat market integration, found that price co-movement strengthened during the 2010/11 wheat export ban period, however, transaction costs of trade also increased.

Price dispersion. In this research, we also contribute to price dispersion, which has attracted attention among agricultural market studies, especially in developing countries. There are different meanings for price dispersion. Generally, it refers to price differences between two markets or price differences that can be observed for the same commodity in the same market. Market power and imperfect competition are considered to be the main causes of price dispersion among many scholars [Wang, 2018]. The availability of price dispersion among commodities, such as irrigation water in segregated spot water markets [Jaghdani, Brummer, 2016; Yoskowitz, 2002] or fish quota markets [Newell, Sanchirico, Kerr, 2005], is a recognised phenomenon. In addition, the existence of price dispersion, even for homogenous products, which is less expected, is among the most replicated findings in empirical economics [Anania, Nistico, 2014]. For example, Aker [2010] studied the effects of mobile phone introduction on price dispersion in grain markets in Niger. She used panel econometrics and bivariate price differences between different markets, with her results showing that mobile phone introduction increased information availability and reduced price dispersion by 10-16 %. Anania and Nistico [2014] studied the price dispersion among 14 perfectly homogenous food items in 437 stores located in different towns in Calabria, Italy. They concluded that heterogeneity between retailers and consumers are two important factors of the observed price dispersion. Cerasa and Buscaglia [2017] used hedonic pricing to test the convergence of coffee prices in the EU market. In contrast to the convergence hypothesis, the results show significant price dispersion in this market. Andersson, Bezabih and Mannberg [2017] have followed Aker's [2010] approach to find the possible effects of the Ethiopian Commodity Exchange (ECX) and its connected warehouses on price dispersion of coffee at the local level. They found price dispersion reduction as a result of ECX availability.

The effect of trade policy on domestic price dispersion of agricultural commodities is an area which has not yet been studied. Therefore, apart from the possible effect of market integration, we have also considered price dispersion in this study.

Methodology and data

Price transmission. In this study, we use the Johansen cointegration test and a vector error correction model (VECM) to test the effects of trade policy on spatial market integration of the Russian dairy market. Most of the market integration analysis was built upon the concept of the law of one price, which can be expressed as follows:

Pt = Po + Pi Pt +et, (1)

where pt is the price in the importing region; pf is the price in the exporting region; fa is the coefficient of the long-term equilibrium. Prices in two regions are equal if fa0 = 0 and fa = 1, which is the strict version of the law of one price. If fa ^ 0 and fa = 1, the two prices have a proportional relationship and their levels differ due to factors, such as transportation costs. This is the weak version of the law of one price. This regression, however, cannot be used when series are non-stationary, in which case using cointegration techniques is advised [Ardeni, 1989; Asche, Bremnes, Wessells, 1999; Ghosh, 2003]. Long-term dynamics in price relationships can be extracted from cointegration models and they can be empirically specified in the form of the basic VECM:

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i=1

where lnpt is a vector of the natural logarithms of n prices at time t; a and P' are the vectors of parameters for the speed adjustment and degree of long-run price transmission, respectively; T/ is a matrix of parameters for the short-run relationship. By considering equation (1), P' in equation (2) can be represented as vector of parameters: [1 - p0 - p1].

Price dispersion. To assess the effect of trade policies on price performance and price dispersion, we follow Aker [2010] and Andersson, Bezabih and Mannberg [2017] and use a panel model approach. The price difference between markets j and k at month t is defined as Yjk,t = \Pj,t - Pk,t\. We have tested the effect of trade policies on Yjkt. As empirical evidence indicates that trade policy in Russia has reduced the size of imports, we use the quantity of imports as an effective proxy for policy implementation. Additionally, since the cheese was mainly imported from the EU and Belarus, the RUB/EUR exchange rate is also considered as proxy regres-sors. The average European Gouda cheese is selected to represent the international cheese price. The regression model is given as:

Yjk,t = Po+ XjKtX + ajk + 6t + ejkit, (3)

where Xjk/t is the vector of variables affecting the price dispersion; ajk are market-pair fixed effects; dt are time-fixed effects. To capture the dynamics of price dispersion, following Aker [2010], we use the dynamic panel approach and utilise the first lag value of the dependent variable to the right-hand side of the dispersion equation:

Yjk,t = Po+ pYjk,t-i + XJkitX + SDt + ajk + dt + ejKt. (4)

Dt is a dummy for testing the shock from the 2014 trade restrictions. This dummy is added as the imported commodities at the monthly intervals cannot reflect the effect of trade restrictions and other developments during 2014, at least for a short period. In order to control for endoge-neity, Blundell-Bond type of instruments and estimators are used [Blundell, Bond, 1998]. The model is tested for temporal autocorrelation in residuals and robust clustered standard errors are used for heteroscedasticity in the model [Wooldridge, 2016: ch14]. This model is estimated with generalized method of moments (GMM).

