Analysis of Export Competitiveness of Agri-Food Products at the EU-27 Level through the Perspective of Technical Complexity
by
, , , , and
Georgiana Raluca Lădaru
1
Mariarosaria Lombardi
2
Ionut Laurentiu Petre
1,*
Carmen Elena Dobrotă
3
Marco Platania
4
Steliana Mocanu
51
Department of Agrifood and Environmental Economics, Bucharest University of Economic Studies, 010374 Bucharest, Romania
2
Department of Economics, University of Foggia, 71122 Foggia, Italy
3
Postdoctoral School ASE-IOSUD, Bucharest University of Economic Studies, 010374 Bucharest, Romania
4
Department of Economics and Business, University of Catania, 95124 Catania, Italy
5
Doctoral School of Economics II, Bucharest University of Economic Studies, 010374 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(13), 5807; https://doi.org/10.3390/su16135807
Submission received: 21 June 2024
/
Revised: 4 July 2024
/
Accepted: 5 July 2024
/
Published: 8 July 2024
(This article belongs to the Special Issue Challenges and Opportunities in Achieving the Sustainable Development Goals in the Agrifood Economy)
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Figure 1
<p>Average annual export value of agri-food products in 2018–2022 for EU Member States. The map was generated using Bing.</p> "> Figure 2
<p>Average annual GDP per capita at EU-27 level, analysis period 2018–2022 (euro). The map was generated using Bing.</p> "> Figure 3
<p>The average annual value of PRODY for the 24 agri-food products at the EU-27 level, period 2018–2022.</p> ">
Versions Notes
<p>Average annual export value of agri-food products in 2018–2022 for EU Member States. The map was generated using Bing.</p> "> Figure 2
<p>Average annual GDP per capita at EU-27 level, analysis period 2018–2022 (euro). The map was generated using Bing.</p> "> Figure 3
<p>The average annual value of PRODY for the 24 agri-food products at the EU-27 level, period 2018–2022.</p> ">
Abstract
:This paper aims to identify the export competitiveness of agri-food products at the level of the European Union Member States, considering indicators/elements belonging to technical innovation (technical complexity) as well as qualitative elements. The main research problem is to examine the differences in the technical complexity of agri-food exports (EXPY) between EU Member States. Through detailed analysis of product categories and Member States’ performance, we have identified significant differences in the technical complexity of exports. Products such as dairy, meat, cereal and vegetable preparations were associated with high technical complexity, while fruit, tobacco and oils were classified in the low technical complexity category. We also identified Member States with exports characterised by high technical complexity, including Cyprus, Ireland and Luxembourg, and those with low technical complexity, such as Slovakia, Latvia and Croatia. These findings suggest the influence of various factors, such as economic specialisation, natural resources and R&D investment on shaping the agri-food export landscape in the European Union. In-depth analysis of these issues provides significant insights for understanding the economic and technical dynamics in the European agri-food sector.
1. Introduction
The agri-food sector is an important component of the economy, of particular importance for food security, employment and economic development. This sector includes agriculture, food production, food processing and distribution [1].
The importance of the agri-food sector is mainly related to ensuring food security. The agri-food sector ensures the supply of food needed by a growing population and contributes to the food security of a country or region [2].
The interconnection between the agri-food sector and employment is also mentioned in the literature. Agriculture and the food industry provide jobs for a significant number of people, helping to reduce unemployment and support rural communities [3].
Last but not least, we also find in the literature the importance of the agri-food sector in terms of economic development. The agri-food sector has a significant impact on economic growth. Through agricultural and industrial production, it contributes to GDP and stimulates other economic sectors [4].
The European Union is a major player in the global agri-food market, exporting a wide variety of products such as cereals, meat, dairy products and wine. EU agri-food products are recognised for their high quality and food safety standards, which strengthens the EU’s position in the international market. The EU’s Common Agricultural Policy support the agri-food sector through subsidies and measures to promote sustainable production, contributing to increased competitiveness [5].
