Cointegration analysis of wine export prices for France, Greece and Turkey

Cointegration analysi s of wine export prices for

France, Greece and Turkey

M. Nisa Mencet

1

M.Ziya Firat

2

Cengiz

Sayin

1

Akdeniz University, Agricultural Faculty, Departm e n t of Agricultural Economics, Antalya, Turkey (Tel: +90 2423106514; Fax: +90 242227456 4)

nmencet@akdeniz.edu.tr ; csayin@akdeniz.edu.tr

2 _{Akdeniz University, Agricultural Faculty, Departm e n t of Animal Science, Antalya, Turkey.}

mzfirat@akdeniz.edu.tr

Paper prepared for presentation at the 98

th

EAAE Seminar ‘Marketing

Dynamics within the Global Trading System: New Perspectiv e s’,

Chania, Crete, Greece as in: 29 June – 2 July, 200 6

Copyright 2006 by [M. Nisa Mencet, M.Ziya Firat, Cengiz Sayin]. All

rights reserved. Readers may make verbatim copies of this docu men t for

non - com m ercial purposes by any means, provided that this copyright

notice appears on all such copies.

Cointegration analysi s of wine export prices for

France, Greece and Turkey

M. Nisa Mencet

1

M.Ziya Firat

2

Cengiz Sayin

1

Akdeniz University, Agricultural Faculty, Departm e n t of Agricultural Economics, Antalya, Turkey (Tel: +90 2423106514; Fax: +90 242227456 4)

nmencet@akdeniz.edu.tr ; csayin@akdeniz.edu.tr

2 _{Akdeniz University, Agricultural Faculty, Departm e n t of Animal Science, Antalya, Turkey.}

mzfirat@akdeniz.edu.tr

Abstract. Mediterranean countries have noticeable affect on the world wine exportation. Among these countries France, Greece and Turkey are selected for this stu dy because of different wine market, trade system s and wine policies they have. In this study, cointegration analysis was conducted for real wine export prices and real exchange rates for France, Greece and Turkey. The long term relations hip s between real exchange rates and real wine export values were explored by using cointegration analysis. Annual data from 1970 to 2003 was used for this analysis and the data sets were found to be integrated of the same order. It was also found that they move together in the long run by Johansen Cointegration Test. Then, Error Correction Model (ECM) was applied to search any short term relations and impacts of exchange rate variations on wine exports. French and Greek monetary policies affect their wine export volume by the years. Therefore, any depreciation of local currency in dollar terms will lead to increase of exports vice versa. On the other hand, Turkish wine real export value and real exchange rate were found not cointegrate d. Since, there was not any cointegrate d vector, any exchange rate volatility do not influence Turkish real export wine value. Subsequen tly, the reasons of wine market failures in these countries and pursue d policies were discusse d.

Keywords: Cointegration Analysis, Error Correction Model (ECM), Wine Export Prices, Real Exchange Rates, Wine Market.

1. Introduction

It has been known that grape cultivation and wine drinking had started by about 6000 BC. The first developm e n t s about wine were taken place aroun d the Caspian Sea and in Mesopota mia. The early Mesopota mian s were the first known people to cultivate grapes.

Wine came to Europe with the spread of the Greek civilization aroun d 1600 BC[1]_{. The}

modern day wine indus try goes to as early as 1900 to become the thriving global indus t ry of today. Wine is one of the world’s oldest drinks. Wine producing countries for their self consu m p t io n have been few since ancient times. Nowadays the trade of the wine volume has increase d, trade syste m improved and new regulations and rules launche d in recent decades. The world’s wine market s have been influenced also from globalization and over the past decade the capacity of the markets enlarged aroun d the world drama tically. At the same time, globalization, technological revolution and massive increases in wealth have changed the wine world beyon d recognition, transfo r mi ng wine from a regional to a truly intern atio n al product. Despite the fact that per capita consu m p tio n has been declining in a num ber of significant wine consu mi ng nation s, consu m p tio n is still increasing in many other countries [2]_.

Wine growing plays a key role in agricultural and econo mic activity. It represen t s an import a n t contrib u tion to the value of final agricultural outpu t in most of the prod ucer count ries. Moreover, at the regional and local levels, the wine growing sector appears to have a conclusive role in agricultural activity and the economy. Therefore, wine export s have been a major source of exporta tion to contribut e to national and foreign exchange earnings for developing countries. Especially Mediterranea n countries have noticeable affect on the world wine export atio n. Wine growing country characteris tics may be very

differen t from one Member State to another and even from one region to anot her, not only as regard s the degree of specialization of wine - growing holdings, but also as regard s the size of the vineyard and the type of wine prod uce d. However, wine prod uctio n depen d s heavily on climatic and geographical condition s [3]_{. The new}

develop me n t s about consu m p tio n, which is a num ber of emerging tren ds in consu m e rs’ wine preferences, have been observed. Instead of a daily compo n e n t of diet, wine deman d is associated with purchasing and consu m p ti o n behavior attached with pleasu re, conviviality, psychological satisfaction, refinem en t and cultural interest [4]_.

Macroecono mic variables are also effective on wine trade. Exchange rate is one of the main macroecono m ic indicators. Exchange rates changes affect exports and imports through changes in their relative prices. Dornbus h et al. (1976), indicate that the exchange rate is identified with the relative prices of goods and thus is a deter mi na n t of the allocation of world expendit ure between domes tic and foreign goods [5]_.