Data. This analysis of regional cheese market integration is conducted for regions of Russia that are comprised of cheese producing as well as consuming regions. Among those, the largest producing regions are the oblasts of Voronezh, Bryansk, Omsk, Pskov, Ryazan, Rostov, and Moscow and the Republics of Udmurt, Adygea, and Tatarstan (Figure 5). The largest cheese consuming regions are Moscow City, Krasnodar krai, Saint Petersburg, and the oblasts of Sverdlovsk, Samara, and Smolensk (Figure 6). The analysis is based on 39 regional price pairs, each consisting of the price series of a cheese exporting and a cheese importing region. We use the natural logarithms of weekly consumer prices of solid and soft rennet cheese for the period from January 2008 to December 2016, comprising 460 observations for each price series1. This type of cheese is produced domestically and also imported from international markets. The Chow breakpoint test [Chow, 1960] suggests the existence of the structural break in the long-run equilibrium in August of 2014 for all of the price pairs. Thus, the date of the structural break coincides with the date of the implementation of the Russian food import restrictions. We account for the structural break in our modelling approach by distinguishing a "free trade" regime (from January 2008 to July 2014) and a "counter-sanction" regime (August 2014 to December 2016) in the price transmission as well as the price dispersion modelling approach. This regime-switching framework allows identifying the possible effects of the counter-sanctions on the degree of market integration. The parameters of the

1 Source: MilkNews. https://milknews.ru/

"free trade" and the "counter-sanction" regime are estimated based on 336 and 124 observations, respectively (Table 1).

Table 1. Descriptive statistics of the average cheese prices in 16 regions under study, rublesVkg Таблица 1. Дескриптивная статистика средних цен на сыры в 16 российских регионах, руб./кг

Indicator No of observations in each region Mean Standard deviation Max Min

Total 460 293.93 84.05 598.41 159.64

Free trade regime 336 251.67 42.56 424.58 159.64

Counter-sanction regime 124 408.44 58.29 598.41 281.75

Source: own estimations.

As price dispersion is being analysed descriptively and analytically, both weekly and monthly price intervals are used2. Primarily, the weekly price dispersion is used for the descriptive analysis of price dispersion before and after the counter-sanctions of 2014, in line with previous price transmission study. Furthermore, the price dispersion is investigated econometrically based on a monthly price series in order to make it possible to include the quantity of cheese imports, which is available at a monthly frequency only. The monthly price series is constructed by selecting the end of the monthly prices of weekly price data used in the price transmission analysis. We further include monthly European cheese price, monthly cheese and milk imports to Russia and the exchange rate as explanatory variables in our model approach. The time period underlying this analysis is January 2008 to December 2016.

Appendix A shows the descriptive elements of the dependent and independent variables in the price dispersion model (not transformed). As will be explained in the results, only the bivariate price dispersions between Moscow City and its cheese providers are selected. Some transformations are used on some of the data series for the model estimation. The exchange rates and import levels are transformed into logarithms.

Results and discussion

Price transmission. The vast majority of the analysed price pairs were not cointegrated when we conducted the test over the entire time frame under study. To estimate the effects of the 2014 import restrictions and all macroeconomic adjustments occurring since summer 2014, we split the data into "free trade" and "counter-sanction" sub-periods and conducted cointegration tests separately for each sub-period. The Chow breakpoint test [Chow, 1960] supported the existence of the structural break in the long-run equilibrium in August 2014 (the starting period of the import restriction and counter-sanctions). The degree of price transmission (parameter fa in equation (2)) and speed of adjustment (parameter a in equation (2)) were then estimated for cointegrated price pairs using a VECM for both the "free trade" and "counter-sanction" regimes. The results suggest that 29 out of the 39 price pairs were not cointegrated during the "free trade" period, but became cointegrated afterwards, during the "counter-sanction" regime (see Appendix B). The remaining 10 price pairs were already cointegrated before the counter-sanctions were implemented. Half of these price pairs illustrate higher integration (increased price transmission and speed of adjustments), whereas the remaining half became less integrated (decreased price transmission and speed of adjustments or no cointegration) during the "counter-sanctions" regime. The results of the z-test [Paternoster et al., 1998] suggest that the changes in price transmission and speed of adjustment parameters between "free trade" and "counter-sanction"

