UPPSALA UNIVERSITY Department of Economics B.Sc. Thesis
Spring 2006
Author: Vanda Czifra
Supervisors: Monica Campos Pál Juhász
The Competitiveness of the Hungarian Agri-Food Sector
– From Transition to Accession
Abstract
The Hungarian agricultural sector has undergone substantial changes between 1992 and 2003, which was a period of transformation from command economy to an EU-conform market economy. The question is whether the Hungarian agricultural sector was able to keep its competitiveness despite the extensive transformation. The aim of this paper is to measure the competitiveness of Hungarian agri-food product groups in relation to the ones of the EU-15 during the transformation period. Results indicate that the competitiveness, measured by revealed comparative advantage (RCA), of the studied agri-food product groups has not changed considerably. The strong position of the Hungarian agricultural sector could be maintained because its competitiveness is based on factor endowments, which are not affected by changes of economic policy. The observed moderate fluctuations of competitiveness can be derived to trade concession changes.
Keywords: Competitiveness, comparative advantage, revealed comparative advantage,
agri-food trade, Hungary, EU-15, transformation process
Acknowledgements
First and foremost, I would like to thank my supervisor Monica Campos for sharing her time and knowledge, and for giving me constructive criticism and valuable comments, which helped the development of this thesis.
Many people in Budapest provided great help for my research. My field supervisor Pál Juhász gave me helpful comments throughout the process of writing. I especially thank Imre Fertő for helping me to find the relevant dataset and for sending me his latest publications. I am also grateful to my former teacher Tamás Réti who found the time to read and comment my thesis.
Special thanks to the librarians at the AKI library who gladly and efficiently helped me to find relevant and valuable information for my research.
In Sweden, I would like to thank Ildikó Asztalos Morell, who before my departure to Hungary gave me helpful background information and showed me where to find statistical and agricultural libraries in Budapest. Also many thanks to Johan Viklund, who gave valuable comments on this thesis.
Last but not least, I would like to thank my sisters Villő (for finding accommodation in Budapest and for helping me with formal Hungarian letters) and Dóra (for helping me with the language in this thesis). Without you I am nothing.
This thesis is dedicated to the memory of my grandparents, who have lived their flourishing
lives on the Hungarian countryside, and especially to the memory of my grandmother who
passed away while this thesis was written.
List of Abbreviations
AA Association Agreements
B Balassa (-index)
BRC Bilateral Resource Costs CAP Common Agricultural Policy
CMEA Council of Mutual Economic Assistance CMS Constant Market Share
DRC Domestic Resource Cost
EU European Union
EU-15 European Union consisting of 15 member states FDI Foreign Direct Investment
GATT General Agreement on Tariffs and Trade GDP Gross National Product
GL Grubel and Lloyd (-index)
GVA Gross value-added
H-O Heckscher-Ohlin (theory)
lnRXA The logarithm of Relative Export Advantage NEM New Economic Mechanism
PRC Private Resource Costs
RC Revealed Competitiveness
RCA Revealed Comparative Advantage RMA Relative Import Advantage
RTA Relative Trade Advantage RXA Relative Export Advantage
SITC Standard International Trade Classification UAA Utilized Agricultural Area
UNCTAD United Nations Conference on Trade and Development
USD United States Dollars
Table of Contents Abstract
Acknowledgements List of Abbreviations
1. Introduction………...6
2. Previous Research……….7
3. Economic Theory………..…9
3.1 International Trade Theory and Comparative Advantage………..9
3.2 Competitiveness………11
4. The Hungarian Agriculture and the European Union………....12
4.1 The Transformation of the Hungarian Agriculture after Communism………...12
4.2 Hungary’s Agri-Food Trade Relations with the EU…...13
5. Measuring Competitiveness……….………..14
5.1 Different Methods for Measuring Competitiveness..………15
5.2 Revealed Comparative Advantage………18
5.2.1 Different Indices of the RCA method……….18
5.2.2 The Interpretation of RCA Indices and Consistency Tests………19
5.2.3 Stability and Dynamics of the RCA Indices………..20
6. Empirical Findings……….21
6.1 Hungarian Agricultural Competitiveness and Trade Relations………21
6.1.1 The Competitive Situation of the Hungarian Agricultural Sector……….…21
6.1.2 Agri-Food Trade between Hungary and the EU-15………..23
6.2 Competitiveness of the Hungarian Agri-Food Sector with Different RCA Indices….24 6.2.1 Presenting the Data………...24
6.2.2 Results from the RCA Method………24
6.3 Consistency and Stability of the Results………...25
6.3.1 Consistency Tests and Interpretation……….……25
6.3.2 Changes of Competitiveness and Stability Tests………27
7. Conclusions and Discussion………....29
References
Appendices
1. Introduction
The Hungarian economy has undergone substantial changes under the short period of the 1990’s. The transformation process of Hungary – from being a communist satellite-state with command economy, to today’s membership in the European Union (EU) with a growing market economy – took roughly only a decade. Hungary’s transformation was a process that started with a transition to market economy from 1989, and ended with the accession to the EU in 2004.
The agricultural sector is traditionally important for the Hungarian economy, due to good natural conditions and high export shares.
1The question is whether the Hungarian agricultural sector was able to keep its strong position despite the extensive transformation. Two facts make the study of Hungary’s agricultural development in relation to the EU very interesting (see e.g. Fertő, 2004): (1) agricultural policy has a substantial weight within the EU and (2) the agri-food trade between these regions is considerable.
A clear distinction between agri-food sector and agricultural sector is made in this paper.
Agricultural sector is defined here as a broad concept that concerns all aspects of agriculture, including rural development, social aspects, environment, etc. On the other hand, agri-food sector denotes food production (both raw materials, including live animals, and food processing) and trade with these products.
Although competitiveness is a broad concept, it is widely used in both political and economic debate. This paper’s analysis uses competitiveness in the sense of ‘underlying competitiveness’, measured by revealed comparative advantage. Thus, competitiveness serves as an important tool to understand and to analyse the effects of the transformation process on Hungary’s agri-food sector in relation to the EU.
Given the question whether the Hungarian agricultural sector was able to keep its strong position despite the extensive transformation, the aim of this paper is to study the development of the Hungarian agricultural sector during the critical period of 1992 to 2003.
The objective is to answer the question how the competitiveness of the Hungarian agri-food product groups in relation to the EU-15
2has changed during this period.
1 The share of Hungarian agricultural export is 7.5% of total Hungarian exports (Eurostat, 2003), for more details see part 6.1.
