exchange rate volatility and turkish industry-level export: panel cointegration analysis
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Exchange rate volatility andTurkish industry-level export:Panel cointegration analysisSaban Nazlioglu aa Department of Econometrics , Pamukkale University ,Denizli , TurkeyPublished online: 01 Mar 2012.
To cite this article: Saban Nazlioglu (2013) Exchange rate volatility and Turkishindustry-level export: Panel cointegration analysis, The Journal of International Trade& Economic Development: An International and Comparative Review, 22:7, 1088-1107,DOI: 10.1080/09638199.2012.660978
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© 2013 Taylor & Francis
The Journal of International Trade & Economic Development, 2013Vol. 22, No. 7, 1088–1107, http://dx.doi.org/10.1080/09638199.2012.660978
Exchange rate volatility and Turkish industry-level export: Panelcointegration analysis
Saban Nazlioglu*
Department of Econometrics, Pamukkale University, Denizli, Turkey
(Received 20 July 2011; final version received 23 January 2012)
The purpose of this article is to investigate the impact of the exchangerate volatility on Turkey’s export. To this end, the panel cointegrationanalysis is applied to the data from Turkey’s top 20 export industries tomajor 20 trading partners for the period 1980–2009. Special attention ispaid to test for whether employment of country-level trade data insteadof industry-level data is subject to the aggregation bias problem in theestimation of long-run cointegration parameters. The results indicatethat employing country-level trade data suffers from the aggregationbias in estimating the cointegration parameters for the level of exchangerate and for the exchange rate volatility. The findings imply that (i) theimpact of the exchange rate volatility on Turkish exports differs acrossindustries, (ii) Turkey benefits from the depreciation of Turkish lira,and (iii) the foreign income plays a key role in determining the Turkishindustry-level exports. The findings increase our insights to explainthe recent dynamics of Turkish exports and provide some policyimplications.
Keywords: exchange rate volatility; industry-level export; Turkey; panelcointegration
JEL Classifications: F14 F31 C33
1. Introduction
Turkey has been implementing the trade-oriented growth model since 1980,and thereby international trade plays a crucial role in Turkey’s developmentmodel. In order to increase export and to cope with trade deficits, exchangerate policy is at the center of the trade-oriented growth model. From 1980 toearly 2000s, Turkey adopted the fixed exchange rate regime. After theeruption of the 2001 crisis, Turkey shifted from the pegged exchange rateregime to the flexible exchange rate system in 2001. Since then, the volatilityof Turkish Lira against the major trading partners’ currencies is of greatimportance in trade flows. The trade dynamics and exchange rate policy
*Email: [email protected]
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The Journal of International Trade & Economic Development 1089
Exchange rate volatility and Turkish industry-level export: Panelcointegration analysis
Saban Nazlioglu*
Department of Econometrics, Pamukkale University, Denizli, Turkey
(Received 20 July 2011; final version received 23 January 2012)
The purpose of this article is to investigate the impact of the exchangerate volatility on Turkey’s export. To this end, the panel cointegrationanalysis is applied to the data from Turkey’s top 20 export industries tomajor 20 trading partners for the period 1980–2009. Special attention ispaid to test for whether employment of country-level trade data insteadof industry-level data is subject to the aggregation bias problem in theestimation of long-run cointegration parameters. The results indicatethat employing country-level trade data suffers from the aggregationbias in estimating the cointegration parameters for the level of exchangerate and for the exchange rate volatility. The findings imply that (i) theimpact of the exchange rate volatility on Turkish exports differs acrossindustries, (ii) Turkey benefits from the depreciation of Turkish lira,and (iii) the foreign income plays a key role in determining the Turkishindustry-level exports. The findings increase our insights to explainthe recent dynamics of Turkish exports and provide some policyimplications.
Keywords: exchange rate volatility; industry-level export; Turkey; panelcointegration
JEL Classifications: F14; F31; C33
1. Introduction
Turkey has been implementing the trade-oriented growth model since 1980,and thereby international trade plays a crucial role in Turkey’s developmentmodel. In order to increase export and to cope with trade deficits, exchangerate policy is at the center of the trade-oriented growth model. From 1980 toearly 2000s, Turkey adopted the fixed exchange rate regime. After theeruption of the 2001 crisis, Turkey shifted from the pegged exchange rateregime to the flexible exchange rate system in 2001. Since then, the volatilityof Turkish Lira against the major trading partners’ currencies is of greatimportance in trade flows. The trade dynamics and exchange rate policy
*Email: [email protected]
developments in Turkish economy during 2000s signal a positive correlationbetween floating exchange rates and export. Determining sensitivity ofTurkish exports to exchange rate volatility deepens our insights for betterunderstanding the dynamics of Turkey’s export and thereby is crucial fordesigning trade and monetary policies in economic development process.
Shifting from fixed to flexible exchange rate system creates volatilities inexchange rates which lead to investigate to what extent trade flows arerelated with exchange rate volatility. The conventional wisdom postulates anegative relationship between exchange rate volatility and trade due to thepoint of view that risk-averse traders may be willing to trade less today inorder to avoid uncertainties stemming from fluctuations in exchange rates(Clark 1973; Ethier 1973; Hooper and Kohlgahen 1978; Gagnon 1993; Wolf1995). The theoretical model developed by De Grauwe (1988), and byDelleas and Zilberfarb (1993), however, implies that traders may trade moretoday to increase (decrease) their revenues (profit losses), which will result ina positive impact of exchange rate volatility on trade. Besides, trade flowsmay not be sensitive to exchange rate volatility if uncertainties in futureprices associated with exchange rate volatility are hedged by financialinstitutions and credit opportunities (Baron 1976; Willet 1986; Giovannini1988). Since the theoretical views do not provide uniform conclusion on theexchange rate volatility and trade nexus, researches have focused ondetermining the nature of this relationship via empirical methods. Similar tothe theoretical literature, the findings from the empirical studies are notclear-cut. For instance, on the one hand a negative relationship betweentrade flows and exchange rate volatility is supported as commonly expected.On the other hand, there are the empirical studies which indicate that eithertrade flows are positively associated with or are unaffected from exchangerate volatility.1
Although a huge empirical literature at international-level is documen-ted for the exchange rate volatility and trade relation, the literature onTurkish data is in fact still rare. Since the international literature iscomprehensively reviewed, herewith I concentrate on summarizing theTurkish literature (see Table 1). In an early study, Ozbay (1999) supportsevidence on that the Turkish export is adversely affected with the exchangerate volatility. This finding is substantiated by the studies conducted byOzturk and Acaravci (2002), and Guloglu (2008). In contrast, Kasman andKasman (2005) and in a more recent study Ozturk and Kalyoncu (2009)find out that Turkey’s export has a positive relation with the exchange ratevolatility.
The studies on Turkey – with the exception of Vergil (2002) – employedaggregate trade data, and thereby may subject to the so-called ‘aggregationbias problem’. The aggregation bias problem implies that a significant effectof a variable in some countries could be offset by insignificant effects of thesame variable in other countries, thus (mis)leading to the conclusion that
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the variable in question is insignificant in general. In order to avoid theaggregation bias problem, disaggregate trade data have been employed. Forinstance, the recent studies carried out by Peridy (2003) and Tadesse (2009)indicate that impact of exchange rate volatility vary across countries. In linewith this literature, the study for Turkey carried out by Vergil (2002) showsthat while the exchange rate volatility negatively affects the Turkish exportsto the US, France, and Germany, it does not matter for Turkey’s exportflows to Italy.
