Economic impact of the remote geographical position

Economic impact of the remote geographical position of Cyprus on the Cypriot agricultural sector

Working Paper

Mattas Konstandinos, Natos Demetrios Aristotle University of Thessaloniki, Department of Agricultural Economics Markou Marinos, Stylianou Andreas Agricultural Research Institute

Nicosia, December 2011

Contents Εκτενής περίληψη (στην ελληνική γλώσσα)

1

Chapter 1 Introduction 1.1 1.2 1.3

Introduction Objectives of the research Structure of the study

6 9 9

Chapter 2 Economic Effects of Geography and Distance 2.1 2.2 2.3 2.4

2.5 2.6

Cyprus geographic position Distance, geography and economic development Cyprus in the Mediterranean Basin Mediterranean geography and Cypriot agriculture 2.4.1 Mediterranean climate 2.4.2 Mediterranean landscape 2.4.3 Other characteristics 2.4.4 Mediterranean agricultural products 2.4.5 Mediterranean diet EU and the Mediterranean region Shipping costs for Cypriot economy

10 10 11 12 12 13 13 14 15 16 16

Chapter 3 Methodology 3.1 3.2 3.3 3.4 3.5 3.6 3.7

The Gravity Equation Model framework Standard Gravity Equation Model Estimation of Gravity Equation Model Augmented Gravity Equation Model Variables of the augmented Gravity Equation Model Augmented Gravity Equation Model Applications Theoretical Background of Gravity Equation Model

18 19 20 21 22 23 25

Chapter 4 Estimated Model and Data Sources 4.1 4.2 4.3 4.4

Estimated Model Dependent Variable Data Sources Data length

27 30 32 33

Chapter 5

ii

Estimation Results 5.1 5.2

5.3

Expected signs Estimated Results 5.2.1 Effect of distance on Cypriots’ total agricultural exports 5.2.2 Effect of distance on Cypriots’ total agricultural imports 5.2.3 Effect of distance on Cypriots’ fisheries exports 5.2.4 Effect of distance on Cypriots’ fisheries imports 5.2.5 Effect of climate on agricultural trade Distance effect on intra-EU agricultural trade

35 36 37 40 43 46 49 50

Chapter 6 Calculating the loss of Cypriot exports due to distance 6.1 6.2 6.3

Measuring the cost of distance for Cyprus The loss of Cypriot total agricultural exports The loss of Cypriot fisheries exports

53 54 56

Chapter 6 Conclusions 7.1 7.2

Conclusions on effect of distance on agricultural trade and agricultural sector Policy implications

59 62

APPENDIX Cypriot agricultural sector and political trends 1.1 Cypriot national economy: Key macro-economic trends 1.2 Structural characteristics of Cypriot agriculture 1.2.1 Employment in agriculture 1.2.2 Agricultural income 1.2.3 Other structural features 1.2.4 Basic products 1.2.5 Agricultural land use 1.2.6 Irrigation water availability 1.3 Agriculture, food industry and marketing 1.4 Agricultural policy of Cyprus 1.4.1 On-going discussions for the revision of CAP 1.5 Trade balance 1.5.1 Trade of main agricultural products 1.5.2 Trade in agricultural products between Cyprus and EU Members States 1.5.3 Trade in agricultural products between Cyprus and EU27 1.5.4 Cypriot agricultural trade with MENA countries 1.6 Cypriot internal market

89 91 92 94

References

100

63 66 68 70 73 74 75 78 79 81 82 84 85

iii

Εκτενής περίληψη Γεωγραφική θέση Κύπρου Η Κύπρος γεωγραφικά χωροθετείται στην ανατολική λεκάνη της Μεσογείου και αποτελεί γεωγραφικά το Νοτιοανατολικό άκρο της Ευρωπαϊκής Ένωσης. Ως νησιωτική χώρα βρίσκεται σε µία σχετική απόσταση από τις γειτονικές της χώρες αλλά και σε µια σχετικά αποµακρυσµένη γεωγραφική θέση σε σχέση µε τους ευρωπαίους εταίρους της. Η Ελλάδα, το εγγύτερο γεωγραφικά µέλος της Ε.Ε. βρίσκεται σε απόσταση 280 χλµ., ως προς το Καστελόριζο, και 800 χλµ, ως προς το ηπειρωτικό της τµήµα. Εξάλλου, σύµφωνα µε τη γερµανική προεδρία του Ευρωπαϊκού Συµβουλίου το 2007, το γεωγραφικό κέντρο της Ευρωπαϊκής Ένωσης βρίσκεται στην πόλη Gelnhausen στην Έσση της Γερµανίας. Ως εκ τούτου, η Κύπρος βρίσκεται σε µία ιδιαίτερα σηµαντική απόσταση - άνω των 2500 χιλιοµέτρων - από το γεωγραφικό κέντρο της Ευρωπαϊκής Ένωσης. Επιπτώσεις γεωγραφικής θέσης Η σηµαντική γεωγραφική απόσταση της Κύπρου από τους ευρωπαίους εταίρους της και η σχετικά αποµακρυσµένη γεωγραφική της θέση στην Ανατολική λεκάνη της Μεσογείου έχουν οπωσδήποτε σηµαντικές επιπτώσεις στην κυπριακή οικονοµία και τον αγροτικό της τοµέα. Οι πλέον άµεσες εντοπίζονται στην επίδραση της απόστασης στις εξωτερικές εµπορικές συναλλαγές. Ακόµα και αν η σχέση της γεωγραφικής απόστασης και του διεθνούς εµπορίου είναι δυνατό να χαρακτηρισθεί ως προφανής, η κυπριακή γεωργία είναι σχετικά πιο ευαίσθητη στην επίδραση της απόστασης από ότι άλλοι τοµείς της κυπριακής οικονοµίας αφού τα κόστη µεταφοράς τείνουν να είναι υψηλότερα στα γεωργικά προϊόντα λόγω των φυσικών τους χαρακτηριστικών (σηµαντικός όγκος και βάρος). Εξάλλου, ακόµα και αν οι συνθήκες παγκοσµιοποίησης χαρακτηρίζονται από σχετικές µειώσεις στα κόστη πραγµατοποίησης διασυνοριακών συναλλαγών, στα γεωργικά προϊόντα, το κόστος πραγµατοποίησης των συναλλαγών είναι ιδιαίτερα σοβαρό, σε τέτοιο βαθµό ώστε να είναι δυνατό να χαρακτηριστεί ως σηµαντικό εµπόδιο για την πραγµατοποίηση του εµπορίου. Επιπλέον, η γεωγραφική θέση ενός κράτους δεν επιδρά µόνο στο µέγεθος της γεωγραφικής απόστασης. Η γεωγραφική θέση προσδιορίζει και τα πολιτισµικά, εθνικά και πολιτικά χαρακτηριστικά που επιδρούν στην οικονοµία µιας χώρας και κατά προέκταση στον αγροτικό της τοµέα. ∆ηλαδή, τα χαρακτηριστικά της ύπαρξης κοινής γλώσσας, κοινών ιστορικών και αποικιακών καταβολών, κοινής θρησκείας, συναφούς πολιτιστικού περιβάλλοντος, κοινών

1

µεταναστευτικών κινήσεων, εθνοτικών σχέσεων αλλά και η ύπαρξη κοινών πολιτικών θεσµών όπως περιφερειακές εµπορικές συµφωνίες ή η κοινή ένταξη στην ΕΕ αποτελούν παράγοντες που είτε προωθούν το εµπόριο είτε λειτουργούν ως εµπόδια για την πραγµατοποίηση του. Ως εκ τούτου, ο κυπριακός αγροτικός τοµέας πέρα από την επίδραση της γεωγραφικής απόστασης, επηρεάζεται και από πλήθος άλλων παραγόντων που συνδέονται µε τη γεωγραφική της θέση. Το γεγονός ότι η Κύπρος είναι µια νησιωτική χώρα χωρίς χερσαία σύνορα είναι ένα από αυτά. Άλλοι παράγοντες περιλαµβάνουν τις µεσογειακές κλιµατολογικές συνθήκες, και το µεσογειακό τοπίο. Οι υψηλές θερµοκρασίες που επικρατούν τους θερινούς µήνες, η έλλειψη βροχοπτώσεων, οι εκτεταµένες περίοδοι ξηρασίας και ο µη πεδινός χαρακτήρας του τοπίου είναι προφανώς χαρακτηριστικά που επηρεάζουν τον αγροτικό τοµέα. Επιπρόσθετα, η Κύπρος, µε τη στρατηγική της θέση στη Μεσόγειο διαθέτει ένα κοινό πολιτιστικό υπόβαθρο µε γειτονικές µεσογειακές χώρες που ευνοεί την επικοινωνία και προφανώς ενισχύει τις οικονοµικές και εµπορικές σχέσεις. Επιπλέον, η ύπαρξη αποικιακών δεσµών µε άλλα κράτη επιδρά τις εµπορικές σχέσεις της προσφέροντας ευκαιρίες για ευρύτερες οικονοµικές σχέσεις και ενισχυµένες εξαγωγές. Στόχοι έρευνας Στο πλαίσιο που δηµιουργεί αφενός η γεωγραφική θέση της Κύπρου και αφετέρου η σχετικά αποµακρυσµένη θέση της από το ηπειρωτικό τµήµα της Ευρωπαϊκής Ένωσης, κύριος στόχος αυτής της έρευνας είναι η εκτίµηση της επίδρασης της γεωγραφικής απόστασης στον κυπριακό αγροτικό τοµέα. Για την επίτευξη του στόχου αυτού, εκτιµάται ένα Επαυξηµένο Υπόδειγµα Εξίσωσης Βαρύτητας (Augmented Gravity Equation Model) χρησιµοποιώντας δεδοµένα εξαγωγών και εισαγωγών αγροτικών προϊόντων της Κύπρου για την περίοδο 19922009. Για την εκτίµηση του υποδείγµατος, αναγνωρίζονται δύο κλάδοι εµπορίου. Ο κλάδος που περιλαµβάνει το σύνολο των αγροτικών προϊόντων και ο κλάδος του περιλαµβάνει µόνο τα αλιευτικά προϊόντα. Η χρήση εµπορικών παρατηρήσεων κατά δεκαοκτώ συναπτά έτη µας δίνει τη δυνατότητα να εκτιµήσουµε την επίδραση της γεωγραφικής απόστασης στο διεθνές εµπόριο της Κύπρου τόσο πριν από την ένταξη της στην ΕΕ (για την περίοδο 1992-2003 δηλαδή) όσο και µετά την ένταξη (2004-2009). Η εκτίµηση ενός υποδείγµατος Εξίσωσης Βαρύτητας προσφέρει επιπλέον τη δυνατότητα εµπλουτισµού του κύριου στόχου µε δευτερεύοντες, όπως η εκτίµηση: i) της επίδρασης γεωγραφικών και κλιµατολογικών χαρακτηριστικών, ii) της επίδρασης πολιτιστικών και ιστορικών χαρακτηριστικών iii) της επίδρασης πολιτικών εξελίξεων στην ευρύτερη γεωγραφική περιοχή της Κύπρου (όπως η 2

διεύρυνση της ΕΕ, η συµφωνία της Βαρκελώνης) στο κυπριακό εµπόριο αγροτικών προϊόντων. Αποτελέσµατα έρευνας Σε αντίθεση µε τις πεποιθήσεις ότι ο ρόλος της απόστασης στην ολοένα και εντονότερα ολοκληρωµένη παγκόσµια οικονοµία βαίνει µειούµενος, στην πραγµατικότητα δε φαίνεται να έχει εξαφανιστεί. Αντίθετα, µετα-αναλύσεις εκτιµήσεων υποδειγµάτων Εξίσωσης Βαρύτητας δείχνουν ότι η απόσταση διαδραµατίζει ουσιαστικό ρόλο στο κόστος που συνδέεται µε το εµπόριο µε µέση ελαστικότητα -0,9. ∆ηλαδή µια µείωση κατά δέκα ποσοστιαίες µονάδες της γεωγραφικής απόστασης µεταξύ των εµπορικών εταίρων θα είχε ως αποτέλεσµα µια µέση αύξηση των διµερών εµπορικών ροών κατά εννέα τοις εκατό. Από την άλλη πλευρά η εκτίµηση της επίδρασης της απόστασης στο ενδοκοινοτικό αγροτικό εµπόριο εκτιµήθηκε µε ελαστικότητα -1.24. ∆ηλαδή µια µείωση κατά δέκα ποσοστιαίες µονάδες της γεωγραφικής απόστασης µεταξύ των χωρών µελών της ΕΕ θα είχε ως αποτέλεσµα την αύξηση του ενδοκοινοτικού εµπορίου κατά 12.4 ποσοστιαίες µονάδες. Στην περίπτωση των κυπριακών εξαγωγών αγροτικών προϊόντων, η επίδραση της απόστασης δεν αποτελεί εξαίρεση από τη γενική εντύπωση που περιγράφεται παραπάνω. Σύµφωνα µε τις εκτιµήσεις η ελαστικότητα της απόστασης για τις κυπριακές εξαγωγές αγροτικών προϊόντων εκτιµάται στο -1,85 για την περίοδο 1992-2003 και στο -1,73 για την περίοδο 2004-2009. Σε σχέση µε τη µέση ελαστικότητα, η ελαστικότατα της απόστασης για τα κυπριακά προϊόντα είναι κατά 105,5% υψηλότερη για την περίοδο 1992-2003 και κατά 92,2% υψηλότερη για την περίοδο µετά το 2004. Επιπλέον, σε σύγκριση µε το τη µέση εκτιµώµενη ελαστικότητα για το ενδοκοινοτικό εµπόριο είναι 39,5% υψηλότερη (για την περίοδο 2004-2009). Με άλλα λόγια, η επίδραση της απόστασης για το εµπόριο της Κύπρου είναι σχεδόν διπλάσια σε σύγκριση µε τη µέση επίδραση στο διεθνές εµπόριο σε όλο τον κόσµο. Φαίνεται δηλαδή ότι η µεγάλη επίδραση της απόστασης στον κυπριακό αγροτικό τοµέα δε µειώθηκε σηµαντικά µετά την ένταξη της Κύπρου στην ΕΕ το 2004. Σε γενικές γραµµές όµως η Ευρωπαϊκή Ολοκλήρωση, µετά το 2004, προκαλεί σηµαντική επίδραση στις κυπριακές εξαγωγές. Οι εξαγωγές προς κράτη µέλη της ΕΕ εκτιµάται ότι είναι κατά 106,4 τοις εκατό υψηλότερες από ότι οι εξαγωγές προς άλλες χώρες εµπορικούς εταίρους της Κύπρου. Ωστόσο, η ένταξη στην ΕΕ δεν οδήγησε σε σηµαντική µείωση της επίπτωσης της γεωγραφικής απόστασης για την κυπριακή γεωργία. Μετά το 2004, ο εκτιµώµενος συντελεστής της 3

γεωγραφικής απόστασης µειώθηκε κατά 6,5 τοις εκατό, σε σχέση µε την επίδραση της απόστασης πριν από το 2004. Ως εκ τούτου, η προσχώρηση της Κύπρου στην ΕΕ δεν οδήγησε σε άµβλυνση των εγγενών µειονεκτηµάτων που η αποµακρυσµένη γεωγραφική θέση της επιβάλλει στον γεωργικό τοµέα και στο εµπόριο των γεωργικών προϊόντων. Μάλιστα, η περίοδος µετά την ένταξη (2004-2010) χαρακτηρίζεται από δραµατική επιδείνωση του εµπορικού ελλείµατος αφού ο µέσος ετήσιος ρυθµός αύξησης των εισαγωγών αγροτικών προιόντων (από χώρες µέλη της ΕΕ) διαµορφώθηκε στο 14,8 τοις εκατό ενώ ο αντίστοιχος ρυθµός των εξαγωγών µόλις στο 4,6 τοις εκατο. Ο µέσος δηλαδή ετήσιος ρυθµός αύξησης των εισαγωγών, για την µετα-ενταξιακή περίοδο, υπολογίζεται σχεδόν τρείς φορές µεγαλύτερος από τον αντίστοιχο ρυθµό αύξησης των εξαγωγών. Στην υποθετική περίπτωση που η Κύπρος µετακινούταν στο γεωγραφικό κέντρο της ΕΕ (και βρίσκονταν δηλαδή στη γεωγραφική θέση της Γερµανίας) βρέθηκε από τους υπολογισµούς ότι αυτό θα είχε ως αποτέλεσµα µέση ετήσια αύξηση στις εξαγωγές γεωργικών προϊόντων, την περίοδο 2004-2009, της τάξης των €145 εκ. ή 19% της αξίας των πραγµατοποιούµενων εξαγωγών. Επιπλέον, σε µία άλλη υποθετική περίπτωση όπου η Κύπρος µετακινούνταν κατά 10% εγγύτερα στους ευρωπαικούς της εταίρους (θα βρίσκονταν δηλαδή πλησίον της Ελλάδας και της Ιταλίας) υπολογίστηκε ότι αυτό θα είχε ως αποτέλεσµα µία µέση ετήσια αύξηση στις εξαγωγές γεωργικών προϊόντων, την περίοδο 2004-2009, της τάξης των €132 εκ ή 17,4% της αξίας των πραγµατοποιούµενων εξαγωγών. Βέβαια, η γεωγραφική θέση της Κύπρου δεν σηµαίνει µόνο τη γεωγραφική της απόσταση από τις χώρες µέλη της ΕΕ. Οι γειτονικές χώρες της Κύπρου, οι Μεσογειακές Χώρες Εταίροι, οι χώρες δηλαδή που έχουν προσχωρήσει στη διακήρυξη της Βαρκελώνης, εκτιµάται ότι δεν αποτελούν ευνοϊκό προορισµό για τις κυπριακές εξαγωγές γεωργικών προϊόντων. Η Κύπρος εξήγαγε πριν από το 2004 γεωργικά προϊόντα κατά 71,4% χαµηλότερης αξίας σε σχέση µε τις εξαγωγές σε άλλες χώρες και κατά 65% τοις εκατό χαµηλότερης αξίας µετά την προσχώρησή της στην Ευρωπαϊκή Ένωση. Επιπρόσθετα, οι εµπορικές σχέσεις της Κύπρου µε τις Μεσογειακές χώρες, κατά την µετα-ενταξιακή περίοδο, χαρακτηρίζονται από ένα κύµα εισαγωγών αγροτικών προιόντων, κυρίως από την Αίγυπτο και το Ισραήλ, γεγονός που θέτει αµφιβολίες για τις επιπτώσεις της συµφωνίας απελευθέρωσης των συναλλαγών αγροτικών προϊόντων της Ε.Ε. µε την Αίγυπτο που τέθηκε σε εφαρµογή το 2010. Επιπλέον, η µη ύπαρξη θαλάσσιας επικοινωνίας µε τις χώρες εταίρους της Κύπρου, εκτιµάται ότι αποτελεί σηµαντικό εµπόδιο για την πραγµατοποίηση εξαγωγών, ενώ οι ιστορικοί αποικιοκρατικοί δεσµοί αλλά 4

και η ύπαρξη κοινής γλώσσας ανάµεσα στην Κύπρο και άλλα κράτη (Ηνωµένο Βασίλειο και Ελλάδα) εκτιµάται ότι επιδρούν θετικά στις εξαγωγές αγροτικών προϊόντων. Συµπεράσµατα και πολιτικές επεκτάσεις Όπως εκτιµάται, η επίδραση της γεωγραφικής θέσης στον κυπριακό αγροτικό τοµέα είναι σηµαντική και η προσχώρηση της Κύπρου στην ΕΕ δεν οδήγησε σε σηµαντική άµβλυνση των εγγενών µειονεκτηµάτων που η αποµακρυσµένη γεωγραφική της θέση επιβάλλει. Εποµένως, είναι κρίσιµη η έναρξη κατάλληλων πολιτικών µέτρων που θα κινούνται προς την κατεύθυνση της αντιστάθµισης ή του µετριασµού αυτού του γεωγραφικού µειονεκτήµατος που παρουσιάζει η Κύπρος. Επίσης το γεωγραφικό αυτό µειονέκτηµα θα πρέπει να αναγνωριστεί και στα πλαίσια της νέας ΚΑΠ, ως κοµµάτι των ευρύτερων φυσικών περιορισµών που αντιµετωπίζει η Κυπριακή γεωργία (όπως η έλλειψη υδάτινων πόρων). Η αναγνώριση αυτής της ιδιαιτερότητας, του Κυπριακού αγροτικού τοµέα, γίνεται εντονότερη υπό το πρίσµα των πιθανών µελλοντικών εξελίξεων. Τις εντεινόµενες δηλαδή µελλοντικά ανταγωνιστικές συνθήκες,

µιας

πιθανής

απελευθέρωσης

του

αγροτικού

εµπορίου

στον

γύρο

διαπραγµατεύσεων της Doha, της διαφαινόµενης αυξηµένης αβεβαιότητας που θα αντιµετωπίσει ο αγροτικός τοµέας παγκοσµίως αλλά και της κλιµατικής αλλαγής.

