MODELLING THE IMPACT OF EU MODELLING THE IMPACT OF EU ACCESSION ON - - PowerPoint PPT Presentation

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MODELLING THE IMPACT OF EU MODELLING THE IMPACT OF EU ACCESSION ON - - PowerPoint PPT Presentation

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MODELLING THE IMPACT OF EU MODELLING THE IMPACT OF EU ACCESSION ON AGRICULTURE ACCESSION ON AGRICULTURE Dissertation thesis Dissertation thesis in 9.1.9 Applied Mathematics in 9.1.9 Applied Mathematics Mgr. D A BARTO A BARTO

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SLIDE 1

COMENIUS UNIVERSITY BRATISLAVA

MODELLING THE IMPACT OF EU MODELLING THE IMPACT OF EU ACCESSION ON AGRICULTURE ACCESSION ON AGRICULTURE

Dissertation thesis Dissertation thesis

in 9.1.9 Applied Mathematics in 9.1.9 Applied Mathematics

  • Mgr. D
  • Mgr. DÁŠ

ÁŠA BARTO A BARTOŠ ŠOV OVÁ Á

Supervisor: Supervisor: Prof. Dr.

  • Prof. Dr. Ing

Ing. . Jarko Jarko Fidrmuc Fidrmuc

Comenius Comenius University Bratislava University Bratislava

Faculty of Mathematics, Physics and Informatics Faculty of Mathematics, Physics and Informatics

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Motivation Motivation

The CEECs – before accession – bilateral agreements, limited liberalization Access into the common market – competition, trade creation, trade diversion

¿¿¿ ¿¿¿ Are dynamic panel data models appropriate tools for modeling th Are dynamic panel data models appropriate tools for modeling this ??? is ???

Methods: CGEM Gravity panel data models Dynamics of trade

¿¿¿ ¿¿¿ Are agriculture products competitive enough to gain from libera Are agriculture products competitive enough to gain from liberalization ??? lization ???

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SLIDE 3

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Goals Goals

To analyze:

  • the impact of EU accession on agriculture trade (if and how much)
  • the influence of dynamics on agriculture trade

To formulate special dynamic gravity panel data model for import and export with agriculture commodities for accession countries

  • includes dynamics of trade and positives of gravity panel data models with detailed

structure of CGEM

  • avoid the common mistakes in gravity models (Baldwin’s gold, silver and bronze

medal mistakes) To compare several methods with each other and with bootsrap estimation

  • Fixed Effects, Hausman-Taylor (bootstraping)
  • Generalized Method of Movements - long-run effects
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SLIDE 4

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Material Material – – data data

Unique database (TRADEAG project) – import, export Panel dimension: 7 reporting countries and 10 partner regions => Slovakia, Czech Republic, Slovenia, Latvia, Lithuania, Bulgaria, Romania with each other, Poland, Hungary, Estonia, EU15, CIS, USA and OC – rest of the world (small cross-sectional dimension) Time dimension: 1996Q1-2005Q4 => Quarterly between 1996 and 2005 (relatively long time-series) Commodities: Meat (bovine, poultry, swine, total), Milk (cream, cheese and curd, total), Cereals, Oilseeds, Sugar, Total (m, x) GDP CPI EU dummy variable B - border and D - distance dummy variables used in the Hausman-Taylor estimation seas*

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SLIDE 5

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Total Total agri agri-

  • food trade

food trade IMPORT EXPORT

200 400 600 800 1000 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1

Milion Euro SK CZ SI

100 200 300 400

1996_Q1 1997_Q1 1998_Q1 1999_Q1 2000_Q1 2001_Q1 2002_Q1 2003_Q1 2004_Q1 2005_Q1

Milion Euro

LV LT

200 400 600 800 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milion Euro

BG RO

200 400 600 800 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milon Euro SK CZ SI

100 200 300 400 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milon Euro LV LT

100 200 300 400 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milion Euro

BG RO

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SLIDE 6

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Methods Methods

CGEM (Computable General Equilibrium Model)

  • includes detailed sectoral information
  • calibrated parameters

Gravity models

  • detailed geographic structure
  • partial model

Dynamics of trade Estimation method: Log-linear form Fixed effects and Hausman-Taylor Bootstraping (FE and HT) Generalized Method of Movement (long-run)

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

CGEM CGEM Computable General Equilibrium Model

Trade flows are related to trend of income and price on export and import market Defined complex model’s structure Includes detailed sectoral information Calibrated parameters Use results of gravity models as inputs (e.g. Keuschnigg and Kohler (1997, 2000, 2002) use gravity models to calibrate the impact of trade liberalization on trade cost such that the resulting trade increase are consistent with available estimates of trade potential from gravity models)