The employment of trade data at country level overcomes theaggregation bias problem to some extent; however, it may still suffer fromthat problem since the impact of exchange rate volatility on trade may differacross industries in a country (Bahmani-Oskoee and Wang 2008). From thispoint of view, the more recent trend in the international trade literature is tobenefit from disaggregating trade data at industry-level in order to deal withthe aggregation bias problem and thereby to assess the impact of exchangerate volatility on industry-level trade. Among others, Wang and Barret(2007), Bahmani-Oskoee and Wang (2008), and Bahmani-Oskooee,Ardalani, and Bolhasani (2010) show that the impact of exchange ratevolatility on trade differs across industries. To best of our knowledge, thereis no study analyzing the impact of exchange rate volatility on the Turkishindustry-level export. Unlike the previous studies on Turkey, this studyemploys industry-level trade data in order to deal with the aggregation bias
Table 1. The summary of the studies on Turkish export data.
Study Data type Period MethodImpact ofvolatility
Ozbay (1999) Aggregate 1988q2–1997q2 Cointegrationa NegativeOzturk and
Acaravci(2002)
Aggregate 1989 Jan–2002Aug
Cointegrationa Negative
Vergil (2002) Bilateral 1990 Jan–2000Dec
Cointegrationa Negative for US,France, andGermany;insignificantfor Italy
Kasman andKasman(2005)
Aggregate 1982q1–2001q4 Cointegrationa Positive
Guloglu (2008) Aggregate 1982 Jan–2006Dec
MS-ARCH Negative
Ozturk andKalyoncu(2009)
Aggregate 1982q1–2005q4 Cointegrationb Positive
Note: aJohansen approach, bEngle-Granger approach, MS-ARCH: Markow Switchingautoregressive conditional heteroscedasticity.
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the variable in question is insignificant in general. In order to avoid theaggregation bias problem, disaggregate trade data have been employed. Forinstance, the recent studies carried out by Peridy (2003) and Tadesse (2009)indicate that impact of exchange rate volatility vary across countries. In linewith this literature, the study for Turkey carried out by Vergil (2002) showsthat while the exchange rate volatility negatively affects the Turkish exportsto the US, France, and Germany, it does not matter for Turkey’s exportflows to Italy.
The employment of trade data at country level overcomes theaggregation bias problem to some extent; however, it may still suffer fromthat problem since the impact of exchange rate volatility on trade may differacross industries in a country (Bahmani-Oskoee and Wang 2008). From thispoint of view, the more recent trend in the international trade literature is tobenefit from disaggregating trade data at industry-level in order to deal withthe aggregation bias problem and thereby to assess the impact of exchangerate volatility on industry-level trade. Among others, Wang and Barret(2007), Bahmani-Oskoee and Wang (2008), and Bahmani-Oskooee,Ardalani, and Bolhasani (2010) show that the impact of exchange ratevolatility on trade differs across industries. To best of our knowledge, thereis no study analyzing the impact of exchange rate volatility on the Turkishindustry-level export. Unlike the previous studies on Turkey, this studyemploys industry-level trade data in order to deal with the aggregation bias
Table 1. The summary of the studies on Turkish export data.
Study Data type Period MethodImpact ofvolatility
Ozbay (1999) Aggregate 1988q2–1997q2 Cointegrationa NegativeOzturk and
Acaravci(2002)
Aggregate 1989 Jan–2002Aug
Cointegrationa Negative
Vergil (2002) Bilateral 1990 Jan–2000Dec
Cointegrationa Negative for US,France, andGermany;insignificantfor Italy
Kasman andKasman(2005)
Aggregate 1982q1–2001q4 Cointegrationa Positive
Guloglu (2008) Aggregate 1982 Jan–2006Dec
MS-ARCH Negative
Ozturk andKalyoncu(2009)
Aggregate 1982q1–2005q4 Cointegrationb Positive
Note: aJohansen approach, bEngle-Granger approach, MS-ARCH: Markow Switchingautoregressive conditional heteroscedasticity.
problem and thereby contributes to the literature by concentrating onTurkey’s industry-level exports to her major trading partners.
The purpose of this study is to empirically investigate the impact ofexchange rate volatility on the Turkish disaggregate export data at industry-level. In this respect, we estimate the export model for the period 1980–2009by employing the exports from Turkey’s top 20 export industries to hermajor 20 trading partners. Nevertheless, as a sensitivity analysis, the exportmodel was re-estimated by employing country-level trade in order toinvestigate whether the result based on the industry-level trade data isdifferent from those on the county-level data. The panel cointegrationanalysis for the industry-level data shows that 10 out of 20 industries’ exportare positively affected from the exchange rate volatility, in the remaining10 cases Turkish industry-level export is negatively affected or is unaffectedfrom uncertainty in the Turkish exchange rates. On the other hand, thecountry-level trade data analysis supports the positive effect of the exchangerate volatility on the Turkish exports. The findings of this study show that itis beneficial to use disaggregate trade data at industry-level to overcome theaggregation bias problem and imply the need to design trade policies atindustry-level by taking into account fluctuations in exchange rates.
The structure of article is organized as follows. The next sectionsummarizes the developments in Turkish trade and exchange rate policies.Section 3 is devoted to explain the theoretical framework and to developthe empirical model. The data are described in Section 4, followed byeconometric methodology. The inferences from empirical findings are givenin Section 6. The discussion and policy implications are presented in Section7. Finally, the concluding remarks are provided in Section 8.
2. Overview of Turkish trade and exchange rate policies
Starting from 1930, Turkey pursued the import-substitution industrializa-tion/inward-oriented development strategy for 50 years. During the inwardoriented development regime, the state-owned public enterprises dominatedTurkish economy and the development targets were determined by means ofthe five-year development plans. The public enterprises accounted for about40% of industry by the late 1970s and the operating losses of the state-owned enterprises constituted a significant share of the government budgetdeficit. Furthermore, the import-substitution policies led to the chronictrade deficits that were financed by foreign borrowing which in turn resultedin balance of payments crises (Metz 1995).
The weaknesses of the inward-oriented growth model prompted Turkeytoward the outward-oriented/trade-oriented economic development strat-egy. Eventually, Turkey decided to implement the outward-oriented growthmodel by the 24 January 1980 decisions. The decisions basically aimed atestablishing the outward-oriented and market-based economic system.
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In this respect, the export subsidies were granted and exchange rates weredevaluated to make Turkish exports more competitive, which lead to thepromotion of export-led growth (CBRT 2002).
Since 1980, Turkey has been pursuing the trade-oriented growth strategyand liberalizing her economy in order to integrate with the world economy.As a consequence, as illustrated in Figure 1, Turkish trade has considerablyincreased over time. In particular, while the Turkish export was US$2.9billion in 1980, it reached at US$102 billion in 2009. The patterns of Turkishimports were similar to that of the export. Specifically, Turkish import roseto US$140 billion in 2009 from US$7.9 billion in 1980. Figure 1 also showsthat the trade deficit has been increasing during the recent years, which isone of key challenges in the Turkish economy. The trade deficit was at itspeak-level in 2008 that it was approximately US$70 billion. Even though itdecreased to US$38.7 billion in 2009, this decline can be attributed to theglobal turbulence of 2008.
The important share of Turkish total exports is explained by majorexporting industries as well as Turkey’s major trading partners. The sharesof top 20 export industries and of Turkish major trading partners in totalexports are illustrated in Table 2 for five-year intervals from 1980 to 2009.2
At first glance, it is observed that the share of top 20 industries in totalexport has been rising since 1980. Particularly, the Turkish top 20 exportindustries accounted for about 78.5% of Turkish total export as of 2009.While the share of major trading partners in total exports increased from57.59% in 1980 to 66.28% in 2000, it decreased to its 1980-level in 2005.As of 2009, the selected trading partners explained approximately 47% ofTurkey’s export earnings. The distribution of Turkish export illustrated inTable 3 indicates that the important share of total export of the top 20 trade
Figure 1. The dynamics of Turkish trade flows (US$1000).Source: Author’s calculations from TurkStat Foreign Trade Database.