5

Chapter 1 Introduction

1.1

Introduction Cyprus is located at the Eastern Basin of the Mediterranean Sea and as an

island country is at a relatively distant location from its neighboring countries. Turkey is situated at a distance of 75 km north, Syria and Lebanon to a distance of approximately 100 km east, Israel to a distance of 200 km southeast, Egypt is 380 km south, and Greece to the west-northwest at a distance of 280 km to the small Dodecanese island of Kastellorizo, 400 km to Rhodes, and 800 km to the Greek mainland. Beyond its relatively distant location from its neighboring countries, Cyprus is also situated to the geographical southeast edge of the European Union and thus to a relatively remote geographical position in relation with its European partners. According to the German EU Council Presidency of 2007, the geographical midpoint of the European Union is situated at the commune of Gelnhausen in Hessen, Germany. Therefore, Cyprus is located to a significant distance - of more than 2500 km - away from the geographical center of European Union. The significant geographic distance of Cyprus from its European partners and its relative remote geographic position at the Eastern Basin of the Mediterranean Sea have considerable effects on Cypriot economy and the agricultural sector. The most immediate and directly affected economic activity from the geographic position of Cyprus is trade. Physical distance is directly connected to trade costs. Moreover, according to Fratianni and Marchionne (2010), transportation costs, a component of trade costs, rise with distance and lower trade cost is associated with higher economic development. According to Anderson and van Wincoop (2003), broadly defined, trade costs include all costs incurred in getting a good to a final user other than the marginal cost of producing the good itself. Therefore, trade costs include, beyond transportation costs, policy barriers, information costs, contract enforcement costs, costs associated with the use of different currencies, legal and regulatory costs and local distribution costs. Even if the link of physical distance and trade is obvious, Cypriot agricultural sector is relatively more susceptible to the effect of distance in relation with other 6

sectors, since, according to Anderson and van Wincoop (2003), transport costs tend to be higher in agricultural products due to the bulky nature of several products. Moreover, according to Disdier and Head (2008), even if globalization has been characterized by a decline in the costs of cross-border trade in farm and other products (Anderson, 2010), trade costs are significant imposing important impediments to trade. Furthermore, the geographic position of Cyprus does not only impose the effect of its physical distance on trade. According to Fratianni and Marchionne (2010), the geographic position and the physical distance between countries associate and counteract with cultural, ethnical and political characteristics. Consequently, the characteristics of common language, common colonial roots, shared religion, immigrant links, ethnic links and institutions such as regional trade agreements or the common accession to EU are trade-enhancing or trade-depressant factors among countries that associate with the geographic position of a country. Thus, the geographic position of Cyprus, beyond offering a relative remoteness, formulates a distinct economic environment and framework where the specific characteristics that the geographic position offers to Cyprus associate, affect and influence its agricultural sector. Therefore, the Cypriot agricultural sector, beyond facing the effect of physical distance with its trading partners, is affected by a numerous geographically related factors. The fact that Cyprus is an island country without land borders is one of them. Other factors include the Mediterranean climatic conditions and the Mediterranean landscape. Cyprus’ high temperatures, lack of significant rainfall, extensive drought periods and the mountainous landscape are obviously characteristics that affect the agricultural sector and consequently hinder or favour agricultural trade. Similarly, Cyprus location to the Mediterranean Basin offer a common cultural affinity background that favours communication among Mediterranean countries and obviously enhance economic and trade relations. Moreover, the colonial links of Cyprus with other neighboring countries affects its trading relationships offering opportunities for greater economic relations and enriched trade. Other factors that interact with the geographic position of Cyprus include the political developments occurred during the last twenty years within the context of European Union and the Mediterranean region of Cyprus. From the one hand are the enlargement of EU in 2004 and 2007 and the accession of Cyprus in it. On the other 7

hand is the Euro-Mediterranean Partnership which initiated from the Barcelona Agreement in 1995. In 1995, the fifteen EU member states with twelve southern and eastern Mediterranean countries. The countries of Maghreb: Morocco, Algeria, Tunisia, the countries of Mashreq: Egypt, Jordan, Palestinian Authority, Syria, Lebanon, and Turkey, Cyprus and Malta (called Mediterranean Partner CountriesMPC) adopted the Declaration of Barcelona (by the city where the meeting took place). One of the main objectives of the Euro-Mediterranean Partnership, was to create a free trade zone by 2010, both among the Mediterranean partner countries and among the Mediterranean countries and the EU. The creation of the zone will be achieved through bilateral Association Agreements between the EU and Mediterranean Partner Countries. While, as regards agricultural products, the Declaration has indicated the importance of the sector and the intention of gradual liberalization of agricultural trade. Besides the regional political, cultural, economic and geographical environment of Cyprus, Cypriot agriculture functions also within the context of global and European trends in agriculture and agricultural trade. The future of CAP reform towards a territorially and environmentally balanced EU agriculture oppose significant threats but also opportunities to the Cypriot agriculture. The objectives of the future CAP (beyond 2013), according to EU Commission, refer to the viable food production in Europe, the sustainable management of natural resources and climate action and the balanced territorial development. Future CAP will try to address the global challenges of food security as well as citizens’ concerns on the climate change and the environment. Furthermore, EU agriculture and consequently Cypriot agriculture will have to cope with an intensified competitive environment formulated by the progress of world economy, the on-going liberalization of world trading system and assisted by the possible conclusion of the Doha round negotiations. Also, Cypriot agriculture must operate within an environment of increased uncertainty and volatility, as it regards food prices, driven by developments on the one hand on the world demand side (increase of world population, income growth, oil prices) and on the other hand on the world supply side (farm productivity growth) (Anderson, 2010).

8

1.2

Research objectives Within this context of the geographical position of Cyprus and its relatively

distant position from the continental part of Europe the main objective of this study is to estimate the effect of geographical distance on Cypriot agricultural sector. In order to achieve that objective, an augmented Gravity Equation Model will be estimated utilizing trade data of exports and imports among Cyprus and its trading partners during the period 1992-2009. Therefore, two sectors of trade are identified, the total agricultural trade sector that incorporates all agricultural and food products and the fisheries products trade sector. The inclusion of trade observations from eighteen consecutive years, gives us the opportunity to estimate the effect of geographical distance before and after Cyprus accession to the European Union in 2004. Also, the estimation of an augmented Gravity Equation Model provides an opportunity to enrich our main research objective with secondary objectives. Those objectives are: i) the estimation of the effect of geographical and climatic characteristics (area of a country, the case of a landlocked trade partner, annual precipitation) on Cypriot agricultural trade, ii) the estimation of the effect of cultural and historical characteristics (the existence of a common language, common colonial roots) on Cypriot agricultural trade, iii) the estimation of the effect of regional political advances (EU enlargement, Barcelona Agreement) on Cypriot agricultural trade.

1.3

Structure of the study Following the achievement of research objectives, this report is separated to

six chapters. Initially, on the first chapter, an introduction to the necessity of the current research is conducted. On the second chapter, the geographic environment of Cyprus is outlined within the context of the economic effects that distance and geographic position oppose to the Cypriot agriculture. On the third chapter the methodology followed in order to achieve our research objectives is presented. On the fourth chapter the estimated model and the data used are described, while on the fifth chapter a detailed presentation of the estimation results is performed for the two investigated sectors. The sixth chapter concludes presenting the policy implications of this study findings. An extending summary of the current status of the Cypriot agriculture is described in the Appendix.

9

Chapter 2 Economic Effects of Geography and Distance

2.1

Cyprus geographic position Cyprus is an island country situated in the Eastern Basin of the Mediterranean

Sea. Is the third largest island in the Mediterranean (after the Italian islands of Sicily and Sardinia) and the world's 81st largest. Cyprus measures 240 kilometers latitudinally and 100 km (62 mi) longitudinally. Its neighboring territories include Turkey in a distance of 75 km north, Syria and Lebanon to the east (105 km and 108 km respectively), Israel (200 km) to the southeast, Egypt (380 km) to the south, and Greece to the west-northwest at a distance of 280 km to the small Dodecanese island of Kastellorizo, 400 km to Rhodes, and 800 km to the Greek mainland. Therefore, Cyprus belonging to the eastern part of the Mediterranean Basin is in a relatively remote position in relation to its European partners and at a significant distance from its neighboring territories. Is a country within a pure Mediterranean environment and as a result, geographical distance and geography represent important factors of Cyprus economic, cultural and geopolitical environment.

2.2

Distance, Geography and economic development It is common sense that physical distance is directly connected to

transportation costs. According to (Fratianni and Marchionne, 2010), transportation costs, a component of trade costs, rise with distance and lower trade cost is associated with higher economic development. Furthermore, a decline in international transportation costs is a likely cause underlying the sharp rise of world trade relative to world output that has occurred over the last fifty years (Hummels, 2007). However, according to Fratianni and Marchionne (2010), there is more than transportation in distance. Common language (Hutchinson, 2002), common colonial roots (Rauch, 1999), shared religion (Kang and Fratianni, 2006), immigrant links to the home country (Head and Ries, 1998) or more generally ethnic networks (Rauch and Trindade, 2001), similarity in economic development (Fratianni and Kang, 2006), institutions such as regional trade agreements (Carrère, 2006; Baier and Bergstrand, 2009), common money (Rose, 2000; Rose and van Wincoop, 2001), national borders (McCallum, 1995; Anderson and van Wincoop, 2003; Chen, 2004) are trade10

enhancing characteristics or big impediments to trade that counteract with transportation costs and associate with the geographic position of a country. According to Anderson and van Wincoop (2003), broadly defined, trade costs include all costs incurred in getting a good to a final user other than the marginal cost of producing the good itself. Therefore, trade costs include transportation costs (both freight costs and time costs), policy barriers (tariffs and non-tariff barriers), information costs, contract enforcement costs, costs associated with the use of different currencies, legal and regulatory costs and local distribution costs (wholesale and retail). Disdier and Head (2008) state that conventional wisdom holds that modern technological progress is causing the disappearance of the impact of distance. Some economists also see a major decline in the importance of transport costs. In particular, Glaeser and Kohlhase (2004) conclude that “certainly it is an exaggeration to claim that moving goods is free, but it is becoming an increasingly apt assumption”. On the contrary, Anderson and van Wincoop (2003), address that “the death of distance is exaggerated. Trade costs are large, even aside from trade policy barriers and even between apparently highly integrated economies”. Certainly, trade costs are the major factor affecting international trade. In addition, according to the notion that international trade assigns a decisive role to the economic development, it is logical to assume that trade costs (and distance at extension) affect the economic development of a country. As well, the fact that many countries began reducing their commercial barriers or intensified their economic integration processes in order to obtain more freedom to trade internationally, indirectly aimed at gaining increased rates of economic development (Afonso, 2001).

2.3

Cyprus in the Mediterranean Basin Mediterranean region has not commonly accepted geographical limits. The

term "Mediterranean” is used as a concept where its the spatial extent varies depending on the purpose or the dimension (geographical, political, economic, cultural or environmental) used (King et al., 2000). The most common, narrow geographic definition, define the Mediterranean countries as the states that have access to the Mediterranean Sea. Although, focusing exclusively on the environmental characteristics of the Mediterranean region, Mediterranean countries are those with a Mediterranean 11

climate (Perez, 1990). Furthermore, King et. al. (2000) using a broader perspective, define the Mediterranean area using geographical, cultural and economic criteria. Under their approach, the Mediterranean region and consequently the Mediterranean countries are spanning to the Mediterranean Basin and the adjacent, with the coastal line, areas where socio-economic and cultural activities are influenced by the Mediterranean Sea. According to the last two viewpoints, the Mediterranean region is described as an area of geographic, cultural and economic characteristics that are not uniform among countries. Although, all Mediterranean countries, Cyprus included, have a common frame of reference, the Mediterranean Sea and the climate which heavily affects Cyprus agriculture.

2.4

Mediterranean geography and Cypriot agriculture Farming is an activity of biological nature. Therefore, one of its main features

is its dependence to environmental and climatic conditions (Papageorgiou et. Al., 2005, p. 23). Thus, Mediterranean agriculture, depends on the characteristics of Mediterranean climate.

2.4.1

Mediterranean climate The Mediterranean climate is a transitional climate between the temperate

climate and tropical areas (Flokas, 1997, p.334). The main characteristics of the Mediterranean climate are its humid and mild temperature winters that do not last long and have little chance of frost. On the other hand, summers are characterized by high temperatures and drought. From north to south, the Mediterranean climate change displaying higher temperatures and longer drought periods (Morch, 1999). The rains occur mainly in winter, autumn and spring displaying great variation in relation to temperature. Autumn and spring are considered climatically as transitional periods between summer and winter and usually do not last long time (Flokas, 1997, p.334). Consequence of the variation of rainfall in the Mediterranean region is the existence of “climatic stress” for vegetation and agriculture (Perez, 1990). Mediterranean agriculture is facing long periods of intense drought, with minimal rainfall, which affects water availability. Moreover, the probability of occurrence of heavy rainfalls is not negligible. Rain storms are heighten the risk of arable land 12

erosion and do not contribute significantly to the water balance since soils do not have the capability to absorb the total amount of rain they receive during a rain storm (Morch, 1999).

2.4.2

Mediterranean landscape of Cyprus Apart from the Mediterranean climate, major determinant of Mediterranean

agriculture is the particular landscape of the Mediterranean areas. Mediterranean regions are generally mountainous and inclined. The lack of flat land and great, in extent, plains affect the quality of soils which are relatively poor in water resources. Most suitable and fertile soils for agriculture occur on the plains of the Mediterranean region and on its coastal areas (Perez, 1990). Similarly, in Cyprus, the landscape is dominated by the mountain masses of Troodos Mountains and Kyrenia mountains and the central plain they encompass, the Mesaoria. The Troodos Mountains, especially, cover most of the southern and western portions of the island and account for roughly half its area.

2.4.3

Other characteristics Mediterranean agriculture and by consequence Cypriot agriculture was

affected by the historical, social, political and economical transformations occurred on the Mediterranean Basin. Common historical roots, the prevalence of Roman and Byzantine culture, the efforts for the European economic integration with the creation of EU or even the “Euro-Mediterranean Partnership” has affected and affect at present time agriculture in the Mediterranean Basin (King et. al, 2000, Beopoulos, 2003). Sea transportation and communication, linguistic similarities, cultural affinities and ethnic relations between Mediterranean countries are only some of the common features shared by the Mediterranean countries that contribute to the development of common characteristics in Mediterranean agriculture (Morch, 1999). Nevertheless, beyond all physical, political and cultural characteristics that influence the Mediterranean agriculture, the environment is the one what plays a prominent role (Quiroga and Iglesias, 2009). Climatic conditions affected the growth of crops in such a way as to adapt to existing conditions of temperature and precipitation (Perez, 1990). Also, the ecosystems of the Mediterranean countries, characterized by rich biodiversity, have greater sensitivity to climate change (Underwood et. Al., 2009). However, Reidsma et. al. (2009), outline that the 13

Mediterranean agriculture, taking into account the variation in the size of production and farm incomes in relation to the variation in climatic conditions, displays better adaptability to weather variation than other farming areas around the world. This adaptability is attributed to the special specific characteristics of Mediterranean farms. Those characteristics include, according to Aranzabal (2008), the display of multifunctionality, the presence of mixed productive directions, the natural adjustment of production cycles and the conservation of rural landscape, biodiversity and energy efficiency.

2.4.4

Mediterranean agricultural products The adjustment of agricultural activities on climate conditions in the

Mediterranean region, has contributed to the type of crops found in the Mediterranean Basin. Already from the 8th century BC, when Greeks took over from the Phoenicians the primacy of trade in the Mediterranean, the products have dominated the markets were cereals, olive oil and wine. The so-called Mediterranean triad (Allen, 2001, s.186, Cannon, 2005, Stallsmith, 2007, p. 157, Daoutopoulos and Koutsoukos, 2008, p.33). Indeed, cereals, vineyards and olive cultivation have prevailed most of the Mediterranean countryside. This prevalence is attributed to their satisfactory adaptation to the semi-arid conditions of the Mediterranean climate (Quiroga and Iglesias, 2009). In particular, the cultivation of olives is “characteristic” of the Mediterranean agriculture (King et. Al., 2000). Moreover, variations in climatic conditions permit cultivation of a variety of other crops such as rice, sugar, citrus, tobacco and potatoes (Perez, 1990). As stated by Dunford and King (2000), the products of Mediterranean agriculture remain stable, despite the modernization of traditional Mediterranean agriculture. Agricultural policy and the world market conditions guide the specialization of modern agricultural production in cereal crops, fruits, vegetables and wine. At the same time, the structural disadvantages of Mediterranean agricultural production (small farms and thus low competitiveness with low employment efficiency) contribute to the conservation of Mediterranean products production. Moreover, Beopoulos (2003) attributes to the small size of farms and the fact that the modernization of Mediterranean agriculture takes place, mainly to the plain areas with the production off-season vegetables and citrus in irrigated areas.