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Gravity models Gravity models

Estimate trade flows for several countries in specific period as a function of S and D in partner countries, transport and transaction costs and integration effect Trade flow is aggregated Detailed geographic structure (doesn’t allow complex analysis for individual sectors of economy) Partial model (Anderson and Van Wincoop (2001) derive gravity equation from general equilibrium model) Estimates use reduced form (parameters of initial model are overall estimated, e.g. as fixed or time effects )

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SLIDE 9

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Random Effects vs. Fixed Effects vs. Random Effects vs. Fixed Effects vs. Hausman Hausman-

  • Taylor

Taylor

Random Effects (RE)

  • estimates are efficient (maybe better)
  • enable to estimate parameters invariant in time (distance)
  • country RE are uncorrelated with other parameters (not satisfied in general – Hausman

test) Fixed Effects (FE)

  • enable to estimate parameters variant across the entities, not over time
  • can be used for >2 time observations for each entity (biased for high cross-sectional

dimension and low time dimension (according to Baltaggi(2001))) Hausman-Taylor (HT)

  • use combination of FE and RE (also correlated parameters)
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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Fixed Effects (FE) Model Specification Fixed Effects (FE) Model Specification

Dynamic models for import and export

  • denotes fixed effects

Domestic S – covered by , standard demand function – relative price effects yt - GDP, et - exchange rate, pt - price, EU - integration effect Bias – autocorrelation of dependent variable (because of lags), but limited (small cross-sectional and long time dimension)

t

θ

−

= + + + − − + +

hom e m hom e m it i t it 1 1 t 2 t it t it

m m y ( e p cpi ) EU α θ ρ β β γ ε

x it it x it it it t i it

EU cpi p y x x ε γ β β ρ θ α + + − − + + + =

−

) (

2 1 1

i

α

Within estimator Fixed effect model (appropriate if T > 15)

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SLIDE 11

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Results for panel Results for panel

Fixed Effect Model

IMPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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SLIDE 12

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Results for panel Results for panel

Fixed Effect Model

EXPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Hausman Hausman-

  • Taylor (HT) Model Specification

Taylor (HT) Model Specification

Dynamic models for import and export Exogenous variables D – distance dummy (time invariant) seas* - seasonal dummy (time variant) Endogenous variables B – border dummy (time variant) Used by e.g. Serlenga and Shin (2004)

  • in heterogeneous panels with common time-specific factors (Intra-EU trade)

−

= + + + − − + + + +

home m home m it i t it 1 1 t 2 t it t it

m m y (e p cpi ) EU B D α θ ρ β β γ ϕ φ ε

x x it i t it 1 1 it 2 it it it

x x y ( p cpi ) EU B D α θ ρ β β γ ϕ φ ε

−

= + + + − − + + + +

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SLIDE 14

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Results for panel Results for panel

Hausman-Taylor Model

IMPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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SLIDE 15

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Results for panel Results for panel

Hausman-Taylor Model

EXPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Results for panel Results for panel

Short comparison FE vs. HT

IMPORT EXPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Bootstraping Bootstraping

If the distribution is likely to be different from standard asymptotic distribution Simple regression in vector form M N random observations of (y, Z) to derive an estimate Many replications (say R) generate a sequence of bootstrap estimators ( ) Sample mean Sample variance

NT

y X u Z u = αι + β + = δ +

1 2 R

ˆ ˆ ˆ ... ˆ E[ ] R δ + δ + + δ δ = δ =

R r r r 1

ˆ ˆ ˆ ˆ ( )( )' Var[ ] R

=

δ − δ δ − δ δ =

1

ˆ δ

1 2 R

ˆ ˆ ˆ , , ..., δ δ δ

FE and HT 50 and 250 replications

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SLIDE 18

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Bootstraping Bootstraping results for panel results for panel

Short comparison FE vs. HT

IMPORT

the first value = bootstrap mean, the third value = bootstrap standard error t-statistics are in parentheses the bootstrap standard error *, **, *** denote significance at the 10, 5 and 1 per cent level

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Bootstraping Bootstraping results for panel results for panel

Short comparison FE vs. HT

EXPORT

the first value = bootstrap mean, the third value = bootstrap standard error t-statistics are in parentheses the bootstrap standard error *, **, *** denote significance at the 10, 5 and 1 per cent level

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Generalized Method of Movements (GMM) Generalized Method of Movements (GMM) Specification Specification