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In this respect, the export subsidies were granted and exchange rates weredevaluated to make Turkish exports more competitive, which lead to thepromotion of export-led growth (CBRT 2002).
Since 1980, Turkey has been pursuing the trade-oriented growth strategyand liberalizing her economy in order to integrate with the world economy.As a consequence, as illustrated in Figure 1, Turkish trade has considerablyincreased over time. In particular, while the Turkish export was US$2.9billion in 1980, it reached at US$102 billion in 2009. The patterns of Turkishimports were similar to that of the export. Specifically, Turkish import roseto US$140 billion in 2009 from US$7.9 billion in 1980. Figure 1 also showsthat the trade deficit has been increasing during the recent years, which isone of key challenges in the Turkish economy. The trade deficit was at itspeak-level in 2008 that it was approximately US$70 billion. Even though itdecreased to US$38.7 billion in 2009, this decline can be attributed to theglobal turbulence of 2008.
The important share of Turkish total exports is explained by majorexporting industries as well as Turkey’s major trading partners. The sharesof top 20 export industries and of Turkish major trading partners in totalexports are illustrated in Table 2 for five-year intervals from 1980 to 2009.2
At first glance, it is observed that the share of top 20 industries in totalexport has been rising since 1980. Particularly, the Turkish top 20 exportindustries accounted for about 78.5% of Turkish total export as of 2009.While the share of major trading partners in total exports increased from57.59% in 1980 to 66.28% in 2000, it decreased to its 1980-level in 2005.As of 2009, the selected trading partners explained approximately 47% ofTurkey’s export earnings. The distribution of Turkish export illustrated inTable 3 indicates that the important share of total export of the top 20 trade
Figure 1. The dynamics of Turkish trade flows (US$1000).Source: Author’s calculations from TurkStat Foreign Trade Database.
Table 3. The distribution of Turkish exports (2009).
Industry
Sharein totalexport Country
Share intop-20
industriesexport
Sharein totalexport
Vehicles other than railway 12.00 Australia 0.38 0.30Machineries, mechanical appliances
boilers and parts thereof7.96 Austria 1.01 0.79
Iron and steel 7.48 Belgium 2.24 1.76Articles of apparel and clothing
accessories knitted6.78 Canada 0.42 0.33
Electrical machinery and equipment 6.50 Denmark 0.85 0.66Pearls, precious stones, coin 5.81 Finland 0.25 0.19Articles of iron and steel 4.45 France 7.75 6.08Articles of apparel and clothing
accessories not knitted4.21 Germany 12.22 9.58
Mineral fuels and oils 3.82 Greece 2.04 1.60Plastics and articles thereof 3.03 Ireland 0.36 0.28Fruit 2.94 Italy 7.36 5.77Salt, sulfur, earth, plastering mat,
lime, cement2.12 Japan 0.29 0.23
Ships, boats, and floating structures 1.79 Netherlands 2.65 2.08Other made-up textile articles 1.61 Norway 0.48 0.38Furniture 1.56 Portugal 0.51 0.40Rubber and articles thereof 1.44 Spain 3.53 2.76Aluminum and articles thereof 1.37 Sweden 0.93 0.73Cotton, cotton yarn, and cotton
fabric1.25 Switzerland 4.91 3.85
Preparations of vegetables and fruits 1.25 UK 7.39 5.79Carpets and other floor covering 1.05 USA 4.02 3.16Total 78.41 59.59 46.73
Source: Author’s calculations from TurkStat Foreign Trade Database.
Table 2. An overview of Turkish exports.
Share of top-20 exportindustries in total exports
Share of major 20 countriesin total exports
1980 44.34 57.691985 46.61 51.601990 64.94 67.941995 69.34 61.032000 71.39 66.282005 78.80 57.682009 78.41 46.73
Source: Author’s calculations from TurkStat Foreign Trade Database.
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industries were explained by the Turkey’s major trading partners as of 2009.Therefore, determining driving forces in exports from these industries tomajor trading partners is crucial for better understanding the fluctuations inTurkish exports and for trade policy designs.
With respect to exchange rate policies, it can be argued that the exchangerate policies in Turkey have been designed in parallel with the developmentsin the macro-economy. In Table 4, I summarized the changes in Turkishexchange rate regime as well as the main developments and important eventsin the Turkish economy. Before 1980s, the fixed exchange rate regime wasimplemented in a way that the value of Turkish lira was determined andadjusted by the government according to the changes in economic condition.After the implementation of the outward-oriented growth strategy, theadjustable peg policy was followed in order to maintain the trade-orientedgrowth model. In particular, Turkish lira was largely devalued againstother currencies and a uniform rate was established. During the period of1980–1988, the Turkish lira was daily adjusted in form of devaluationsand consequently it depreciated more than 8% in real terms. In 1989, thegovernment put into effect the system of partial market setting of the officialexchange rate and the free capital movements along with higher interestrates and convertible Turkish lira. These policy changes resulted in theappreciation of the Turkish lira (Asikoglu and Uctum 1992).3
The 1994 crisis of Turkish economy – which was one of the majorfinancial crises in her history – led to the structural policy changes inTurkey. To alleviate the impacts of the crisis and to recover the economy,the stabilization program was announced and the economic rescue programby the International Monetary Fund (IMF) came into effect. As a part of thestabilization and rescue programs, the Turkish lira was dramaticallydevaluated by 39%. In 1999, the stabilization program guided by IMFwas announced in order to decrease inflation and real interest rates and toprovide a stable macroeconomic environment. The program was essentiallyexchange rate-based and was dependent upon announcing value of exchangerate basket for the first one and a half-year period (CBRT 2002).
Table 4. Turkish exchange rate policies.
Date Development in Turkish economy Exchange rate policy
Before 1980 Inward-oriented growth strategy Fixed exchange rates1980 Outward-oriented growth strategy Adjustable exchange rates1989 Financial liberalization policy Convertibility of Turkish lira1994 Economic crisis Devaluation of Turkish lira1999 Stabilization program Crawling exchange rates2001 Economic crisis Floating exchange rates
Source: Asikoglu and Uctum (1992) and CBRT (2002).
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industries were explained by the Turkey’s major trading partners as of 2009.Therefore, determining driving forces in exports from these industries tomajor trading partners is crucial for better understanding the fluctuations inTurkish exports and for trade policy designs.
With respect to exchange rate policies, it can be argued that the exchangerate policies in Turkey have been designed in parallel with the developmentsin the macro-economy. In Table 4, I summarized the changes in Turkishexchange rate regime as well as the main developments and important eventsin the Turkish economy. Before 1980s, the fixed exchange rate regime wasimplemented in a way that the value of Turkish lira was determined andadjusted by the government according to the changes in economic condition.After the implementation of the outward-oriented growth strategy, theadjustable peg policy was followed in order to maintain the trade-orientedgrowth model. In particular, Turkish lira was largely devalued againstother currencies and a uniform rate was established. During the period of1980–1988, the Turkish lira was daily adjusted in form of devaluationsand consequently it depreciated more than 8% in real terms. In 1989, thegovernment put into effect the system of partial market setting of the officialexchange rate and the free capital movements along with higher interestrates and convertible Turkish lira. These policy changes resulted in theappreciation of the Turkish lira (Asikoglu and Uctum 1992).3
The 1994 crisis of Turkish economy – which was one of the majorfinancial crises in her history – led to the structural policy changes inTurkey. To alleviate the impacts of the crisis and to recover the economy,the stabilization program was announced and the economic rescue programby the International Monetary Fund (IMF) came into effect. As a part of thestabilization and rescue programs, the Turkish lira was dramaticallydevaluated by 39%. In 1999, the stabilization program guided by IMFwas announced in order to decrease inflation and real interest rates and toprovide a stable macroeconomic environment. The program was essentiallyexchange rate-based and was dependent upon announcing value of exchangerate basket for the first one and a half-year period (CBRT 2002).