1 Russian ruble will be referred to as RUB inside the tables.

2 Source: MilkNews. https://milknews.ru/

regimes are statistically significant1 (see Appendix C). Overall, out of the 39 price pairs, we have 34 cases were integration emerged or improved and five cases were integration disappeared or worsened. While the highest degree of price transmission during the "counter-sanction" regime was observed between pairs like Moscow City and Bryansk oblast (1.12) and between Samara oblast and the Tatarstan Republic (1.15), the highest speed of adjustment was for pairs like Sverdlovsk and Pskov oblasts (0.29) and Samara oblast and the Tatarstan Republic (0.27).

Saint Petersburg and the Tatarstan Republic are among the regions for which cheese markets became more weakly integrated in regional markets during the "counter-sanction" regime. For example, we find market integration to have decreased during the counter-sanction regime for two out of five price pairs involving Tartastan and three out of seven price pairs involving Saint Petersburg compared to the time period when trade was possible.

Moreover, compared to other consumer regions, the average degree of price transmission (0.51) and speed of adjustment (0.06) in Saint Petersburg in the "counter-sanction" regime is considerably low (Table 2).

Table 2. Average price transmission coefficients of major consuming and producing regions

Таблица 2. Средние коэффициенты ценовой трансмиссии для регионов - лидеров по производству и потреблению сыров

Region Average degree of price Average speed of

transmission (ß1) adjustment (a)

Moscow City - producing regions 0.81 0.12

Krasnodar krai - producing regions 0.84 0.12

St Petersburg - producing regions 0.51 0.06

Sverdlovsk oblast - producing regions 0.84 0.16

Samara oblast - producing regions 0.82 0.13

Smolensk oblast - producing regions 0.73 0.09

Note: the averages are calculated based on estimations in Appendix C. Source: own estimations.

We assume that the decrease in market integration in Tartastan can be explained by the decrease in Tartastan's cheese exports during the import restriction period. In particular, regional exports of cheese from Tatarstan decreased by nearly 30 % in 2014-2016 (Figure 11). This might have resulted in weaker co-movements of the cheese prices in importing regions with the prices in Tatarstan.

8000 6000 4000 2000 0

7454.7

5257.8

2014

2016

Fig. 11. Regional net exports of cheese from the Republic of Tatarstan, tonnes2 Рис. 11. Чистый экспорт сыров из Республики Татарстан, т

Before the counter-sanctions was implemented, Saint Petersburg was mainly a cheese-importing region and cheese were primarily imported from the bordering countries like Estonia

1 Only in the case of the "Sverdlovsk Oblast-Tatarstan Republic" price pair was the z-test of the price transmission parameters not significant due to similar betas in both regimes. However, the difference in the speed of adjustment was positive and statistically significant.

2 Source: own illustration based on data from Rosstat.

and Finland. According to media reports, despite the counter-sanctions against those European countries, Estonian and Finnish cheese continued to be supplied in Saint Petersburg even after the import restrictions were implemented1. Therefore, cheese prices in Saint Petersburg remained influenced by cheese imports, which explains the decrease in integration with other regional cheese markets in Russia.

Price dispersion. Primarily, the simple weekly price dispersion (\Pjt - Pkt\) is conducted between the same pairs used in price cointegration (see Appendix D). The results of the descriptive analysis of weekly price dispersion show that the pairwise price dispersion generally increased during the counter-sanction regime. This increase was extreme for Moscow City and Sverdlovsk oblast and their cheese providing regions. As a result, Moscow City is selected for the price dispersion model estimation. As it was explained in the methodology section, the price dispersion and its explanatory variables can show systematic dynamic structure. Therefore, the dynamic panel data is prepared for monthly intervals. Furthermore, the logarithmic forms of the exchange rate, milk and cheese imports and European cheese prices are used as covariates for this estimation. The logarithm transformation for these covariates has been used to reduce huge variations on the absolute value. The "plm" package in R statistical software was used for estimation [Croissant, Millo, 2008; 2018]. Table 3 shows the results of the estimation for the dynamic panel model of price dispersion.