Monitoring the export competitiveness of the agri-food sector can be useful for identifying factors that can contribute to an increase in exports. The European Union, with integrated agricultural policies and high-quality products, plays a significant role in the global context of agri-food trade.
In terms of export competitiveness with agri-food products, several key elements have been identified that can ensure this competitiveness compared to other competitors. International certifications and standards can help to ensure a competitive advantage. Obtaining international certifications and complying with globally recognised food quality and safety standards increases the confidence of trading partners and opens new doors for export [6].
At the same time, product quality makes an essential contribution to product competitiveness. To be competitive in the international market, agri-food products must meet high standards of quality and food safety [7].
Last but not least, we identify technological innovation as a factor promoting the development of competitiveness of agri-food products. Adopting advanced technologies in agriculture and food processing can increase the efficiency and quality of products, providing a competitive advantage [8].
Therefore, the present paper aims to identify the export competitiveness of agri-food products at the level of the European Union Member States, taking into account indicators/elements belonging to technical innovation (technical complexity) as well as qualitative elements.
Next, aspects of the literature review will be presented (Section 2), to determine how to assess competitiveness in terms of technical and qualitative elements. The methodology used will be presented (in Section 3), and then in Section 4 the main results determined from the application of the methodology will be presented, and discussions will be developed on these. Finally, the main conclusions (Section 5) will be drawn on these aspects.
2. Literature Review
Export competitiveness is a ubiquitous theme in the literature, and various methodologies have been proposed to determine it. The most common method used in the literature is that determined by Balassa [9], which refers to the index of revealed competitive advantage (RCA) or the Balassa index. It is calculated as the ratio between the share of a product in a country’s total exports and the share of the same product in world exports. If the RCA index is greater than 1, the country is considered to have a revealed competitive advantage in that product.
Subsequently, other researchers have modelled this Balassa index, determining similar calculation methodologies, such as the Symmetric Explicit Comparative Advantage Index (SERI) [10], and then the Agricultural Competitiveness of a Country (ACA) [11].
Studying export competitiveness is crucial for an economy and companies involved in international trade. The first reason found in the literature relates to the growth of international sales. Companies that understand and improve their export competitiveness are more likely to increase sales in international markets. This gives them the opportunity to access new and diverse customers [12].
Another important aspect of developing export competitiveness relates directly to the development of the economy. Exports are a significant component of economic growth. Studying export competitiveness at the national or sectoral level can provide insights into how a country or sector can improve its position in the global market [13].
The importance of export competitiveness in terms of innovation and efficiency should not be overlooked. Export competitiveness contributes to opportunities for innovation and increased efficiency at the micro and macro level. Companies are motivated to develop higher-quality products and services and improve processes to remain competitive [14].
Job growth can also be a boost to a country’s export competitiveness. Companies that become more export-competitive are more likely to expand their operations, thus helping to create new jobs [15].
Another motivation for increasing export competitiveness is risk diversification. A stronger export orientation helps companies diversify their risks. If a domestic market is struggling, access to multiple foreign markets can help reduce the negative impact on the business [16].
At the macroeconomic level, one can also discuss the motivation represented by the internationalisation of business. Studying export competitiveness is essential for companies that want to expand their activities internationally. It helps them to identify opportunities and develop strategies tailored to the specific requirements of different markets [17].
Addressing export competitiveness analysis is therefore essential for sustainable economic growth, business development and building a more robust global business climate.
In studying competitiveness according to the methodologies presented above, a lack of coverage of technical or production specifics was identified, as well as a failure to consider product quality. These issues have been dealt with in the literature in the work of Minondo (2007) [18], but he investigated the quality of the products analysed on an order of three magnitudes. Other current research dealing with these issues on the order of technical complexity and quality are presented in the research of Wang et al. [19]. He studied quality on an order of five magnitudes.