Table 4. Turkish exchange rate policies.
Date Development in Turkish economy Exchange rate policy
Before 1980 Inward-oriented growth strategy Fixed exchange rates1980 Outward-oriented growth strategy Adjustable exchange rates1989 Financial liberalization policy Convertibility of Turkish lira1994 Economic crisis Devaluation of Turkish lira1999 Stabilization program Crawling exchange rates2001 Economic crisis Floating exchange rates
Source: Asikoglu and Uctum (1992) and CBRT (2002).
In February 2001, Turkey experienced the most destructive economic crisissince 1945. As an aftermath of the 2001 crisis, the government decided toadopt floating exchange rate regime.
3. Theoretical framework and empirical model
In order to explain the theoretical underpinnings of the impact of exchangerate volatility on export, we can utilize the simple model developed by Pereeand Steinherr (1989). First, let us consider the profit of an export firm as afunction of export price, unit costs, and level of export. This profit functioncan be formulated as:
~P ¼ ð~p� cÞx ð1Þ
where * denotes a random variable, Pis the profit, p is the export price, c isthe unit costs assumed to be constant, and x is the level of export. Theexport price is described as ~p ¼ p�:~e that p* is the given world market priceand e is the nominal exchange rate. The nominal exchange rate is treated asrandom which makes the firm’s profit to be random. The firm’s decisionproblem is to maximize the utility function which takes the following form:
Uð ~PÞ ¼ �exp �l ~P ð2Þ
where l measures the risk aversion (i.e. exchange rate volatility). If theexport price (p) is normally distributed, then the expected utility functioncan be written as:
EUð ~PÞ ¼ �exp �lðmP�12ls
2PÞ ð3Þ
where the m and s2 are respectively the mean and variance of the profit.More specifically, mP ¼ Eð ~PÞ ¼ ðE~p� cÞx and s2P ¼ E½ð~p� E~pÞx�2 ¼ x2s2p.The maximization of the expected utility function in equation (3) withrespect to the level of export (x) yields the following relationship betweenexchange rate volatility and export:
x ¼ ðE~p� cÞ=ls2p ð4Þ
In this relationship, the impact of the exchange rate volatility on exportdepends on degree of risk aversion. If the firm is sufficiently risk averse(l4 1), the expected utility function is convex and thereby export ispositively correlated with exchange rate volatility. This is because anincrease in exchange rate volatility raises expected marginal utility of exportrevenues and thereby encourages the firm to increase export activity. If, incontrast, the firm is not very risk averse (l5 1), the expected utility function
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is concave in which an increase in exchange rate volatility reduces expectedmarginal utility of export earnings and therefore leads the firm to exportless. Finally, if the degree of risk aversion is equal to zero (l¼ 0), the level ofexport is not sensitive to the extent of exchange rate volatility (De Grauwe1988, 66).
In order to empirically analyze the impact of exchange rate volatility onindustry-level trade, following Kennen and Rodrick (1986) and Bahmani-Oskooee, Ardalani, and Bolhasani (2010), the export model in this study isdescribe as:
lnXjit ¼ a0 þ a1 lnY�it þ a2 lnREXRit þ a3 lnVOLit þ eit ð5Þ
where t refers to the time period, Xji is the Turkish exports from industry j totrading partner i, Y�
i is the real income of trading partner i, REXRi is the realbilateral exchange rate between Turkish lira and the currency of tradingpartner i, and VOLi is the volatility of the exchange rate.
In this specification, the impact of the foreign income on export isexpected to be positive since an increase in the real income of a tradingpartner results in an increase in the demand for Turkish products. The realdepreciation of Turkish lira increases her exports by making Turkishproducts cheaper in international markets. It is therefore expected that theexport is positively related with the movements in the exchange rates. Asexplained in introduction section, the coefficient on exchange rate volatilitycould be significantly positive/negative or insignificant.
4. Data
The export variable is measured by different proxies in the empiricalliterature. The conventional proxy is to use export volumes. In Turkey,likewise in most of the developing countries, data availability on quantity ofexport is scarce at trading partner – and at industry-level trade. To deal withthis data collection problem, one way is to deflate export values by exportunit value indices or by export prices. The problem with this proxy, on theone hand, is that unit value indices change with commodity composition oftrade even though prices of commodities are unchanged (Goldstein andKhan 1985). On the other hand, export prices are generally not available atcountry- and/or industry-level. Furthermore, this proxy ignores the fact thata country exports different commodities to different trading partners(Bahmani-Oskooee and Goswami 2004). To overcome the difficulties arisingfrom data unavailability of trade volume and price indices at country- andindustry-level, Bahmani-Oskooee and Goswami (2004) propose a direct wayof analyzing sensitivity of trade flows to exchange rate (volatility) by usingexport values. Since export volumes, price indices, and export unit valueindices are not available at country- and industry-level in Turkey, the export
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is concave in which an increase in exchange rate volatility reduces expectedmarginal utility of export earnings and therefore leads the firm to exportless. Finally, if the degree of risk aversion is equal to zero (l¼ 0), the level ofexport is not sensitive to the extent of exchange rate volatility (De Grauwe1988, 66).
In order to empirically analyze the impact of exchange rate volatility onindustry-level trade, following Kennen and Rodrick (1986) and Bahmani-Oskooee, Ardalani, and Bolhasani (2010), the export model in this study isdescribe as:
lnXjit ¼ a0 þ a1 lnY�it þ a2 lnREXRit þ a3 lnVOLit þ eit ð5Þ
where t refers to the time period, Xji is the Turkish exports from industry j totrading partner i, Y�
i is the real income of trading partner i, REXRi is the realbilateral exchange rate between Turkish lira and the currency of tradingpartner i, and VOLi is the volatility of the exchange rate.
In this specification, the impact of the foreign income on export isexpected to be positive since an increase in the real income of a tradingpartner results in an increase in the demand for Turkish products. The realdepreciation of Turkish lira increases her exports by making Turkishproducts cheaper in international markets. It is therefore expected that theexport is positively related with the movements in the exchange rates. Asexplained in introduction section, the coefficient on exchange rate volatilitycould be significantly positive/negative or insignificant.
4. Data
The export variable is measured by different proxies in the empiricalliterature. The conventional proxy is to use export volumes. In Turkey,likewise in most of the developing countries, data availability on quantity ofexport is scarce at trading partner – and at industry-level trade. To deal withthis data collection problem, one way is to deflate export values by exportunit value indices or by export prices. The problem with this proxy, on theone hand, is that unit value indices change with commodity composition oftrade even though prices of commodities are unchanged (Goldstein andKhan 1985). On the other hand, export prices are generally not available atcountry- and/or industry-level. Furthermore, this proxy ignores the fact thata country exports different commodities to different trading partners(Bahmani-Oskooee and Goswami 2004). To overcome the difficulties arisingfrom data unavailability of trade volume and price indices at country- andindustry-level, Bahmani-Oskooee and Goswami (2004) propose a direct wayof analyzing sensitivity of trade flows to exchange rate (volatility) by usingexport values. Since export volumes, price indices, and export unit valueindices are not available at country- and industry-level in Turkey, the export
variable is described as the export values in US dollars of the industry j withthe trading partner i. The data on this variable are retrieved from TurkishStatistical Institute (TurkStat) on-line trade database.