2 The EU-15 consists of the following member states: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden and United Kingdom. For a methodological discussion concerning the composition of the EU during the period of study, see part 6.2.1
The applied methodology for measuring competitiveness is the Revealed Comparative Advantage (RCA) method, selected among several methods and extended for agri-food studies. The interpretation of the results is ensured by consistency tests after Ballance et al (1987), while the changes are studied with stability tests after Hoeckman and Djankov (1997) and Hinloopen and van Marrewijk (2001). This methodology was previously used by Fertő (and Hubbard 2002, 2004) to assess the competitiveness of the Hungarian agri-food sector between 1990 and 1998, but the assessment in this paper is extended until 2003.
Most literature on the transformation of the Hungarian agricultural sector, and also on the competitiveness of the Hungarian agri-food sector, (e.g. Ash, 1992; Kiss, 1993; Tóth, 2002) has only a descriptive character. Another problem is that while there are quantitative studies of Hungarian agri-food competitiveness, they give no qualitative explanation of the results.
The examination of possible changes of Hungarian agri-food competitiveness and their explanation is also neglected. Therefore, this paper tries to link qualitative explanation to the quantitative results of the transformation period, and aspires to pay attention to the changes in Hungary’s agri-food competitiveness.
The structure of this paper is the following: Section 2 presents previous research conducted in this field, and this paper’s response to them. Section 3 provides the theoretical background, followed by section 4 which is a presentation of the Hungarian agriculture’s transformation and the agri-food trade between Hungary and the EU-15. Section 5 presents and compares different methods of measuring competitiveness. Section 6 presents and analyses the results of an assessment of the competitiveness of the Hungarian agri-food sector with the chosen measures. Finally, section 7 provides a concluding summary with discussions.
2. Previous Research
Some previous research on the subject of this paper can serve as a starting point:
Imre Fertő and Lionel Hubbard (2002) examined the competitiveness of Hungarian agri-food sector in relation to the EU, based on four RCA indices
3, for the period 1992 to 1998. Their results indicate that Hungary had revealed comparative advantage for live animals, meat, cereals, vegetables and fruits, sugars, beverages, oil seeds, cork and wood, and animal and vegetable oils and fats. The authors also examined the consistency and the stability of the results and concluded that despite significant changes in Hungarian agriculture
3 The applied indices of the RCA method were the B-index, RXA, RTA and RC. For definitions see section 5.
during the transformation, the pattern of comparative advantage in relation to the EU has remained stable. Fertő has developed these findings from both a methodological point of view (2005) and from a broader trade policy perspective (2004).
Banse et al. (1999) studied the competitiveness of the Hungarian agriculture by applying various price and cost indices
4for the period 1990 to 1996. Their results indicate that cereal production (wheat, barley, maize) was internationally more competitive than breeding of live animals (pig and cattle). Oil seeds (sunflower and rapeseed) and cattle had the highest competitiveness in relation to the EU.
Gorton et al. (2004) studied the international competitiveness of Hungarian agriculture by estimating DRC ratios between 2000 and 2002, and compared that to estimates for the years 1994 to 1996. Their results show that Hungary’s international competitiveness in cereal production has decreased compared to the mid-1990s, however, meat (pork and poultry) production became competitive while dairy production remained uncompetitive.
Bozsik (2004) has studied the competitiveness of Hungarian agri-food products on the EU market from 1996 to 2002, with price indices, GL-index and RCA indices (up to 1998). His results suggest that Hungarian production of live animals, meat, cereals, vegetables and fruits, honey and alcoholic beverages are competitive on the EU market.
In summary, previous research indicate that Hungarian production of live animals, meat, cereals, and vegetables and fruits is competitive. Although cereal production is more competitive than that of live animals, its competitiveness is decreasing.
As a response to previous studies, this paper assessed the competitiveness of Hungarian agri-food products vis-à-vis the EU-15 with RCA indices during a longer time period (1992- 2003). It has been found that in addition to live animals, meat, cereals, and vegetables and fruits, oil seeds are also competitive. This paper also aspires to link qualitative explanation to the quantitative results, and therefore suggests that competitiveness is based on the fact that the production of these product groups is land intensive, and that Hungary is relatively well endowed with agricultural land. Concerning cereals and live animals, this paper has found that it is the breeding of live animals that is more competitive than cereal production.
5The decreasing competitiveness of cereals was only a temporary drop between 1992 and 1996.
4 The applied indices were Domestic Resource Costs (DRC), Private Resource Costs (PRC) and Bilateral Resource Costs (BRC). The first two indices used the world as reference market, while the last one used the EU.
5 The difference can be due to the use of different methods and reference markets. Hungarian cereal production is internationally cheap (which the previous studies seized) but production of live animals show greater underlying competitiveness in relation to the EU-15, which is shown by this paper. However, the consistency tests in this paper also show that it is not advisable to rank the product groups based on competitiveness.
3. Economic Theory
This section presents the theoretical framework for studying the competitiveness of the Hungarian agri-food sector. The concept of competitiveness is closely related to international trade and comparative advantage. Therefore, the first part of this section provides the core of classical trade theory that concerns comparative advantage. This is followed by the theory of intra-industry trade, because agri-food trade is increasingly of an intra-industry nature (Fertő, 2004 p.12). The second part of this section defines competitiveness and links it to international trade theory and the concept of comparative advantage.
3.1 International Trade Theory and Comparative Advantage
The concept of comparative advantage within international trade theory was first developed by David Ricardo in 1817. He presented the Ricardian model to describe the patterns of international trade in “On the Principles of Political Economy and Taxation”.
6The Ricardian model includes two countries and two goods where each country has only one factor input, which is labour. Therefore, the autarky price, the price before trade, is determined by labour productivity. A country will have comparative advantage in producing a good if the autarky price is lower compared to that of other goods produced in the country and in other countries. A lower autarky price is a result from the fact that the country’s labour force has higher productivity, i.e. produces more efficiently, than the other country’s labour force. It is also assumed that countries specialise their production and export the good in which they have comparative advantage.
The idea of the Ricardian model is that countries will benefit from international trade if each country produces and exports the goods in which they have comparative advantage. It is therefore not necessary for a country to posses absolute advantage in producing a good, i.e. to be able to produce a good with fewer inputs than any other country, to benefit from trade.
In sum, according to the Ricardian model, a country that has higher labour productivity than other countries in producing a good, has a lower autarky price for that good and therefore has comparative advantage in producing it.