Export competitiveness has become increasingly important lately given the military conflict between Russia and Ukraine that started in February 2022 [20]. Since this period, Ukraine has changed its export routes through the European Union, specifically through the eastern countries of the European Union [21].
These additional imports from Ukraine have put pressure on the prices of agricultural products in the importing countries, as well as on their ability to sustain exports in the same volume and value. Several influences of exports from Ukraine have been described in the literature. “The Russian invasion of Ukraine has accelerated agricultural commodity prices and increased global food insecurity” [22]. Also, in the literature, we identify the influences of Ukraine’s exports on the Romanian market: “there are disruptions in the grain market, and economic theory is once again confirmed that increasing grain supply can lead to increasingly lower prices” [23]. Another interesting finding is mentioned in this paper [24]. The authors find that aggregate measures of geopolitical risk reduce short-term food prices in Eastern Europe but increase food prices in Western Europe.
3. Materials and Methods
This paper seeks to determine the export competitiveness of EU Member States concerning agri-food products by taking into account more than the revealed comparative advantage, i.e., technical complexity and productivity. To this end, the first part of the paper will quantitatively analyse data on exports of agri-food products for each EU-27 Member State for the last five years for which data have been identified, i.e., 2018–2022. These data were taken from the International Trade Centre (ITC) database [25].
According to the methodology and the ITC Combined Nomenclature at product code level (HS4), the first 24 chapters of the nomenclature are classified as agri-food products. Below in Table 1 are the chapters of the Combined Nomenclature, as well as the HS codes and the abbreviated name chosen in this research.
The second part of the paper aims to determine the export competitiveness of agri-food products by taking into account the degree of technical complexity. Thus, indicators will be determined that will measure this aspect at the level of 24 agri-food products, using product-relevant income levels (PRODY), as well as at the level of 27 European Union Member States, using the overall technical export level (EXPY), these indicators being determined in the research of “[19]”, having the following form.
n = number of states; Ymj = GDP per capita in year m and country j; xmjk = value of exports of agri-food product k in state j and year m, and Xmj = value of exports of all agri-food products from state j in year m.
As can be seen, this methodology takes into account the Revealed Competitive Advantage (RCA), which is enshrined by Balassa to determine export competitiveness, but is used as a value that weights on average the GDP per capita of the country, thus determining the level of revealed product income (PRODY). The value of this indicator is higher if the relative export of the agri-food sector (product) is higher, or if the GDP per capita (level of economic development of the country) increases.
The overall technical level of exports determines the degree of technical complexity for agri-food products at the level of a country. Thus, we can again see the use of the revealed comparative advantage as the value by which the revealed level of income for each agri-food product in each year analysed, determined above, is weighted.
4. Results and Discussion
The motivation behind the analysis is to better understand the economic and technical dynamics of the agri-food sector in the European Union. By classifying agri-food products and Member States according to the technical complexity of exports, the study aims to identify differences in the technical complexity of exports, influencing factors, economic policies and strategies, as well as insights for development.
As discussed so far, to determine the export competitiveness of EU Member States, it is desirable to take into account technical productivity (Minondo, 2007) [18] or technical complexity (Wang et al., 2023) [19]. Thus, in the first part of the research, an analysis of data on agri-food exports is proposed.
Figure 1 shows the European Union Member States and the average annual value of exports of agri-food products, i.e., the first 24 chapters of the Combined Nomenclature (HS4), according to the International Trade Centre.
On average, during the period 2018–2022, the highest annual export value was recorded by the Netherlands, which exported €91.34 billion worth of agri-food products, with annual variations ranging from €86.7 billion to €99.1 billion, leading to a low coefficient of variation of ±5%. In second place is Germany, with an average annual value of agri-food exports of €75.5 billion and a coefficient of variation of ±5.6%. In third place is France with an average annual value of exports of €64.87 billion and a coefficient of variation from this average of ±9.4%. The lowest variation is recorded by the fourth-ranked EU country, Spain, which exports an average of €50.45 billion worth of agri-food products annually, with a variation from this average of only ±3.9%. The least exporting EU Member States are Cyprus and Malta, given their small size and agri-food potential. These countries exported products worth €483 million and €281 million, respectively.