The real income of the trading partners is measured as the grossdomestic product (GDP) index (2005¼ 100). The data on GDPs of thetrading partners are collected from World Development Indicators (WDI)on-line database.
The bilateral real exchange rate between Turkey and trading partner i iscalculated by P�
i � Ei
� ��P, where P* and P are the foreign and domestic
consumer price indexes, respectively (2005¼ 100), and E is the bilateralnominal exchange rate which is measured as the number of Turkish lira perunit of trading partner’s currency. Therefore, the real exchange rate isdescribed in a way that an increase represents a real depreciation of Turkishlira. The nominal exchange rates are collected from Electronic DataDistribution System of Central Bank of the Republic of Turkey and theconsumer price indexes are obtained from WDI. Note that the nominalexchange rates between Turkey and each of Euro Area countries after 2002were obtained by dividing Euro/TL rates to fixed conversion rates.4
In the literature, different proxies are used to measure exchange ratevolatility. In this respect, the standard deviation of percentage change inexchange rate and the moving sample standard deviation of growth ofexchange rate are widely used in order to capture exchange rate volatility.5
However, these proxies are not able to parametrically model the time-varyingvariance of exchange rates and are subject to the measurement error problemwhich causes biased estimates of the impact of exchange rate uncertainties indecision-making process of traders (Pagan and Ullah 1988; Boug andFagereng 2010). To overcome these shortcomings, more recent studies in theliterature rely on generalized autoregressive conditional heteroskedasticity(GARCH) models (among others, Bahmani-Oskooee and Mitra 2008; Hallet al. 2010). The justification of utilizing GARCHmodels is due to the fact thatexchange rate series are characterized by a stochastic process and hetero-scedastic dynamics that can be adequately modeled by volatility of time series(Bollerslev 1986). The Turkish exchange rate volatilities are thereforemeasured by estimating GARCH models for the real exchange rates.6
All the variables are expressed in their natural logarithmic forms. Theunbalanced panel data is collected for the period from 1980 to 2009 byemploying the exports from Turkey’s top 20 export industries to her major20 trading partners.
5. Econometric methodology
In order to examine the relationships among the variables in concern, thefirst generation panel unit root and cointegration methods7 are applied tothe data set. The panel data methods are more powerful compared to the
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time series unit root and cointegration approaches, by combininginformation from both time and cross-section dimensions.
To analyze the long-run equilibrium relationship in the export demandmodel, I utilize the panel cointegration test developed by Pedroni (1999) thatallows heterogeneous cointegration relation across countries. The testingprocedure starts with estimating equation (5) for each country by using theordinary least squares (OLS). Then, the following auxiliary regression on theresiduals is estimated by the OLS.
eit ¼ fieit�1 þ nit ð6Þ
To test for the null of no-cointegration among the variables, sevencointegration statistics are developed. The four statistics – within-dimensionpanel cointegration tests – pool the autoregressive coefficients (fi) acrossdifferent members for the unit root tests on the residuals. The next threestatistics – between-dimension panel cointegration tests – take the average ofthe individually estimated coefficients for each cross-section in the panel.The null hypothesis of no cointegration H0: fi¼ 1 for all i is tested againstthe alternative of H1: fi¼f5 1 for all i in the within-dimension approachand of H1: fi5 1for all i in the between-dimension approach. Thus,an additional source of potential heterogeneity across cross-sectionscan be adequately captured by the between-dimension approach. The panelcointegration statistics which have the asymptotic standard normaldistribution are constructed as follows:
Within-dimension panel cointegration statistics:
Panel n� stat :Zn ¼ T2N3=2XNi¼1
XTt¼1
L�211ie
2i;t�1
!�1
ð7:1Þ
Panel rho-stat : Zr ¼ TffiffiffiffiN
p XNi¼1
XTt¼1
L�211ie
2i;t�1
!�1XNi¼1
XTt¼1
L�211iðei;t�1Dei;t � liÞ
ð7:2Þ
Panel pp-stat : Zt ¼ s2N;T
XNi¼1
XTt¼1
L�211ie
2i;t�1
!�1=2XNi¼1
XTt¼1
L�211iðei;t�1Dei;t � liÞ
ð7:3Þ
Panel adf-stat : Z�t ¼ ~s�2N;T
XNi¼1
XTt¼1
L�211ie
�2i;t�1
!�1=2 XNi¼1
XTt¼1
L�211ie
�i;t�1De
�i;t
ð7:4Þ
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time series unit root and cointegration approaches, by combininginformation from both time and cross-section dimensions.
To analyze the long-run equilibrium relationship in the export demandmodel, I utilize the panel cointegration test developed by Pedroni (1999) thatallows heterogeneous cointegration relation across countries. The testingprocedure starts with estimating equation (5) for each country by using theordinary least squares (OLS). Then, the following auxiliary regression on theresiduals is estimated by the OLS.
eit ¼ fieit�1 þ nit ð6Þ
To test for the null of no-cointegration among the variables, sevencointegration statistics are developed. The four statistics – within-dimensionpanel cointegration tests – pool the autoregressive coefficients (fi) acrossdifferent members for the unit root tests on the residuals. The next threestatistics – between-dimension panel cointegration tests – take the average ofthe individually estimated coefficients for each cross-section in the panel.The null hypothesis of no cointegration H0: fi¼ 1 for all i is tested againstthe alternative of H1: fi¼f5 1 for all i in the within-dimension approachand of H1: fi5 1for all i in the between-dimension approach. Thus,an additional source of potential heterogeneity across cross-sectionscan be adequately captured by the between-dimension approach. The panelcointegration statistics which have the asymptotic standard normaldistribution are constructed as follows:
Within-dimension panel cointegration statistics:
Panel n� stat :Zn ¼ T2N3=2XNi¼1
XTt¼1
L�211ie
2i;t�1
!�1
ð7:1Þ
Panel rho-stat : Zr ¼ TffiffiffiffiN
p XNi¼1
XTt¼1
L�211ie
2i;t�1
!�1XNi¼1
XTt¼1
L�211iðei;t�1Dei;t � liÞ
ð7:2Þ
Panel pp-stat : Zt ¼ s2N;T
XNi¼1
XTt¼1
L�211ie
2i;t�1
!�1=2XNi¼1
XTt¼1
L�211iðei;t�1Dei;t � liÞ
ð7:3Þ
Panel adf-stat : Z�t ¼ ~s�2N;T
XNi¼1
XTt¼1
L�211ie
�2i;t�1
!�1=2 XNi¼1
XTt¼1
L�211ie
�i;t�1De
�i;t
ð7:4Þ
Between-dimension panel cointegration statistics:
Group rho-stat : ~Zr ¼ TN�1=2XNi¼1
XTt¼1
e2i;t�1
!�1XTt¼1
ðei;t�1Dei;t � liÞ ð7:5Þ
Group pp-stat : ~Zt ¼ N�1=2XNi¼1
ðs2iXTt¼1
e2i;t�1Þ�1=2
XTt¼1
ðei;t�1Dei;t � liÞ ð7:6Þ
Group adf-stat : ~Z�t ¼ N�1=2
XNi¼1
XTt¼1
s�2i e�2i;t�1
!�1=2XTt¼1
ðe�i;t�1De�i;tÞ ð7:7Þ
The presence of a cointegration relation requires estimating thecointegration vector. The cointegration parameters are obtained by thepanel fully modified ordinary least squares (FMOLS) developed by Pedroni(2000). The panel FMOLS estimator is constructed from the followingequation:
yit ¼ ai þ bxit þ mit ð8Þ
where yit is the dependent variable and xit (xit¼ xit71þ uit) is the vector ofregressors. The panel FMOLS estimator is given by:
b�GFM ¼ N�1XNi¼1
XTt¼1
ðxit � �xiÞ2 !�1 XT
t¼1
ðxit � �xiÞy�it � Tgi
!ð9Þ
where y�it ¼ ðyit � �yiÞ � O21;i
O22;iDxit, Oi is the long-run covariance matrix which
is estimated using the Newey–West heteroscedasticity consistent estimator.The t-statistic associated with the cointegration parameters is obtained bytb�GFM
¼ N�1=2PN
i¼1 tb�FM;i, where tb�FM;i
¼ðbFM;i �b0Þ�ðO�1
11i
PTi¼1 ðxit � �xiÞ2
�1=2.