Heckscher and Ohlin have further developed the Ricardian model in the early 20
thcentury, to better fit reality. The assumption of only one factor input was dropped and replaced by the assumption that there are two factors of production: for example land and labour. It is also
6The account of the Ricardian model relies here on the works of Husted and Melvin (1998) and Krugman and Obstfeld (2000).
assumed that the two goods that are produced are either land or labour intensive, i.e. their production relies more on one of the factors. A great importance is given to the countries’
factor endowments in order to explain comparative advantage. As Husted and Melvin (1998, p.100) explains it: “A country will have comparative advantage in, and therefore will export, that good whose production is relatively intensive in the factor with which that country is relatively well endowed”. In sum, according to the Heckscher-Ohlin (H-O) theory, a country’s comparative advantage is decided by its factor endowments.
A simple illustration of the H-O theory presents itself from the agri-food sector. It is intuitive that a country that is relatively well endowed with land has a comparative advantage in producing agri-food products, given that their production is land intensive.
The classical trade theory has been criticized for not being able to describe trade patterns in the real world. It has been found that trade within the same industry between countries is an important part of total world trade (Grubel and Lloyd, 1975). This kind of trade is called intra-industry trade and it is a plausible approach to explain agri-food trade (Fertő, 2004 p.12), since both represent trade with goods within one single industry.
Intra-industry trade can be explained by economies of scale and by domestic demand for differentiated products (Krugman and Obstfeld, 2000 p.137). These two arguments can be exemplified from agri-food trade. A country can both export and import agri-food products, either because it can produce a particular product (e.g. wheat) on large scale or because it wants to satisfy domestic demand on a differentiated product (e.g. wholemeal muesli). These intra-industry trade patterns may therefore not reflect comparative advantage, based on labour productivity or factor endowments, and therefore it seems to contradict the models of classical trade theory. However, this does not mean that the classical trade theory of comparative advantage is not valid.
Husted and Melvin (1998, p.142) point out that the aggregated trade statistics are not
constructed in a way that take labour productivity or factor endowments into consideration,
and therefore, production methods and factor requirements diverge within industries and
within product groups on the aggregate level. For example, even if wheat and wholemeal
muesli are within the same aggregated product group, the production of wheat is less labour
intensive than that of wholemeal muesli. Consequently, if the aggregated statistics would take
labour productivity and factor endowments into consideration, many of the intra-industry
trade patterns could be explained by comparative advantage based on the classical theory.
3.2 Competitiveness
The concept of competitiveness is complex and the notion is widely used in both economic research and public debate. It is therefore necessary to define a meaning of competitiveness.
Despite the wide use of the concept, there is a consensus that competitiveness can be analysed on three different levels (e.g. Fertő, 2004; Hammarlund, 2004; Palánkai, 2004). Firstly, competitiveness can be observed on the macroeconomic level, where nations’ competitiveness is studied. Secondly, it is present on the mesoeconomic level, where the competitiveness of industries and sectors, like the agri-food sector, is studied. Thirdly, competitiveness is observed on the microeconomic level, where the competitiveness of firms is examined.
Although there are diverging ideas of how competitiveness can be observed on the three levels (Fertő, 2004, p.51-52; Hammarlund, 2004, p.5, 10; Palánkai, 2004, p.305-306), the concept of comparative advantage can be linked to all the three levels of observation. On the macroeconomic level, competitiveness can be observed as economic growth, as other macro-indicators, or as comparative advantage due to price differences. Competitiveness on the mesoeconomic level is observed as the comparative advantage of an industry of a country, and also as the ability of an industry to gain and maintain a share of domestic and export markets. Finally, there are several ways of studying competitiveness on the microeconomic level. These are the firm’s cost efficiency, the quality of its products, ability to meet demand, ability to produce on large scale and the possession of comparative advantage in production.
Two important differences between comparative advantage and competitiveness are worth highlighting (Fertő, 2004, p. 51). Firstly, competitiveness can be affected by changes in macroeconomic variables, whereas comparative advantage is structural in nature. Secondly, government support and protection affect competitiveness, but not comparative advantage.
Moreover, Vollrath (1989) notes that government intervention and competitiveness tend to be inversely related. Thus, as an example, international competitiveness can change due to exchange rate fluctuations or regulated prices, but comparative advantage is still unaffected since it is based on factor endowments or on other non-macroeconomic variables, depending on the underlying theory. The fact that competitiveness is more sensitive to macroeconomic changes and government policies shows that comparative advantage denotes a kind of underlying factor of competitiveness, which is more important, but also harder to observe.
Finally, it has to be noted that both competitiveness and comparative advantage are
relative concepts. They are expressed in relation to a reference market, like in our case: the
competitiveness of the Hungarian agri-food sector is evaluated against that of the EU-15.
4. The Hungarian Agriculture and the European Union
For a better understanding of the transformation process during the period of study, this section gives an introduction to the Hungarian agricultural and agri-food sector. Hungary’s agricultural transformation is described in the first part, followed by an account of the Hungarian agri-food trade relations with the EU-15 in the second part.
4.1 The Transformation of the Hungarian Agriculture after Communism
Hungary had a centrally planned economy that was imposed from the communist take-over in 1948, until the liberalizations in the 1960’s. In order to understand the scope of the agricultural transformation, some general features of the command economy are described, based on Kornai (1992).
The command economy meant an extremely centralized economy where supply and demand were replaced by central planning. The price mechanism was also replaced by quantitative plan targets or centrally set prices. The system failed to work efficiently and it affected agriculture negatively by creating shortages, distorted prices and low quality products. Agriculture was collectivized, meaning that private ownership of farms and land was highly restricted and farmers were forced to join the state owned cooperatives, called collectives. Agricultural prices were considerably depressed, since low food prices were considered as part of the social policy. Moreover, foreign trade was almost solely conducted within the CMEA
7and it was restricted in other respects because of the aim for self-sufficiency within the eastern block. Trade had the characteristic of barter exchanges and was accounted in ruble that had little connection to the actual trade flows.
The liberalizations of the 1960’s are called New Economic Mechanism (NEM). The NEM included, among other things, indicative planning based on market indicators and more market oriented prices. These early liberalizations, together with high incomes, more incentives and a prioritized position of agriculture, contributed to the Hungarian agriculture’s good situation (Juhász, 2001) compared to other Central and Eastern European economies.