Appreciations of exchange rate cause any trade balance deficit and it affects particularly agricultural prod uct s. Therefore the import ance of the study is to search the real exchange rate volatility and monetary policy on wine exporta tion.

The aim of study is to examine the impact of the real exchange rate variations on real wine exports value in France, Greece and Turkey for 1970 - 2003. In the first section of the study, t he EU market (particularly France, Greece and Turkey) will be explored as aspects of having market shares in internation al trade, consu m p tio n, produc tion and regarding with each nation’s regulatory wine policies. Afterwards, in the empirical part of the study, the long run and short run relations hi ps between variation s in the real exchange rate and wine exports will be examined. To attach import a nce for wine exportation, cointegration analysis will be done for France, Greece and Turkey. A long run analysis is investigated by applying the Johansen Cointegratio n Test. Empirical evidence of unit roots justifies the cointegration tests and the subseque n t use of an Error Correction Models (ECM) in estimating test equatio ns are used to analyze the short - run dyna mics depart u re s from the long - run equilibriu m relation under investigation. The procedu re s used for stationary testing, cointegration testing, and the ECM model estimation are described in detail in the following section.

2. Material and methods

There are 2 variables named Real Wine Export Value (RWEV) and Real Exchange Rate (RER). These series are lasted 33 years. Figures are begun from 1970 until 2003. They are annual series gathere d from FAO, EUROSTAT, U.S. Depart m e n t of Agriculture (USDA), International Organization of Vine and Wine (OIV) and then they are manipulate d to make the num bers real. Each count ries export quantities were given in Metric ton (Mt) unit. The real exchange rate data are real weighted exchange rate data. The real weighted exchange rate is equal to the average nominal exchange rate (defined as the price of the dollar in terms of foreign currencies). Such changes in the real exchange rate can then be cumulated into an index which shows the level of the real exchange rate compare d to a particular base year USDA. Annual data from 1970 to 2003 was used for this analysis and the data sets were found to be integrated of the same order. It was also found that they move together in the long run by Johansen Cointegration Test. Then, ECM was applied to search any short term relations and impacts of exchange rate variations on wine exports [6].

The comm on objective of cointegration tests is to determine if there exists a long - run relation s hi p among all test variables. All of these tests are designed to find the stationary linear combinatio n s of vector time series, and in all of these tests a numbe r of cointegrating factors must be determi ne d. If the hypoth esis is accepted, the error term (ut) is not stationary and this mean s that yt and xt series are not integrate d. The latter

one is rejected, yt and xt are integrate d. Note that since the unit root tests test the

cointegration. xt and yt are said to be cointegrate d if there exists a parame ter α such that

αχ

−

=

yt

i

ut

_{is a stationary proces}

s

[7;8]

.

In particular, this study will consider the possible long run relation for the real exchange rate and the other variables by using cointegration analysis, as suggeste d by Saunders et al. (2001) [9]_{, Batten and Belognia (1986)}[10]_{, Xu (1996)}[11}_{, Johansen et.al (2000)}[12]_{. In this}

stu dy, we have chosen to use Johanse n (1988)[13] _{metho d ology.}

For the further step, ECM analysis was feasible to implemen t indicating the impact level and any impact which the exchange rate variations can have on the wine export value in the short run.

) 1 ( 1 2 1 2 1

1z t DRWEVt DRWEVt DRERt DRERt t

p

DRWEV= + + + + +

ε

It is assum e d that at least one of the coefficients (p1 ) is non zer o. The error terms are

white noise. _{The z}

t terms are the residuals from the previously estimate d cointegration tests. The focus of the analysis is on zt terms, as they provide an explanation of short -run deviation s from the long - -run equilibriu m. These variables indicate the extent to which the syste m under considera tion deviates from the long - run equilibriu m. In general, the zt coefficient s indicate the short - run disequilibriu m respo n s es of the model. By using lagged values of zt, it is implied that the last period’s equilibriu m error will affect the current period. If zt equals zero, then the syste m is in equilibrium [9].

First of all, both RWEV and RER data sets are found in this study to be integrated of the same order I(1), and then it became possible to investigate the existence of a long - run relation s hi p between exchange rates and agricultu ral exports. This investigation can be undert ake n within a cointegratio n testing framework. If empirical evidence of cointegration is found to exist, this will have import an t implication s for the relations hi p between the exchange rate and agricultural exports. Cointegration implies the existence of a stable long - run relation s hi p between movement s in exchange rates and changes in agricultural export s over longer periods of time [6; 9].

3. Wine trade in the EU and Turkey

In the past, the market for wine was primarily one of local prod uc tion and consu m p tio n. This has changed to a bigger extent in the last decades [14]_{. Several wine prod ucing}

count ries arou n d the world have begun to make an impact on the export market in an attem p t to expan d their limited local markets. The result of this shift in market focuses for some of the older wine producing countries plus the rise of new wine producing count ries aroun d the world has caused an increase in the competitive natu re of the global wine market [15]_.

The world wine busines s is valued at a consu m e r value of € 150 billion and a wholesale value of € 60 billion while the total global prod uc tion of wine averages at aroun d 275 hl per annu m [16]_{. Europe accoun te d (in value term s) for all but 5 % of wine exports and}

three quarters of wine imports globally (Figure 1). After 1997, Europe’s share of global export s declined from 88 percent to 70 percent and nowadays, wine is becoming an interna tio n ally traded prod uct [17]_.