14

However, for the Common Agricultural Policy (CAP), under its 2003 reform (Council of the European Union, 2003), as Mediterranean agricultural products are recognized the cultivation of tobacco, cotton and the production of olive oil. While, Patterson and Joshling (2005) consider that, at agricultural policy level, the critical Mediterranean agricultural products that display the impact of agricultural and trade policies are separated into five categories. Olive oil and olives, table grapes and wine, fruit fruit (fruit), citrus fruits and tomatoes (fresh and processed).

2.4.5

Mediterranean diet The Mediterranean diet is closely linked to the Mediterranean agriculture and

the Mediterranean agricultural products, mainly olive oil (Willet et. al., 1995). The characteristics of Mediterranean agriculture are considered by Lorgerila et. al (2002) as a starting point - but not the single cause - for the formation of the Mediterranean diet. Although many areas of the Mediterranean area have different eating habits, they are considered by Trichopoulou and Lagiou (1997) as variations of a common diet in which olive oil occupies a central position. Common features of the diet are the high consumption of olive oil, fruits and vegetables, nuts, legumes and grains, moderate consumption of dairy products and low consumption of meat and meat products (Trichopoulou and Vasilopoulou, 2000). Intense scientific research in recent decades, have linked Mediterranean diet with longevity and the prevention of numerous diseases (cardiovascular diseases and cancer) (Serra-Majem et . Al., 2006; Brill, 2009). Table 2.1

Member Countries of the Euro - Mediterranean Partnership

Mediterranean Partner Countries

European Union

Egypt

Libya*

Austria

Ireland

Hungary

Albania

Morocco

Belgium

Spain

Poland

Algeria

Mauritania

Bulgaria

Italy

Portugal

Bosnia-Herzegovina

Montenegro

France

Cyprus

Romania

Jordan

Monaco

Germany

Latvia

Slovakia

Israel

Palestinian Authority

Denmark

Lithuania

Slovenia

Croatia

Syria

Greece

Luxemburg

Sweden

Lebanon

Turkey

Estonia

Malta

Czech Rep.

Tunisia

United Kingdom

Netherlands

Finland

Source: European Commission, 2005

15

2.5

EU and the Mediterranean region In geopolitical level, European Union, following the delimitation of

Mediterranean region in line with its socio-economic, cultural and environmental characteristics, defines the Mediterranean countries beyond its narrow geographical context. Therefore, when European Union launched the “Euro-Mediterranean Partnership”, by the declaration of the “Barcelona Process”, in 1995, as Mediterranean Partner Countries – MPC (members of the “Euro - Mediterranean Partnership” which are not EU members) defined all “Μediterranean” countries even if they did not had access to the Mediterranean sea. Today, as members of the “EuroMediterranean Partnership”, besides the twenty seven EU states, are include all countries with coastline on the Mediterranean both with Jordan and Mauritania (European Commission, 2005).

2.6

Shipping costs for Cypriot economy

Shipping costs are defined by Radelet and Sachs (1998) as the ratio of CIF and FOB importing prices. Actually, the accurate mathematical type that indicates the average shipment costs is equation 2.1 below where, SC stands for shipment cost, CIF for the importing price Cost-Insurance-Freight and FOB for the importing price Free On Board.  CIF  SC =   −1  FOB 

(2.1)

The FOB (free on board) price measures the cost of an imported item at the point of shipment by the exporter, specifically as it is loaded on to a carrier for transport. The CIF (cost-insurance-freight) price measures the cost of the imported item at the point of entry into the importing country, inclusive of the costs of transport, insurance, handling, and shipping costs, but excluding customs charges. Therefore, the CIF/FOB band, as defined by equation 2.1 measures the ratio of the price devoted exclusively on shipping costs. Shipping costs are measured for each country from the point of view of one country’s imports, due to data availability even though shipping costs apply both in the direction of imports and exports. In the empirical work of Radelet and Sachs (1998), the average CIF/FOB band is calculated for the total value of imports for a period of 25 years. The above Table 2.2 presents the average CIF/FOB band for the EU12 plus Cyprus, Sweden and Finland.

16

Table 2.2

CIF/FOB band, 1965-1990 average (%)

Country CIF/FOB Band Austria 4.1 Cyprus 10.5 Denmark 4.5 Finland 4.8 France 4.2 Source: Radelet & Sachs, 1998

Country CIF/FOB Band Germany 3 Greece 13 Ireland 5 Italy 7.1 5.6 Netherlands

Country Portugal Spain Sweden UK

CIF/FOB Band 10.3 6.4 3.5 6

Straightforwardly, the higher a country cif/fob ratio is, the higher are its shipments costs. Furthermore, according to Bloom and Sachs (1998), a higher country cif/fob ratio is typically considered less desirable than a lower country cif/fob ratio. Figure 2.1

CIF/FOB band, 1965-1990 average (%)

United Kingdom Sweden Spain Portugal Netherlands Italy Ireland Greece Germany France Finland Denmark Cyprus Austria 0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

Source: Radelet & Sachs, 1998

As depicted on the figure 2.1, Cyprus, in contrast with the European countries of the EU-12, Finland and Sweden displays significantly increased CIF/FOB ratio and consequently importantly higher shipment costs. In particular, shipment costs for Cyprus are almost twice the shipment costs of Ireland which is also an island country.

17

Chapter 3 Methodology

3.1

The Gravity Equation Model framework According to Filippini and Molini (2003), frequently, the social sciences are

lending laws or principles from the natural or biological sciences. The Gravity Equation Model (GEM) constitutes such an example as its basic idea is traced back to Newton’s law of gravity. The law of gravity, formulated as the GEM, is used by the social sciences for the empirical analysis of various forms of international and regional social or economic flows (flows of immigrants, commuters, tourists, hospital patients). But, among the plenty paradigms of GEM application in empirical research, its most wide use has been cited to the field of empirical analysis of international trade and foreign direct investments flows (Cheng and Wall, 2005). Krugman and Obstfeld (2005, p.12) state that for several decades in international economics, economists dealing with the empirical research of international trade, have recognized a powerful empirical relation between the size of the economy of a country and the value of its trade. This empirical relation, which is based on Newton’s law of gravity, is formulated with an equation of form:

Tij = A

Yi Y j Dij

(1)

where the size of the economy of a country is expressed as the level of a county’s income and the value of its trade flows with the value of a country’s imports or exports with its partners. Therefore, equation (1) consist an empirical relationship that predicts the volume of international trade between any two countries i and j and formulates the GEM of international trade. As A, a constant term is symbolized, as Tij the value of bilateral trade flows (imports or exports) between any country i and any country j, as Yi and Yj the size of income of country i and country j respectively, and as Dij the geographical distance between the two countries. So, according to relation (1), GEM of international trade implies that, the value of trade flows between two countries is

18

proportional, maintaining the all other factors constant, with the incomes of the two countries and inversely proportional with the size of geographic distance between them. For the empirical application of GEM, the level of each country income is represented by the Gross Domestic Product (GDP) of each corresponding state. GDP is considered to be representative, from the side of the exporting country, of an indicator of exports supply and on the other hand, from the side of the importing country, of an indicator of imports demand. The size of distance, at the empirical application, is represented by the great circle distance between two trading partners (usually between the capitals of countries). It is expressed in kilometers and it is considered to represent the overall cost for the accomplishment of a trade transaction (transport costs, duties, tariffs, etc.) between two trading countries (Frankel et. al., 1996, Eichengreen and Irwin, 1996, Gopinath and Echeverria, 2004, Serano and Pinilla, 2009). The first empirical applications of Gravity Equation Models to the empirical analysis of international trade flows of goods is credited by Deardorff (1998) to the concurrent but independent work of Tinbergen and Pöyhönen.

3.2

Standard Gravity Equation Model The exclusive relation between the value of bilateral trade, the level of income

of two trading partners and the size of geographic distance between two countries, according to Wall (2000), constitutes the standard GEM. The standard formulation of GEM constitutes the simplest and basic form of GEM in the empirical research of international trade. Considering as Yi and Yj the size of Gross Domestic Product of countries i and j, Dij the geographical distance between them and Tij the value of import or export flows from country i to country j, the standard GEM of international trade takes the form:

Tij = a

Yi β Y jγ Dijδ

(2)

Form (2) of the gravity equation is an estimated model, where researchers using the actual data of the value of trade flows (imports and exports of goods), the

19

size of GDP of both trading partners and the geographical distance between the two trading partners, are trying to estimate the parameters α, β, γ and δ.

3.3

Estimation of Gravity Equation Model For the empirical application of the standard GEM and the estimation of

parameters, the common approach according to Head et. al. (2008), is to use natural logarithms in equation (2) in order to obtain a linear form of the model, as represented in model (3).

ln Tij = a + β ln Yi + γ ln Y j + δ ln Dij + ε ij

(3)

The formulation of standard GEM to its linear form, as model (3) constitutes the most common version of the standard GEM as widely used in empirical research of international trade flows (Wall, 2002). The linear formulation of the model (5) facilitates the estimation of the parameters α, β, γ and δ as it consists a multiple regression model which will be estimated using ordinary least squares method (Frankel, 1997, p. 50). In order to estimate the model, as dependent variable, the natural logarithm of the value observations of trade flows between two countries (lnTij) are used. The natural logarithm of GDP of both trading partners (lnYi, lnYj) and the natural logarithm of kilometers between the two countries (lnDij) are treated as independent variables. In the model as εij is represented the error term of the model. Model (3) thus implies that the value of trade flows between two countries is determined by the GDP of both countries and the geographical distance between them, without specifically to assume that the value of trade is proportional to the product of GDP of both countries and inversely proportional to the geographical distance between them. In fact, the signs of the estimated parameters β, γ and δ are those that will determine the form of the relationship between the value of trade flows, the size of GDP and the geographical distance between the two trading partners. However, experience from empirical applications in literature has shown that the signs of β and γ are usually positive and the sign of δ is negative confirming the logic behind GEM (Krugman and Obstfeld, 2005, p. 13; Battersby and Ewing, 2005).

20

3.4

Augmented Gravity Equation Model Going one step forward, Krugman and Obstfeld (2005), identified that the data

of bilateral trade flows between the U.S. and some European countries fit quite well but not completely to the standard GEM, implying that, for a comprehensive research of international trade extra factors (besides geographical distance and the size of GDP of the trading partners) that affect international trade must be considered. Meaning that the empirical application of the standard GEM omits a portion of the variance of international trade flows value. The omission is treated by empirically applying a GEM consisting of additional independent variables than those used in standard GEM. GEM in this case is called augmented GEM (augmented gravity equation model) since it is enriched with additional independent variables (Cheng and Wall, 2005). The linear logarithmic form, of the augmented GEM, can be viewed as an extension of the standard GEM, as in essence, model (4) represents a standard GEM added with real independent quantitative variables or indicators and bilateral dummy variables. The added variables, indicators and dummies represent the various other factors that impact international trade beyond geographical distance and GDP.

ln Tij = a + β ln Yi + γ ln Y j + δ ln Dij + ∑ ζ n ln Ζin + ∑ θ n lnZ nj + n

∑λ

m

m

n

Β + ∑ ξ r Β + ∑ ϕ r Β + ε ij m ij

r i

r

(4)

r j

r

Thus, in model (4): •

Zi and Zj represent a ζ and θ number respectively, of real quantitative variables and indicators that express unilateral characteristics and factors (such as population size, area, indicators of trade policy, etc.) of the trading partners i and j.

•

Bij represent a λ number of dummy variables expressing common events or bilateral common characteristics between countries i and j (e.g. the fact of common borders between the two countries, joint participation in the European Union, etc.)

•

Bi and Bj represent a ξ and φ number respectively, of dummies that express unilateral characteristics of the countries involved in

21

international trade (such as the case of a landlocked country, an island country, etc.).

3.5

Variables of the augmented Gravity Equation Model One of the most common used, additional independent variables that enrich

augmented GEM, is the size of the total population of a country. This independent variable represents a further measure of the economic size of a country engaging in international trade (Cheng and Wall, 2005). Other independent variables that are used in an augmented GEM representing the economic size of a country are the size of per capita GDP (Glick and Rose, 2002), the size of a country’s surface area expressed in units of square kilometers (Cavallo and Frankel, 2008) or the length of its coastline expressed in kilometers (Parpiev and Sodikov, 2008). Quite often, as independent variables, policy indicators are used. Policy indicators represent measures of the broader institutional and political environment of a country (Gwartney and Lawson, 2003) or specific trade or economic policies (de Haan & Sturm, 2003). One commonly used such indicator is the Economic Freedom of the World Index issued annually by the Fraser Institute in Canada. This index measures the relative consistency of a country’s policies towards the concept of economic freedom (Gwartney et. Al. 2006). Similarly, another common indicator is the Index of Economic Freedom issued annually by the Heritage Institute in the U.S. and the Wall Street Journal. Its composite indicator represent the degree of liberalization in all sectors of the economy of countries in the world (Kane et. al., 2007). Other indicators used as independent variables are an index of country’s democratization which represents the relative degree of democratic conditions in a country (Mansfield et. Al., 2000), infrastructure indicators that represent the level of infrastructure (transport infrastructure, ports, telecommunications, etc.) available to a country and thus contributing to the conduction of international trade (Nordas & Piermartini, 2004). As depicted in model (4) augmented GEM is furtherly enriched with dummy variables that represent common events and bilateral or unilateral characteristics of the countries that engage to international trade. One of the dummy variables, frequently used in an augmented GEM, is one that expresses the fact of simultaneous participation of both trading partner countries to a preferential trade agreement. The dummy takes the value one if both trading partners belong to the agreement otherwise 22

takes the value zero (Martinez-Zarzoso, 2003, Koo, 2006). By the same way, with the use of a dummy variable, is expressed the fact of a joined participation of two partners to the European Union (Endoh, 1999) or the adoption of a common currency (Yeyati, 2003). Furthermore, in the empirical literature of international trade, frequent is the use of dummy variables, in augmented GEM, that represent the fact that two countries share a common language (Melitz, 2002) or the existence of a common border between the two countries (McCallum, 1995). Besides, usual is the correspondence of a dummy to the existence of colonial ties (in the sense that a country is or was a colony of another country) (Rose, 2004) or the fact that a country is an island or landlocked (Wong, 2008).

3.6

Augmented Gravity Equation Model Applications The primary objective of an empirical research, with the use of a gravity

framework, strongly affects the independent variables that will enrich – among the various independent variables that can be used – the augmented GEM (Rafiquzzaman, 2002, Wall, 2002, Sandberg, 2004, Bussière et. al., 2004). The primary objective usually include an empirical investigation on the effects of policies, institutions or other factors on international trade flows. Such examples are, the study on the effects of a free trade agreement on international trade flows or the research on the effects of other factors, like the presence of common borders between trading partners (Skripnitchenko et. al., 2004). These effects are estimated as deviations from the volume of trade flows that a standard GEM predicts (Cheng and Wall, 2005). So, in order to estimate the impact of policies or other factors to international trade flows, the standard GEM is enriched, transforming to an augmented GEM, with specific independent variables that represent the specific policies or factors researched. Thus, an augmented GEM may, according to Sandberg et. al. (2006), estimate deviations by the “standard” or “physical” volume of bilateral trade (Papazoglou, 2007) due to the impact of certain political factors or other characteristics modeled accordingly. When the objective of the empirical research of international trade flows (with the use of augmented GME) is to estimate the effects of a regional trade agreement, the estimated augmented GME will incorporate an appropriate dummy variable. The variable will reflect the fact of common participation of two countries in this agreement, taking the value of one if both trading partners belong simultaneously to 23

the agreement otherwise taking the value zero. By enriching the model with this dummy, it can be estimated whether bilateral international trade is relatively higher or lower, from the volume of trade expected by the standard GEM, due to the agreement (Carrere, 2006; Baier and Bergstrand, 2007). The same methodology is followed in the case of the research on the effects of a country’s participation in the European Union or the adoption of the Euro (Thom & Walsh, 2002). Similarly, with the use of an augmented GEM, the effects of the degree of liberalization of an economy or trade policy can be estimated. The estimation is achieved by enriching GEM with a policy index which represents the general institutional environment or an indicator of economic freedom (Gwartney and Lawson, 2003). Using these types of indexes or indicators the relative change in value of bilateral trade can be estimated due to a relative change of the index or indicator (Depken and Sonora, 2005). By enriching an augmented GEM with an independent variable representing the level of infrastructure, the effect of infrastructure (transport infrastructure, telecommunications, road networks etc.) on international trade can be also investigated (Clark et al., 2004). In essence, it is estimated whether the value of bilateral trade is higher than expected if trading partners have relatively better quality or more infrastructure than other countries (Limao and Venables, 2001, Nordas and Piermartini, 2004). The variables that reflect the level of infrastructure are based on indicators of the World Bank that express the density of infrastructure, e.g. roads, ports and airports per square kilometers, internet connections per 1000 inhabitants, etc. (World Bank, 2009). A significant portion of the literature, of empirical researches of international trade with the use of augmented GEM, estimates the impact of a country’s common or unilateral cultural, historical or geographical characteristics on exports or imports. In particular, it is estimated the extent where the value of bilateral trade is higher or lower, than expected, if trading partners have a common language (Cavallo and Frankel, 2008), common historical roots (Colonial link) (Glick and Rose, 2002; Rose, 2004) or one (or both) of the two partners are landlocked or an island country (Glick and Rose, 2002; Baier and Bergstrand, 2007; Wong, 2008). Special case of augmented GEM use is the estimation of the impact of borders on international trade. McCallum was the first, in 1995, using an augmented GEM with an appropriately defined dummy variable, to estimate the magnitude of the effect 24

of U.S. border with Canada. Combining data of international bilateral trade flows between Canadian provinces and U.S. states and inter-regional trade data flows between the provinces of Canada, he estimated that the average Canadian province conducted in 1988 value of bilateral trade flows twentytwo times higher in another Canadian province than with an American state of equal size and in equidistant location (McCallum, 1995). The significantly large size of the estimated US-Canada border effect by McCallum, and the notion that this figure incorporates all kind of factors that create obstacles in conducting trade (transport costs, tariffs, etc.) provided a long-term debate over the so-called “border puzzle” (Head and Mayer, 2002; Feenstra, 2004). In fact, Obstfeld and Rogoff (2000) consider the outcome of McCallum’s estimation as one of six “puzzles” of international macroeconomics. The publication of the McCallum’s results in 1995, sparked a significant volume of empirical researches using GEM (Armstrong, 2007; Head et al., 2008), which aimed at understanding the “border puzzle” (Heliwell, 1996; Hillberry, 1998; Feenstra, 2002). A well known published research, coping with the “border puzzle” was the publication of Anderson and van Wincoop in 2003 which tried “solving” the puzzle with an augmented GEM consistent with economic theory (Anderson and van Wincoop, 2003).