Therefore, the Hungarian agriculture had a head start after the collapse of the communist system in 1989, when an extensive transformation had to be confronted with. Three main
7 The Council of Mutual Economic Assistance (CMEA), also called Comecon, was an economic and trade organisation of communist states, 1949-1991. The final members were: Bulgaria, Cuba, Czechoslovakia, East Germany, Hungary, Mongolia, Poland, Romania, Soviet Union and Vietnam.
areas of reform can be distinguished within the agricultural transformation from 1989 (Ash, 1992; Ferenczi, 1996; Buckwell and Tangermann, 1997):
The first area concerned price and market liberalization. Prices were almost fully liberalized already at the end of the 1980’s, but it was the Price Act of 1991 that completed this process. After this short-term introductory price control, all agricultural prices were set according to international price setting. In connection to the Agricultural Market Regulation Act of 1993, the market interventions were restructured to fit intervention schemes of Western European agricultural markets. The second area of agricultural reform was the transformation of state owned lands and collectives to private owned lands and farms. It was mainly done by different restitution programmes, outlined by the Compensation Law of 1991 and the Cooperative Transition Law of 1992. The idea was that individuals whose property was confiscated or collectivized under communism received vouchers, worth a share of the estimated value of their lost property. These vouchers did not offer full compensation, but with additional financial sources, they could be used to buy back the lost property. The third area of transformation concerned the foreign trade. The liberalization of foreign trade with non-CMEA countries was completed already in 1991. An initial increase in export subsidies and non-tariff barriers had to be cut down after 1995, when the GATT Agreement on Agriculture was implemented in Hungary.
It is impossible to determine when the transformation ended. Although the last law concerning agricultural land reform was adopted in 1994, the transformation could not be accomplished immediately after the adoption. A clear sign of a completed transformation process is the EU-accession in 2004.
4.2 Hungary’s Agri-Food Trade Relations with the EU
After the collapse of the CMEA markets for agri-food products in 1989, Hungary was forced to restructure its foreign trade and turn to the Western European markets. Hungary negotiated several trade agreements in the beginning of the 1990’s, and one of these was the Association Agreements (AA) with the EU that entered into force in 1992. The agri-food trade relations with the EU during the period of study were highly shaped by this agreement.
The AA had a broad content with political, legal and economic aspects. The aim was to
establish a free trade area by 2000, but the liberalization of trade was asymmetric. This meant
that it was only the EU-15 that had to open its markets during the first five years, whereas
Hungary could wait until 1995 to remove its trade barriers. No free trade was envisaged for agricultural goods, but preferential export quotas were agreed upon for certain Hungarian agri-food products
8, which have played an important role in the Hungarian market (Kiss, 1993, p. 80). Even though the quotas were restrictive; Hungary was not able to fully utilise its quotas. The import concessions were however overfullfilled, which caused a decrease in the positive agricultural trade balance vis-à-vis the EU-15. (ibid, p. 89) According to Kiss (ibid, p.87), the reasons for the initial low export of Hungarian agri-food products
9were not the low quotas, but the limited competitiveness which resulted from low quality, increased production costs and low level of export promotion.
The 1992-93 reorganization of the Common Agricultural Policy (CAP) of the EU did also affect the agri-food trade with Hungary. The well known protectionist features of the CAP (see for example: Palánkai, 2004; Weyerbrock, 1998; Tóth, 2002) had to be reformed at the beginning of the AA period. This was due to the GATT Uruguay Round where it was decided that the EU has to abolish its variable levies and reduce its internal subsidies, export subsidies, and its tariff levels (Palánkai, 2004, p.111). Hungary was also a signatory of the Uruguay Round Agreements and as a result, the agri-food trade with the EU-15 was notably liberalized after 1994. The internal measures of the CAP reforms included the reduction of subsidies and the guaranteed price supports for agri-food products. Kiss (1993, p. 90) concludes therefore that “the CAP reform provides some export possibilities for Hungarian cereal, oil seed and fodder producers
10”.
5. Measuring Competitiveness
This methodological section discusses different measures of competitiveness. The first part compares different methods and their measures that are frequently used for assessing the competitiveness of agri-food sectors. The method that shows to be the most suitable for this study, is chosen and developed in the second part. This second part consists of extensions of the chosen method, together with tests that asses the consistency and the stability of the method’s measures.
8 The following products had preferential quotas: beef, sheep, chicken, pork, goose, duck, game, live horse, sausages, cheese, paprika and wheat. For more details see Kiss, 1993
9 For a statistical presentation of Hungarian agri-food trade with the EU during the period of study, see part 5.2.
10 Kiss (1993, p.90) defines cereals as: “wheat, rye, barley, rice, etc”, oil seeds as: “soy, rape, sunflower”.
5.1 Different Methods for Measuring Competitiveness
There is an abundance of different methods for measuring competitiveness on the mesoeconomic level. Still, there is no consensus about which method should be preferred.
Five frequently used quantitative methods
11can be distinguished for assessing the competitiveness of agri-food sectors. This presentation does only aim for giving an intuitive understanding of the measures, which are specified as the basic versions of the existing elaborate methods.
Simple economic indicators are widely used in both descriptive (e.g. Zawojska, 2002; Kiss, 1993) and analytic studies (e.g. Kaspersson et al 2002; Fertő, 2004) that assess the competitiveness of agri-food sectors. The most frequently used indicators in this field are:
various agricultural export and import data, utilized agricultural area (UAA), farm size, persons employed in agriculture and share of agricultural production of GDP. These provide an overview of the competitiveness of agri-food sectors in comparison to other sectors or countries of interest.
The Constant Market Share (CMS) model is a method that has been frequently used during the last decade to analyze agricultural trade (e.g. Ahmadi-Esfahani, 1995; Chen and Duan, 2000; Fertő 2004). The idea behind the model is that given the same level of competitiveness, an industry’s market share should remain constant. Therefore, a change in export should be caused by a change of competitiveness, among other factors. The model distinguishes three components of a change in exports
12:
Δq = S
0ΔQ + ΔSQ
0+ ΔSΔQ (1) Change in exports = structural effect + residual + second-order effect
where q is the particular country’s exports, S is the country’s share of the reference market and Q is the export of the reference country, Δ denotes a change in the variable over time and the superscript 0 denotes the base year. The residual effect can be interpreted as the effect of the change in competitiveness. This model has different and more detailed specifications that are used for empirical estimations (see Fertő, 2004).