Others 12,6% CCEE 2,7% USA 9,1% EU 67,2% Australia 5,5% China 1,4% Chili 2,6% S-Afr 3,2% Argentina 5,7% Switzerland 0,5% Romania 2,2% 7

Figure 1. World Wine Production (2002 - 2003) [18]

Until about 15 years ago, wine exporting was an almost exclusively European activity and the wine from other countries was not commo n. The major European wine producing nation s of France, Italy, Spain, Portugal, and Germany hold 67 percent of the wine export market share shown in Figure 2. Australia holds 8 percent. The remaining countries have 25 percent of the wine export volume market in 2003 [18]_.

Figure 2 depicts the chronological numerical value of wine export. From 1975 - 1985, about 80 per cent of exports came from five European Union members (France, Italy, Spain, Germany, and Portugal), anot her 10 per cent came from Bulgaria, Hungary and Romania, and a further 8 per cent came from other European countries and the former French colonies of North Africa. Since then, however, California and several south er n hemisphe re count ries (Australia, Argentina, Chile, South Africa, and New Zealand) have begun to challenge that European domina nce. Between 1986 and 1999, this new group’s combined share of world wine exports grew from 1.6 to 15 per cent in value terms [19].

The EU has a leading position in the world wine market. Data has indicated that European wine culture makes up 45 % of wine - growing areas, 60 % of prod uc tion (178 million hl), 60 % of consu m p t ion (127 million hl, 9 l/capita / y e ar) and 70 % of export s (4.4. billion €). At the global level, the EU is both the largest exporter and the main importer of wine. It export s on average just over 10 million hl per year, mainly to the United States (23%), Japan (15%), Switzerland (13%) and Canada (9%). During 2000 - 2003 period, wine exports is averaged € 4.5 billion (14 million hl) and this value is accoun t e d for 34 of drinks exports and 0.4 % of total EU revenue from exports. Average value export (value / vol u m e) is 325 €/ hl, import (value /v olu m e) is 215 €/ hl in 2004. France, Italy and Spain are major exporting countries and their export increase significantly while export volume decreased in Greece (- 30%), Hungary (- 19%) and Germany (- 10%) in the last decade. On the other hand, Australia, the United States, Chile and Eastern Europe are the main importers of wine to EU [20]_.

0 2000 4000 6000 8000 10000 12000 14000 16000 1 9 6 1 1 9 6 4 1 9 6 7 1 9 7 0 1 9 7 3 1 9 7 6 1 9 7 9 1 9 8 2 1 9 8 5 1 9 8 8 1 9 9 1 1 9 9 4 1 9 9 7 2 0 0 0 Years Q t (M t 1 0 0 0 ) 0 1000 2000 3000 4000 5000 6000 7000 V a lu e ( $ m il li o n )

Value ($ million) Quantity (Mt 1000)

Figure 2. Worldwide Export Quantity, Volume and Prices [18]

There are big differences in unit - price between the prod uc ts and also between the volumes exported. The unit price of Quality Alsace is 3, 13 Euro /Liter for intra EU trade. However, the price is 5, 77 Euro/Liter for extra EC. The difference is 2, 67 Euro /Liter which is relatively high. In the EU, there is price discrimination for export. The price for intra - EU trade is lower than for the extra - EU trade. The most significant difference is burgu n dy’s which is 5, 79 Euro /Liter [21].

1994 1995 1996 1997 1998 1999 _{2000 2001} 2002 ₂₀₀₃ Prév. I mp o r t i n V a l u e E x p o r t i n V a l u e 0 500.000 1.000.000 1.500.000 2.000.000 2.500.000 3.000.000 3.500.000 4.000.000 4.500.000 5.000.000

TRADE in VALUE (1.000 Eur)

Import in Value Export in Value

3.1. Wine in France and Greece and Turkey

France is the largest wine prod ucer in the world since at least the end of the 19th

centu ry . The reputatio n of French wine grew with its export to England, Scotlan d, Scandinavia and the Middle East. After the French Revolution, vineyards that had belonged to the nobility and religious comm u ni ties were parceled out to small landowners. France maintaine d its reputation as the world's largest wine producer since the trade of wine began. The markets for French wines have been traditionally segmen te d into quality and table wines. The best quality French wines belong to regulate d categories such as Apellation d’Origine Controlee (AOC- Controlled Denominatio n of Origin), or Vin Delimite de Qualite Superieure (VDQS- High Quality Wine from given area). In France, the wine trade is regulate d by legal regulations, and the quality attribu t es of the best wines are regulated under the existing AOC scheme. Due to such regulation the French market is constraine d in the internatio nal market at the same time. France also leads beside the production the world in per capita consu m p ti on of wine. French wine consu m p tio n is estimate d at 60 liters, down more than 50 percent since 1970. French exports represen t more than 30 percent of total French prod uction, or one bottle out of every three bottles. Some emerging market s for French wine include Hong Kong, Taiwan, and Malaysia. French export s to the United Kingdom ($84 million), Germany ($84 million), and the United States ($64 million) increase d by 10 percent. Other EU member states remain the largest export markets for French wine [22]_.