3.7

Theoretical Background of Gravity Equation Model In the decades of 1960 and 1970, the first empirical studies of international

trade using the GEM framework originate to the work of Tinbergen (1962), Pöyhönen (1963), Linnemann (1966) and Aitken (1973). Those early applications did not have sufficient theoretical economic justification for the use of GEM in predicting the value of bilateral trade flows between two countries. According to Baier and Bergstrand (2007), early applications justified the use of GEM just describing its analogy with Physics. The first attempt to explain GEM within the context of economic theory and the theory of international trade is credited by Deardorff (1998) to James Anderson in 1979. Anderson, proved under a set of assumptions that GEM is consistent with economic theory (Anderson, 1979). Subsequent extensions by Bergstrand (1985; 1989), Helpman, and Krugman (1985), Deardorff (1998), Evenett and Keller (2002), Eaton and Kortum (2002) but particularly by Anderson and van Wincoop (2003), 25

verified the consistency of GEM framework with economic theory and the theory of international trade using differentiated assumptions from the initial proof of Anderson in 1979. Common ground of all theoretical attempts, according to Baier and Bergstrand (2007), is the distinct role of traded goods prices in countries that trade bilaterally. This is particularly evident in the theoretical proof of GEM by Anderson and van Wincoop with the inclusion of multilateral resistance to trade terms as independent variables. The terms correspond to indicators of prices faced by consumers in countries that trade bilaterally, expressing an average barrier to trade that the two countries face both in their bilateral trade and trade with all other countries (Anderson and van Wincoop, 2003). Thus, the theoretical proof of GEM, by Anderson and van Wincoop, is considered a benchmark in GEM subsequent literature (Balistreri and Hillberry, 2007; Novy, 2008).

26

Chapter 4 Estimated Model and Data Sources

4.1

Estimated Model Following the primary objectives of this paper, an augmented Gravity

Equation Model will be estimated. The model will be extracted from the general form of the augmented GEM, as described in the previous chapter and presented below as model (1).

ln Tij = a + β ln Yi + γ ln Y j + δ ln Dij + ∑ ζ n ln Ζin + ∑ θ n lnZ nj + n

∑λ

m

m

Β + ∑ ξ r Β + ∑ ϕ r Β + ε ij m ij

r i

r

n

(4.1)

r j

r

In an augmented GEM, apart from the three independent variables that constitute the standard form (lnYi, lnYj and lnDij), three additional sets of independent variables are included. The variables lnYi, lnYj represent the size of a country’s economy, lnDij represent the size of geographical distance between the trading partners, and: •

Zi and Zj represent a ζ and θ number respectively, of real quantitative variables and indicators that express unilateral characteristics and factors of the trading partners i and j,

•

Bij represent a λ number of dummy variables expressing common events or bilateral common characteristics between countries i and j and,

•

Bi, Bj represent a ξ and φ number respectively, of dummies that express unilateral characteristics of the countries involved in international trade (such as the case of a landlocked country, an islanld country, etc.).

As the general form of the augmented GEM implies, the model will be enriched with added variables, in accordance with the primary and secondary objectives of the study. Therefore, in conformity with this study primary objective, all trade flows among Cyprus and 116 countries are identified in order to compose our augmented GEM dependent variable. Trade flows data will be completed with exports and

27

imports of the total agricultural and fisheries sector between Cyprus and the 116 countries in our sample. Consequently, four models are formulated, two for the Cypriot imports of total agricultural and fisheries products and two for the respective exports. Each model will be estimated twice. Once for the estimation of distance effect before Cyprus accession to the EU and once for the period after the accession. Further, the augmented GEM will be enriched with four quantitative variables that represent the yearly level of precipitation in Cyprus and the size of a trading partner’s area and size of population. Additionally, from the numerous available dummy variables that usually augment a GEM, our model will include one dummy representing the case of a Cyprus’ partner being a landlocked country and three dummies representing the common characteristics between Cyprus and its trading partners. Also, in order our model to catch the effect of EU participation for Cypriot exports and imports one dummy will correspond to the joint participation of Cyprus and its partners to EU and one dummy will stand for the participation only of the partner country. Finally, one dummy will represent the participation of a Cyprus trading partner to the Mediterranean Partner Countries so that we can estimate the effect of Mediterranean Partnership to Cypriot exports and imports. Thus, expanding all Z and B independent variables of the general model, that compose the five summations of model (4.1), the augmented GEM will be enriched with eleven independent variables. The model consequently will take the form of model (4.2) and (4.3) representing the augmented GEM for Cypriot exports and imports respectively.

ln X ijk,t = a 0k + a1k ln Yi ,t + a 2k ln Y j ,t + a3k ln Dij + a 4k ln Area j + a5k ln Popi ,t + a 6k ln Pop j ,t + a 7k ln Raini ,t + a8k Landlocked j + a9k ComLang ij

(4.2)

+ a10k Colonial + a11k ComCol + a12k EU j + a13k EU ij + a14k MPC j + ε ij ,t ln M ijk,t = a0k + a1k ln Yi ,t + a 2k ln Y j ,t + a3k ln Dij + a 4k ln Area j + a5k ln Popi ,t + a 6k ln Pop j ,t + a 7k ln Raini ,t + a8k Landlocked j + a9k ComLang ij

(4.3)

+ a Colonial + a ComCol + a EU j + a EU ij + a MPC j + ε ij ,t k 10

k 11

k 12

k 13

k 14

Where the variables are defined as follows:

28

X ijk,t

denotes the value of exports of Cyprus (country i) to country j in each sector k in year t,

M ijk,t

denotes the value of Cyprus imports (country i) from country j in each sector k in year t,

a 0k

is the constant term of the equation,

Yi ,t Y j ,t

represents country’s i and j GDP in year t,

Pop i ,t Pop j ,t

represents population of country i and j in year t,

Dij

denotes the physical distance between country i and country j. It is measured using the great circle distance between capitals,

Area j

denotes the area of countries j,

Landlocked j is a dummy variable that equals one if country j is landlocked, ComLang ij

represents a dummy variable that equals one if Cyprus and country j share a common language and zero otherwise,

Colonial

represents a dummy variable that equals one if Cyprus has or ever had a colonial link with a country j

ComCol

represents a dummy variable that equals one if Cyprus has or ever had a common colonizer country with a country j

Raini ,t

represents the amount of precipitation of Cyprus in year t

EU ij

represents a binary variable that equals one if Cyprus and country j are at the same time members of the European Union and zero otherwise,

EU j

represents a binary variable that equals one if a country j is a member of the European Union and zero otherwise,

MPC j

represents a binary variable that equals one if a country j is a member of the Mediterranean Partner Countries and zero otherwise,

a nk

represents the parameters to be estimated,

k

represents the sectors investigated, k=1 for the total agricultural products and k=2 for the fisheries products,

ε ij,k t

is the error term of the equation which is assumed to be log normally distributed with mean zero. It represents the numerous other

29

determinants of bilateral imports that are not captured by the variables included in our model (Feenstra et. al., 2001).

4.2

Dependent Variable In models 4.2 and 4.3 the ln M ijk,t and ln X ijk,t variables denote the natural

logarithm of the value of imports and exports, respectively, of sector k of Cyprus from (to) country j at time t. Meaning that, the dependent variable of the model consists of the values of Cypriot imports or exports with 116 trade partners over an eighteen years period (1992-2009). As mentioned before, in accordance with this study’s research objectives, trade flows observations that compose the depended variables, are identified in two sectors (total agricultural trade and fisheries trade). Observations of import and export flows are classified by Standard International Trade Classification Revision 3 (SITC Rev. 3) at one and two digit level. Following that classification and according to the identification of two sectors in the current papers’ empirical research, the ‘total agricultural trade’ investigated sector, according to World Bank (2009), consists of import and export flows classified in SITC code 0 “Food and live animals”, code 1 “Beverages and tobacco”, code 4 “Animal and vegetable oils, fats and waxes” and code 22 “Oil-seeds and oleaginous fruits”. Furthermore, for the other investigated sector, “fisheries”, its dependent variable is constructed with export and import observations from the SITC classification code 03 “Fish (not marine mammals), crustaceans, molluscs and aquatic invertebrates, and preparations thereof”. The sector of ‘total agricultural trade’ includes both import flows of agricultural products and flows of food products, beverages and tobacco. Following prior literature and considering the high level of intra-industry trade in food and agricultural products (Hirschberg et al., 1994, Ferto and Hubbard, 2001, Cho et al., 2002, World Bank, 2009) the identified sector used in model estimation includes both agricultural and food products. Accordingly, for the sake of economy we refer to agricultural and food products trade as agricultural trade. The values of Cypriot agriculture imports and exports, that compose the dependent variables, are collected over an eighteen years period, from 1992 to 2009. The dataset is restricted to this time period partially due to data availability. After 2009, the needed trade observations were not fully available from the data source

30

used. In addition, 1992 was selected as the initial year of our dataset as the inaugural year of European Union.

Table 4.1

The 116 trading partners of Cyprus

Nr.

Country

Nr.

Country

Nr.

Country

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

Afganistan Albania Algeria Argentina Armenia Australia Austria Bahrain Belarus Belgium Belize Bolivia Bosnia Herzegovina Brazil Bulgaria Burkina Faso Canada Czech Republic Chile China Colombia Congo Costa Rica Côte d'Ivoire Croatia Cuba D. R. Congo Denmark Djibouti Dominican Republic Dominica Egypt Equator Estonia Ethiopia Finland France FYR Macedonia Gambia Georgia Germany

42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82

Ghana Greece Guatemala Honduras Hong Kong Hungary India Indonesia Iran Iraq Ireland Island Israel Italy Jamaica Japan Jordan Kenya Korea South Kuwait Latvia Lebanon Libya Lithuania Luxembourg Madagascar Malaysia Malawi Malta Morocco Mexico Myanmar Moldavia Netherlands New Zealand Nigeria Norway Oman Pakistan Palestine Panama

83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116

Peru Philippines Poland Portugal Qatar Romania Russia Saudi Arabia Serbia Singapore Slovakia Slovenia South Africa Spain Sri Lanka Sudan Sweden Switzerland Syria Thailand Trinidad & Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Vietnam Yemen Zambia Zimbabwe

31

Trade observations are collected using the import and export flows between Cyprus and all its trading partners (Table 4.1). The final number of trading partners, that consist the dataset, is 116 countries, after the exclusion of overseas territories (like Gibraltar) or countries with minor values of trade flows (below 100 US dollars).

4.3

Data Sources

i)

Dependent Variable Value observations of the imports and exports come from the UNcomtrade

database (United Nations Commodity Trade Statistics Database) of United Nations (UN, 2009). The web statistical service of UNcomtrade collects 1.5 billion trade flow observations (imports and exports among all countries in the world) at current U.S. dollar prices, from 1962 onwards, sorted by nine different systems of classification at six digits level (UNcomtrade, 2009). For the formulation of the dependent variables of models 4.1 and 4.2 data of the third revision of the Standard International Trade Classification of the United Nations are used at one and digits level. Since the values of imports and exports, which consist the dependent variable, are collected from the database at current prices U.S. dollars, it is necessary to deflate them. For deflation, since the values are expressed in U.S. currency, the U.S. consumer price index (Consumer Price Index - CPI) is used with base year the 2009. Consumer Price Index is issued by the U.S. government Bureau of Labor Statistics. Thus, the values of imports and exports between 116 countries and Cyprus in the sample, between 1992 and 2009, are deflated to 2009 prices.

ii)

Real Independent Variables GDP data of Cyprus and of the 116 countries that consist the j countries in

models 4.1 and 4.2, are gathered from the statistical web service of the International Monetary Fund (IMF, 2009). The GDP figures are expressed, in the IMF database, at current U.S. dollar prices. Therefore, following the deflation procedure for trade values, all GDP values are deflated using the US consumer price index (CPI) with 2009 as the base year. The same source was also used to extract the data for the completion of the independent variables of population. The independent variable of the geographical distance ln Dij expresses the physical distance between Cyprus and country j. The variable is constructed utilizing

32

the sizes of the great circle distance (the shortest distance between two points on the surface of a sphere), expressed in kilometers, between two countries’ capital cities. Data on the geographical distance came from the database of the French research center “Centre D'Etudes Prospectives et D'Informations Internationales”-CEPII (CEPIIa, 2009). The same database used for the formation of the independent variable that reflects the area (in square kilometers) of countries j (CEPIIb, 2009). The independent variable ln Raini ,t

that represents the amount of precipitation in

Cyprus was constructed utilizing yearly rainfall data from the Meteorological Service of Cyprus government.

iii)

Dummy Variables

The augmented GEM 4.2 and 4.3 is enriched with dummy variables that represent common events between trading partners or unilateral characteristics of Cyprus’ trading partners. Those dummy variables are: a) Landlocked j that takes the value one if country j is a landlocked country, zero otherwise, b) ComLang ij that takes the value one when Cyprus have a common official language with a country j (like with Greece) otherwise takes the value zero, c) Colonial that takes the value one when Cyprus have a colonial relationship after the 18th century with a country j (like with United Kingdom), d) ComCol that takes the value one when Cyprus have a common colonizer country with a coutry j (like with Malta or India), e)

EU ij , EU j and MPC j that takes the value one representing partishipation to EU

and the Mediaterranean Partnership respectively. Data for the composition of these dummy variables were retrieved from the database of the Centre D'Etudes Prospectives et D'Informations Internationales-CEPII (CEPIIb, 2009) and supplemented, where necessary, with data from the electronic version of the World Factbook of the U.S. Central Intelligence Agency (CIA, 2010).

4.4

Data length The dataset consists theoretically of (116 partners x 18 years) 2,088 trade

observations for each trade direction (exports and imports) and each investigated sector (total agriculture and fisheries). Although, due to the existence of zero import

33

and export flows, for certain countries in certain years, the actual dataset is significantly reduced. Therefore, the zero observations are omitted and estimations are conducted using only the actual import and export flows for each one of the two investigated sectors. Including the observations for the fourteen explanatory variables of the estimated model, the two investigated sectors’ data and the two directions of trade, a dataset of (116 countries X 18 years X 18 variables) 37,584 observations is finally constructed. In table 4.2 the summary statistics of the included variables are presented.

Table 4.2

Summary statistics

Variable

Obs.

Mean

Std. Dev.

Min

Max

ln X ij1 ,t

1527

13.213

2.711

0.755

19.562

ln X ij2,t

300

11.109

2.389

3.620

17.185

ln M ij1 ,t

1643

13.372

2.915

3.312

20.456

ln M ij2,t

940

11.887

2.357

3.607

16.872

ln Yi ,t

2088

23.399

0.290

23.004

23.935

ln Y ji ,t

2061

24.694

2.067

17.520

30.309

ln Area j

2088

12.066

2.105

5.756

16.653

ln Pop i ,t

2088

13.576

0.068

13.458

13.677

ln Pop j ,t

2088

16.264

1.658

11.172

21.010

ln Dij

2088

8.148

0.913

5.495

9.718

ln Raini ,t

2088

6.112

0.207

5.606

6.457

Landlocked j

2088

-

-

0

1

ComLang ij

2088

-

-

0

1

Colonial ComCol EU 12 j

2088

-

-

0

1

2088

-

-

0

1

2088

-

-

0

1

EU 15 j

2088

-

-

0

1

EU 25 ij

2088

-

-

0

1

EU 27 ij

2088

-

-

0

1

MPC j

2088

-

-

0

1

34

Chapter 5 Estimation Results

5.1

Expected signs Following relevant papers and researches utilizing an augmented Gravity

Equation Model, the models 5.1 and 5.2 (as described to the previous chapter as models 4.2 and 4.3) are recognised as linear regression models which are estimated using the Ordinary Least Squares method. This method minimizes the sum of squared vertical distances between the observed responses in the dataset, and the responses predicted by the linear approximation. The dataset, including the observations for the fourteen explanatory variables of the estimated models, the two investigated sectors’ data of the two directions of trade, consist of 37,584 theoretical observations. For the estimation, the STATA MP ver.11 statistical package was selected.

ln X ijk,t = a 0k + a1k ln Yi ,t + a 2k ln Y j ,t + a3k ln Dij + a 4k ln Area j + a5k ln Popi ,t + a 6k ln Pop j ,t + a 7k ln Raini ,t + a8k Landlocked j + a9k ComLang ij

(5.1)

+ a Colonial + a ComCol + a EU j + a EU ij + a MPC j + ε ij ,t k 10

k 11

k 12

k 13

k 14

ln M ijk,t = a0k + a1k ln Yi ,t + a 2k ln Y j ,t + a3k ln Dij + a 4k ln Area j + a5k ln Popi ,t + a 6k ln Pop j ,t + a 7k ln Raini ,t + a8k Landlocked j + a9k ComLang ij

(5.2)

+ a10k Colonial + a11k ComCol + a12k EU j + a13k EU ij + a14k MPC j + ε ij ,t

Prior to estimations of the gravity model, is expected that the variable of physical distance between Cyprus and its trading partners will present a negative sign towards the depended variable of the model 5.1 and 5.2. This expectation is partly relied on the concept of gravity model, where the variable of distance is assumed to be inversely proportional to the value of trade and partly to previous empirical findings. However, the majority of estimated parameters signs are expected to have mixed signs. In particular, the coefficients that measures the estimated effect of Cyprus’ and trading partners’ GDP on international trade is expected to have a positive sign due to the definition of GEM, where the value of international trade is proportional to the

35

GDP of the trading countries bilateral trade. Though, a positive or negative sign is expected on the parameters of the variables that measure the estimated effect of population size and the figure of the area of the partner country. Also, a mixed sign is expected on the coefficients that measures the estimated effect of yearly precipitation level on exports and imports. An analogous picture of positive and negative indications, concerning the expected sign of the estimated coefficients, is also present on the dummy independent variables. The sign of the coefficient that estimates the effect of a landlocked partner country on Cypriots trade is expected to be negative, but the sign of the variable that represents common language between Cyprus and a trading partner is expected to be positive. Furthermore, the sign for the parameter that represent colonial relationship is expected to be positive while the coefficient for the case of common colonizer between Cyprus and a trading partner is hypothesised to be negative or positive. A negative or positive sign is also expected for the case of a trading partner is a country of the Mediterranean Partner Countries. Finally positive signs are also expected for the dummy variables that represent the state that Cyprus and a trading partner country are simultaneously members of the European Union (after Cyprus accession in 2004) and the state that a sole trading partner is member of EU (before 2004). The distinction between EU participation of the trading partners, after and before 2004, is also reflected to the methodology of estimations that are performed for the two models. As described to the previous chapter, each model (4.2 and 4.3) will be estimated two times. Once for the period 1992-2003, in order to capture the effect of distance on Cypriots’ agricultural exports and imports before its accession to EU, and once for the period 2004-2009, in order to capture the effect of distance after its accession. Additionally, this dual estimation of each model generates the opportunity of estimating the effect of EU accession on Cypriot’s agricultural trade. That is also the logic behind the enrichment of our estimated models with four distinctive binary variables, which represent EU participation, one for every stage of EU enlargement.