Table 3. Results of panel estimation of price dispersion by GMM

Таблица 3. Результаты панельной оценки дисперсии цен с использованием обобщенного метода моментов

Indicator Coefficients

Instruments of the first lag of dependant variable (yiit-1) 52,309 (4,753) ***

Dummy for counter-sanction period 31,332 (6,334) ***

EUR-RUB exchange rate (logarithm transformation) 27,696 (4,622) ***

Total monthly milk import in 1,000 tonne (logarithm transformation) -2,314 (3,394)

Total cheese imports in 1,000 tonne (logarithm transformation) -20,620 (3,862) ***

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EU Gouda cheese price Euro/100 kg (logarithm transformation) 0,001 (0,001)

Balanced panel: n = 8, T = 108, N = 864, number of observations used: 1704

Autocorrelation test (1): normal = -1.3 (p-value = 0.19)

Autocorrelation test (2): normal = -1.5 (p-value = 0.12)

Note: Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 In () standard deviation Source: own calculations.

1 Even after the counter-sanction implementation, Estonian and Finnish cheeses were supplied in the markets around Saint Petersburg. Cheese from those countries could also be ordered through various internet shops. Transporting of goods over the border by individuals for the purpose of self-consumption is not banned, and as a result it became the main way for citizens of Saint Petersburg to import cheese and other food products from Estonia and Finland during the import ban. Moreover, in the first half of 2017, Narva, the Estonian city that borders Saint Petersburg, was again ahead of Tallin in the amount and volume of tax free deals registered, which included food products as well [BumagaMedia. (2015). Market of Sanctions: How to bring Finnish cheese and roll back the visa at the expense of smugglers. http://paperpaper.ru/say-cheese/ (in Russ.); DELFI. (2017). Large cross-border business on a banned basis: Narva-shuttles, cheese tours from Saint Petersburg and resale from under the floor. http:// rus.delfi.ee/daily/estonia/bolshoj-transgranichnyj-biznes-na-zapreschenke-narvityane-chelnoki-syrnye-tury-iz-peterburga-i-pereprodazha-iz-pod-poly?id=78976776 (in Russ.); Inosmi.ru. (2016). Russian tourists again bring food from Finland - so they go around sanctions. http://inosmi.ru/social/20160913/237835927.html (in Russ.); Vedomosti. (2016). The food embargo has created a whole network of cheese suppliers. https://www.vedomosti.ru/ management/articles/2016/03/11/633113-prodovolstvennoe-embargo-porodilo-tseluyu-set-postavschikov-sira (in Russ.)].

The Sargen test shows the right identification of instruments and the autocorrelation test shows no serial autocorrelation in the residuals of the estimated GMM model. The results of the price dispersion model show that increases in cheese imports reduce the price dispersion between Moscow and its cheese providing regions. The effect of raw milk imports is not significant, but it shows the same direction as cheese imports. The dummy variable for the counter-sanction period was also significant. It is worth mentioning that the counter-sanction period covers multiple effects that can directly be captured by the dummy variable. The Russian ruble devaluation caused the increase of dispersion between the domestic markets. It shows that, by devaluation, the foreign cheeses become more expensive and their imports are probably reduced. The increase in European cheese prices does not affect the domestic price dispersion. This could be due to lower levels of volatility in this price. No effects are recognised from changes in the Bela-rusian ruble and it was not included in the model.

Generally speaking, the increase in price cointegration between domestic cheese markets, especially for a city like Moscow, does not show increases in market efficiency directly. The available price dispersion shows that the supply demand gap in this metropolis is not really covered by domestic supply.

Conclusion

In the paper, we investigate the effects of the combined events that started in the summer of 2014 with the Russian food import restrictions (counter-sanctions) in August of 2014. We also look at the macroeconomic adjustments of currency devaluation and oil price reductions on spatial integration in the Russian dairy market following the Johansen cointegration and VECM approach on the one side and the price dispersion approach on the other. We have used consumer cheese prices to represent the Russian dairy market, since cheese so far is the only dairy item that was significantly affected by the import restrictions. While the production of cheese increased by about 20 %, the imports dropped by around 40 % since the introduction of the counter-sanctions. Our results suggest that the Russian dairy market became more integrated during the "counter-sanction" regime. Our results are in line with Svanidze, Götz, Serebrennik-ov [2021], who found that the domestic wheat market integration strengthened in Russia amid restrictions of wheat exports in 2010/11. After the implementation of the counter-sanctions, the Russian market was mainly dominated by locally produced cheese, which led prices in domestic importing and exporting regions to co-move. In 29 out of the 39 price pairs, we observe an emergence in cointegration between major cheese importing and exporting regions. In addition, in five price pairs, the degree of long-term price transmission improved significantly, whereas for the remaining five pairs cointegration disappeared or price transmission parameters decreased significantly during the "counter-sanction" regime.