Dynamic models for import and export Elimination of fixed effects Autocorrelation of transformed errors => lagged dependent and independent variables are used as IV (Arellano and Bond (1991)) Less applicable because of small cross-sectional dimension => just for analyzing the stability of the results Long-run effects

m it home t m it home t it it

EU cpi p y m m ε γ β β ρ ∆ + + ∆ − ∆ − ∆ + ∆ = ∆

−

) (

2 1 1 x it it x it it it it

EU cpi p y x x ε γ β β ρ ∆ + + ∆ − ∆ − ∆ + ∆ = ∆

−

) (

2 1 1

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SLIDE 21

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Results for panel Results for panel

Dynamic Arellano-Bond Models

IMPORT

t-statistics are in parentheses ARM1 and ARM2 denote the Arrelano-Bond test that the average autocovariance in residuals of order 1 and 2 is 0 with H0 of no autocorrelation *, **, *** denote significance at the 10, 5 and 1 per cent level

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SLIDE 22

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Results for panel Results for panel

Dynamic Arellano-Bond Models

EXPORT

t-statistics are in parentheses ARM1 and ARM2 denote the Arrelano-Bond test that the average autocovariance in residuals of order 1 and 2 is 0 with H0 of no autocorrelation *, **, *** denote significance at the 10, 5 and 1 per cent level

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SLIDE 23

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

General Results General Results

The lagged exports and imports have a large influence on trade flows of agri-food commodities The income elasticities: imports – significant for all meat and milk commodities exports – significant only for cheese and sugar => saturated market The price elasticities are relatively high. Thus, price effects due to trade changes may have large effects on trade flows Bootstrap confirmed our estimations The results are largely confirmed by the GMM estimations

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Results on the EU enlargement Effects Results on the EU enlargement Effects

We found positive and significant EU enlargement effects on both imports and exports, which vary strongly between agricultural commodities The highest EU elasticities are found for sugar both for the imports (2.56) and exports (1.34) The import effects of the EU dominate only for sugar, oilseeds, cheese and milk The export effects of the EU are significant for all commodities except for meat of swine, cereals and oilseeds The net effects (x - m) – positive for all except sugar, cheese, cereals and oilseeds

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Conclusions Conclusions

Panel dynamic model is appropriate for the explanation of the agri-food trade during the period of pre-accession and post-accession to the EU We found important differences between individual commodities The lagged exports and imports have a large influence on trade flows of agri-food commodities Accession to the EU increased the new member states’ exports, has less impact on their import The new member states have gained significantly from liberalized access

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Possible Effects on Third Countries Possible Effects on Third Countries

In our sample, which includes mainly the reporting countries of the new member states

  • f the EU, we cannot estimate directly possible trade diversion effects

The EU effects cover both trade creation effects and trade diversion effects Nevertheless, the addition of Bulgaria and Romania did not change the results Since the EU effects dominate for the export, trade creation is expected to be more important

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Thank you for your attention Thank you for your attention

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Questions 1 Questions 1

It is theoretically correct to use gravity model for homogeneous products and under what conditions?

  • countries are specialized in special products (Feenstra and Deardorff (1998))
  • gravity models for homogeneous products (cereals, oilseeds,…) don’t go well with literature
  • commodity groups - considered as differentiated goods (cheese and milk products) – can be used

gravity models under new theory of international trade (increasing returns to scale in production) What is the reason of negative income elasticity especially for imports?

  • the negative elasticity is not significant
  • could it be caused because of Engel’s law (the higher income the lower share of spending for

foodstuff ) The EU dummy variable is always positive for imports and mostly positive for exports (except for cereals). Significant estimates of EU dummy are for exports of meat, milk, sugar and total agrarian trade. What is the main reason for this?

  • it could be explained by relatively high protectionist rates prior to EU accession as those products

are viewed as sensitive products in agricultural trade and characterized by high trade barriers

  • this is the main economic gain of thesis
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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Questions 2 Questions 2

  • What is the explanation for different income elasticity and negative income elasticity in import

equation?

  • see Questions 1
  • What is the explanation for positive distance elasticity in import equation?
  • the positive elasticity is not significant
  • because of data, we have fixed effects – (don’t know exactly what all they include)
  • significant for Meat of poultry - could it be caused because peripheral countries are concentrated on

agriculture (the centre of EU on industry)

  • Combination of CGEM and gravity model
  • CGEM
  • include complicated detailed structure of (agriculture) sector
  • the parameters are calibrated (could be calibrated by gravity model)
  • gravity model
  • known as partial models
  • trade is aggregated
  • Anderson and Van Wincoop derive gravity equation from CGEM (many initial

parameters are estimated in aggregated form – as part of fixed or time effects - reduced estimation)

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

International trade theories International trade theories

Classical theories Adam Smith - absolute advantage theory

  • 2 countries, 2 commodities, one-factor theory

David Ricardo - the theory of comparative advantage

  • bilateral trade is profitable for both of countries, also for country without absolute

advantage, absolute price, one-factor theory Neoclassical theories Bertil Ohlin - Heckscher-Ohlin theorem