The Grubel and Lloyd (GL) index is the measure that has gained most economic acceptance among the methods for measuring intra-industry trade. The idea is that if the share of export
11 For qualitative methods for assessing competitiveness in agriculture, please refer to Pitts and Lagnevik (1998).
12 For a formal derivation of equation 1, please refer to Ahmadi-Esfahani (1995).
and import of a product group have approximately the same size, then the intra-industry trade is high and the product group is competitive. This measure suits best for analysis on the product level (Hammarlund, 2004). It has different specifications but the original formula offered by Grubel and Lloyd (1975) is the following:
( )
∑
∑
=
=
+
−
−
=
nj j j
n j
j j
M X
M X GL
1
1
1(2)
where X
jand M
jare the values of exports and imports respectively of product group j of a particular country. The index calculates the relationship between the absolute value of the net exports and the total trade flow of a product group of a country. The GL varies between 0 and 1, and the product group is competitive if GL is close to 1.
A lot of effort has been put into measuring comparative advantage according to the classical trade theory. The primary problem is that relative prices under autarky are not observable. For that reason, Balassa proposed (1965) a method where only the revealed comparative advantage should be measured. Revealed comparative advantage (RCA) can therefore be calculated based on actual trade flows. The original RCA index, called Balassa (B) -index, is defined as follows (Balassa, 1967, p. 205):
nt nj
it ij
X X
X B X
/
= / (3)
where X represents exports, i is a country, j is a product, t represents all export products and n represents all the countries that are trade partners. Thus the B-index measures a country’s exports of a product relative to its total exports and to the corresponding exports of a set of countries, e.g. the EU. A comparative advantage is “revealed” for the particular country and product if B>1, while other results denote a comparative disadvantage. This index and its different versions (see Vollrath, 1991) have been used for measuring agri-food competitiveness by for example Hammarlund (2004) and Fertő and Hubbard (2002).
The final commonly used measure for competitiveness is the domestic resource cost (DRC) ratio, which concentrates on cost/price competitiveness. It has been used for measuring competitiveness of agri-food products by for example Gorton et al (2004), Banse et al (1999), and Kaspersson et al (2002). Gorton et al (2004, p. 2) explain the DRC ratio as a variable that
“compares the opportunity costs of domestic production to the value added it generates at
international prices”. As we can see from its formula, the calculation of the DRC of product i requires detailed information of production technology (a
ij), shadow prices for domestic resources (V
jS) and international prices (P
S) of traded inputs (j) and outputs (i):
∑
∑
= +
=
−
=
kj
S j ij S
i n k j
S j ij i
P a P
V a DRC
1
1
(4)
The numerator is therefore the sum of costs of using domestic resources and the denominator is the value-added in international prices.
13The production of product i is internationally competitive if DRC is smaller than one.
As the competitiveness of the Hungarian agri-food sector is of interest, there are some important and desirable characteristics, based on previously discussed theories, which the choice of competitiveness measure should be based on. Firstly, the measure should best suit for analysis on the mesoeconomic level. Secondly, since competitiveness is a relative concept, the measure should include data of a reference market as well. Thirdly, as comparative advantage denotes an underlying factor of competitiveness and is therefore more desirable to determine, it is an advantage if the measure includes underlying, non-price factors. Fourthly, the measure’s insensitiveness to government policies is also desirable, in order to measure the underlying factors of competitiveness. Finally, the measure should be insensitive to aggregated data, since it is the only data available. The listed characteristics for each of the methods are summarized in Table 1, to ease the choice of measure/method.
Table 1: Desirable characteristics of competitiveness measures and methods Characteristic Economic
Indicators CMS GL RCA DRC
Best suited for analysis on the
mesoeconomic level 3 3 3
Relative measure - the reference
market is taken into consideration 3 3
Non-price determinants or underlying
factors are considered 3 3 3
Insensitive to government policies 3 3
Insensitive to aggregated data 3 3 3
Hence, the RCA method satisfies most of the requirements and is therefore chosen as the method for this investigation. It is also the only method that clearly links to classical trade
13 For detailed explanation of the DRC ratio, see Gorton et al (2004), Banse et al (1999), or Kaspersson et al (2002).
theory. However, some simple economic indicators will also be presented for the Hungarian and the European agriculture, in order to give a good overview of the competitive situation.
5.2 Revealed Comparative Advantage
5.2.1 Different Indices of the RCA Method
Now that the RCA method is chosen, it will be closely examined and extended. From now on, competitiveness will denote the ‘underlying competitiveness’, measured by comparative advantage. Consequently, if a product is described as competitive, it means that it has a revealed comparative advantage.
The main shortcoming of the original RCA measure (the B-index) is that it is sensitive to government policies. Agricultural trade is especially subject to protectionist government interventions and therefore, Vollrath (1989) offered three alternative RCA measures after examining agricultural trade.
Relative Trade Advantage (RTA) is offered as the first alternative RCA measure by Vollrath. It is the difference between Relative Export Advantage (RXA), which equals to the B-index
14, and Relative Import Advantage (RMA), which is RXA’s counterpart concerning imports:
RMA RXA
RTA = − (5)
where
nt nj
it ij
X X
X RXA X
/
= / and
nt nj
it ij
M M
M RMA M
/
= /
M represents net imports and the subscripts are defined as for the B-index, in (3). Thus,
nt nj
it ij nt nj
it ij
M M
M M X
X X RTA X
/ / /
/ −
= (5.1)
The second alternative RCA measure offered by Vollrath, is the logarithm of the relative export advantage (LnRXA):
⎟ ⎟
⎠
⎞
⎜ ⎜
⎝
= ⎛
nt nj
it ij
X X
X RXA X
/ ln /
ln (6)
14 It is important to note some differences between Vollrath’s RXA and the B-index. RXA eliminates double-counting: the studied country and product are not part of the reference; and it is a global measure: the reference includes all countries and commodities. In this paper, the set of countries (n) is restricted to the EU-15, whereas the set of commodities (t) refers to all agri-food trade. Double-counting is eliminated in the sense that Hungary is not part of the EU-15.
Finally, Revealed Competitiveness (RC) is offered by Vollrath as the third alternative RCA measure, which is a logarithmic form of RTA:
⎟ ⎟
⎠
⎞
⎜ ⎜
⎝
− ⎛
⎟ ⎟
⎠
⎞
⎜ ⎜
⎝
= ⎛
−
=
nt nj
it ij nt
nj it ij
M M
M M X
X X RMA X
RXA
RC /
ln / /
ln / ln
ln (7)
The motivation for the logarithmic forms is that they become symmetric through the origin, which is desirable for further use of the index values, such as regression analysis. If the three measures have positive values, it can be interpreted as a revealed comparative advantage for the product of study.