The highest price transmission during the "counter-sanction" regime was observed between pairs like Moscow City and Bryansk oblast and between Samara oblast and the Tatarstan Republic. In contrast, the price pairs that included Saint Petersburg demonstrated the lowest price transmission levels during the "counter-sanction" regime on average. Despite the food import restrictions, cheese from Estonia and Finland was supplied in supermarkets in Saint Petersburg, which explains the relatively low integration with other regional cheese markets on average in Russia. Furthermore, cointegration of the prices in the Tatarstan Republic with the prices in the other consuming regions decreased in three out of five price pairs, which can possibly be explained by a notable decrease in the level of exports from Tatarstan between 2014 and 2016. We also find increased market integration reflected by the increase in the speed of adjustment parameter for five out of ten price pairs. The highest speed of adjustment was for pairs like Sverdlovsk and Pskov oblasts and Samara oblast and the Tatarstan Republic.

In order to check the efficiency of the dairy market of Russia and the effects of import restrictions, apart from market cointegration, we also study the effects of counter-sanctions on price dispersion between major cheese producing (exporters) and cheese consuming regions (importers). The results show that an extreme dispersion occurred between Moscow City and Sverdlovsk oblast as major inland metropolises (not on the border) and the major cheese producing regions. Moscow City was selected and the bivariate price dispersion and the price dispersion variables was regressed on potential factors that can affect the price dispersion apart from counter-sanctions. The results show that the import restrictions have increased price dispersions between metropolitan cities such as Moscow and cheese producing regions. We find substantially lower price dispersion for other major consuming cities with lower populations on the western borders of Russia. The reduction in import levels and the devaluation of the Russian ruble caused increases in the price dispersion. The inverse effects of counter-sanctions on market integration and price dispersion is an interesting observation that can be considered for further policy developments for market efficiency in dairy markets.

Appendix A. Descriptive statistics, model covariates and source of the monthly cheese prices for Moscow and its cheese providers Приложение А. Дескриптивная статистика, ковариаты модели и источники ежемесячных цен на сыры для г. Москвы и его региональных поставщиков

Variable Pairs Mean Sd Min Max Source of data

Moscow City - Voronezh 87.14 41.58 30.5 165.0

СЛ oblast (RUB)

Moscow City - Bryansk oblast 91.05 43.77 43.9 177.4

tU и (RUB)

Й tU Moscow City - Omsk oblast 104.26 63.5 17.93 234.58

te (RUB)

ТЗ Moscow City - Adygea 59.08 24.36 11.73 114.85

! (prie panel) Republic (RUB) MilkNews

Moscow City - Pskov oblast 50.29 21.3 11.45 98.9

(RUB)

ir Moscow City - Tatarstan 110.11 48.17 50.08 209.09

> й e ТЗ Republic (RUB)

Moscow City - Ryazan oblast 99.04 48.87 37.27 193.6

й e (RUB)

p e ГУ Moscow City - Rostov oblast 88.13 40.47 41.18 167.84

(RUB)

Dummy for counter-sanctions 0.27 0.45 0 1 -

EUR-Ruble exchange rate 48.69 13.22 34.49 82.39 OANDA

СЛ e Total monthly milk import in 17.1 6.14 6.27 28.64 TradeMap.

ir 1,000 tonne Eurostat

a > о Total cheese import in 1,000 30.91 10.03 9.63 47.9 TradeMap.

и tonne Eurostat

EU Gouda cheese price 298.25 40.28 217.94 381.97 EU website

(Euro/100 kg)

Sources: Milknews website. https://milknews.ru (in Russ.); Oanda Corporation. https://www.oanda. com/rw-en/; TradeMap. Trade statistics for international business development. https://www.trademap. org/Index.aspx; Eurostat. https://ec.europa.eu/eurostat; Official website of the European Union. https:// europa.eu/european-union/index_en.