Table 2 provides descriptive statistical data on the value of exports of agri-food products at the level of the 27 Member States of the European Union and for the last five years analysed, namely 2018–2022.
As can be seen at the level of the 27 Member States of the European Union, the average per country for the value of agri-food exports ranged from €18 billion to around €22 billion. However, there is a very large standard deviation, which means that the values differ greatly from one country to another. There is also a very large difference between the average value of exports for each country and the median of the 27 countries, which again means that there is quite a large difference between countries.
Next, to take into account export competitiveness and technical complexity, the gross domestic product per capita for each Member State in the same analysis period will be taken into account for the application of the methodology presented in Section 3 (Figure 2).
The GDP per capita indicator is obviously influenced by its two components, namely the value of the gross domestic product at the state level and the population resident in that state. So there are states with a very high GDP either because the value of GDP is very high and this value is related to a small number of inhabitants, as is the case of the state that ranks first, Luxembourg, with a GDP per capita value of almost 85,000. This average was calculated over the time interval 2018 to 2022 when GDP per capita varied between €82,000 and €86,700, the variation being only 2.2%. In second place is Ireland with an average GDP of 66,000 EUR, also a fairly strong economy with a small resident population. Closer to the European average, Denmark ranks 3rd with almost €50,000 per capita and a change of 3.3%. At the bottom of the ranking is Bulgaria with a GDP value of almost €7000 per capita and a rather high variation of 8%, and in the second last place is Romania with a GDP of €9000 per capita and a variation of 5%. In terms of stability of GDP per capita, Finland is in first place with the most stable GDP per capita during the period under review, with a variation of only 1.5% and an average GDP per capita of almost EUR 37,000.
Similarly, a descriptive statistical analysis of GDP per capita at the level of the 27 Member States of the European Union will be carried out, as shown in Table 3.
At the level of the 27 Member States of the European Union, the average gross domestic product per inhabitant in the period 2018–2022 ranged from 26.6 thousand euros to 29.4 thousand euros on average per Member State. It can be seen that the median is also quite close to the mean, so both the median and the mean are significant. However, the living standards are quite high; on average the variation of the states is over 60%, so there are very developed states with a very high GDP per capita as well as underdeveloped states with very low GDP, as can be seen from the minimum and maximum value and the absolute difference between them, respectively, the range being 77–80 thousand euro per capita. Analysing the bolting index, it can be observed that it has quite a high value of 2.8, which is close to the standard value 3, i.e., the distribution is normal in terms of bolting. As regards the symmetry of the distribution between the 27 Member States, there is a positive asymmetry with a Skewness coefficient of 1.5–1.6, which again means that there are few countries with high GDP values above the average and the median and several countries with lower GDP per capita values; as measured by the Skewness coefficient, 10 EU countries exceed the average GDP per capita.
As described in Section 2 of the literature review, the measurement of export competitiveness has been carried out over time using the Balassa model, i.e., determining the revealed comparative RCA export advantage. According to the methodology presented in Section 3 and the method of calculation proposed in this article, this index mentioned above is taken into account, i.e., RCA is considered as a weight in the determination of PRODY. This indicator is calculated as a weighted average of gross domestic product per capita for each Member State and each agri-food product with the weight of RCA.
According to the methodology, the PRODY indicator is calculated for each agri-food product, so it has been calculated for the 24 food products at the level of the 5 years analysed, this centralisation being shown in Table 4.