Coming back to the problem of sensitivity to government interventions, Vollrath (1991) recommends the use of the B-index and lnRXA in preference to other RCA measures. The reason for this is the fact that B and lnRXA do not include imports, which tend to be more affected by government interventions (i.e. protectionist measures) than exports. However, as it was mentioned in part 4.2, the Hungarian agri-food exports to the EU under the period of study were highly affected by government interventions.
On the other hand, if any of the indices show comparative advantage for a product, it suggests that this product would be even more competitive if markets were open. This comes from the fact that government intervention and competitiveness tend to be inversely related (Vollrath, 1989), as mentioned in part 3.2.
5.2.2 The Interpretation of RCA Indices and Consistency Tests
Four RCA indices are now selected as measures. In order to compare the results of the different indices, we need to know if these results are consistent, i.e. if the indices identify comparative advantage in the same way. It is also important to know how the numerical results of the indices can be interpreted. In addition to the standard interpretation, that an RCA index shows whether or not the particular product group is competitive, two other possible interpretations of RCA indices and their corresponding consistency tests are offered by Ballance et al (1987)
15.
Firstly, RCA indices can be interpreted as cardinal measures. In this case the indices identify the extent to which a country has comparative advantage (or disadvantage) in a product. The consistency can be informally tested by computing simple correlation
15 Originally, Ballance et al offer three consistency tests. The third and omitted test concerns the standard
interpretation. Results showing that the standard interpretation is correct are available from the author on request.
coefficients, r
16, among all RCA indices for every year
17. The indices are consistent as cardinal measures if the correlations are high, e.g. if r > 0,75 as suggested by Fertő (2004).
Secondly, the RCA indices can be interpreted also as ordinal measures. This implies that the indices provide a ranking of products by comparative advantage. The consistency of this interpretation can be tested by computing Spearman’s rank correlation coefficients, r
s18, among all RCA indices for every year. The indices are consistent as ordinal measures if Spearman’s rank correlations are high, e.g. if r
s> 0,75.
5.2.3 Stability and Dynamics of the RCA Indices
The basic task in assessing the competitiveness of the Hungarian agri-food sector was to select the appropriate measures and to ensure that their results can be interpreted in the right way. It is now time to investigate the methods for analyzing the changes, or more formally, the stability and the dynamics, of competitiveness. Three major ways can be distinguished for analyzing the changes of competitiveness as shown by RCA indices.
The easiest and most intuitive way of analyzing the change of competitiveness is to examine how the values of the RCA indices have changed over the years. The consistency tests ensure a correct interpretation, which sets the base for an analysis of the changes.
Hoeckman and Djankov (1997) offer an additional straight-forward method for analyzing the changes in competitiveness. They compute the simple correlation coefficients between the index in the first year (set as base year) and the indices of the following years. The idea is that if correlations are high
19, the competitiveness has not changed to a great extent.
The distribution of the indices can be analysed as well, to examine the changes (Hinloopen and van Marrewijk, 2001). However, it is only the distribution of the B-index that is recommended by the authors to be examined this way. The method is to calculate the empirical cumulative distribution of the B-index presented in percentile points and also to present descriptive statistics (mean, maximum, minimum and standard deviation) for every year. If the values of these figures are changing, then the distribution of the B-index has
16
y x
y r x
σ σ
) ,
= cov( where σx and σy are standard deviations of x and y respectively.
17 The correlation between two series of index values is computed for the same year. In other words, the index values of different product groups create a series for one year and correlations among these series are computed for every year and pair of index. Other correlations in this paper are computed in the same way.
18
b a s
b r a
σ σ
) ,
= cov(
where a and b are the ranks of x and y respectively. Thus, the data has to be ranked before computing the correlations, based on the ranks. Keller and Warrack (2003, p.636) give further information.19 It is not specified what level of correlation is considered as “high”, but r > 0,75 can be a point of orientation.
moved and consequently, the competitiveness has changed. Fertő (2005) suggests formal testing the movement of the B-index with a two-tailed Wilcoxon signed rank test, since it does not require the assumption of normality in the distribution of the data, where the samples are matched pairs. The null hypothesis is that there is no difference between the distributions, i.e. the median of the last year and the median of a selected previous year are the same.
206. Empirical Findings
The following section presents the results of studying the competitiveness of the Hungarian agri-food sector between 1992 and 2003. The first part describes both the competitive situation of the Hungarian agricultural sector and the agri-food trade between Hungary and the EU-15. The dataset and the results from the RCA method are presented in the second part.
Finally, the results, together with their consistency and changes, are analyzed in the last part.
6.1 Hungarian Agricultural Competitiveness and Trade Relations 6.1.1 The Competitive Situation of the Hungarian Agricultural Sector
In order to give an overview of the competitive situation between Hungary and the EU-15, some simple economic indicators for 2003 are presented in Table 2.
Table 2: Simple economic indicators for Hungary and the EU-15, 2003 Utilized
agricultural area (UAA) as
percentage of total area
Number of holdings (thousands of
holdings)
UAA per holding (ha)
Persons employed in agriculture as
percentage of employed working
population
Share of agriculture in the GDP (GVA
of agriculture /GDP) (%)
Hungary 63,0 773,0 5,6 5,4 2,7
EU-15 40,0 6284,0 20,2 4,0 1,6
Austria 38,8 174,0 18,7 5,5 1,2
Belgium 45,7 55,0 25,4 1,7 1,0
Denmark 61,8 49,0 54,7 3,3 1,6
Finland 6,7 75,0 29,9 5,3 1,0
France 54,2 614,0 45,3 4,3 2,0
Germany 47,7 412,0 41,2 2,4 0,7
Greece 30,0 825,0 4,8 16,3 5,4
Ireland 61,7 135,0 32,3 6,4 1,9
Italy 43,7 1964,0 6,7 4,7 2,2
Luxembourg 49,5 3,0 52,3 2,4 0,5
Netherlands 46,4 86,0 23,5 2,7 2,0
Portugal 41,5 359,0 10,4 12,8 2,5
Spain 50,7 1141,0 22,1 5,6 3,6
Sweden 7,1 68,0 46,1 2,5 0,6
United Kingdom 69,7 281,0 57,4 1,2 0,7
Source: European Commission (2005) and author’s own calculations
20 For further explanation of the Wilcoxon signed rank test, please refer to Keller and Warrack (2003) p. 577-582
The statistics in Table 2 show the fact that agriculture has a great economic importance in Hungary, compared to an average of the EU-15 countries. The percentage of the Hungarian UAA is 63%, which is larger than the average share of UAA of the EU-15, 40%. The share of agriculture in GDP is also higher in Hungary, 2,7%, compared to the EU-15 average of 1,6%.