Wine has been made in Greece since ancient times. The tradition of fine wine started making stretches from Homer to the fall of Byzantiu m. The history of Modern Greek wine therefore really starte d in the 1960s when moder n technology was first applied in the Greek wineries. Greece has just over 150,000 hectares under vine, of which about 77,500 are devoted to wine prod ucing grapes. Total annual productio n varies, but is in the region of 4.5 million hectoliters of which about 60% is white and 40% is red or rose. There are about 300 native grape varieties grown in Greece, but many are extremely local or used for table grapes or dried fruit. Out of these grape vaieties, 27 wines with an appellation of origin scattere d througho u t Greece. When we compare quality wine for France and Greece, it should acclaim that there is a big difference between them in terms of productio n volume. Greece has had a stable volume of quality wine production in contras t to France, whose wine prod uctio n has been incurring a downward trend [23]_.

The earliest historic evidence of winemaking is found in Turkey from 3000 BC. There are some mythological stories about how wine was found in Anatolia. Turkey comes 4th in vineyard acreage in the World but this potential is used not only for wine [24]_{. Grapes are}

processe d generally for pectin, converted into raisins, dried for eating or processe d in a grape based Turkish delicacy and delights. Only 3 % of all grapes go into the prod uctio n of wine. Most of the country’s grapes are grown in the Marmara, Central Anatolia and the Aegean regions. Turkey is one of the graceful count ries in term s of grape varieties among the other rich count ries. Annually, about 69 million liters is consu m e d in Turkey while per capita consu m p tio n is 0.24 liter. The value of the wine market more than doubled over the review period. There are eighty wineries in Turkey. This num ber of wineries makes them domina n t in market and led to low level of competition. The governm e n t mono poly “Tekel” was respon sible for the production, importa tion and export of alcoholic drinks in Turkey over the review period. But there were also private operators in the beer and wine markets. Tekel was privatized by the end of 2003, and state control over the alcoholic drinks market decreased. Given the dema n d is increasing, production is foreseeing increasing trend. Currently, Turkey has no appellation controller. All serious wine - producing countries have adopte d codes and stan da r d s regulating wine prod uctio n, the mainten a nce of vineyards and the adoption of the controlled appellation. Local wine - makers recognize that Turkey must draft appellation controller stan da r d s if it is suppos e d be taken seriously in the internatio nal market [25]_{. Nowadays, wine drinking has become fashionable and trendy in Turkey, with}

discerning Turks placing a premiu m on quality. As a result of that, Turkish wines are gaining a positive interna tio nal repu ta tion.

3.2. Wine Policies in the EU

The EU is considering increase d domes tic supp or t subsidies for wine and therefore many regulation s and legislation s have been launche d to organize the wine market recently. Wine market regulations in the EU started with the Commo n Market Organization (CMO) in 1970 and it has been modified until today. A major aspect of the new CMO’s strategy is to sup p ort and protect "quality wines produce d in specified regions" by setting quality stan d a r d s and taking into accoun t "traditional conditions of prod uctio n" [26; 27]_.

Since the 1980’s, the wine market has been facing a continuo u s decline and noticeable qualitative change in deman d. These changes have been dealt with by significantly developing the CMO but with some inconsistencies. Initially, CMO practices starte d out with very strict limitation s. It then allowed coupled freedom for plantings virtually guaran teeing sales, thereby generating a serious struct ur al surplus. From 1978, it became very interven tionis t with the ban on planting and the obligation to distil the surplus. Towards the end of the 1980s, financial incentives for giving up vineyard s were reinforced, facilitating a move towards a balance, but withou t achieving it completely. With the GATT agreeme n t s having removed the existing external protection and with deman d (which is in constan t decline) developing towards a qualitative level which the vineyards could not always guarantee at the time, a refor m of the CMO became necessary. This was included in Agenda 2000 and the CAP general reform [26; 3]_.

The objective behin d the Council Regulation (EC) No 1493 / 1 9 9 9 was “To reform and simplify the comm o n organization of the wine market, with a view to achieving a better balance between supply and deman d in the Comm unity market and improving the competitivenes s of this sector in the long term”. The new CMO for wine aims to maintain a better balance between sup ply and deman d in the Comm u ni ty market, giving prod ucers the chance to bring prod uction into line with market develop me n t s and to allow the sector to become perm an e n tly competitive. This goal is purs ue d by financing the restruct u ri ng of a large part of presen t day vineyards, and should conseque n tly give rise to product s sought by domes tic and interna tional deman d [20]_{. Because of the curren t}

market situation between supply and deman d in the Commu nity is unbalanced and the rules governing the definitions, processing and marketing of wine need to be refined, updat e d and made more flexible to take into accoun t changing qualitative consu m er deman d. Hence the adoption of a wine refor m propos al has been put on the Commissio n working progra m m e for 2006.