Regarding the reported income levels per product (PRODY), it can be observed that, both over the whole period analysed and on average, the product category 04, namely dairy products, records the highest reported income of all the analysed agri-food products exported from the European Union; ranging between 34,000 EUR and 38,000 EUR for this product, the value of the indicator varies by ±5.6%. On the 2nd position at average level is the product category 02, meat and offal, with an average revenue revealed for this category of 33.3 thousand EUR and a variation of 5.8%. The product category with the lowest variation in the reported income is category 20, namely vegetable and fruit preparations, with a variation of 1.8%, the average PRODY indicator being 30.8 thousand euros (see Figure 3).
At the opposite pole, the least competitive products in terms of technical complexity are cereals, which rank last with an indicator value of 16.7 thousand euros, followed by oilseeds, with a low competitiveness and technical complexity indicator value of 19,000 euros.
Next, we propose to classify the 24 products according to the determined value of the PRODY indicator and according to quartiles (Table 5), i.e., the first 25% of the indicator value, then quartile 2 (25–50% of the indicator value), and so on. This classification, according to the literature, can help in determining not only the technical complexity that we have previously calculated but also the quality of the products; this concept of quality has not been taken into account so far.
By analysing the technical complexity of agri-food products according to the PRODY 4 quartile indicator classification, it is possible to determine which products have a high potential in terms of technical productivity and which have less potential in terms of complexity or even competitiveness. At the EU level, there were 6 products with a PRODY indicator level of more than 30,193 euros, namely dairy products, meat, plants, resins and gums, cereal preparations and fruit and vegetable preparations. It can be seen that several products have been included in this group which have certain processing and transformation requirements, leading to the technical complexity that the indicator measures.
In the 2nd category, i.e., products with medium to high potential, there were also 6 products, with a level of technical complexity ranging from €26,932 to €30,192, namely beverages, coffee and tea, products of the milling industry, fish, live animals and green planting material.
As the technological complexity of the products decreases, the value of the income revealed is lower. Group III comprises 7 products, with an indicator value between 25,248 and 26,931 euros. In this group, there were products such as meat preparations, edible preparations, animal products, food industry residues, cocoa, vegetables and sugars.
Within the last category of products of low technical complexity, with a reported income indicator below EUR 25,247, fall 5 products, namely fruit, tobacco, oils, oilseeds and cereals; again, these products correspond to product categories with a low level of processing, leading to this range of reported income being placed in this category.
Next, the level of technical competitiveness at the Member State level was deter-mined according to the total agri-food products exported (EXPY—Table 6).
Regarding the level of technical complexity at the level of the 27 Member States for exported agri-food products, it can be observed that they vary on average between 22 thousand euros for Romania and 31.5 thousand euros for Cyprus. The average over the whole period analysed for a country (EU-27 average) is 27.8 thousand euros, with a standard deviation of 2 thousand euros, representing a coefficient of variation of 7%.
However, there are important differences between the Member States, the first three countries, Cyprus, Ireland and Luxembourg, are countries whose level of technical complexity determined by the income revealed exceeds 30 thousand euros, but the causes may be different, Cyprus there are agri-food products with a high level of technical complexity which denotes this high value, products such as olives that are processed to be marketed, also these products are in low supply, the producing countries are very few, so Cyprus has a competitive advantage in this regard. The following two countries have a competitive advantage due to the high level of GDP per capita which influenced the weighted average and the final result of the indicator.
These three countries are also the countries that recorded the highest increase in the indicator measuring the year 2022 compared to 2018, the value of the indicator increased by 3906 euros for Cyprus, representing an increase of 13%, then Ireland recorded an increase in the indicator by 12% and Luxembourg by 11%.
Similarly, Member States will be ranked according to the four quartiles of EXPY scores (Table 7).
By analysing the technical complexity of the agricultural export products exported by each Member State using the EXPY indicator, it was possible to determine their classification. Thus, 11 EU Member States were placed in the first quartile according to the high technical complexity of the countries, even though they have a high GDP per capita, in most cases they export wholesale food products with added value or a high degree of processing.