Furthermore, the share of the Hungarian population employed in agriculture is also higher than the EU-15 average; 5,4% compared to 4,0%. Although Hungary has higher shares and values than the EU-15 average, it has to be noted that there are a few European countries that have higher values or shares than Hungary.
A great economic importance of the agricultural sector can reflect (1) a lower level of economic development and (2) a specialization in a competitive agricultural sector. For Hungary the last explanation is the case, since although the share of agriculture in the GDP is 2.7%, it generates an agri-food export which is 7.5% of total exports (Eurostat, 2003). A specialization and the strategic importance of agriculture is given by Hungary’s good natural conditions for agriculture, and the less contaminated land due to less use of pesticides and fertilizers under communism (Palánkai, 2004). Good natural conditions, good land quality and available labour force are considered as positive factor endowments for the Hungarian agriculture, which are sources of comparative advantage according to the H-O theory. Former sources of competitiveness, like low land prices and low wages, are now disappearing because of the closing of the price and wage gap between Hungary and the EU-15.
21The number and size of the holdings reflect another important competitive factor, namely economies of scale. Hungary has more holdings than many of the EU-15 countries, but their size is considerably smaller: only 5,6 ha of UAA per holding, compared to the EU-15 average of 20,2 ha. Previous studies agree that small scale is an important source of competitive disadvantage of the Hungarian agriculture (Chaplin et al, 2004; Palánkai, 2004). After the privatization of the agricultural collectives, the farm-structure became fragmented with many small subsistence farms and unclear ownership conditions in some cases. Farm-size is favourable only where the cooperative forms are kept after privatization. Another problem resulting from land privatization is the undercapitalization and the depreciation of capital stock. Many of the agricultural machines are too old or unsuitable for small farms and small farmers have no means to modernize to be competitive and to be able to stay in the market.
Low FDI inflow to the agriculture is another reason for slow development. Previous studies also agree that bad transport- and storage conditions are important competitive disadvantages.
21According to classical trade theory, high-wage countries can preserve their comparative advantage because wages must be higher in countries where the labour force is more productive. Moreover, the wage gap is closing to a less extent than the price gap.
6.1.2 Agri-Food Trade between Hungary and the EU-15
From Table 3 it can be seen that, concerning agri-food trade, the EU-15 is an important trade partner for Hungary. Agri-food trade increased during the first year of the AA, but it dropped from 1993, in contrast with expectations of increased trade under the agreements. However, just before the completion of the free trade area, from 1998 an increase is visible. Although the share of exports was higher than the share of imports during the period of study, except for the last year of observation, the values are converging.
Table 3: Hungarian agri-food trade with the EU-15 as percentage of total agri-food trade
35 40 45 50 55
Percentage
Export 51,1 54,2 53,1 44,6 47,0 41,5 44,6 49,6 46,5 48,0 50,0 51,0 Import 42,4 47,0 45,8 40,9 38,2 37,9 37,2 41,1 45,7 46,9 49,0 52,0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Source: 1992-1998: Fertő’s calculations (2004, p.64) based on OECD, 1999-2003:
author’s calculations based on AKI (Agricultural Economics Research Institute, Budapest)
Hungarian agri-food exports to the EU-15 (Table A2.1 in Appendix2) are highly concentrated to certain product groups. The shares of export of meat, cereals, vegetables and fruits are high, around 20-30% respectively. This is consistent with the findings of previous studies (Bozsik, 2004; Fertő and Hubbard, 2002; etc.) and theories of comparative advantage, according to which countries specialise their production and exports in products in which they have a comparative advantage.
The imported agri-food products to Hungary from the EU-15 (Table A2.2) show a different picture. The structure is more balanced than that of the exports and consequently, there are no dominating import products. On the one hand there are few product groups whose share of imports exceeds 15% (coffee, feeding stuff for animals, cork and wood) and on the other hand there are several product groups whose share does not exceed one or two percent.
The fact that the Hungarian agri-food imports from the EU-15 are balanced and lower than the
exports can be seen as an advantage to the Hungarian agri-food sector. It might be a sign that
Hungarian agri-food production is fairly self-sufficient and competitive. Furthermore, an
important sign of the Hungarian agri-food sector’s competitiveness is the positive agri-food
trade balance with the EU-15 (Fertő, 2004, p. 65).
6.2 Competitiveness of the Hungarian Agri-Food Sector with Different RCA Indices
6.2.1 Presenting the Data
The dataset used for this study is provided by UNCTAD and it is presented in Standard International Trade Classification (SITC), revision 2, on the 3-digit group level. The SITC is a code that divides industries into ten very broad categories numbered from 0 to 9. Within each broad category there are subcategories with two-, three-, four- or five-digit codes, where the higher digit categories include more specified product groups and products. The dataset consists of export and import data of selected product groups, provided in thousands of
USD22.
UNCTAD’s specifications of “all food items” and “agricultural raw materials” are the following SITC categories: section 0, 1, 2 (less divisions 27, 28 and groups 233, 244, 266, 267) and section 4. This study defines agri-food products based on UNCTAD’s definitions, but it does not include the product groups 035, 244, 261 and 264, because no data are available for Hungarian trade flows for these product groups during the period of study.
However, since these product groups are not main agri-food products for Hungary
23, the omission does not cause a problem. For a complete list of included products, please see Appendix 1. Furthermore, it is worth to bear in mind that there are seven sporadic missing values in the Hungarian data for less important products.
24The studied time period is from 1992 to 2003, i.e. from the third year after the fall of communism to the year before Hungary’s EU-accession. 1992 was chosen as the starting year because only estimated export and import data are available for Hungarian agri-food trade during 1990 and 1991. These years are omitted because they would cause a rupture in the series. 1992 is also the year when the Association Agreements entered into force.
The reference market is the EU-15 during the whole time period. In this study, the EU-15 includes Austria, Finland and Sweden during the whole time period, even though they did not enter the EU before 1995. The dataset for the EU-15 is complete for the whole time period.