4. Empirical Analysis of wine export prices in France, Greece

and Turkey

The impacts of an exchange rate changes on import s and exports depen d on the magnitu de of the exchange rate changing. The size of the exchange rate impact depend s also on crop, year, count ry, governme n t al influence in markets, elasticity’s, meas u re d price variables, alternative prices considere d, and the definition of the exchange rate effect [28]_{. A rise in the price of the foreign exchange rate is a depreciation of the home}

currency. Foreign currencies have become more expensive hence the relative value of the home currency has fallen. A fall in the price of foreign exchange is an appreciation of the

home currency. As a result of cheaper foreign currencies, the relative value of home currency has risen. Theoretically, as the value of the dollar rises, the dollar price of any given export becomes more expensive to foreign buyers thereby reducing the dema n d for goods. Similarly, when the local currency depreciates, its depreciation leads to an increase in the sales of exports in foreign market s. The empirical section clarifies the relation s hi p between exports and exchange rates for each country. The exchange rate has a significant effect on count ry’s total export s and conseque n tly its trade balance. In addition, it is very import an t to managers making internation al busines s decisions. Exchange rates are deter mi ne d by the supply of and deman d for a count ry’s currency. When comparing currencies of only two countries, the supply of one currency equals the deman d for the other country. In order to dema n d one currency, one must be supplied by anot her currency [29]_{. Under a floating exchange rate, currency realignme n t}

(appreciation and depreciation) leads to short run adjus t m e n t s in prices, outp ut, and trade volume. The exchange rate is deter mi ne d in the foreign exchange market. Exchange rate changes affect exports and imports through changes in their relative prices [30]_.

The level of dema n d for exports and its variability are more import a n t than the variability of the exchange rate for a commo dity whose storage cost is not negligible. If a comm odity can be stored for a long time an exporter might wait until the currency has settled to a more approp riate parity. If the com modi ty is a perishable one or incurs high storage cost, its exports are likely to be hostage to the vagaries of deman d. Volatility of import dema n d is equally if not more import an t than one’s currency variability on trade. For exporters the problem of exchange rate variability becomes one of hedging the exchange risk when selling goods and services invoiced in foreign currency. If the exporters and importers are risk averse, an increase in exchange rate variability will reduce the volume of trade. The more risk averse the importers are the fewer import s they will buy; similarly, increased risk aversion on the part of exporters will cause them to reduce their supply. Therefore, in the presence of both risk - averse importers and exporters, exchange rate volatility will act as a wedge between deman d for imports and supply of export s, unless the wedge (same currency) is simply shifted to either the importers or the exporters, depen di ng on the currency on which the trans action is deno min ate d [31]_.

4.1. Unit root tests

The Unit root analysis was firstly used to test stationary of time series. For that reaso n, the Augmen te d Dickey Fuller (ADF) test was impleme nt e d to determine whether the series has a unit root. Below is a detailed description of the analysis cond ucte d for each count ry. If the ADF test fails to reject the test in levels but rejects the test in first differences, then the series contains one unit root and is of integrated order one I (1). If the test fails to reject the test in levels and first differences but rejects the test in second differences, then the series contains two unit roots and is of integrate d order two I (2). For the ADF test, one mus t specify the number of lagged first difference terms to add in the test regression. In this study, time lag was specified according to Akaike Inform ation Criteria (AIC) for each series. According to the criteria of AIC, the lowest AIC value was chosen for this implemen t a tio n [32]_.

According to Saunders et al. (2001), the ADF test also deter min es whether the data series are drifting (i.e. whether they are integrated). The main objective of this test is to discover whether the data series need to be differen tiate d, and how many times this mus t be done in order to induce their stationary. If the data series are foun d to be integrated of the same order, e.g., I (1), then cointegration tests can be perform e d [9].

Firstly, RWEV and RER was calculated the level of unit root test. According to this, ADF results are larger (in absolute values) than the MacKinnon critical value and the results

were the hypot he sis that RWEV has a unit root cannot be rejected at the 5 % level. The unit root test results in level are depicted in Table 1. Here we have used the “intercept” option for unit root test and AIC is used for the Lag choices. The hypoth es es used in the stu dy are given as follows:

t y ) 1 ( t y 1 t y t y 1 t ε ρ ∆ = − + ≡ − − ₋ ∆yt=δy_t−1+εt δ=(ρ−1) 1 : H

0 ρ ≥ (Non stationary) (At least one unit root is exist)

1 : 1

H ρ< (Stationary)

Here, when we apply a test on the coefficient of RWEV_{t− γ1}( =0.002346) . Under the null hypoth esis, γisbelow the t statistics (- 0.085159) meaning that the ADF test results gave

the same statistical meaning. The null hypothesis and the existence of unit root are accepte d. On the other hand, Philip - Perron (PP) Unit Root Test is impleme nt e d to check the result we got after the ADF Test. According to this test, PP Test Statistics is foun d for RWEV (0.123138) and for RER (0.123138) while the MacKinnon critical value is –2.9558. The null hypot he sis can not be rejected.

Table 1. ADF test results level for RWEV and RER in level

Variables France Greece Turkey

RWEV - 0.30492 0 (AIC: 45.06455) - 2.940742 (AIC: 44.69233) - 1.25220 8 (AIC: 30.73967) RER - 3.29971 1 (AIC: 10.9626 2) - 2.969897 (AIC: 11.03846) - 2.80814 8 (AIC: 11.11336)

Notes: *MacKinnon critical values for rejection of hypothesis of unit root 1 % critical value is - 3.6852;

5 % critical value is - 2.9705.

As there is a non station ary series, the first difference is depicted in Table 2. The difference at 5 % level is statistically significant meaning the hypothesis claiming that RWEV has a unit root can be rejected. So y1 is I (I).

Table 2. ADF test results: first difference

Variables France Greece Turkey

RWEV - 3.82329 1 (AIC:44.99241) 5.07184 0 (AIC: 44.87804) - 3.197492 (AIC: 30.82892) RER - 4.666776 (AIC: 45.20876) - 5.970388 (AIC: 46.97874) - 4.580115 (AIC: 48.33749)

Notes: *MacKinnon critical values for rejection of hypothesis of unit root. 1 % critical value is - 3.6852; 5 % Critical Value is - 2.9705.