5.2

Estimated Results Ordinary Least Squares’ regressions, for the model 4.2 and 4.3, are presented

in the following Tables 5.1, 5.2, 5.3 and 5.4. Each table contains the estimations’ results for an investigated sector (total agricultural products and fisheries products) and for each direction of trade (exports and imports). 36

The first column of each table present the independent variables of the estimated models. Next to each variable, the respective estimated coefficients are presented with their t-statistic values. Each coefficient is labeled with one, two or three asterisks, corresponding to a level of significance α = 0.15, α = 0.05 and α = 0.01 respectively. The level of significance corresponds to the level where the independent variable is statistically significant for the interpretation of the dependent variable. In the last two rows of each table, the sample size N of trade flows and the coefficient of determination R square are presented. R square is an indicator of goodness of fit of the model in a given sample data and a measure of how well future outcomes are likely to be predicted by the model.

5.2.1

Effect of distance on Cypriot total agricultural exports The total amounts of observations for the sector of total agricultural trade of

Cyprus (k=1 of the model 5.1) consist of 1511 export observations. Meaning that our exports dataset consists of 27,6% zero export observations. As depicted on Table 5.1 the regressions’ R square of the total agricultural export sector is 0.516 for the period 1992-2003 and 0.454 for the period 2004-2009. The results indicate that the majority of variables included in the specification of the augmented GEM, have the expected sign and are statistically significant. Specifically, the coefficients of the variables, which are mostly in our focus and consistent with the scope of the present research (the variables that stands for physical distance and the dummies of colonial relationship, language and landlocked case) are, in majority, statistically significant. Therefore, as the results indicate, the effect of distance on Cypriot agricultural exports is, as expected, negative and statistically significant at level α=0.01. The coefficient for the distance variable, for the period 1992-2003, is -1.85, meaning that a percentage change to the geographical distance of Cyprus between its trading partners would have resulted to a -1,85 percentage change to Cypriots exports. Consequently, if it was achievable to decrease the physical distance between Cyprus and its trading partners by 10 percent, this would have resulted, for the period 1992-2003, to an increase to the value of Cypriot agricultural exports by 18,5 percent. Concerning the other impediments or propellants of trade (the case of landlocked countries, the case of common language and the colonial ties between Cyprus and its partners), among the statistically significant coefficients, all are having 37

Table 5.1

Regression results of total agricultural exports augmented GEM-5.1

1992-2003 lnYi lnYj lnDij lnAreaj lnPopi lnPopj lnRaini Landlockedj ComLangij Colonialij ComColij EU12j EU15j EU25ij EU27ij MPCj α0 N R2

2004-2009 lnYi lnYj lnDij lnAreaj lnPopi lnPopj lnRaini Landlockedj ComLangij Colonialij ComColij EU12j EU15j EU25ij EU27ij MPCj α0 N R2

αn

t

0.6414 0.8337*** -1.8513*** -0.0881** -1.2826 -0.4181*** 0.1572 -0.5478*** 0.6185* 0.1230 -0.1397 0.2417 -0.0987 -1.2565*** 16.9798 960 0.5161

0.85 15.96 -19.09 -1.96 -0.77 -5.79 0.46 -3.61 1.06 0.22 -0.9 0.86 -0.52 -4.64 0.94

αn 1.7845 0.8519*** -1.7394*** -0.1030* -25.0008* -0.2599** -0.1367 -0.2677* -1.6954** 0.5590 0.4286** 0.3862* 0.7258*** -1.0570*** 310.368 551 0.4549

t 0.59 10.39 -11.76 -1.32 -1.22 -2.11 -0.32 -1.22 -1.93 0.66 1.67 1.32 2.48 -2.61 1.46

95% Confidence Interval

-0.8394 0.7311 -2.0416 -0.1760 -4.5492 -0.5598 -0.5163 -0.8458 -0.5226 -0.9600 -0.4431 -0.3097 -0.4741 -1.7875 -18.340

2.1223 0.9361 -1.6610 -0.0001 1.9841 -0.2763 0.8308 -0.2497 1.7595 1.2060 0.1637 0.7931 0.2768 -0.7254 52.300

95% Confidence Interval -4.2031 7.7722 0.6909 1.0129 -2.0299 -1.4489 -0.2565 0.0505 -65.3554 15.3538 -0.5013 -0.0185 -0.9732 0.6997 -0.6982 0.1627 -3.4216 0.0308 -1.0989 2.2168 -0.0767 0.9338 -0.1878 0.9602 0.1498 1.3018 -1.8514 -0.2626 -108.3682 729.1052

*, ** and *** represent significance at the 15%, 5% and 1% level respectively

38

the expected sign. In particular, the coefficient of the dummy that represents lack of access to sea transportation by a country is -0.547, meaning that Cypriots exports are by 100(e

-0.547

-1) = 42.13 percentage lesser (during the period 1992-2003) with a

landlocked country than with a country with access to the sea. Moreover, the coefficient 0.618, for the dummy of common language between Cyprus and its partners, indicates that Cyprus traded (during the same period) agricultural products of 85,6% more with countries that share a common official language than with the other partners. The coefficient of exports’ destinations’ countries is estimated 0.83, meaning that during 1992-2003 a 10 percentage increase in partner’s GDP would have resulted to a 8.3% increase of Cypriot agricultural exports. Contrary, trade relations with Mediterranean Partner (MP) countries are not estimated as strong enough. With a coefficient -1.26, it is estimated that Cypriot agricultural exports are by 71.6% lower with MP countries than with its other trade partners. After Cyprus accession to EU and during the period 2004-2009, the reduced trade with MP countries continued, but slightly diminished. With a coefficient of 1.05, it is estimated that agricultural exports from Cyprus are by 65% lower if their destination is MP countries than if their destination was other countries. Similarly, after Cyprus EU accession, the effect of distance to Cypriot agricultural exports marginally declined. The estimated coefficient -1.739, for the distance independent variable which is statistically significant at 1 percent level, indicates a negative effect for the Cypriot exports but slightly less severe from the period before accession. Thus, during 2004-2009, it is estimated that a 10 percentage reduction to the distance between Cyprus and its partners would have resulted to an increase of agricultural exports by 17.39 percent. From the total number of coefficients, for the 2004-2009 model results, the majority are estimated as statistically significant. However, plenty of them are not having the negative or positive expected sign. In particular, the coefficients for the population and area variables are estimated with negative signs. Moreover, a negative coefficient, with a strong magnitude, is estimated for the dummy of common language between Cyprus and its partners. The coefficient indicates that Cyprus traded (during 2004-2009) agricultural products by 81.5 percent less with countries that share a common official language than with the other partners. The dummy of landlocked countries is still estimated with a negative parameter (-0.26), indicating that Cyprus is 39

trading a lesser 22.9 percent with landlocked countries than with a country with access to sea transportation. Also a positive parameter was estimated for the coefficient of the variable that stands for the common colonizer dummy. According to the results, Cyprus exported 52.4% more to the countries that share a colonizer than to other countries. Contrary, the effect of EU to Cypriot exports is estimated positive, statistically significant and with an important magnitude. Specifically, for the dummy that represents joint participation of Cyprus and its partners to the EU of 25 members (for the period 2004-2006) the estimated coefficient is 0.386, meaning that Cyprus traded more with its EU25 partners than by the other countries by 47.1%. The same trend continued for the period after 2007. The coefficient of 0.725 suggests that Cyprus traded more with its EU27 partners than by the other countries by 106.4 percent during the period 2007-2009.

5.2.2

Effect of distance on Cypriots’ total agricultural imports As depicted on Table 5.2 the regressions’ R square of the total agricultural

import sector is 0.517 for the period 1992-2003 and 0.461 for the period 2004-2009. The total amounts of observations for the sector of total agricultural trade of Cyprus (k=1 of the model 5.2) consist of 1090 import observations, meaning that our imports dataset consists by 21.7% of zero export observations. Estimation results indicate that for the majority of variables included in the specification of the augmented GEM 5.2, except the variable of distance, the estimated coefficients have the expected sign and are statistically significant. Thus, as the results indicate, for the period 1992-2003, the effect of distance on Cypriots agricultural imports is, as not expected, positive, statistically significant at level α=0.01, with a relatively weak magnitude of the estimated coefficient. In particular, the estimated coefficient for the distance variable, for the period 1992-2003, is 0.03, meaning that a percentage change to the geographical distance of Cyprus between its trading partner would have resulted to a 0.03 percentage change to Cypriot imports. Consequently, decreasing the physical distance between Cyprus and its trading partners by 10 percent, this would have resulted, for the period 1992-2003, to a decrease to the value of Cypriot agricultural imports by 0.3 percent. On the other hand, for the period 1992-2003, the effect of distance on Cypriot agricultural imports is, as expected negative and statistically significant at level α=0.01. 40

Table 5.2

Regression results of total agricultural imports augmented GEM-5.2

1992-2003 lnYi lnYj lnDij lnAreaj lnPopi lnPopj lnRaini Landlockedj ComLangij Colonialij ComColij EU12j EU15j EU25ij EU27ij MPCj α0 N R2

2004-2009 lnYi lnYj lnDij lnAreaj lnPopi lnPopj lnRaini Landlockedj ComLangij Colonialij ComColij EU12j EU15j EU25ij EU27ij MPCj α0 N R2

αn 0.7708 0.8386*** 0.0360*** -0.0584* -8.452*** 0.0528 -0.1062 -0.2142 1.0071** -0.6558* -0.4558*** 1.6529*** 1.8648*** 0.9044*** 88.8365 1090 0.5173

t 1.02 14.34 4.02 -1.3 -4.7 0.8 -0.3 -0.97 2.05 -1.33 -2.4 6.07 8.55 2.74 4.93

95% Confidence Interval -0.7134 2.2550 0.7238 0.9533 -0.1327 0.2046 -0.1462 0.0294 -11.9815 -4.9225 -0.0764 0.1819 -0.8033 0.5909 -0.6483 0.2199 0.0427 1.9715 -1.6257 0.3141 -0.8281 -0.0836 1.1188 2.1870 1.4368 2.2928 0.2569 1.5519 53.5145 124.1585

αn 0.2259 0.7084*** -0.1431*** -0.1067* -1.7456 0.2436*** -0.3002 -0.9321*** 0.7044* -0.6745* -1.1967*** 1.6473*** 1.9295*** 0.5654* 14.2322 543 0.4616

t 0.07 7.94 -2.93 -1.32 -0.08 2.67 -0.68 -2.79 1.05 -1.06 -3.99 4.91 6.01 1.32 0.06

95% Confidence Interval -6.3662 6.8181 0.5331 0.8837 -0.4442 0.1579 -0.2650 0.0516 -44.3175 40.8264 0.0645 0.4227 -1.1738 0.5734 -1.5879 -0.2762 -0.6129 2.0217 -1.9213 0.5724 -1.7863 -0.6071 0.9879 2.3068 1.2989 2.5601 -0.2729 1.4038 -416.8937 445.3581

*, ** and *** represent significance at the 15%, 5% and 1% level respectively

41

The estimated coefficient for the distance variable, for the period 2004-2009, is -0.143, meaning that a percentage change to the geographical distance of Cyprus between its trading partner would have resulted to a -0.143 percentage change to Cypriot imports. Consequently, decreasing the physical distance between Cyprus and its trading partners by 10 percent, this would have resulted, for the period 2004-2009, to an increase to the value of Cypriot agricultural imports by 1.43 percent. So, even if the sign of the parameter is as expected negative, its magnitude is still rather minor, indicating a moderate effect of physical distance on Cypriot agricultural imports. Concerning the other trade enhancing or depressant factors included in our estimated 5.2 model, the variable of landlocked countries is estimated as statistically significant only for the period 2004-2009 with a negative coefficient of -0.932. The magnitude of the coefficient indicates that Cyprus imported (during 2004-2009) agricultural products by 100(e

-0.932

-1) = 60.6 percent less from countries that are

landlocked than from countries that have access to the sea. Contrary to the landlocked variable, the dummy variable that indicates presence of common official language between Cyprus and its partners is estimated statistically significant both for the period before and after EU accession. Thus, with a coefficient 1.007 for the period 1992-2003, it is estimated that Cyprus imported agricultural products by 173.7% more from countries that share a common language than from the other countries. Also for the period after EU accession, 2004-2009, the estimated coefficient of 0.704 indicates that during that six years Cyprus traded more by 102.1% with partners that share a common official language than with the other partners. As it regards the historical colonial links of Cyprus with its trading partners, estimation results indicate a negative statistically significant effect to the value of agricultural imports. In particular, for the variable that stands for colonial relationship among Cyprus and a trading country, for the period 1992-2003, the estimated coefficient is -0.655 and for the period 2004-2009, the estimated coefficient is similar (-0.674). The magnitude of those coefficients, suggests that Cyprus imported agricultural products (from countries that ever had a colonial relationship) of lesser value by 48% during 1992-2003 and by 49% during 2004-2009. Also, for the other variable that express colonial relationship, the dummy of the case of common coloniser state with a trading partner, the estimated coefficients indicate a negative statistically significant effect to the value of agricultural imports. In detail, for the period 1992-2003, a -0.455 coefficient was estimated, indicating less agricultural 42

imports by 36.5% for Cyprus and its partner that share a common coloniser. For the period 2004-2009, that specific coefficient increased to -1.19, meaning that Cyprus imported agricultural products of lesser value by 69.8% with the countries sharing a common coloniser. European integration, both before and after Cyprus accession, estimated to comprise a significant positive effect on Cypriot agricultural imports. Specifically, all dummy variables utilised in the model and represent the stages of EU enlargement, from 15 to 27 members, are estimated with statistically significant coefficients of strong positive magnitude. For the period 1992-2003, for the dummy that stands for participation of Cyprus trade partners to EU of 12 and 15 members coefficients of 1.65 and 1.86 were estimated respectively. The intensity of those coefficients suggests that Cyprus imported agricultural products 4.2 times more from members of the European Union during 1992-1994 and 5.4 times more from members of EU-15 during 1995-2003. For the dummy that represents joint participation of Cyprus and its partners to the EU of 25 members (for the period 2004-2006) the estimated coefficient is 1.64, meaning that Cyprus traded more with its EU25 partners than by the other countries by 4.1 times. The same trend continued for the period after 2007. The coefficient of 1.92 suggest that Cyprus traded more with its EU27 partners than by the other countries by 588 percent during the period 2007-2009. Amplified imports from Cyprus trade partners, was also estimated for the case of the Mediterranean Partner countries. The dummy explanatory variable that represented the case that a Cypriot importer belonged to the Mediterranean partner countries estimated to have a positive and statistically significant effect on Cypriot imports. The measure of the parameters showed that Cyprus imported agricultural products from its Mediterranean partners 1.4 times more for the period 1992-2003 and 92 percent more after 2004.

5.2.3

Effect of distance on Cypriots’ fisheries exports

Destinations of Cypriots fisheries exports are relatively restricted. The few exporting markets for the fisheries products of Cyprus have resulted to a limited database of export flows. The total amount of observations for the sector of fisheries trade of Cyprus (k=2 of the model 5.1) consist of 300 export observations, meaning that our exports dataset consists by 14.37% of non zero export observations. As depicted on

43

Table 5.3 the regressions’ R square of the fisheries export sector is 0.384 for the period 1992-2003 and 0.371 for the period 2004-2009. Table 5.3

1992-2003 lnYi lnYj lnDij lnAreaj lnPopi lnPopj lnRaini Landlockedj ComLangij Colonialij ComColij EU12j EU15j EU25ij EU27ij MPCj α0 N R2

2004-2009 lnYi lnYj lnDij lnAreaj lnPopi lnPopj lnRaini Landlockedj ComLangij Colonialij ComColij EU12j EU15j EU25ij EU27ij MPCj α0 N R2

Regression results of fisheries’ exports’ augmented GEM-5.1

αn -1.6701* 0.5585*** -0.5338*** -0.0502 2.7824 -0.1800* 0.1126 -1.8512*** 0.7014 2.2723*** 0.0102 -0.5600* -1.1243*** 1.0047** 4.4059 215 0.3843

t -1.11 5.08 -3.39 -0.47 0.88 -1.03 0.15 -2.61 0.87 3.89 0.03 -1.08 -2.92 1.99 0.1

95% Confidence Interval -4.6331 1.2928 0.3416 0.7754 -0.8443 -0.2233 -0.2626 0.1623 -3.4458 9.0107 -0.5237 0.1636 -1.3468 1.5720 -3.2483 -0.4541 -0.8963 2.2992 1.1205 3.4240 -0.6038 0.6242 -1.5793 0.4592 -1.8841 -0.3646 0.0078 2.0015 -84.5769 93.3887

αn 3.5070 1.0529*** -0.1931*** -0.2241 -7.4845* 0.1463 -1.3977 3.8577*** 6.3991*** -3.2316*** -0.2554 -1.3324 0.2924 1.9352 931.837 85 0.3716

t 0.36 3.23 -0.25 -0.71 -1.14 0.3 -0.9 3.15 6.28 -3.33 -0.31 -0.9 0.23 0.76 1.36

95% Confidence Interval -16.1054 23.1194 0.4026 1.7032 -1.7286 1.3424 -0.8516 0.4034 -20.7779 5.0861 -0.8381 1.1306 -4.5079 1.7125 1.4154 6.3001 4.3661 8.4321 -5.1660 -1.2971 -1.8780 1.3672 -4.2712 1.6065 -2.2727 2.8575 -3.1167 6.9870 -431.6494 2295.323

*, ** and *** represent significance at the 15%, 5% and 1% level respectively 44

The results indicate that the majority of variables included in the specification of the augmented GEM, have the expected sign and are statistically significant. Specifically, the coefficients of the variables, which are mostly in our focus and consistent with the scope of the present research (the variables that stands for physical distance and the dummies of colonial relationship, language and landlocked case) are, in majority, statistically significant. Thus, as the results indicate, for the period 1992-2003, the effect of distance on Cypriot fisheries exports is, as expected, negative and statistically significant at level α=0.01. Particularly, the estimated parameter for the distance variable, for the period 1992-2003, is -0.533, meaning that a percentage change to the geographical distance of Cyprus between its trading partner would have resulted to a 0.53 percentage change to Cypriots exports. As a result, a 10 percent decrease of the physical distance between Cyprus and its trading partners would have resulted, for the period 1992-2003, to an increase to the value of Cypriot fisheries exports by 5.3 percent. On the other hand, after Cyprus accession to EU (for the period 2004-2009), the effect of distance on Cypriot fisheries exports is slightly diminished. The estimated parameter for the distance variable is -0.193, meaning that a 10 percent decrease of the physical distance between Cyprus and its trading partners would have resulted, for the period 2004-2009, to an increase to the value of Cypriot fisheries exports by 1.9 percent. Therefore, from the estimated parameters of the distance variable it is obvious that even if the sign of the parameters is, as expected, negative, its effect to Cypriot fisheries exports is relatively small (in relation with the effect of distance on total agricultural exports). The dummy representing the case of landlocked countries, as destinations of Cyprus exports, is estimated with mixed signs before and after Cyprus accession to EU. For the pre-accession period, the estimated parameter is as expected negative with a strong magnitude of -1.85, but for the period after 2004, the estimated coefficient is clearly positive (3.85). The diverse signs indicate that the case that a landlocked country is a destination for Cypriot exports functions as an obstacle for fisheries exports on the pre accession period and as a enhancing factor during 20042009 period. On the period 1992-2003, it is estimated that Cyprus exported 84.2 percent less with countries that are landlocked (in contrast with countries that are not landlocked) and on the period 2004-2009, 46 times more.