6.2.2 Results from the RCA Method
The competitiveness of the Hungarian agri-food sector vis-à-vis the EU-15 was assessed with four RCA indices and Table 4 summarizes the findings. The means of the four RCA indices
22 Current selling prices
23 The explanations for the omitted product groups’ codes are 035: Fish salted, dried, smoked, 244: Cork natural, raw, waste, 261: Silk, 264: Jute and other textile bast fibres
24 The missing values are Hungarian exports in 1995 of group 122 (tobacco), in 1998 of group 042 (rice), in 2000 of groups 122, 072 (cocoa), in 2001 of group 122, and Hungarian imports in 1995 of group 012 (dried meat) and in 2001 of group 041 (unmilled wheat). These missing values are replaced by zero.
over the studied period demonstrate a similar pattern, with all showing comparative advantage for five product groups out of 21. The competitive product groups are live animals, meat, cereals, vegetables and fruits, and oil seeds. Their production is land intensive, which gives support to the H-O theory that comparative advantage is based on factor endowments.
The competitiveness of six product groups is ambiguous (sugars, miscellaneous edible products, beverages, cork and wood, animal and vegetable oils and fats), meaning that the four indices show different competitiveness. However, all the four indices are close to the critical value of revealed comparative advantage (1 for B and 0 for other indices) for the ambiguous product groups. Whether the indices show a comparative advantage or not, may depend on the inclusion of imports in the calculations. For example cork and wood are competitive according to indices that do not include imports (B and lnRXA), but since import of this product group is high, other indices show a comparative disadvantage. Hungary has a comparative disadvantage in producing the other not mentioned agri-food product groups.
The findings are confirmed by the annual values of the indices, presented in Appendix 3.
Table 4: The means of revealed comparative advantages* of the Hungarian agri-food sector by product group and index, 1992-2003
B RTA lnRXA RC
00: Live animals other than animals of division 03 1,98 1,52 0,66 1,50
01: Meat and meat preparations 2,21 1,73 0,79 1,61
02: Dairy products and bird's eggs 0,29 -0,13 -1,30 -0,40
03: Fish, crustaceans and molluscs and preparations 0,10 -0,14 -2,39 -0,95
04: Cereals and cereal preparations 1,34 0,53 0,22 0,45
05: Vegetables and fruits 1,17 0,52 0,14 0,58
06: Sugars, sugar preparations and honey 0,70 0,03 -0,41 0,07 07: Coffee, tea, cocoa, spices, and manufactures 0,57 -1,08 -0,56 -1,06 08: Feeding stuff for animals 0,95 -2,46 -0,21 -1,43 09: Miscellaneous edible products and preparations 1,09 -0,51 0,05 -0,35
11: Beverages 0,50 0,10 -0,76 0,17
12: Tobacco and tobacco manufactures 0,24 -0,97 -1,96 -2,07 21: Hides, skins and furskins, raw 0,34 -0,70 -1,11 -0,58
22: Oil seeds and oleaginous fruits 5,73 5,05 1,69 2,11
23: Crude rubber 0,04 -1,93 -3,35 -4,02
24: Cork and wood 1,34 -0,45 0,28 -0,30
26: Textile fibres and wastes 0,53 -2,08 -0,68 -1,63 29: Crude animal and vegetable materials 0,90 -0,32 -0,12 -0,31
41: Animal oils and fats 0,90 0,31 -0,28 0,43
42: Fixed vegetable fats and oils 1,08 0,18 -0,05 0,29 43: Animal or vegetable fats, waxes and oils 0,07 -2,04 -3,38 -4,03 Source: Author’s calculations based on UNCTAD data, SITC on the three-digit level.
*Revealed comparative advantages in bold: when B >1 and when other indices show positive values.
6.3 Consistency and Stability of the Results 6.3.1 Consistency Tests and Interpretation
Two consistency tests were carried out, following the methods of Ballance et al (1987), in
order to assure a correct interpretation of the RCA measures.
As it can be seen in Table 5, the correlation coefficients among the paired RCA indices for each year are reasonably high. Of the six possible pairings, four show generally higher simple correlation coefficients than 0,75. Therefore, the test suggests that RCA indices can be interpreted as cardinal measures of comparative advantage. Hence, the size of the values can be understood as the extent of comparative advantage, where a higher value means a higher extent of comparative advantage. For example: the values of the B-index for feeding stuff for animals have steadily increased since 1997 (Table A3.1), which can be interpreted as a growth in comparative advantage.
Table 5: Cardinal interpretation: correlation coefficients among RCA indices
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
B:
RTA
0,77 0,92 0,92 0,89 0,88 0,79 0,71 0,77 0,87 0,83 0,87 0,89lnRXA
0,77 0,69 0,61 0,70 0,71 0,80 0,84 0,82 0,74 0,78 0,71 0,73RC
0,79 0,71 0,60 0,64 0,65 0,73 0,69 0,62 0,67 0,71 0,62 0,63RTA:
lnRXA
0,51 0,62 0,56 0,68 0,72 0,65 0,62 0,65 0,63 0,62 0,57 0,57RC
0,79 0,79 0,68 0,80 0,86 0,86 0,87 0,88 0,81 0,83 0,74 0,75lnRXA:
RC
0,90 0,89 0,94 0,91 0,88 0,88 0,84 0,75 0,84 0,86 0,82 0,78 Source: Author’s calculations based on UNCTAD data, SITC on the three-digit level.Table 6 shows Spearman’s rank correlation coefficients among the paired RCA indices for every year of observation. The rank correlations are generally lower than 0,75 (22 cases out of 48, the perfectly consistent pairings excepted), which means that the RCA indices may not be interpreted as ranks based on competitiveness between the product groups. Hence, for example: the fact that the index values of oil seeds are higher than that of live animals (Table 4), should not be interpreted as oil seeds have a higher rank of comparative advantage. In other words, it is not certain that oil seeds are more competitive than live animals.
Table 6: Ordinal interpretation: rank correlation coefficients among RCA indices
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
B:
RTA
0,69 0,65 0,79 0,77 0,70 0,74 0,66 0,62 0,54 0,48 0,46 0,42lnRXA*
1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00RC
0,84 0,82 0,91 0,87 0,77 0,86 0,75 0,60 0,71 0,71 0,71 0,74RTA:
lnRXA
0,69 0,65 0,79 0,77 0,70 0,74 0,66 0,62 0,54 0,48 0,46 0,42RC*
1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00lnRXA:
RC
0,84 0,82 0,91 0,87 0,77 0,86 0,75 0,60 0,71 0,71 0,71 0,74 Source: Author’s calculations based on UNCTAD data, SITC on the three-digit level.* The pairings of B and lnRXA, and RTA and RC are perfectly consistent by definition.