The Philip - Perron (PP) Unit Root Test is implement e d to justify the results of the ADF test. After that, first differences allowed us to test further. RWEV and RER are stationa ry and integrate d at I (1) level.

4.2. Cointegration analysis

The results of cointegration tests determin e the actual form of the data used in all subsequ e n t regression analysis. If the time series are not cointegrate d, then the first -differences form is appro p riate for all test variables [33]_{. Alternatively, the model can be}

reevaluated and the inclusion of additional test variables may be considered. There is maybe several such cointegrating vectors exist so that there are a number of alternative cointegration tests.

4.2.1. France

There is a long run relations hi p between the RER and RWEV for France. First the sum m ary of the Johan se n Cointegration Test is shown in Table 3. Lag 1 is chosen since the AIC has the lowest value. The model with lag 1 was chosen with the linear deterministic test assu m p ti o n.

Table 3. Johanse n cointegratio n test for RWEV and RER for France

Eigenvalue Likelihood Ratio 5 Percent Critical Value 1 Percent Critical Value Hypothesi z ed No. of CE(s) 0.477400 20.1171 6 15.41 20.04 None *(**)

2.12E- 06 6.56E- 05 3.76 6.65 At most 1

*(**) denotes reject ion of the hypot hesis at the 5%, 1% significance level

Under the Johanse n Cointegratio n Test, it could be said that there is a cointegrate d vector (Table 3). In Johanse n’s Method, the eigenvalue statistic is used to determi ne whether cointegrat ed variables exist. Cointegration is said to exist if the values of compu t e d statistics are significantly different from zero [6]. The Likelihood Ratio is higher than 5 % critical value and the eigenvalues are found as (0.477400, 2.12E- 06). Cointegrate d vector for RWEV and RER is (1, - 68.41360). The French wine market is affected from exchange rate volatility. Therefore the money supply, called M2, changes the real export wine value and the volume of exported wine.

4.2.2. Greece

Under the Johanse n Cointegration Test, it could be said that there are cointegrate d vectors (Table 4). The Likelihood Ratio is higher than the one foun d 5 % critical value and the cointegrate d vector for RWEV and RER is (1, - 4.00019 7). The Greek wine market is affected from exchange rate volatility the Likelihood Ratio test indicated 1 cointegrate d equation at the 5 % significance level.

Table 4. Johansen cointegration test for Greece

Eigenvalue Likelihood Ratio 5 Percent Critical Value 1 Percent Critical Value Hypothesi z e d No. of CE(s) 0.28502 1 18.25031 15.41 20.04 None * 0.20928 7 7.51425 0 3.76 6.65 At most 1 **

The coefficient of zt is negative indicating that an increase in the value of the dollar will

decrease the value of wine exports in the short run. The variable results are written on the line and t- statistics are in parent he s e s. This finding justified the theory of exchange rate and the value of export.

4.2.3. Turkey

The Likelihood Ratio rejects any cointegratio n at the 5% significance level. There is no cointegrate d vector derived from for the REWV and RER. The Turkish wine export value and the Real exchange rate are not cointegrate d. Cointegrate d vector for RWEV and RER is foun d as 1 and - 588.5248. The possible reason s for noncointegratio n are small quan tity of wine export quantity and fixed exchange regime for some years before 1980s. Since there was not any volatility for the some years, variables couldn’t cointegrate d. If there are any changes in the exchange rate policy, this can not lead to any changes for the value of Turkish export value. Thus, exchange rate policy changes can not imply changes in the value of Turkish export. To increase wine export, exchange rate changes should not be used as a foreign trade policy tool.

The result of the cointegratio n does not allow us to implement the ECM Test. However, since it is not feasible to implement ECM Test, it could be stated that there is no short run relation between 2 variables in the long run.

Table 5. Johansen cointegration test for Turkey

Eigenvalue Likelihood Ratio Critical Value (5 %) Critical Value (1 %) Hypothesiz e d No. of CE(s) 0.323779 12.7716 2 15.41 20.04 None* 0.007847 0.252098 3.76 6.65 At most 1** *(**) denotes rejection of the hypoth esis at the 5% (%1) significance level

4.3. Error correction model

The ECM deter mi nes whether a portion of the disequibria from one period is corrected in the next period. For example, the change in price in one period may upon the degree of excess deman d in the previous period [34]_.

France

To apply ECM, the first differences of variables are taken. Both of the two differences of variables are then tested for ECM. As a conclusion, the results of the ECM estimatio ns are stated at about 6 % of disequilibria “corrected” each year by changes in D (RER) and about 1 % of disequilibria “corrected” each year by changes in D (RWEV).