45

Another variable with diverse estimated consequence, before and after 2004, is the dummy that represents colonial link among Cyprus and its partners. The coefficient for this dummy, on the period 1992-2003, is estimated to have a positive sign and a strong magnitude of 2.272. After 2004, the coefficient is estimated with a negative sign (-3.23). The intensity of the parameters, indicate that before 2004, Cyprus exported with countries that ever had a colonial relationship 8.7 times more. In contrast, for the period 2004-2009, the estimated parameter suggests that trade with those countries was weaker (in relation with the other trading partners) by 96%. As it regards another trade-boosting factor, the case where Cyprus and its trading partners share a common language, its effect on exports is estimated important and positive only for the period after Cyprus’ accession. So, for the period 2004-2009, the estimated coefficient 6.39 manifests that Cyprus exported fisheries products 594 times more to countries that share a common language than to the other countries. Common coloniser dummy was not estimated statistically significant and EU integration dummy was statistically significant only for the twelve and fifteen member’s union stage. Specifically, for the period 1992-2003, for the dummy that stands for participation of Cyprus’ trade partners to EU of 12 and 15 members, parameters of -0.56 and -1.124 were estimated respectively. The magnitude of those coefficients suggests that Cyprus exported fisheries products 42.8% less to members of the European Union during 1992-1994 (in relation to exports to other countries) and 71.6% less to member countries of EU-15 during 1995-2003. This negative signalling of those coefficients suggests that EU12 and EU15 countries, as destinations of Cyprus exports of fisheries, were not a favourable exports market during the period 1992-2003. Contrary, Mediterranean Partner countries consist an important destination of Cypriot exports since, for the period 1992-2003, coefficient 1.004 is estimated. The intensity of this parameter indicates that Cyprus traded 1,7 times more with its Mediterranean partners than with the other non- MP countries.

5.2.4 Effect of distance on Cypriot fisheries imports As on fisheries exports, sources of Cypriot fisheries imports are limited. Therefore the database of import fisheries flows is consisting of a total amount of 937 observations meaning that zero trade flows account for the 55% of the dataset. As results of Table 5.4 indicate, the estimated parameters for the distance variable are not, as expected, negative. Both coefficients before and after 2004 are 46

estimated positive and statistically significant, meaning that distance is not a hinder factor for the fisheries imports. Table 5.4

Regression results of fisheries’ imports’ augmented GEM-5.2

lnYi lnYj lnDij lnAreaj lnPopi lnPopj lnRaini Landlockedj ComLangij Colonialij ComColij EU12j EU15j EU25ij EU27ij MPCj α0 N R2

αn 0.1739 0.3027*** 0.5579*** -0.0620* -3.3092* 0.0673 0.4370 -1.6210*** 0.1232 1.8556*** -0.2067 0.8729*** 0.8475*** 0.6597** 36.9599 632 0.2713

t 0.18 4.73 4.89 -1.22 -1.46 0.83 0.98 -3.5 0.24 9.32 -0.83 2.69 3.42 1.78 1.55

95% Confidence Interval -1.6933 2.0411 0.1770 0.4285 0.3337 0.7821 -0.1617 0.0378 -7.7571 1.1387 -0.0919 0.2266 -0.4378 1.3119 -2.5310 -0.7110 -0.8694 1.1158 1.4647 2.2466 -0.6940 0.2807 0.2365 1.5094 0.3615 1.3335 -0.0685 1.3879 -9.9808 83.9006

lnYi lnYj lnDij lnAreaj lnPopi lnPopj lnRaini Landlockedj ComLangij Colonialij ComColij EU12j EU15j EU25ij EU27ij MPCj α0 N R2

αn -0.1032 0.0672 0.7819*** -0.1928* 11.6696 0.3735*** -0.4391 -1.9886*** -0.0470 2.8143*** -0.7999** 1.0768** 0.7669* 1.7854*** -154.4210 305 0.2226

t -0.02 0.49 3 -1.44 0.4 2.53 -0.73 -3.4 -0.04 4.52 -1.73 2.06 1.41 3.22 -0.51

95% Confidence Interval -8.7113 8.5049 -0.2013 0.3356 0.2688 1.2949 -0.4555 0.0699 -46.1781 69.5174 0.0825 0.6644 -1.6294 0.7513 -3.1393 -0.8378 -2.1087 2.0148 1.5888 4.0397 -1.7109 0.1111 0.0479 2.1057 -0.3067 1.8405 0.6956 2.8752 -753.3744 444.5323

*, ** and *** represent significance at the 15%, 5% and 1% level respectively

47

In particular, for the period 1992-2003, the estimated parameter is 0.557, meaning that a percentage change to the geographical distance of Cyprus between its trading partners would have resulted to a 0.557 percentage change to Cypriot imports. As a result, a 10 percent decrease of the physical distance between Cyprus and its trading partners would have resulted, for the period 1992-2003, to a decrease to the value of Cypriot fisheries imports by 5.5 percent. After Cyprus accession to EU (for the period 2004-2009), the effect of distance on Cypriot fisheries imports is slightly enlarged. The estimated parameter for the distance variable is 0.781, meaning that a 10 percent decrease of the physical distance between Cyprus and its trading partners would have resulted, for the period 2004-2009, to a decrease to the value of Cypriot fisheries imports by 7.8 percent. Concerning the variable of landlocked countries, the coefficients are estimated as statistically significant with a negative, as expected, sign. The magnitude of the coefficient -1.62 indicates that Cyprus imported (during 1992-2003) fisheries products by 80.2 percent less from countries that are landlocked than from countries that have access to the sea. For the period 2004-2009, the estimated coefficient is -1.98, meaning that imports from landlocked countries are diminished by 86.1% in relation with imports from the other countries. Common language is estimated as a not statistically significant factor on fisheries imports. On the contrary, colonial relationships are estimated as important for Cypriot fisheries imports. For the variable that stands for colonial relationship among Cyprus and a trading country, for the period 1992-2003, the estimated coefficient is 1.855 and for the period 2004-2009, the estimated coefficient is greater (2.814). The magnitude of those coefficients, suggests that Cyprus imported fishing products from countries that ever had a colonial relationship 5.4 times more during 1992-2003 and 15.6 times more during 2004-2009. Also, for the other variable that express colonial relationship, the dummy of the case of common coloniser state with a trading partner, the estimated coefficient for the period 2004-2009 indicate a negative effect to the value of fishing imports, suggesting that Cyprus traded less by 55% from partners that share a common coloniser state. European integration, both before and after Cyprus accession, estimated to comprise a significant positive effect on Cypriot fisheries products imports. Specifically, for the period 1992-2003, for the dummy that stands for participation of Cyprus’ trade partners to EU of 12 and 15 members, coefficients of 0.87 and 0.84 48

were estimated respectively. The intensity of those coefficients suggests that Cyprus imported fisheries 1.4 times more from members of the European Union during 19921994 and 1.3 times more from members of EU-15 during 1995-2003. For the dummy that represents joint participation of Cyprus and its partners to the EU of 25 members (for the period 2004-2006) the estimated coefficient is 1.07, meaning that Cyprus traded more with its EU25 partners than by the other countries by 1.9 times. The same trend continued for the period after 2007. The coefficient of 0.76 suggests that Cyprus traded more with its EU27 partners than by the other countries by 113.8 percent during the period 2007-2009. Enlarged imports from specific trade partners, was also estimated for the case of the Mediterranean Partner countries. The dummies representing the case that a Cypriot fishing products’ importer belonged to the Mediterranean partner countries are estimated to be positive (0.65 for the period 1992-2003 and 1.78 for the period 2004-2009) and statistically significant. The magnitude of the parameters indicates that Cyprus imported fishing products from its Mediterranean partners by 91.5 percent more for the period 1992-2003 and 492.9 percent more after 2004.

5.2.5

Effect of climatic conditions on Cypriots’ agricultural trade The fact that agricultural production and consequently agricultural trade is

affected by climatic conditions and the notion that agricultural trade is expected to be affected directly or indirectly by climate change is widespread. According to Anderson (2010) the effects of climate change on agricultural production is in general unknown but the on going weather volatility and especially the sequence of extreme weather events (like an extensive drought) will lead to further trade policy interventions. Undoubtedly, in many parts of the world, as in Cyprus, water has been one of the most-abundant factors of production used in agriculture. Precipitation, in Cyprus as in other locations, is a major source of water resources used in agriculture. Therefore, in our models 5.1 and 5.2 we tried to estimate the possible effect of yearly fluctuation of precipitation in Cyprus on agricultural imports and exports. As depicted on the chapter’s tables, precipitation variable was not estimated as statistically significant even to the lower level of significance. Hence, even if the relation among yearly rainfall, crop’s yield and consequently imports or exports is logical, estimation results does not suggest a plausible and statistically significant correlation. However, since climate change and 49

water scarcity affect agriculture in myriad ways the inclusion of one independent variable to represent weather conditions may be proved insufficient.

5.3

Distance effect on intra-EU agricultural trade As the estimated results of the paragraph 5.2 indicate, the coefficient of

distance on Cypriot agricultural exports is estimated -1.85 for the period 1992-2003 and -1.73 for the period 2004-2009. Yet, even if the significant negative magnitude of the estimated coefficients proves the importance of geographical distance and contribute to the calculation of the theoretical “loss” of exports, only their comparison with the average effect of distance among EU countries can clearly reveal the relative scale of burden Cyprus faces due to their geographical position. Therefore, in order to compare the magnitude of distance effect Cypriot agricultural exports face with the average agricultural exports distance effect among EU countries an extra Gravity Equation Model is estimated. The following GEM – 5.4 utilizes bilateral agricultural trade observations among EU courtiers for the period 2004-2007. Meaning that the dependent variable lnXij,t will consist from exports among EU countries both from EU country i to EU country j and from EU country j to EU country i. Therefore, theoretically, the dependent variable will consist of 2502 trade flows observations. But, due to the exclusion of Luxembourg and the presence of zero trade flows our dataset is restricted to 1736 export flows.

ln X ij ,t = a 0 + a1 ln Yi ,t + a 2 ln Y j ,t + a3 ln Dij + a 4 ln Popi ,t + a5 ln Pop j ,t + a6 ln Areai + a 7 ln Area j + a8 Landlocked i

(5.4)

+ a9 Landlocked j + a10 ComLang ij + a11k Colonial + a12k ComCol + ε ij ,t Where the variables are defined as follows:

X ijk,t

denotes the value of bilateral exports among EU countries in year t

a 0k

is the constant term of the equation,

Yi ,t Y j ,t

represents country’s i and j GDP in year t,

Pop i ,t Pop j ,t

represents population of country i and j in year t,

Dij

denotes the physical distance between country i and country j. It is measured using the great circle distance between capitals,

50

Areai , Area j denotes the area of countries i and j, Landlocked i , Landlocked j

is a dummy variable that equals one if country i or j is landlocked,

ComLang ij

represents a dummy variable that equals one if countries i and j share a common language and zero otherwise,

Colonial

represents a dummy variable that equals one if country i has or ever had a colonial link with a country j

ComCol

represents a dummy variable that equals one if country i has or ever had a common colonizer country with a country j

a nk

represents the parameters to be estimated,

ε ij,k t

is the error term of the equation which is assumed to be log normally distributed with mean zero. It represents the numerous other determinants of bilateral imports that are not captured by the variables included in our model (Feenstra et. al., 2001).

The OLS estimation results of the GEM-5.4 are presented on Table 5.6. The total amount of observations for the total intra-EU agricultural trade consist of 1736 export observations. Meaning that our exports dataset consists by 30,6% of zero exports observations. As depicted on Table 5.6 the regressions’ R square is 0.793, significantly higher than the R square figures GEM – 5.1 and GEM-5.2 presented after Cypriot exports’ estimations. The results indicate that the majority of variables included in the specification of the augmented GEM, have the expected sign and are statistically significant. Specifically, the coefficients of the variable, which is mostly in our focus and consistent with the scope of the present research (the variable that stands for physical distance) is statistically significant. Therefore, as the results indicate, the effect of distance on intra-EU agricultural trade for the period 2004-2007 is, as expected, negative and statistically significant at level α=0.01. In particular, the estimated coefficient for the distance variable, for the period 2004-2007, is -1.24, meaning that a percentage change to the geographical distance among EU partners would have resulted to a 1.24 percentage change to intra - EU agricultural trade. Consequently, decreasing the physical distance

51

between EU members by 10 percent would have resulted, for the period 2004-2007, to an increase to the value of intra-EU agricultural trade by 12.4 percent. Table 5.6

Estimation results for the GEM-5.4 (Bilateral intra-EU trade)

2004-2007

αn

t

lnYi lnYj lnDij lnPopi lnPopj lnAreai lnAreaj Landlockedi Landlockedj ComLangij Coonylij ComColij α0 N R2

0.512*** 1.012*** -1.243*** 0.393*** 0.134** -0.105*** 0.010 -0.539*** -0.098* 0.528*** 0.687*** 3.174*** -20.683***

10.01 19.87 -24.33 5.64 1.93 -3.24 0.29 -6.29 -1.14 2.92 3.86 11.74 -22.12

95% Confidence Interval 0.411 0.912 -1.343 0.256 -0.002 -0.169 -0.055 -0.707 -0.266 0.173 0.338 2.644 -22.517

0.612 1.112 -1.142 0.530 0.271 -0.042 0.075 -0.371 0.071 0.883 1.036 3.704 -18.849

1736 0.793

*, ** and *** represent significance at the 15%, 5% and 1% level respectively

Comparing the magnitude of distance effect among the intra-EU agricultural trade and the Cypriot agricultural exports, it is obvious how extent the burden of physical distance is for the Cypriot agricultural sector. While the magnitude of the distance coefficient for the Cypriot exports is -1.85 for the period 1992-2003 and 1.73 for the period 2004-2009, the average coefficient for the intra-EU trade is -1.24. Meaning that the effect of distance on Cypriot exports is greater (for the period 20042007) by 49.1%.

52

Chapter 6 Calculating the loss of Cypriot exports due to distance

6.1

Measuring the cost of distance for Cyprus As stated on the introduction, Cyprus is situated to the geographical southeast

edge of the European Union and thus to a relatively remote geographical position in relation with its European partners. Therefore, since the geographical midpoint of the European Union is situated in Germany, Cyprus is located to a significant distance of more than 2500 km - away from the geographical center of European Union. As estimated on the previous chapter, the effect of distance on Cypriot exports is significantly negative. Given that the geographical position of Cyprus is fixed its geographical distance from its European partners is also an unchanging factor. But, theoretically, if we could “shift” Cyprus from its geographical position and place it to the geographical center of European Union (in Germany) or even closer to the continental Europe, it is obvious that its geographical distance from its European partners would have decreased significantly. In that theoretical situation, given that all other factors included on the estimated GEM would have remained constant, Cypriot trade would have increased considerably. The magnitude of that increase presents in fact the potential trade of Cyprus if its geographical disadvantage, due to its considerable distance from its EU partners, was absent. At the same time that theoretical increase in the value of exports can be characterized as a “loss” in trade value due to Cyprus relatively remote geographical position. Therefore, in order to calculate the theoretical “loss” of Cypriot agricultural exports two scenarios are developed: Scenario 1:

The theoretical “placement” of Cyprus to the center of European Union.

Scenario 2:

The theoretical “placement” of Cyprus to a ten percent less distance from its European partners.

Under Scenario 1, we can calculate the potential agricultural exports of Cyprus, utilizing the estimated coefficients of GEM-6.1, if Cyprus had the same geographical distances among its trade partners that the country situated at the center of EU (Germany) has. Obviously, all other economic, demographic, cultural and 53

geographical factors (included in our estimated GEM-6.1 as independent variables) will remain constant. Under Scenario 2, utilizing the estimated coefficient of distance from the GEM-5.1, the calculation of potential Cypriot agricultural exports can be accomplished by “moving” Cyprus closer to the countries of South Europe (e.g. nearer to Greece and Italy).

6.2

The loss of Cypriot total agricultural exports The measurement of agricultural export “loss”, under Scenario 1 will be

accomplished by utilizing the estimated coefficients of the GEM-6.1 for the Cypriot exports, for the period 2004-2009 on the following equation 6.1: ln X ij ,t = 3 1 0 .3 6 8 + 1 .7 8 4 5 ln Y i ,t + 0 .8 5 1 9 ln Y j ,t − 1 .7 3 9 4 ln D ij − 0 .1 0 3 ln A rea j − 2 5 .0 0 0 8 ln P o p i ,t − 0 .2 5 9 9 ln P o p j ,t − 0 .1 3 6 7 ln R a in i ,t

(6.1)

− 0 .2 6 7 7 L a n d lo cked j − 1 .6 9 5 4 C o m L a n g ij + 0 .5 5 9 0 C o lo n ia l + 0 .4 2 8 6 C o m C o l + 0 .3 8 6 2 E U 2 5 + 0 .7 2 5 8 E U 2 7 − 1 . 0 5 7 0 M P C j

Using equation 6.1, the calculation of the value of Cypriot exports is possible, given that all independent variables are known. As stated earlier, on the theoretical case of “moving” Cyprus to the geographical center of European Union (in Germany) we can straightforwardly use as independent variable of distance (lnDij) the data of distances between Germany and the Cypriot partners, than the actual distances between Cyprus and its partners. Meaning that, we are switching the distance variable of Cyprus with the distance variable Germany faces with Cypriot partners. In that way, the physical distances between Cyprus and its EU partners will significantly be reduced while distances with Cypriot trading partners from the South – East Mediterranean area will be increased considerably. The calculations of potential exports from equation 6.1 are depicted on Table 6.1. The first column presents the actual total agricultural exports of Cyprus in Euros. The second column depicts the potential exports as are calculated using equation 6.1 and utilizing Germany’s distances than Cyprus. The third column presents the percentage increase from the actual exports and the last column the theoretical loss on exports due to Cyprus relatively remote geographical position from its EU partners.