  • 2 countries, 2 commodities, two-factor theory, constant economies of scale, country

exports commodity, which is produced by using more abundant factor Paul Samuelson - Stolper-Samuelson theorem

  • price balancing (with assumption H-O theorem, element of dynamics)

Paul Krugman

  • economies of scale and consumers’ preferences
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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Comparison of results for panel and Slovakia Comparison of results for panel and Slovakia

Fixed Effect Model

IMPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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SLIDE 32

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Comparison of results for panel and Slovakia Comparison of results for panel and Slovakia

Fixed Effect Model

EXPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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SLIDE 33

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Comparison of results for panel and Czech republic Comparison of results for panel and Czech republic

Fixed Effect Model

IMPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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SLIDE 34

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Comparison of results for panel and Czech republic Comparison of results for panel and Czech republic

Fixed Effect Model

EXPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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SLIDE 35

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Comparison of results for panel and Latvia Comparison of results for panel and Latvia

Fixed Effect Model

IMPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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SLIDE 36

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Comparison of results for panel and Latvia Comparison of results for panel and Latvia

Fixed Effect Model

EXPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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SLIDE 37

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Comparison of results for panel and Lithuania Comparison of results for panel and Lithuania

Fixed Effect Model

IMPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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SLIDE 38

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Comparison of results for panel and Lithuania Comparison of results for panel and Lithuania

Fixed Effect Model

EXPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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SLIDE 39

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Baldwin Baldwin’ ’s medal mistakes s medal mistakes

Gold

  • the work without multilateral resistance factor (only time-invariant variables, without

cross-section) Silver

  • the work with logarithm of average of the sum of import and export

instead of average of logarithm of import and export Bronze

  • the use of real trade flows instead of nominal values => biases via spurious

correlations, because of global trends in inflation rates

m x 2

lo g ( )

+

lo g ( m ) lo g ( x ) 2 +

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SLIDE 40

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Results for Slovakia Results for Slovakia

Fixed Effect Model

IMPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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SLIDE 41

September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Results for Slovakia Results for Slovakia

Fixed Effect Model

EXPORT

t-statistics are in parentheses *, **, *** denote significance at the 10, 5 and 1 per cent level

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Total milk trade Total milk trade IMPORT EXPORT

6 12 18 24 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milióny BG RO 20 40 60 80 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milióny SK CZ SI

6 12 18 24 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milióny LV LT 50 100 150 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milióny SK CZ SI

15 30 45 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milióny LV LT 6 12 18 24 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milióny BG RO

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Total meat trade Total meat trade IMPORT EXPORT

22 44 66 1996_Q1 1997_Q1 1998_Q1 1999_Q1 2000_Q1 2001_Q1 2002_Q1 2003_Q1 2004_Q1 2005_Q1 Milióny SK CZ SI 5 10 15 20 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milióny LV LT 50 100 150 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milióny BG RO 10 20 30 1996_Q1 1997_Q1 1998_Q1 1999_Q1 2000_Q1 2001_Q1 2002_Q1 2003_Q1 2004_Q1 2005_Q1 Milióny SK CZ SI 3 6 9 12 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milióny LV LT 21 42 63 1996_Q1 1997_Q1 1998_Q1 1999_Q1 2000_Q1 2001_Q1 2002_Q1 2003_Q1 2004_Q1 2005_Q1 Milióny BG RO

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September 21, 2009 COMENIUS UNIVERSITY BRATISLAVA

Total sugar trade Total sugar trade IMPORT EXPORT

10 20 30 40 1996_Q1 1997_Q1 1998_Q1 1999_Q1 2000_Q1 2001_Q1 2002_Q1 2003_Q1 2004_Q1 2005_Q1 Milióny SK CZ SI 100 200 300 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milióny BG RO 15 30 45 1996_Q1 1997_Q1 1998_Q 1 1999_Q 1 2000_Q 1 2001_Q 1 2002_Q 1 2003_Q1 2004_Q1 2005_Q 1 Milióny LV LT 30 60 90 120 1996_Q1 1997_Q1 1998_Q1 1999_Q1 2000_Q1 2001_Q1 2002_Q1 2003_Q1 2004_Q1 2005_Q1 Milióny SK CZ SI 15 30 45 60 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milióny LV LT 30 60 90 120 1 9 9 6 _ Q 1 1 9 9 7 _ Q 1 1 9 9 8 _ Q 1 1 9 9 9 _ Q 1 2 _ Q 1 2 1 _ Q 1 2 2 _ Q 1 2 3 _ Q 1 2 4 _ Q 1 2 5 _ Q 1 Milióny BG RO