06 . 33262452 )) 2 ( 1 ( * 7 0835810748 . 0 )) 1 ( ( * 4113213194 . 0 ) 2 ( 1 ( * 27994229 . 0 )) 1 ( 1 ( * 0626694236 . 0 ) 2700689137 ) 1 ( 1 * 6507395 . 45 ) 1 ( 1 ( * 0162660566 . 0 ) 1 ( + − + − + − − − − + − + − − = RER D RER D RWEV D RWEV D RER RWEV RWEV D 43 . 36015958 )) 2 ( 1 ( * 4 0724569493 . 0 )) 1 ( ( * 580208754 . 0 )) 2 ( 1 ( * 4661458588 . 0 )) 1 ( 1 ( * 3864629512 . 0 ) 2700689137 ) 1 ( 1 * 65073954 . 45 ) 1 ( 1 ( * 0606854013 . 0 ) 1 ( + − + − + − + − + + − + − − = RER D RER D RWEV D RWEV D RER RWEV RER D Greece

The coefficient of zt is negative indicating that an increase in the value of the dollar will

decrease the value of wine exports in the short run. The variable results are written on the line and t- statistics are in parent he s e s. This finding justified the theory of exchange rate and the value of export. The results of the ECM estimation s are stated at about 14 % of diseq uilibria “corrected” each year by changes in D (RER) and about 7 % of disequilibria “corrected” each year by changes in D (RWEV).

84140827 )) 2 ( 1 ( * 0111472 . 0 )) 1 ( ( * 462140 . 0 ) 2 ( 1 ( * 462140 . 0 )) 1 ( 1 ( * 143336 . 0 ) 7700689137 . 5 ) 1 ( 1 * 205271 . 8 ( ) 1 ( 1 ( * 070505 . 0 ) 1 ( + − + − − − − − + − − − + − − = RER D RER D RWEV D RWEV D RER RWEV RWEV D

94488248

2))

(

*

0.446320

)

*

0.367400

2))

(

*

43595

1))

(

*

0.060527

08

5.77E

1)

(

*

8.205271

1)

(

*

0.143336

D(RER1)

+

−

+

−

+

−

−

−

−

+

+

−

+

−

=

D(RER1

1)

D(RER(

D(RWEV1

D(RWEV1

RER1

RWEV1

0

.

0

5. Results and Discussion

In this study, the impact of the real exchange rate variations on real wine exports value is examined from 1970 - 2003. In some previous studies, cointegration s tests between wine and alcoholic beverages were tested by using the Johan se n Cointegration test. However, the relation between the Mediterrea nea n wine export values and the real exchange rates were not explored with time series data in such an analysis [6] _.

First, both RWEV and RER data sets were found to be integrate d of the same order. Then, it could become possible to investigate the existence of a long - run relations hip between exchange rates and agricultu ral exports, and afterwar d s cointegration analysis was conducte d. For that reason, unit root ADF tests were carried out for stationarity. Integration was found to exist. After that, because the series were integrated in the same order we searched the long term relations hip between variables. The RER and the RWEV were found to be cointegrate d for all countries in the context of this stu dy. It was found that French and Greek monetary policies have affected on wine export prices and volume thro ug h ou t the years. Following the 33 years of annual observation s, these 2 variables were not found to be stationary separately, but when the analysis was conducte d with both variables together, they were found to be cointegrate d and they moved together in the long run. It could be concluded that any changes for each count ry’s monetary policy will affect export volume. Therefore, any depreciation of local currency in dollar term s will lead to an increase of exports and vice versa.

The theory of exchange rate volatility was justified one more time in this study. One further step of this study was the implemen t a tio n of the ECM to deter mine whether there were any short term relations and impacts of exchange rate variations on wine export s. We provided this short term relations hi p by the ECM test results. On the other hand, the Turkish wine export value and the real exchange rate were not cointegrate d. Thus, any exchange rate policy changes can not imply changes for the Turkish export value. To increase wine export, exchange rate changes should not be used as a foreign trade policy tool. For France and Greece, short term relation through real exchange rates and real export wine volume exist. However, for Turkey, we could not find any cointegration vector and for that reason our hypot he sis about this country did not hold true.

6. Reference s

1. Anonymou s / a , 2004. History of wine. (http: / / w ww.his to ry - of-wine.co.uk / h t m l / t i m eline.ht ml ).

2.

Anderso n, K., Wittwer, G., 2001. The World Wine Market in 2005: Effects of Faster Asian Deman d Growth. Proceedings of the 26th World Congress of the OIV, Adelaide, 11 - 18 October 2001, Paris: Office International de la Vigne et du Vin. 3. European Commissio n / a, 2006. Wine economy of the the sector, Working paper,

European Commission, Directorate - General for Agrciulture and Rural Develop me n t, Brussels.

(europa.eu.int / c o m m / a g ricult u re / market s / wi n e / s t u d i e s / r e p_econ 2 00 6_en.p df)

4.

Fotopo ulos, C., Krystallis, A. and Ness, M., 2003. Wine Produced by Organic Grapes in Greece: Using Means - end Chains Analysis to Reveal Organic Buyers. Food Quality and Preference , 14 (7): 549 - 566.

5.

Dornbu s h, R., Krugman, P., Cooper, R.N., Withmann, M.N., 1976. Flexible exchange rate in the short run , Brookings paper on econo mic activity, Vol.1976, No (3), 537 - 584.

6. Mencet, M. N., 2006. “Price analysis of wine prices the case: France, Greece and Turkey ”, Mediterra nea n Agrono mical Institute of Chania, Depart m e n t of Business Economics and Managemen t , Economics Depart m e n t Master Thesis (Unpublished), Chaina, Crete, Greece.

7.

Kadilar, C., 2000. Multivariable Time Series Analysis , Hacettepe University, ISBN: 9758201 2 0 4. Ankara.

8.

Sorenso n, B.E., 2005. Economics 266 . htt p: / / w ww. u h.ed u / ~ b s o r e n s e / c oi n t.p d f

9.