6. Empirical findings
The cointegration analysis requires examining unit root properties ofvariables. In order to determine order of integration of the variables inconcern, I carried out a battery of panel unit root tests.8 The unit rootanalysis shows that the variables are integrated at order one and therebysignals a possible cointegration relationship among the variables. The panelcointegration statistics reported in Table 5 indicate that at least four of sevenstatistics support the existence of the cointegration relationship among theindustry-level trade, the foreign income, the exchange rate, and the exchange
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Table
5.
Panel
cointegrationtests.
Panel
statistics
Groupstatistics
Industry
v-stat
rho-stat
pp-stat
adf-stat
rho-stat
pp-stat
adf-stat
Vehiclesother
thanrailway
0.27
74.98
710.99
79.61
72.77
713.14
711.04
Machineries,mechanicalappliancesboilersand
partsthereof
1.68
73.24
77.34
77.12
71.23
77.84
77.62
Ironandsteel
1.01
70.82
74.97
73.75
1.04
76.12
74.29
Articlesofapparelandclothingaccessories
knitted
1.41
72.82
76.52
75.40
70.91
77.28
75.09
Electricalmachineryandequipment
0.72
75.55
79.43
76.73
73.12
710.27
77.15
Pearls,preciousstones,coin
0.56
74.44
79.45
77.31
72.66
710.29
77.68
Articlesofironandsteel
0.85
74.45
79.92
78.01
72.63
711.80
710.86
Articlesofapparelandclothingaccessories
not
knitted
1.34
72.79
77.07
76.52
70.99
79.27
79.53
Mineralfuelsandoils
71.38
4.77
75.72
72.77
7.41
75.92
72.32
Plasticsandarticlesthereof
0.48
74.32
79.98
78.27
72.54
711.12
78.72
Fruit
1.69
72.80
75.95
75.43
70.49
75.99
75.37
Salt,sulfur,earth,plasteringmat,lime,
cement
0.14
71.73
74.62
74.20
0.18
74.48
74.93
Ships,boats,andfloatingstructures
71.04
2.51
711.44
75.47
4.80
716.47
75.88
Other
made-uptextile
articles
70.23
71.98
75.20
72.75
70.05
76.96
75.45
Furniture
1.28
73.90
78.14
76.66
72.22
79.06
76.95
Rubber
andarticlesthereof
70.06
72.55
76.60
72.78
71.41
77.69
74.40
Aluminum
andarticlesthereof
1.29
73.45
77.38
76.65
71.52
77.98
77.65
Cotton,cottonyarn,andcottonfabric
70.97
71.10
76.59
75.37
70.51
79.40
77.60
Preparationsofvegetablesandfruits
1.62
74.12
77.76
77.51
71.88
78.29
78.02
Carpetsandother
floorcovering
70.54
70.94
75.77
75.36
0.14
76.94
76.88
Notes:
The1%
,5%
,and10%
criticalvalues
are
respectively1.28,1.645,and2.33forthepanel-v
statistic,and71.28,71.645,and72.33forother
statistics.
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Table
5.
Panel
cointegrationtests.
Panel
statistics
Groupstatistics
Industry
v-stat
rho-stat
pp-stat
adf-stat
rho-stat
pp-stat
adf-stat
Vehiclesother
thanrailway
0.27
74.98
710.99
79.61
72.77
713.14
711.04
Machineries,mechanicalappliancesboilersand
partsthereof
1.68
73.24
77.34
77.12
71.23
77.84
77.62
Ironandsteel
1.01
70.82
74.97
73.75
1.04
76.12
74.29
Articlesofapparelandclothingaccessories
knitted
1.41
72.82
76.52
75.40
70.91
77.28
75.09
Electricalmachineryandequipment
0.72
75.55
79.43
76.73
73.12
710.27
77.15
Pearls,preciousstones,coin
0.56
74.44
79.45
77.31
72.66
710.29
77.68
Articlesofironandsteel
0.85
74.45
79.92
78.01
72.63
711.80
710.86
Articlesofapparelandclothingaccessories
not
knitted
1.34
72.79
77.07
76.52
70.99
79.27
79.53
Mineralfuelsandoils
71.38
4.77
75.72
72.77
7.41
75.92
72.32
Plasticsandarticlesthereof
0.48
74.32
79.98
78.27
72.54
711.12
78.72
Fruit
1.69
72.80
75.95
75.43
70.49
75.99
75.37
Salt,sulfur,earth,plasteringmat,lime,
cement
0.14
71.73
74.62
74.20
0.18
74.48
74.93
Ships,boats,andfloatingstructures
71.04
2.51
711.44
75.47
4.80
716.47
75.88
Other
made-uptextile
articles
70.23
71.98
75.20
72.75
70.05
76.96
75.45
Furniture
1.28
73.90
78.14
76.66
72.22
79.06
76.95
Rubber
andarticlesthereof
70.06
72.55
76.60
72.78
71.41
77.69
74.40
Aluminum
andarticlesthereof
1.29
73.45
77.38
76.65
71.52
77.98
77.65
Cotton,cottonyarn,andcottonfabric
70.97
71.10
76.59
75.37
70.51
79.40
77.60
Preparationsofvegetablesandfruits
1.62
74.12
77.76
77.51
71.88
78.29
78.02
Carpetsandother
floorcovering
70.54
70.94
75.77
75.36
0.14
76.94
76.88
Notes:
The1%
,5%
,and10%
criticalvalues
are
respectively1.28,1.645,and2.33forthepanel-v
statistic,and71.28,71.645,and72.33forother
statistics.
rate volatility. The existence of cointegration implies that the disturbancesin the export model which are driven by the short-term shocks tend to becorrected over the long-run.
The existence of the long-run steady state relationship among thevariables of the export model entails estimating the cointegrationparameters to determine how sensitive are the Turkish industry-level exportto changes in the explanatory variables. Table 6 illustrates the cointegrationparameters obtained from the panel FMOLS estimations. The exchange ratevolatility exhibits a significant positive coefficient in 10 out of 20 industriesthat the export from these industries accounts for 33% of Turkish totalexport as of 2009. On the other hand, the export from articles of apparel andclothing accessories knitted and from electrical machinery and equipmentis negatively correlated with the exchange rate volatility. The exchange ratevolatility does not play a significant role in the export of eight industrieswhich account for 32% of Turkish total export by 2009.
The results clearly indicate that the impact of the exchange rate volatilityon the export varies across industries as predicted in the theory. Thereby,disaggregating trade data at industry-level is crucial for dealing with theaggregation bias problem and for obtaining more reliable empirical results.In fact, the panel cointegration analysis for the country-level trade presentedin Table 7 indicates that the exchange rate volatility has a significant positivecoefficient on Turkey’s export. Moreover, the magnitude of the coefficientfor the country-level trade is generally smaller than those for the industry-level trade. While the positive impact of the exchange rate volatility onTurkey’s country-level trade is in line with that of Kasman and Kasman(2005) and Ozturk and Kalyoncu (2009), this finding is in contrast withOzbay (1999), Ozturk and Acaravci (2002), and Guloglu (2008).