54

Table 6.1

Loss of Cypriot exports under Scenario 1 (in Euros) Actual Exports

Potential Exports

Increase

Theoretical “Loss”

2004 2005 2006 2007 2008 2009

551,735,616 637,104,085 704,605,903 823,038,712 987,402,896 880,770,796

693,342,135 757,571,491 839,164,602 984,714,979 1,179,157,430 1,002,113,868

25.70% 18.90% 19.10% 19.60% 19.40% 13.80%

141,606,519 120,467,406 134,558,699 161,676,267 191,754,534 121,343,072

Average

764,109,668

909,344,084

19.01%

145,234,416

Source: UNCOMTRADE, 2011 As table 6.1 and figure 6.1 indicate, agricultural exports of Cyprus are significantly increased if we “move” Cyprus to the country that the geographical center of EU lays. That increase varies from 25.7% for the 2004 to 13.8% for the 2009 exports. The theoretical “loss” of Cyprus is quite significant counted at 141.6 million Euros for 2004 exports to 121.3 million Euros for 2009 exports. On average, for the period 2004-2009, the theoretical “loss” on exports value is calculated to 145.2 million Euros. Figure 6.1

Potential Cypriot exports under Scenario 1 (in million Euros) 30%

1,200 25%

1,000

20%

800

15%

600 400

10%

200

5%

0

0% 2004

2005 Actual Exports

2006

2007

Potential Exports

2008

2009

Increase (%)

Under Scenario 2, utilizing the estimated coefficient of -1,74 for the period 2004-2009, we can calculate the potential exports of Cyprus if we could have “moved” Cyprus closer, by ten percent” to the South EU members. Therefore, for an increase (according to the magnitude of the estimated coefficient) of 14.7%, potential

55

exports of 897 million Euros are calculated. Consequently, as depicted on Table 6.2 and figure 6.2, the loss on exports on average would be 132.9 million Euros. Table 6.2

Average (2004-2009)

Figure 6.2

Loss of Cypriot exports under Scenario 2 (in Euros) Actual Exports

Potential Exports

Increase (%)

Theoretical Loss

764,109,668

897,064,750

17.40%

132,955,082

Potential Cypriot exports under Scenario 2 (in million Euros)

1000 900 Theoretical loss 800 700 600 500 400 300 200 100 0 Actual Exports

6.3

Potential Exports

The loss of Cypriot fisheries exports The measurement of fisheries export “loss”, under Scenario 1 will be

accomplished by utilizing the estimated coefficients of the GEM-5.1 for the Cypriot fisheries exports, for the period 2004-2009 on the following equation 6.2:

ln X ij ,t = 931.8 + 3.50 ln Yi ,t + 1.05 ln Y j ,t − 0.19 ln Dij − 0.22 ln Area j − 7.48 ln Popi ,t + 0.14 ln Pop j ,t − 1.39 ln Raini ,t + 3.85 Landlocked j + 6.39ComLang ij − 3.23Colonial − 0.25ComCol − 1.33EU 25 +

(6.2)

+ 0.29 EU 27 + 1.93MPC j

56

The calculations of potential exports from equation 6.2 are depicted on Table 6.3. The first column presents the actual fisheries exports of Cyprus in Euros. The second column depicts the potential exports as calculated using equation 6.2 and utilizing Germany’s distances than Cyprus. The third column presents the percentage increase from the actual exports and the last column the theoretical loss on exports due to Cyprus relatively remote geographical position from its EU partners. Table 6.3

Loss of Cypriot fisheries exports under Scenario 1 (in Euros) Actual Exports

Potential Exports

Increase (%)

Theoretical Loss

2004

34,590,933

268,460,234

776.1%

233,869,301

2005

38,105,961

318,489,619

835.8%

280,383,658

2006

31,841,898

231,395,073

726.7%

199,553,175

2007

43,257,060

324,038,636

749.1%

280,781,576

2008

63,006,241

476,390,186

756.1%

413,383,945

2009

19,004,787

170,872,044

899.1%

151,867,256

Average

38,301,147

298,274,299

678.8%

259,973,152

Figure 6.3

Potential Cypriot exports under Scenario 1 (in million Euros)

600

1000.0% 900.0%

500

800.0% 700.0%

400

600.0% 500.0%

300

400.0% 200

300.0% 200.0% 100.0%

100 0

0.0% 2004

2005 Actual Exports

2006

2007

Potential Exports

2008

2009

Increase (%)

Potential fisheries exports of Cyprus are significantly increased. As depicted on Figure 6.3 that increase varies from 726% for the 2006 to 899% for the 2009 exports. Overall the average increase is 678% for the six year period of 2004-2009.

57

The theoretical “loss” of Cyprus is quite significant also. The calculations have resulted to an average loss of 259.9 million Euros. However, it must be noted that the relatively small figure of coefficient of determination for the fisheries’ gravity equation model (0.37) yield considerable doubts over the reliability of equation’s 6.2 calculations. Though, under Scenario 2, utilizing the estimated coefficient of -0,19 for the period 2004-2009, the potential fisheries exports of Cyprus are calculated on the scenario of “moving” Cyprus closer, by ten percent, to the South EU members. Table 6.4

Average (2004-2009)

Loss of Cypriot fisheries exports under Scenario 2 (in Euros) Actual Exports

Potential Exports

Increase (%)

Theoretical Loss

38,301,146

39,040,358

1.9%

739,212

The “shifting” of Cyprus closer to Italy and Greece is resulting, according to the magnitude of the estimated coefficient, to a 1.9% increase on fisheries exports. Therefore, as presented on table 6.4, potential exports of 39 million Euros are calculated. Consequently, as depicted on Table 6.4 and figure 6.4, the loss on exports on average would be 739,212 Euros. Figure 6.4

Potential Cypriot exports under Scenario 2 (in million Euros)

45 Theoretical Loss

40 35 30 25 20 15 10 5 0 Actual Exports

Potential Exports

58

Chapter 7 Conclusions 7.1

Conclusions on effect of distance on agricultural trade and agricultural sector As stated in the introduction of the current research, contrary to globalization

beliefs, the role of distance on the value of trade flows across the countries does not seem to have disappeared. On the contrary, numerous researches indicate that distance play a substantial role on the costs associated with trade. In particular, Disdier and Head (2008), running a meta-analysis on 1052 GEM estimates (using methods, controls, units of observation, and sample periods similar to ours) of the distance effect on trade, showed that the average elasticity of trade in physical goods with respect to distance is -0.9. The magnitude of this average coefficient means that a ten percent decrease of the physical distance among trade partners would have resulted to an average increased of bilateral trade by nine percent. In the case of Cypriot agricultural exports, the effect of distance does not impose an exception to the general impression described above. According to the estimations of our augmented Gravity Equation Model, the coefficient of distance on Cypriot agricultural exports is estimated -1.85 for the period 1992-2003 and -1.73 for the period 2004-2009. In relation to the average coefficient calculated by Disdier and Head (2008), the figures of the coefficients estimated by our model are 105.5% greater for the period 1992-2003 and 92.2% greater for the period after 2004. Additionally, in comparison with the average estimated coefficient for the distance variable of intra-EU agricultural trade model, the magnitude of the coefficient estimated by our model (for the period 2004-2009) is 39.5% higher. In other words, the effect of distance for Cyprus’ trade is almost double in comparison with the average effect of distance on international trade worldwide. The contrast among the average effect of distance on international and intraEU agricultural trade and our estimated effect implies an important consequence of the relative remote geographical position of Cyprus to its agricultural trade and in consequence to its agricultural sector. It seems that Cyprus considerable distances from its European partners (who consist its major trade partners) impose significant trade costs for the transportation of Cypriot agricultural goods. Those trade costs are almost double in relation to the average estimated world costs, introducing an 59

important trade impediment to the expansion and development of Cypriot agricultural trade and agricultural sector. In general, the large effect of distance and trade costs on agricultural sector was not decreased significantly after Cyprus accession to EU in 2004. European integration, after 2004 enlargement, imposes an important effect on Cypriot agricultural exports. Exports to EU members are estimated to be 106.4 percent higher than exports to other countries. However, accession to EU did not resulted to a significant decrease to the effect of trade costs on Cypriot agricultural sector. After 2004, the estimated coefficient of distance was 6.5 percent decreased, in relation to the effect of distance before 2004. Therefore, Cyprus accession to EU did not resulted to major mitigation of the inherent disadvantages that its remote geographical position imposes on agricultural sector and agricultural trade. However, there is more on geography than distance. The case of trade partners that consist landlocked countries, historical links with adjacent states and the cultural affinity with the near geographical nations impose, at the same time, impediments and incentives to the conduction of trade. Mediterranean partner countries, the non EU members’ countries of the Mediterranean region and Cyprus’ neighbours, are not a favourable destination for Cypriot agricultural exports. Cyprus exported before 2004 agricultural products of 71.4% lower value to MP countries and 65% percent lower after its accession to EU. The lack of sea transportation for certain countries, since 1992 is estimated as impediment for the Cypriot exports. Further, historical colonial links and cultural affinity between Cyprus and its trading partners appear to be present, both as it regards the effect of countries that have a common coloniser with Cyprus and as it regards the case of presence of a joint official language. On the other hand, Cypriot agricultural imports are not estimated to be significantly affected by distance. The moderate magnitudes of the estimated coefficients suggest a minor consequence to agricultural imports due to the geographic position of Cyprus. Beyond distance, the case of landlocked countries, common language and colonial links between Cyprus and its trading partners are estimated with important magnitudes. The case of landlocked countries affects negatively the value of Cypriot imports, while the presence of common language among Cyprus and its partners consist a notable incentive for imports. Contrary, historical colonial links among Cyprus and its partners are estimated as depressants of the value of agricultural imports. 60

Among its partners, Cyprus is estimated to favour imports from Mediterranean Partner countries. Although, after 2004, a slight decline to this preference is detected. Members of the EU, both before and after Cyprus accession to EU in 2004, consist also an advantageous origin of Cypriot imports. Well before the enlargement of 2004, EU12 and EU15 members (for the period 1992-2003) were exporting agricultural products of 4.2 times and 5.4 times more value to Cyprus. This trend continued after 2004 with a modest expansion. For the period after 2007, imports from EU27 are estimated to value 5.8 times more than the imports from other trading partners. Beyond total agricultural products, distance affects also fisheries trade. Even though the effect of distance on fisheries exports is estimated to be negative, its magnitude declines after 2004. On the contrary, as it regards fisheries imports, the effect of distance, after 2004, increases. This evolvement indicates a moderate shift to the disadvantages that the remote geographical position of Cyprus imposes on fishing products trade sector. Besides distance, all the other estimated factors that favour or oppose obstacles to trade (landlocked countries, common language and colonial links) are estimated in mixed signs. The lack of sea transportation for certain countries, during 1992-2003, is estimated as impediment for the Cypriot fisheries exports and imports, but during 2004-2009, landlocked countries are a favourable destination for Cypriot exports and an even more scarce origin (as expected) for fisheries imports. As it regards the effect of colonial links, their presence indicates, in general, enhanced fisheries exports, while the presence of common language indicates similarly a quite strong incentive for Cypriot trade. As it regards its partners, Cyprus is estimated to be a favour destination for European and Mediterranean fisheries imports. Even more, after 2004, this preference is estimated to be increased both for the members of EU and the Mediterranean Partners. Those results indicate a strong trade relationship among Cyprus and its fisheries trade partners with a specific direction. In particular, as it concerns fisheries exports, EU countries were not a favourable destination for Cypriot exports (since they traded 67.5 percent less value of exports in relation to other countries) before Cyprus accession to EU. During that period (1992-2003), only Mediterranean Partner countries were an important destination of exports since they imported fishing products of 173 percent more value.

61

7.2

Policy implications As estimated, the effect of geographical position on Cypriot agricultural sector

is significant and Cyprus accession to EU did not resulted to major mitigation of the inherent disadvantages that this remote geographical position imposed on agricultural sector trade. Therefore, it is critical the launch of appropriate policy initiations that will mitigate or compensate for Cyprus relative geographical disadvantages. Those geographical disadvantages must be acknowledged by the new CAP as part of the general natural constrains that Cyprus and Cypriot agriculture face (such as water resources scarcity). Products of Cypriot agriculture must be acknowledged as “sensitive” or “special” in terms of the context of geographical and physical conditions they produced. Moreover, Cyprus must highlight the fact that Cypriot agriculture withstands a considerably different economic and natural environment and the necessity of specific policy measures in order to moderate this difference. A differentiated environment not only from the point of natural resources limitation but in terms of geographical constrains generated from its relative remote geographical position from its European partners. In addition, policy provisions must be taken in order the intensified world competition, from the constantly more competitive world markets and a possible liberalization of agricultural trade on the Doha on-going round of negotiations, to be addressed successfully. Also, the liberalization of agricultural trade will undoubtedly offer significant opportunities for the Cypriot agriculture that the sector must take advantage. Finally, policy provisions for the future challenge of increased uncertainties must be taken. Cypriot agriculture will have to face an increased uncertain environment that will emerge from the world market developments (extensive use of agricultural biotechnology, responses due to EU CAP reform) and climate change. Thus, trade policy interventions or policies aimed at stabilizing food markets policies must be initiated or supported at EU level in order the crop yield fluctuations from climate change to be tackled and food prices volatility to be mitigated.

62

APPENDIX Cypriot agricultural sector and political trends 1.1

Cypriot national economy: Key macro-economic trends Cyprus, as a full member of EU since 2004 and of EMU since 2008, is

successfully meeting the challenge of being part of the European unification process. Its economy is in general characterized by macroeconomic stability, which is often confirmed by positive reviews and comments from the European Commission and other international organizations. During the period 2000 - 2008, the real GDP growth rate of the Cypriot economy was, on average, increased by 4%. A percentage that is favorably compared with the average GDP growth in EU countries. The remarkable is that this growth rate was achieved in conditions of near full employment, low inflation and absence of currency fluctuations. Furthermore, in 2007, the Cypriot fiscal balance recorded a surplus of about 3.34% of GDP, its external debt stood at 59% of GDP while unemployment remained low at around 4% (European Commission, 2009). Also, per capita income of Cyprus was, at current prices, 22,400 Euros and reached the 90.7% of the respective average for EU-27. Specifically, as shown in Figure 1.1, the annual growth rate of GDP over the last decade (1998-2008) was quite significant (around 4%), reflecting the continuous and steady growth pace of the Cypriot economy. Figure 1.1

Economic development profiles

Source: Eurostat, Forecasts Pricewaterhouse Coopers (front)

63

The intense trend of development for the Cypriot economy continued also after 2007. Thus, due to international economic and political developments and the crisis in international financial markets a mild slowdown occurred. Moreover, the international financial crisis leaded to a more generalized recession in EU and negative growth rates in most EU countries (since 2008) and Greece (since 2009). Nevertheless, it should be noted that Cyprus was one of the only three Eurozone countries that recorded positive growth rates in 2008. In general, after the accession of Cyprus to the EU, its national economy continued to show strong growth rates of GDP, as shown in Figure 1.2. At the same time and for the same period (2005 -2008), unemployment and inflation remained low and the country’s external debt followed a downward trend (from 69% of GDP in 2005 reached 49.1% in 2008). Figure 1.2

Cyprus: Evolution of GDP, 2004-2008 (in million euro)

Source: Statistical Service of Cyprus Despite the good performance of Cyprus economy, a key negative development was the deterioration of trade balance. The trade deficit worsens from a 25% of GDP in 2005 to a 37% in 2008. Specifically, the deficit increased from -3.641 million in 2004 to -6.181 million Euros in 2008 (i.e. an increase of 70%). This development is attributed to the significant increase of imports (from 4577 million Euros in 2004 to 7349 million Euros in 2008) and the virtually stagnant exports. Trade deficit developments partly reflect the problem of low competitiveness of the

64

Cyprus economy, especially under the increased competition imposed by markets opening and trade liberalization. During the past 20 years, the focus of Cypriot economy has shifted from agriculture towards the services sector and light industry. The services sector, including tourism, contributes by 80.3% to the GDP and employs 62% of the economically active population. The share of Gross Value Added (GVA) of the service sector in total economy provides Cyprus a leading position in the EU-25. The primary sector (agriculture, fisheries, forestry and hunting) contributes 2.3% to GDP (Statistical Service of Cyprus, 2009). As shown in Figure 1.3, the agricultural sector solely contributes a 2,3% of the Cypriot GDP representing a 6.3% of the economically active population (Agricultural Statistics, 2008). Figure 1.3

Composition of GDP in Cyprus Economy, 2009

Source: Cyprus Statistical Service National Accounts, 2009

The Cypriot economy and in particular the Cypriot rural economy is in transition due to the new economic environment that has been created as a result of the full accession of Cyprus to the EU. After EU accession, Cypriot economy is continuously adjusting to the new economic environment and the intense international competition. The continued liberalization of international trade and the extension of free trade agreements between the EU and the Mediterranean Partner Countries (under the EuroMediterranean Partnership), intensify the competitive pressure on the Cypriot

65

agricultural sector, due to the similarity in agricultural production both among Cyprus and EU countries as well as among Cyprus and Mediterranean Partner Countries. The contribution of agriculture, forestry sector and food industry, throughout the Cyprus economy, is quite significant, accounting for the 6.7% of GDP, the 10.3% of the total workforce and the 37% of total exports. As it is reflected, the agricultural sector, while presenting a decreased contribution to the formation of the total Cypriot GDP (due to the fast growth of service sector) continues to be essential for the Cypriot economy in terms of social cohesion, food security, employment and environmental preservation. It is assessed that, taking into account the subsequent (not primary sector economic activities) influenced by the agriculture, forestry and food industry, the overall contribution of agriculture to the Cyprus economy is quite large and the multiplier of the direct contribution to GDP is estimated around 4. This means that any decline in agricultural sector’s GDP by one unit can cause a fourfold equivalent decline to the total GDP of the whole national economy. In conclusion, the agricultural sector as a whole has an important role to play in growth and employment and thereby to achieve the goals of the Lisbon Strategy.

1.2

Structural characteristics of Cypriot agriculture In Cyprus, as rural areas are considered all areas not designated, under the

local urban plan, as urban areas. In terms of area, rural areas are estimated to cover 95% of the total area of Cyprus. Consequently, the population that resides in these areas, regardless of profession, considered as rural population and recorded as such in national censuses and periodic reports. The total population of Cyprus is 803,200, of whom a significant proportion (29.8%) are rural population (data from 2007) residing in rural areas. The historical development of urban and rural population of Cyprus is shown in Table 1.1 and Figure 1.4.

66

Table 1.

Evolution of the population

Year 1901 1911 1921 1931 1946 1960 1973 1982 1992 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Total 237.022 274.108 310.715 347.959 450.114 573.566 631.778 522.845 615.013 697.500 703.529 715.100 730.400 749.200 766.400 778.700 789.300 796.900 803.200

Urban 44.103 51.973 61.512 76.351 115.808 206.983 266.803 332.215 416.082 480.100 485.304 494.300 505.900 520.000 522.200 542.900 551.500 557.900 563.500

Rural 192.919 222.135 249.203 271.608 334.306 367.583 347.959 190.733 198.931 217.400 218.225 220.800 224.500 229.200 233.200 235.800 237.800 239.000 239.700

% rural /total 81,3 81,0 80,2 78,0 74,3 64,0 55,0 36,5 32,3 31,2 31,0 30,9 30,7 30,6 30,4 30,3 30,1 30,0 29,8

Source: Cyprus Statistical Service, «Demographic Report 2009»

As is clear from the data in Figure 1.4, the most significant population change took place in the period between the end of the 1960s and the middle of the 1980s. The main features of that period are the impressive “rural exodus” and the subsequent rapid urbanization of the population. The dramatic change is shown clearly in Figure 3.5, which depicts over time, the percentage change of rural population to the total Figure 1.4

Evolution of urban and rural population of Cyprus

600,000 500,000 400,000 300,000

urban

200,000

rural

100,000

1901 1911 1921 1931 1946 1960 1973 1982 1992 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

Source: Cyprus Statistical Service, «Demographic Report 2009»

67

population of the country. As it is shown, rural population, from 80% at the beginning of last century, followed a decline until the early 1980s and since then it has been stabilized at about 30% of the total population. Of course, a major contribution to this urbanization trend and rural population reduction was caused by the Turkish invasion of 1974. Due to the military invasion, a large proportion of refugees expelled from their settlements in rural areas and forced to settle there.