Saunders, P., Biswas, B., and Mohapat ra , S., 1999. A Cointegration Analysis of the Impact of Real Exchange Rate Variations on US Agricultural Exports, volume 59

article 2, Agriculture Financial Review.

(htt p: / / a f r.ae m.corn ell.ed u / 5 9 / v o l u m e_59_article2.ht m ).

10.

Batten, D., S., Belongia, M.T., 1986. Monetary Policy, Real Exchange Rates and U.S Agricultural Exports, Amer. J. Agr. Econ .

11.

Xu, Z., 1999. The exchange rate and long run price moveme n t s in the US and Japan, Applied Economic Letters , 1999, 6, 227 - 230.

12.

Johanse n, S., Mosconi, R., Nielsen, 2000. Cointegration Analysis in the Presence of Structural Breaks in the Determinis tic Trend, Econometrics Journal (2000), vol.3:216 - 249).

13.

Johanse n, S., 1998. Statistical Analysis of cointegration Vectors. J. Econ. Dyna mics and Contr ol 12 (1998): 23154.

14. Anderso n, K., 2001. Prospects Ahead for the Wine Industry. The Australian Grapegrower and Winemaker 448: 67- 74, May 2001.

15.

Silverman, M., Castaldi, R.M., Baack, S., 2000. Competition in the Global Wine Indust ry: A U.S. Perspective , San Francisco State University.

(htt p: / / b i e.sfs u.ed u / files / GlobalWineCase%20T&S%20Text%202002.p df ).

16.

Heijbroek, A., 2003. “Conseq ue nces of the globalization in the wine indust ry” , Symposiu m International Great Wine Capitals Global Network, Bilbao, 8 October.

17.

Anderso n, K., Norman, D. and Wittwer, G., 2001. Globalizatio n and the World's Wine Markets. The World Economy 26(5): 659 - 87, May 2003.

18.

OIV, 2003. World Wine Production 2002 - 2003 Economic Services Depart m e n t, Wine Institu te ; Office International de la Vigne et du Vin, Paris.

19. FAO, 2004. “Food and Agriculture Organization of the United Nations”. FAOSTAT Database.

(htt p: / / f a o s t a t.fao.org / f a o s t a t / f o r m ?collection =T r a de.CropsLivestockProd uc t s& Domain = Tr a d e&servlet = 1&hasb ulk = 0&version = ext&language =EN ).

20. European Commision / b , 2006. Reform of the wine sector. European Commission, Directorate - General for Agrciulture and Rural Develop me n t, Brussels.

21. EUROSTAT, 2004. European Union Statistical Depart me n t, Wine Databases.

22.

Auriol, E., Lesourd, J. and Schilizzi, S.G.M., 2002. French Wine Quality Effects, Oenométrie IX – Montpellier. (htt p: / / w ww.vd qs.fed -eco.com / d o c u m e n t s / 2 0 0 2Mont p ellier /Lesour d.p d f ).

23.

Anonymou s / b , 2004. http: / / w ww.wineonline.co.uk / g r eece.ht m

24.

Wine Institut e, 2005. The voice for Californian wine, Key facts, “World vineyard acreage by country”.

(htt p: / / w ww.wineins tit u te.org / c o m m u n ica tio ns / s t a t i tics / k eyfact s_worldacreage4 .htm )

25. Anonymou s / c, 2006. Turkish wine market.

(htt p: / / w ww.buyus a.gov / t u r k ey / e n / t u r ki s hwi ne m a rke t.h t ml )

26. EU Commision, 2005. Reg. (EC) No 1493 / 1 9 9 9 of 17 May 1999 on the Commo n

Organisation of the Market in wine.

(htt p: / / e u r o p a.eu.int / s c a d p l u s / l e g / e n / l v b / l 6 0 0 3 1. ht m ).

27.

EU Council, 2005. Reg. EC 1493 / 1 9 9 9. Achievement s in Agricultural Policy Under Commissioner Franz Fischler (Period 1995 - 2004).

(http: / / e u r o p a.e u.int / c o m m / a g ricult ur e / p u b li / a c hieveme n t s / t e x t_en.p df ).

28.

Kristinek, J.J., Anderson, P., 2002. Exchange Rates and Agriculture: A Literatu re Review, AFPC Working Paper 02- 2. Agricultural and Food Policy Center

Depart me n t of Agricultural Economics.

(htt p: / / w ww.afpc.ta m u.e d u / p u b s / 0 / 3 7 3 / w p - 2002 - 02.pdf ).

29.

Colonder, 2001. 2001. University of Phoenix, Microecono mics Fourth Edition, McGraw Hill Irwin, U.S.

30. Schwart z, N. E., 1986. The conseque nces of a Floating Exchange Rate for the U.S. Wheat Market. Amer. J. Agr. Econ.68 (19869:42833).

31.

Giorgioni, G., Thom p s o n, J., 2002. Which volatility? The case of the export s of wheat. Applied Economics Letters , Vol. 9, pp.681 - 684.

32.

Enders, W., 2003. Applied Econometric Time Series , 2nd Edition (Hardcover).

33.

Engle, R.F., Granger, W.J., 1987. Cointegratio n and Error Correction: Represen t a tio n, Estimation, Testing. Econometrica 55, 251 - 276.

34.

Batchelor, R., 2000. EVIEWS Tutorial: Cointegration and Error Correction , City University Business School, London & ESCP, Paris.