With respect to the impact of the level of the exchange rate on the export,the results indicate that (i) the exports of 13 industries are positively relatedwith a decline in the value of Turkish lira, (ii) depreciation of Turkish liranegatively affects the export of four industries, and (iii) for the remainingthree industries, the exports are not sensitive to changes the value of Turkishlira. Accordingly, as theoretically expected, the majority of Turkey’s top 20exporting industries are positively influenced by the changes in the value ofTurkish lira. However, the panel cointegration estimates of the exchangerate for the country-level trade in Table 7 shows that Turkey’s export aresurprisingly negatively correlated with a depreciation of Turkish lira.Thereby, the estimation results for the exchange rate clearly indicate that theinferences from the aggregate trade seem to be subject to the aggregationbias problem.
Finally, the panel cointegration analysis for the foreign income variableindicates that the income carries significant positive coefficient in all casesfor the industry-level trade as well as the country-level trade. The resultsfurthermore show that the coefficient on the foreign income is greater than
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those on the exchange rate and its volatility with the exception of mineralfuels and oils trade. Accordingly, the analysis for both the industry- andcountry-level trade strongly implies that the foreign income is morepronounced than the exchange rate and exchange rate volatility in thefluctuations of Turkish exports.
Table 6. Panel FMOLS estimation.
lnY* lnREXR lnVOL
Vehicles other thanrailway
5.839 (18.646) 0.169 (2.240) 0.942 (1.327)
Machineries, mechanicalappliances boilers andparts thereof
5.710 (32.901) 70.001 (2.042) 0.330 (2.743)
Iron and steel 1.632 (12.263) –0.173 (1.479) 70.395 (0.472)Articles of apparel and
clothing accessoriesknitted
3.688 (45.851) 0.945 (4.271) –0.203 (1.944)
Electrical machinery andequipment
5.772 (43.018) 1.735 (6.604) –0.152 (3.581)
Pearls, precious stones,coin
6.877 (42.285) –0.873 (1.879) 0.011 (2.301)
Articles of iron and steel 4.058 (28.862) 1.564 (4.294) 0.296 (5.195)Articles of apparel and
clothing accessoriesnot knitted
5.331 (48.739) 2.631 (10.201) –0.090 (0.099)
Mineral fuels and oils 0.231 (2.506) –7.514 (8.620) 3.191 (8.875)Plastics and articles
thereof7.284 (36.615) 1.759 (2.759) 0.984 (7.462)
Fruit 1.359 (34.056) –0.057 (1.230) 0.305 (4.624)Salt, sulfur, earth,
plastering mat, lime,cement
0.984 (13.318) 0.016 (3.669) –0.447 (1.127)
Ships, boats and floatingstructures
7.556 (15.517) 1.258 (2.040) 0.011 (0.105)
Other made-up textilearticles
6.277 (28.298) 1.721 (3.758) 0.047 (0.909)
Furniture 6.954 (45.080) 1.598 (5.984) –0.051 (0.454)Rubber and articles
thereof5.195 (51.869) 0.757 (6.296) 0.184 (2.266)
Aluminum and articlesthereof
6.568 (34.247) 2.420 (5.015) 1.061 (5.964)
Cotton, cotton yarn andcotton fabric
1.493 (8.137) –0.249 (0.640) 0.031 (1.160)
Preparations ofvegetables and fruits
3.545 (37.244) 1.124 (7.472) 1.779 (6.311)
Carpets and other floorcovering
2.659 (27.889) –0.661 (7.703) 0.365 (22.133)
Notes: Figures in parentheses are absolute values of t-statistics. The 1%, 5%, and 10% criticalvalues are respectively +1.645, +1.96, and +2.578.
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those on the exchange rate and its volatility with the exception of mineralfuels and oils trade. Accordingly, the analysis for both the industry- andcountry-level trade strongly implies that the foreign income is morepronounced than the exchange rate and exchange rate volatility in thefluctuations of Turkish exports.
Table 6. Panel FMOLS estimation.
lnY* lnREXR lnVOL
Vehicles other thanrailway
5.839 (18.646) 0.169 (2.240) 0.942 (1.327)
Machineries, mechanicalappliances boilers andparts thereof
5.710 (32.901) 70.001 (2.042) 0.330 (2.743)
Iron and steel 1.632 (12.263) –0.173 (1.479) 70.395 (0.472)Articles of apparel and
clothing accessoriesknitted
3.688 (45.851) 0.945 (4.271) –0.203 (1.944)
Electrical machinery andequipment
5.772 (43.018) 1.735 (6.604) –0.152 (3.581)
Pearls, precious stones,coin
6.877 (42.285) –0.873 (1.879) 0.011 (2.301)
Articles of iron and steel 4.058 (28.862) 1.564 (4.294) 0.296 (5.195)Articles of apparel and
clothing accessoriesnot knitted
5.331 (48.739) 2.631 (10.201) –0.090 (0.099)
Mineral fuels and oils 0.231 (2.506) –7.514 (8.620) 3.191 (8.875)Plastics and articles
thereof7.284 (36.615) 1.759 (2.759) 0.984 (7.462)
Fruit 1.359 (34.056) –0.057 (1.230) 0.305 (4.624)Salt, sulfur, earth,
plastering mat, lime,cement
0.984 (13.318) 0.016 (3.669) –0.447 (1.127)
Ships, boats and floatingstructures
7.556 (15.517) 1.258 (2.040) 0.011 (0.105)
Other made-up textilearticles
6.277 (28.298) 1.721 (3.758) 0.047 (0.909)
Furniture 6.954 (45.080) 1.598 (5.984) –0.051 (0.454)Rubber and articles
thereof5.195 (51.869) 0.757 (6.296) 0.184 (2.266)
Aluminum and articlesthereof
6.568 (34.247) 2.420 (5.015) 1.061 (5.964)
Cotton, cotton yarn andcotton fabric
1.493 (8.137) –0.249 (0.640) 0.031 (1.160)
Preparations ofvegetables and fruits
3.545 (37.244) 1.124 (7.472) 1.779 (6.311)
Carpets and other floorcovering
2.659 (27.889) –0.661 (7.703) 0.365 (22.133)
Notes: Figures in parentheses are absolute values of t-statistics. The 1%, 5%, and 10% criticalvalues are respectively +1.645, +1.96, and +2.578.
7. Discussion and implications
The findings first of all provide a clue in interpreting the recent dynamicsof Turkish exports. As illustrated in Figure 1, Turkish exports considerablyrose during the period 2002–2008 in which the Turkish economy wasstrengthened as a result of rapid economic growth. In the same time,Turkish lira was appreciated against the major currencies, shifting frompegged to flexible exchange rate system which resulted in more volatileexchange rates, and the income increases were observed in Turkish majortrading partners as well as in the world. The empirical findings imply thateven though the appreciated Turkish lira seems to depress the Turkishexports, the increase in foreign income and the volatility of exchange rateslead traders to trade more from Turkey. In 2009, a sharp decline in Turkishexports went hand in hand with the depreciation of Turkish lira and withdecline in trading partners’ income. These developments in 2009 can beconsidered as the consequences of the global economic crisis that hit theworld in 2008. Even though the empirical findings indicate a positiverelation between the Turkish exports and the weakness of Turkish lira,it appears that the decline in foreign income suppressed the impact ofexchange rates. It is thereby possible to draw a conclusion from the recentdevelopments in Turkish exports that foreign income is the key factorcontributing to the fluctuations in Turkish exports.
The major trading partners of Turkey are developed/high incomecountries in which traders are able to hedge the exchange rate risks throughthe developed financial markets. However, in Turkey as well as in emergingmarket economies, the forward markets are not well developed, the financialmarkets are underdeveloped, and transactions costs are high. Since thesefactors reinforce the impact of exchange rate volatility on trade, policymakers should encourage the development of financial markets and of newinstruments which help Turkish traders to hedge the risks arising from thevolatilities in exchange rates.