Appendix B. Estimated parameters for regional cheese market cointegration

Приложение B. Оцениваемые параметры коинтеграции регионального рынка сыров

№ Pairs (Importer - Exporter) Johansen cointegration test (p-value) Chow-test (p-value) (08/2014)

Whole period (01/200812/2016) Free trade (01/200808/2014) Counter-sanction (08/201412/2016)

1 Moscow City - Voronezh oblast 0.613 0.678 0.001 0.000

2 Moscow City - Bryansk oblast 0.049 0.092 0.008 0.000

3 Moscow City - Omsk oblast 0.033 0.092 0.000 0.000

4 Moscow City - Adygea Republic 0.104 0.279 0.010 0.000

5 Moscow City - Pskov oblast 0.458 0.810 0.005 0.000

6 Moscow City - Tatarstan Republic 0.004 0.007 0.001 0.000

7 Moscow City - Ryazan oblast 0.233 0.495 0.001 0.000

8 Moscow City - Rostov oblast 0.001 0.070 0.000 0.047

9 Krasnodar krai - Bryansk oblast 0.504 0.812 0.003 0.000

10 Krasnodar krai - Tatarstan Republic 0.038 0.041 0.411 0.000

11 Krasnodar krai - Moscow oblast 0.815 0.719 0.011 0.002

12 Krasnodar krai - Rostov oblast 0.169 0.799 0.005 0.002

13 St Petersburg - Voronezh oblast 0.452 0.780 0.045 0.000

14 St Petersburg - Bryansk oblast 0.043 0.015 0.024 0.000

15 St Petersburg - Omsk oblast 0.161 0.311 0.032 0.000

16 St Petersburg - Tatarstan Republic 0.086 0.098 0.042 0.000

17 St Petersburg - Moscow oblast 0.102 0.340 0.009 0.000

18 St Petersburg - Ryazan oblast 0.269 0.372 0.071 0.000

19 St Petersburg - Rostov oblast 0.081 0.015 0.152 0.000

20 Sverdlovsk oblast - Voronezh oblast 0.901 0.935 0.061 0.000

21 Sverdlovsk oblast - Omsk oblast 0.192 0.236 0.000 0.000

22 Sverdlovsk oblast - Adygea Republic 0.280 0.468 0.001 0.000

23 Sverdlovsk oblast - Pskov oblast 0.497 0.605 0.014 0.000

24 Sverdlovsk oblast - Tatarstan Republic 0.002 0.016 0.001 0.001

25 Sverdlovsk oblast - Ryazan oblast 0.653 0.600 0.049 0.000

26 Sverdlovsk oblast - Rostov oblast 0.124 0.153 0.031 0.000

27 Samara oblast - Voronezh oblast 0.831 0.954 0.007 0.000

28 Samara oblast - Bryansk oblast 0.105 0.354 0.038 0.000

29 Samara oblast - Omsk oblast 0.284 0.348 0.011 0.000

30 Samara oblast - Udmurt Republic 0.370 0.242 0.047 0.000

31 Samara oblast - Tatarstan Republic 0.049 0.049 0.000 0.000

32 Samara oblast - Moscow oblast 0.223 0.588 0.011 0.000

33 Samara oblast - Ryazan oblast 0.452 0.676 0.001 0.000

34 Smolensk oblast - Voronezh oblast 0.723 0.937 0.019 0.002

35 Smolensk oblast - Udmurt Republic 0.889 0.802 0.065 0.000

36 Smolensk oblast - Adygea Republic 0.318 0.651 0.022 0.000

37 Smolensk oblast - Pskov oblast 0.086 0.225 0.040 0.000

38 Smolensk oblast - Moscow oblast 0.202 0.235 0.042 0.000

39 Smolensk oblast - Rostov oblast 0.059 0.229 0.000 0.000

Source: own estimations.

Appendix C. Estimated price transmission parameters

Приложение С. Оцениваемые параметры ценовой трансмиссии

№ Pairs (Importer - Exporter) Degree of price transmission (parameter ß1) Speed of adjustment (a)

Free trade Counter-sanction z-test Free trade Counter-Sanction z-test

1 Moscow City - Voronezh oblast - 0.94 - - 0.15 -

2 Moscow City - Bryansk oblast 0.85 1.12 1.65 0.02 0.05 2.42

3 Moscow City - Omsk oblast 0.34 0.58 1.76 0.01 0.11 3.47

4 Moscow City - Adygea Republic - 0.81 - - 0.16 -

5 Moscow City - Pskov oblast - 0.70 - - 0.20 -

6 Moscow City - Tatarstan Republic 0.76 0.60 -1.68 0.07 0.03 -2.08

7 Moscow City - Ryazan oblast - 0.85 - - 0.15 -

8 Moscow City - Rostov oblast 0.77 0.89 1.71 0.02 0.11 2.59

9 Krasnodar krai - Bryansk oblast - 0.82 - - 0.11 -

10 Krasnodar krai - Tatarstan Republic 0.88 - - 0.02 - -

11 Krasnodar krai - Moscow oblast - 0.87 - - 0.21 -

12 Krasnodar krai - Rostov oblast - 0.85 - - 0.06 -

13 St Petersburg - Voronezh oblast - 0.41 - - 0.09 -

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14 St Petersburg - Bryansk oblast 0.92 0.44 -3.30 0.08 0.04 -1.67