1.2.1

Employment in agriculture The role of agriculture remains important for the Cypriot economy, even

though its percentage contribution to GDP has been reduced due to the faster growth in other non agricultural economic sectors such as tourism and services. Agriculture is contributing 2% to the formation of Cypriot GDP, while the sector continues to employ 6.3% of the total workforce and exports 21.3% of total exports value of the country (Agricultural Statistics, 2008). The "Census of Agriculture 2003/2004", performed by the Statistical Service of Cyprus, on the structure of agricultural enterprises, land use and employment in agriculture, showed that the majority of workers in agriculture are working part time and cannot or do not survive solely from farming activities, resulting to the adoption of survival strategies by the members of the farm households in order to ensure additional employment. The following Figure 1.5 illustrates the development of full-time employees in the Cypriot agricultural sector for the period 2000-2010. Furthermore, Table 2 depicts the labor inputs in the EU-27. Figure 1.5 illustrates the fact that the number of people employed in agriculture has stabilized at 25,000 full-time employees.

68

Table 2.

Agricultural Labor input (in 1000 annual work units-AWUs)

GEO/TIME

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

E.U-27

11058,3

11224,6

11525,1

11780,7

12405,6

12685,3

12776,1

13298,3

12765,1

14290,5

14945,7

Belgium

62,6

64,9

64,9

66,0

68,0

70,0

71,9

72,9

74,8

75,1

74,8

Bulgaria

358,3

399,7

441,1

494,4

560,4

626,4

791,6

791,6

0,0

739,6

770,8

Czech Republic

129,5

130,3

135,3

138,1

147,9

151,9

163,6

170,3

152,2

157,6

165,5

Denmark

60,1

60,4

56,9

58,2

60,9

62,9

66,9

70,0

72,2

75,8

75,5

Germany

525,3

536,0

544,0

554,0

568,0

583,0

592,0

610,3

632,5

658,9

684,7

Estonia

28,7

29,3

31,2

32,9

37,4

37,8

38,3

38,8

56,2

58,4

64,6

Ireland

145,7

146,5

147,9

150,2

152,9

148,6

160,0

164,2

158,3

153,3

152,5

Greece

568,8

570,6

572,7

574,8

590,5

606,6

613,4

620,4

571,2

578,2

585,7

Spain

897,3

909,1

945,7

998,2

1013,3

1017,2

1032,2

1022,7

1069,3

1098,7

1101,5

France

855,8

871,5

887,7

903,3

918,6

936,4

955,3

975,3

992,0

1009,6

1028,4 1383,0

Italy

1162,4

1164,0

1188,0

1216,0

1257,0

1242,0

1284,0

1288,0

1350,0

1396,0

Cyprus

25,3

25,2

25,9

25,9

27,3

28,7

30,4

30,6

29,7

30,2

30,7

Latvia

87,9

93,4

96,8

107,4

122,8

138,2

139,6

140,9

143,1

145,2

148,6 186,7

Lithuania

143,4

147,1

150,9

158,0

165,8

173,6

165,4

186,7

180,6

170,8

Luxembourg

3,5

3,5

3,6

3,8

3,9

4,0

3,9

4,0

4,1

4,2

4,3

Hungary

414,4

424,7

421,8

459,3

504,4

522,2

553,8

581,9

646,7

642,9

676,0

Malta

4,2

4,2

4,2

4,2

4,1

4,1

4,3

4,3

4,3

4,5

4,7

Netherlands

179,7

181,7

183,9

187,0

189,5

194,1

194,8

203,9

208,1

211,4

219,5

Austria

149,2

152,0

152,3

154,9

157,4

162,5

166,1

167,9

169,9

171,6

173,5 2494,9

Poland

2086,9

2213,8

2349,3

2299,3

2291,9

2291,9

2283,6

2279,4

2266,8

2524,3

Portugal

337,4

344,0

359,0

374,0

402,0

429,0

453,0

478,8

479,7

506,2

502,8

Romania

2241,0

2152,0

2152,0

2205,0

2527,0

2596,0

2336,0

2696,0

2765,0

3121,0

3645,0

Slovenia

80,9

81,9

83,2

84,0

88,7

90,0

90,2

95,6

106,0

107,1

103,8

Slovakia

83,2

86,0

90,3

91,3

91,3

98,8

105,4

118,6

131,7

132,3

143,0

Finland

84,5

86,9

88,7

90,9

93,1

96,2

103,3

106,4

106,6

108,9

111,1

Sweden

60,6

63,2

65,8

68,6

74,6

75,6

76,8

77,8

78,8

79,4

80,1

United Kingdom

281,6

282,7

282,0

281,0

287,0

297,5

300,4

301,0

315,4

329,2

333,8

Norway

56,4

57,9

59,6

61,4

63,3

66,0

64,5

67,0

68,9

69,8

72,1

Switzerland

80,6

81,5

85,3

86,3

88,5

88,9

91,8

93,2

95,3

99,4

101,1

Source: Eurostat, economic accounts for agriculture Figure 1.5

Evolution of employment in agriculture, 2000-2010

35

AWUs in 1000s

30 25 20 15 10 5 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Source: Table 2

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The gradual reduction of employment in agriculture, as shown in Figure 1.5 above, is attributed to the modernization of agriculture and the overall development trends of Cypriot economy. Main factors contributing towards those directions are, from the one hand, the constant evolvement of technology and expertise those results to increased productivity in agriculture and the replacement of human labor with mechanical or electronic work. From the other hand, despite the continued development of agriculture, the manufacturing and services economic sectors are growing even faster, attracting more inputs, especially employment. Under these conditions, the observed decrease of employment in agriculture is a structural change closely linked with the economic development and modernization of the agricultural sector. Furthermore, another trend that has contributed to the reduction of employment to the agricultural sector and the reduction of rural population is the abandonment of agriculture that has been observed after Cyprus accession to EU. The annual rate of this abandonment has been calculated to 3%.

1.2.2

Agricultural income The evolution of the real income from agriculture (by means of AWU) in

Cyprus during the period 2000-2010 is shown on Figure 1.6. As depicted on this diagram, the agricultural income increased slightly during 2000-2002 and then presented a downward trend during 2003-2004, a landmark year for Cyprus, as during 2004 the accession of Cyprus to EU concluded and the implementation of CAP started. After 2006, farm income showed signs of recovery. It must be noted that, before accession, Cypriot farmers were enjoying prices much higher than the market prices enjoyed by farmers in member states of the EU.

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Figure 1.6

Cyprus: Indicator A of real income from agriculture by an annual work unit (AWU). (2005 = 100)

120 100 80 60 40 20 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Source: European Union, Eurostat (economic accounts for agriculture). Figure 1.7

Evolution of Indicator A in the 10 new member states of the 2004 enlargement. (2005 = 100)

180 160 140 120 100 80 60 40 20 0

2000 2001 2002 2003 2004 2005 2006 2007 2008

Source: European Union, Eurostat (economic accounts for agriculture).

Figure 1.7 depicts the evolution of the index A in the new member states of the 2004 EU enlargement. According to the graph, it is obvious that with the exception of Cyprus, the values of Index A increased sharply from 2004 and onwards in countries like the Czech Republic, Estonia, Latvia and Poland. In those countries the values and therefore agricultural incomes enjoyed by farmers, prior to EU membership, was significantly low.

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Figure 1.8

Evolution of Indicator A in Cyprus and the EU-27 (2005 = 100)

140 120 100 80 EU-27

60

Cyprus

40 20 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Source: European Union, Eurostat (economic accounts for agriculture)

Figure 1.8 depicts the evolution of the index A in Cyprus and the EU-27. As shown to the diagram, the development of agricultural income in Cyprus during the period 2000-2003, before EU accession, was more similar to the average of all the member states of the EU-27 than with the new member states of the 2004 enlargement. However, after the accession, the agricultural income of Cyprus is formulated to substantially lower level than the EU-27 average. For 2007, the first year of the implementation of EU-27, the agricultural income of Cyprus was 20% lower (in real terms) than EU’s average. It must be noted that the development of agricultural income not only showed a downward trend since 2003, but lagged considerably behind the development of the index of wages and salaries of both Cyprus and the EU-27. The main factors contributing to the declining farm incomes, in addition to lower prices of agricultural products, was the substantial reduction of the production of cereals and fodder crops, the rapid increase in the cost of intermediate inputs, the widespread drought that prevailed during that period and the lowest level of support applied after EU accession. The evolution of index A of farm income as compared to the wage index is shown in Figure 1.9. As the figure depicts both farm income and the index of wages is lower than the equivalent in the EU-27 in Cyprus.

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Figure 1.9

Evolution of the income indicator (Indicator Α) in relation to the evolution of wages and salary index

1.2.3

Other structural features The productivity of Cypriot agricultural enterprises and on extension the

competitiveness of the Cypriot agricultural sector depends largely to the inherent structural features of agricultural enterprises. In 2003 the Cypriot agricultural enterprises was censured 45,199 versus 52,089 in 1994. According to recent data from Eurostat, in 2007, the number of agricultural enterprises fell to 40,120 (a reduction of 12% during the period 2003-2007). Based on these numbers, after Cyprus accession to EU, and for the period 2003-2007, the average annual rate of decline of the number of agricultural enterprises reached a 3% compared with the 1.6% for the pre 19942003 period. Therefore, post-accession, the output rate of agricultural enterprises rose in almost a double rate compared to the pre 1994-2003 period. This structural trend can be attributed mostly to the modifications of the operational framework and the general policy environment Cypriot agriculture face after accession. Those modifications can include the reduction of protectionism policies and the increased competitive environment due to the opening of Cypriot agricultural markets. Furthermore, important elements are the plausible severe influence of drought and in general the climate change. Cyprus agriculture can be described as “small scale agriculture” compared with the EU agriculture. The distribution of agricultural enterprises per utilized

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agricultural land, based on 2007 data, is presented on Table 3. According to table, 86% of agricultural enterprises with a size smaller than 5 hectares cultivate the 28% of the utilized agricultural land. While, the remaining 14% of agricultural enterprises with a size greater than 5 hectares cultivates the 72% of agricultural land. Furthermore, beyond the small size of agricultural enterprises, as it regards its cultivated acreage, the significant fragmentation of agricultural enterprises oppose a further important obstacle to its efficient operation. Table 3.

Distribution of agricultural holdings to the area of land use

Utilized agricultural Utilized agricultural land (in 000s ha) land (in 000s ha) 2003 2007 0-4.99 39600 34700 43 42 5-9.99 2900 2800 20 19 10-19.99 1500 1500 21 20 20-49.99 800 800 26 24 50 + 400 400 47 41 Total 45200 40100 158 146 Source: Agriculture in the European Union, Statistical and Economic Information, 2009 Size of a.h (ha)

1.2.4

Number of a.h 2003

Number of a.h 2007

Basic products In terms of surface area, the two major non-irrigated crops are cereals and

wine grapes, followed by potatoes and citrus fruits. Those products are also the main export products of Cyprus. Furthermore, a significant portion of agricultural land in cultivated with vegetables, mainly tomatoes, cucumbers etc. In the mountainous and hilly areas pome fruit are planted, especially apples, pears, cherries and peaches. The cultivation of table grapes has decreased in recent years due to competitive export markets and poor absorption from local wineries. The cultivation of olives and carobs can be described as traditional due to the fact that they are part of the rural Cypriot environment and their contribution is important to the maintenance of rural landscape. The cultivation of olives in recent years, present significant growth and new irrigated plantations has been created. Most of the production of olives is used for olive oil production. Besides, the cultivation of citrus fruits is declining during the recent years due to the international competitive environment in international markets, the prolonged drought and other adverse climatic conditions, resulting to a significant reduction of cultivated land and production.

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In terms of Gross Value of production, among all products of Cypriot agriculture, potatoes occupy the first position and tomatoes, cucumbers and melons / watermelons in second place. Third place is occupied by citrus, followed by cereals, wine grapes, fruit and table grapes. Noted that potatoes, citrus fruits and vegetables are the main package export products, while imported large quantities of grain to meet the needs of consumers and livestock. Livestock production appears to be quite robust ensuring the self-sufficiency of Cyprus in pork and goat meat, poultry and eggs. Greater dependence from imports is present on the sector of beef cattle. While there is sufficiency in fresh milk, there are significant imports of dairy products, mainly cheese, and beef meat. Important exporting Cypriot agricultural products are potatoes, citrus, vegetables, wine and juices, which cover about 65% of the total export value of agricultural products and foodstuffs, while in terms of animal origin predominates halloumi which exporting performance present an upward trend.

1.2.5

Agricultural land use After Cyprus accession to EU, certain political, market and physical factors,

changes in the political protection regime of agricultural products, lower prices and subsidies, greater opening markets, the functioning of the agricultural sector in a highly competitive environment, adverse weather conditions and severe drought, have resulted in changes to the use of agricultural land. A summary of developments in the use of agricultural land before and after accession is presented on Table 4. It is worth noting that in 2003, four products cultivation accounted for the 83.5% of total agricultural land (49.3% grain, forage 16.1%, 8.5% vineyards and olive trees and carob 9.6%), while in 2008, the same products are accounting for 80.2% (37.8% - 22.8% - 8.2% - 11.4%, respectively). After EU accession, the total area of agricultural land was at the same levels. An exception was during 2008 (drought year) where a significant reduction was observed, while setting aside farmland more than doubled.

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Table 4.

Land use for the main agricultural products

Land use

1995

2000

2003

2008

Total Temp crops Cereals Legumes Industrial crops Fodder crops Vegetables & melons Permanent crops Vines Citrus Fresh fruit Nuts Olives and carobs

134,4 92,0 60.9 1 0,5 16,4 13,2

135,2 93,4 51.5 0,8 0,4 30,2 10,5

147,5 107,0 72.7 0,7 0,4 23,8 9,4

102,4 70,6 38.7 0,6 0,1 23,3 8,0

% Variation 1995/03 2003/08 9,7 - 30,6 16,3 -34,0 19.3 -46.8 -30,0 -14,3 -20,0 -75,0 45,1 -2,1 -28,8 -14,9

42,4 19,3 7,2 4 3,7 8,2

41,8 19,2 5,5 3,6 3,9 9,6

40,5 12,5 4,8 3,9 5,2 14,1

31,8 8,4 4,2 3,4 4,0 11,7

-4,5 -35,2 -33,3 -2,5 40,5 71,9

-21,5 -32,8 -12,5 -12,8 -23,1 -17,0

Source: Agricultural Statistics 2008

The development of cultivated land of the individual products for the period before accession (1995-2003) and after accession (2003-2008) appears as: (i) all cultivated areas showed negative trend after integration, (ii) crops showed the most significant rate reduction of cultivated land after the integration was the industrial plants, grain and vines (-75.0% -46.8% and -32.8% respectively), (iii) vegetables / melons, citrus fruits and legumes continued their negative trend and post-accession, but to a lesser extent (-14.9% -12.5% -14.5% respectively) while the crops that have tended to increase pre-accession and post-accession was followed by decreasing course was cereals, fodder plants, nuts and olives and carob (percentages -46.8% 2.1% -23.1% and -17% respectively). According to the latest available data from the Statistical Service of Cyprus, in 2008, the total area of utilized agricultural area is 145,200 hectares. The distribution by main categories of use and distribution of cultivated area by main crop groups is shown in Table 5. Table 5 depicts the fact that the percentage of cropland in the total agricultural land is 70.5%. The cultivated agricultural land allocated to annual and perennial crops at a rate of 69.9% and 31.1% respectively. The first place in the annual cereal crops occupied (54.8%) with second forage plants (33.0%). The dominant perennial crops are olives and carobs (36.8%), followed by vineyards (26.4%) and citrus (31,1%). Set aside and fallow / barren land cover 8.1% and 20.4% respectively of the total area of agricultural land.

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Table 5.

Distribution of agricultural land Land use

Hectares

Crop Area

Percentage % 102,400

Temporary crops Cereals Legumes Industrial crops Fodder crops Vegetables & melons

38,700 600 100 23,300 8,000

70,600

Permanent crops Vines Citrus Fresh fruit Nuts Olives & carobs

8,400 4,200 3,400 4,000 11,700

70,5 68,9 54,8 0,8 0,1 33,0 11,3

31,800 31,1 26,4 13,2 10,7 12,6 36,8

Fallow land

11,800 8,1

Grazing land

1,400 1,0

Uncultivated & deserted land

29,600

Total agricultural land

145,200

20,4 100,0

The irrigated agricultural land is 24,900 hectares, representing 21,8% of the total cultivated land, as shown in table 6. Table 6.

Distribution of irrigated land Hectares

Land use Crop area Temporary crops: Cereals Legumes Industrial crops Fodder crops Vegetables & melons Permanent crops: Vines Citrus Fresh fruit Nuts Olives & carobs Fallow land Total

Total 102,400 70,600 38,700 600 100 23,300 8,000 31,800 8,400 4,200 3,400 4,000 11,700 11,800 114,200

Irrigated 24,400 9,000 300 100 100 600 8,000 15,400 1,000 4,200 3,400 800 6,000 500 24,900

Irrigated as % of the total 23,9 12,7 0,8 16,7 100,0 2,6 100,0 48,4 11,9 100,0 100,0 20,0 51,3 4,2 21,8

Source: Agricultural Statistics, 2008

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The above data shows that vegetables, melons and watermelons, citrus and pome / stone fruit, irrigated at 100%, while a lower percentage of irrigated olives and other crops. Furthermore, analysis of data for the period 2000-2008 shows that: (I) the land set aside doubled due to the application of standards and requirements of cross compliance and the abandonment of agricultural land, (ii) the uncultivated barren and agricultural land, reduced by 56.0 thousand hectares in 2000 to 29.6 thousand hectares in 2008, (iii) the irrigated area decreased by 35.8 thousand hectares in 2000 to 24.4 thousand hectares in 2008 and (iv) the cultivated area of 135.2 thousand hectares in 2000 reduced to 102.4 thousand hectares in 2008. The great variation of topography and climate and the presence of extensive microclimatic conditions have favored the production of a wide range of agricultural products in Cyprus. In the central plain of the island, wheat and barley is grown without the need of irrigation. Potatoes, vegetables, legumes and fodder plants, grow throughout the island. In the narrow valleys and the higher elevation land of the Troodos Mountains, deciduous trees, walnut trees, vines and a wide variety of pome and stone fruits are growing. The viticulture (grape varieties) is very important in the hilly areas of Paphos and Limassol and table grape varieties grow in the southwestern areas near the sea.

1.2.6

Irrigation water availability The statistical analysis of annual precipitation (1916-1974) indicates that "dry"

years (with rainfall of 390-470 mm) and “mostly dry” (with rainfall