Table 7. Panel cointegration tests and FMOLS estimation for country-level trade.
Panel cointegration tests Panel FMOLS estimation
Panel v-stat 2.03 lnY* 2.566 (60.51)Panel rho-stat 71.73 lnREXR 70.155 (2.58)Panel pp-stat 73.35 lnVOL 0.143 (5.03)Panel adf-stat 72.69Group rho-stat 0.49Group pp-stat 72.46Group adf-stat 72.49
Notes: Panel cointegration test: the 1%, 5%, and 10% critical values are respectively 1.28,1.645, and 2.33 for the panel-v statistic, and –1.28, –1.645, and –2.33 for other statistics. PanelFMOLS estimation: figures in parentheses are absolute values of t-statistics. The 1%, 5%, and10% critical values are respectively +1.645, +1.96, and +2.578.
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In Turkey, there is an ongoing discussion on whether Turkish lira iscorrectly valued against the major currencies. On the one side, the exportersstrongly argue that the Turkish lira is overvalued during the recent years andthe appreciation of Turkish lira deteriorates the competiveness of Turkey.On the other side, the governors of Central Bank of Republic of Turkeyadvocate that the value of Turkish lira is determined by the market forcesand that intervention to the exchange rates are not consistent with prevailingmonetary policy. It is worthwhile noting that in most of Turkey’s top 20export industries, the exports heavily depend upon imports of raw materialsand intermediate goods. The appreciation of Turkish lira during the recentyears cheapened the cost of production which in turn provides advantage toincrease the international competiveness of Turkish producer/exporters.Moreover, the decrease in the costs of production may also offset thenegative impacts of the appreciated Turkish lira on exports. Furthermore,the history of Turkish trade and exchange rate policies show that themarket-oriented trade and exchange rate policies appear to be useful forTurkish economy.
As illustrated in the empirical analysis, the foreign income has a crucialrole in increasing Turkish exports. In addition to continuing and promotingtrade relations with the high income countries, policy makers and firmsshould focus on exporting to the countries which have growth potentials/rising income levels for the sustainability of the trade-oriented growthmodel. Thereby, the BRIC countries (Brazil, Russia, India, and China) inwhich high growth rates have been recorded during the recent years andsome other countries such as Algeria, Hong-Kong, Iraq, Malaysia,Morocco, and Syria can be considered as new candidates for Turkey’smajor trading partners in the near future. In this regard, Turkey’s recentinternational relations based on establishing free trade agreements andlifting or easing visa requirements are expected to increase Turkish exports.
8. Conclusion
This study examines the impact of the exchange rate volatility on Turkey’sexport from top 20 export industries to major 20 trading partners during theperiod 1980–2009 by utilizing panel cointegration techniques. In addition tothe industry-level analysis, the country-level analysis is also conducted to seewhether there is an aggregation bias in the estimation of the long-runcoefficients obtained from the country-level trade data. The main findings ofthe study can be summarized as: (i) employing country-level trade datainstead of industry-level suffers from the aggregation bias in estimating thecointegration parameters for the level of exchange rate and for the exchangerate volatility, (ii) the impact of the exchange rate volatility on Turkishexports differs across industries, (iii) decline in the value of Turkish lira hasfavorable impacts on Turkish industry-level exports, and finally (iv) the
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In Turkey, there is an ongoing discussion on whether Turkish lira iscorrectly valued against the major currencies. On the one side, the exportersstrongly argue that the Turkish lira is overvalued during the recent years andthe appreciation of Turkish lira deteriorates the competiveness of Turkey.On the other side, the governors of Central Bank of Republic of Turkeyadvocate that the value of Turkish lira is determined by the market forcesand that intervention to the exchange rates are not consistent with prevailingmonetary policy. It is worthwhile noting that in most of Turkey’s top 20export industries, the exports heavily depend upon imports of raw materialsand intermediate goods. The appreciation of Turkish lira during the recentyears cheapened the cost of production which in turn provides advantage toincrease the international competiveness of Turkish producer/exporters.Moreover, the decrease in the costs of production may also offset thenegative impacts of the appreciated Turkish lira on exports. Furthermore,the history of Turkish trade and exchange rate policies show that themarket-oriented trade and exchange rate policies appear to be useful forTurkish economy.
As illustrated in the empirical analysis, the foreign income has a crucialrole in increasing Turkish exports. In addition to continuing and promotingtrade relations with the high income countries, policy makers and firmsshould focus on exporting to the countries which have growth potentials/rising income levels for the sustainability of the trade-oriented growthmodel. Thereby, the BRIC countries (Brazil, Russia, India, and China) inwhich high growth rates have been recorded during the recent years andsome other countries such as Algeria, Hong-Kong, Iraq, Malaysia,Morocco, and Syria can be considered as new candidates for Turkey’smajor trading partners in the near future. In this regard, Turkey’s recentinternational relations based on establishing free trade agreements andlifting or easing visa requirements are expected to increase Turkish exports.
8. Conclusion
This study examines the impact of the exchange rate volatility on Turkey’sexport from top 20 export industries to major 20 trading partners during theperiod 1980–2009 by utilizing panel cointegration techniques. In addition tothe industry-level analysis, the country-level analysis is also conducted to seewhether there is an aggregation bias in the estimation of the long-runcoefficients obtained from the country-level trade data. The main findings ofthe study can be summarized as: (i) employing country-level trade datainstead of industry-level suffers from the aggregation bias in estimating thecointegration parameters for the level of exchange rate and for the exchangerate volatility, (ii) the impact of the exchange rate volatility on Turkishexports differs across industries, (iii) decline in the value of Turkish lira hasfavorable impacts on Turkish industry-level exports, and finally (iv) the
foreign income is a major factor determining the Turkish industry-levelexports. The empirical findings provide better understanding of the recentdynamics of Turkish export and policy implications for both thegovernment and exporting firms.
Acknowledgements
I would like to thank anonymous reviewers and the editor Professor David Giles fortheir constructive and valuable comments that help me to improve the paper. Anyremaining errors are the author own responsibility.
Notes
1. An interested reader is referred to McKenzie (1999), Ozturk (2006), Bahmani-Oskooee and Hegerty (2007), and Hall et al. (2010) for comprehensive reviews ofthe literature on the exchange rate volatility and trade linkage.
2. See first and third columns of Table 3 for Turkey’s top 20 export industries andmajor trading partners, respectively.
3. I refer an interested reader to Asikoglu and Uctum (1992) for a broad overviewof Turkish exchange rate policies during the 1980–1990 and CBRT (2002) for anoverview of the liberalization process.
4. The conversion rates: 13.7603 for Austria, 40.3399 for Belgium, 5.94573 forFinland, 6.55957 for France, 1.95583 for Germany, 340.75 for Greece, 0.787564for Ireland, 1936.27 for Italy, 2.20371 for Netherlands, 200.482 for Portugal, and166.386 for Spain.
5. See Bahmani-Oskooee and Hegerty (2007) for a wide range of the proxies inmeasuring exchange rate volatility.
6. In order to save space, results for GARCH(1,1) estimations are not reported herebut available upon request.
7. It is important to note here that the first generation non-stationary panel methodsassume independency among cross-sectional units. The second generation panelunit root and cointegration tests take into account cross-sectional dependency;however, they require balanced panel data sets (Demetriades and James 2011).Since this study employs unbalanced panel data set, the first generation panelmethods are more suitable than the second generation panel tests.
8. In order to save space, the results from the unit root tests of Hadri (2000), Levin,Lin, and Chu (2002), and Im, Pesaran, and Shin (2003) are not reported here, butare available upon request.
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