15 St Petersburg - Omsk oblast - 0.39 - 0.05

16 St Petersburg - Tatarstan Republic 0.96 0.48 -2.14 0.06 0.03 -1.75

17 St Petersburg - Moscow oblast - 0.56 - - 0.08 -

18 St Petersburg - Ryazan oblast - 0.79 - - 0.09 -

19 St Petersburg - Rostov oblast 0.99 - - 0.05 - -

20 Sverdlovsk oblast - Voronezh oblast - 0.82 - - 0.20 -

21 Sverdlovsk oblast - Omsk oblast - 0.69 - - 0.15 -

22 Sverdlovsk oblast - Adygea Republic - 0.88 - - 0.18 -

23 Sverdlovsk oblast - Pskov oblast - 0.86 - - 0.29 -

24 Sverdlovsk oblast - Tatarstan Republic 0.78 0.78 -0.05 0.03 0.14 2.29

25 Sverdlovsk oblast - Ryazan oblast - 0.90 - - 0.08 -

26 Sverdlovsk oblast - Rostov oblast - 0.94 - - 0.05 -

27 Samara oblast - Voronezh oblast - 0.86 - - 0.09 -

28 Samara oblast - Bryansk oblast - 0.87 - - 0.08 -

29 Samara oblast - Omsk oblast - 0.44 - - 0.08 -

30 Samara oblast - Udmurt Republic - 0.92 - - 0.13 -

31 Samara oblast - Tatarstan Republic 0.99 1.15 1.71 0.03 0.27 3.79

32 Samara oblast - Moscow oblast - 0.57 - - 0.06 -

33 Samara oblast - Ryazan oblast - 0.94 - - 0.20 -

34 Smolensk oblast - Voronezh oblast - 0.99 - - 0.05 -

35 Smolensk oblast - Udmurt Republic - 0.35 - - 0.02 -

36 Smolensk oblast - Adygea Republic - 0.72 - - 0.11 -

37 Smolensk oblast - Pskov oblast - 0.74 - - 0.22 -

38 Smolensk oblast - Moscow oblast - 0.73 - - 0.07 -

39 Smolensk oblast - Rostov oblast - 0.86 - - 0.08 -

Source: own estimations.

Appendix D. Discriptive results of bivariate price dispersion ( \Pj,t - Pk,t\ )

Приложение D. Дескриптивная статистика двумерной ценовой дисперсии ( IP,t - Pk,t| )

Consumption region Production region Average SD Free regime Counter-sanction

Before ban After ban Before ban After ban Min Max Min Max

City of Moscow Voronezh oblast 66.74 143.76 25.84 15.18 30.41 113.12 107.16 164.99

Bryansk oblast 67.22 156.96 14.86 24.39 41.27 97.48 90.77 177.4

Omsk oblast 71.17 195.08 31.71 30.89 14.75 143.29 133.21 234.98

Adygea Republic 47.38 90.63 14.97 14.78 11.73 82.64 55.9 117.97

Pskov oblast 42.48 72.78 15.4 19.18 11.45 73.13 30.96 102.25

Tatarstan Republic 84.22 180.28 19.8 27.51 48.53 126.99 122.27 209.09

Ryazan oblast 73.55 168.89 24.23 23.79 36.28 126.15 117.27 193.6

Rostov oblast 66.41 147.73 17.3 20.7 39.73 109.23 104.73 168.32

Krasnodar krai Bryansk oblast 11.46 20.1 7.87 9.0 0.09 30.56 0.27 34.22

Tatarstan Republic 27.61 43.41 9.1 14.12 8.61 53.11 11.97 73.03

Moscow oblast 9.73 9.11 6.12 5.4 0.05 23.66 0.04 19.21

Rostov oblast 10.09 12.59 6.49 6.1 0.03 25.67 0.05 27.41

City of Saint Petersburg Voronezh oblast 18.32 16.73 11.77 16.68 0.04 47.22 0.46 65.8

Bryansk oblast 18.79 23.74 5.47 11.2 5.13 31.44 3.01 54.52

Omsk oblast 24.39 61.86 15.1 10.88 0.03 64.87 35.91 76.88

Tatarstan Republic 35.8 47.06 7.0 9.78 18.68 50.06 24.11 63.15

Moscow oblast 11.22 16.3 6.98 8.69 0.05 27.52 1.69 33.44

Ryazan oblast 25.15 35.67 11.1 10.38 0.2 46.95 18.01 57.34

Rostov oblast 17.99 14.54 6.84 12.15 6.06 38.28 0.04 44.23

Sverdlovsk oblast Voronezh oblast 39.99 99.41 26.14 7.96 0.32 87.57 82.74 122.2

Omsk oblast 44.4 150.73 31.53 20.91 0.4 118.76 105.5 173.47

Adygea Republic 20.86 46.28 15.08 7.07 0.0 60.88 23.9 64.55

Pskov oblast 21.1 28.43 10.11 10.18 0.02 47.96 2.8 45.54

Tatarstan Republic 57.43 135.93 18.49 18.22 28.7 102.78 96.32 153.93

Ryazan oblast 46.76 124.55 24.93 16.24 5.3 99.93 82.63 151.08

Rostov oblast 39.62 103.39 19.42 13.39 4.69 84.7 74.36 130.47

Samara oblast Voronezh oblast 16.02 9.91 8.99 6.52 0.02 36.57 0.41 31.15

Bryansk oblast 13.49 14.43 9.1 8.38 0.03 39.24 0.63 35.93

Omsk oblast 19.83 50.92 13.76 13.4 0.11 52.76 23.52 75.07

Udmurt Republic 15.61 17.69 11.0 11.16 0.1 38.16 0.53 37.85

Tatarstan Republic 29.25 36.12 11.21 8.7 0.52 54.5 14.85 55.4

Moscow oblast 7.2 16.98 5.54 12.54 0.01 23.53 0.02 39.49

Ryazan oblast 18.72 24.74 11.04 8.07 0.01 38.94 5.14 47.68

Smolensk oblast Voronezh oblast 9.55 11.34 5.53 8.01 0.0 24.12 0.24 54.47

Omsk oblast 13.64 61.68 8.73 14.46 0.16 43.06 32.02 85.83

Udmurt Republic 10.63 28.18 8.9 13.45 0.03 33.04 2.29 48.12

Adygea Republic 16.94 42.77 11.67 10.97 0.0 41.64 19.52 64.05

Pskov oblast 21.3 60.63 8.56 7.97 1.73 47.9 43.95 82.42

Moscow oblast 7.17 8.45 5.43 5.59 0.01 23.96 0.06 23.87

Ryazan oblast 10.64 35.49 6.05 7.74 0.04 26.79 15.65 51.6

Rostov oblast 4.71 14.33 4.07 5.17 0.01 16.0 4.0 27.1

Source: own estimations.

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Information about the authors

Alisher Tleubayev, Sr. Lecturer of Business School at Suleyman Demirel University, Doctoral student at Martin-Luther-Universität Halle-Wittenberg (Germany), 1/1 Abylai Khan St., Almaty, 040900, Kazakhstan

Phone: +7 (727) 3079565-237, e-mail: alisher.tleubayev@sdu.edu.kz

Tinoush Jamali Jaghdani, Dr., Research Associate at Agricultural Markets, Marketing and World Agricultural Trade Dept., Leibniz Institute of Agricultural Development in Transition Economies, 2 Theodor-Lieser-Str., Halle (Saale), 06120, Germany Phone: +49 (345) 2928-218, e-mail: jaghdani@iamo.de

Linde Götz, Dr., Deputy Head of Agricultural Markets, Marketing and World Agricultural Trade Dept., Leibniz Institute of Agricultural Development in Transition Economies, 2 Theodor-Lieser-Str., Halle (Saale), 06120, Germany

Phone: +49 (345) 2928-327, e-mail: goetz@iamo.de

Miranda Svanidze, Dr., Research Associate at Agricultural Markets, Marketing and World Agricultural Trade Dept., Leibniz Institute of Agricultural Development in Transition Economies, 2 Theodor-Lieser-Str., Halle (Saale), 06120, Germany

Phone: +49 (345) 2928-571, e-mail: svanidze@iamo.de

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