Within the 2nd class of countries with medium to high potential, 6 countries have been classified as having very extensive agriculture, such as France with a high diversity of agricultural products, cooperating with almost the entire range of products, as well as countries with agriculture concentrated on certain products with a medium to high degree of technical complexity.
The fewest countries registered in a single class are in the medium to low potential class, with only 4 countries registered here with somewhat low agriculture but some competitive products.
The last class includes countries with a low degree of technical complexity, with 6 countries in this category, some of which, such as Romania and Hungary, have a large agricultural area but low added value and low technical complexity.
5. Conclusions
Analysing the value of exports of agri-food products at the total EU level and the level of each Member State, it was possible to observe an increase in their value at the total EU-27 level over the period analysed (2018–2022) from €491 billion to €690 billion, i.e., an increase of 40%. This increase is attributed to the military conflict between Russia and Ukraine, so in 2022, with the outbreak of this conflict, exports began to increase, as Ukraine changed the routes for transporting products through Europe. The largest increases in exports are recorded in the least developed countries in terms of GDP per capita; i.e., in Bulgaria export growth was 84% and in Romania, export growth was 73% during the period under review. Given the unfavourable influences of these external trade activities, they will put high pressure on these already weakly developed countries.
Analysing the indicator of the level of income revealed for each agri-food product at the level of the 27 EU Member States, from the point of view of descriptive statistics we can see that the average between products varied between 26.7 thousand euro and 28.95 thousand euros, and the standard deviation led to the determination of coefficients of variation ranging between 15.7% and 16.4%. This variation has increased annually over the period analysed, so it can be seen that agri-food products are starting to distinguish themselves more and more from each other in the determination of technical complexity. This is attributed to the development of technologies in some agri-food products at the expense of others.
Analysing the indicator of the level of technical competitiveness among Member States from the point of view of descriptive statistics, it was found that the average per Member State during the period under review ranged from €27.2 thousand to €29.3 thousand. The standard deviation was less than the average, compared to the previous indicator, thus a coefficient of variation between 6.7% and 8.4% was determined, similar as in the case of agri-food products, and EU Member States are increasingly distinguishing themselves from each other. This rather small variation shows that the technical complexity at the EU Member State level for agri-food products is not very different, given the uniform standards and regulations at the European Commission level, as well as the technologies and know-how made available by multinational companies.
Classifying products and Member States according to the level of technical complexity of the exported agri-food products, it was found that according to the income levels revealed for the product in the category of products with high technical complexity are: 04-Dairy; 02-Meat; 06-Plants; 13-Lac; Gums, Resins; 19-Cereals preparations; 20-Vegetables and fruits preparations, and in the category of products with low technical complexity are: 08-Fruits; 24-Tobacco; 15-Oils; 12-Oleaginous; 10-Cereals. At the same time, looking at the overall technical level of exports, the Member States with high technical complexity were: Cyprus; Ireland; Luxembourg; Finland; Denmark; Austria; Netherlands; Italy; Germany; Malta; Belgium, and those with low technical complexity being: Slovakia; Latvia; Hungary; Croatia; Bulgaria; Romania.
This situation reflects a complex combination of economic, geographical, cultural and technological factors that contribute to the situation presented in the classification of agri-food products and Member States according to the technical complexity of exports.
To improve the competitiveness of agri-food exports, countries such as Romania and Bulgaria should invest in infrastructure and advanced technologies, diversifying exported products. Slovakia, Latvia, Hungary and Croatia should adopt technological innovations, invest in training and expand access to international markets. Countries with highly technically complex exports, such as Cyprus, Ireland and Germany, should strengthen their position in international markets, promote sustainability and support continuous innovation. All EU Member States should benefit from government support and European funds, cooperate inter-state to share best practices and implement constant monitoring systems to improve export performance.
Future research directions should focus on the impact of digitalization and advanced technologies, such as artificial intelligence and blockchain on the competitiveness of agri-food exports, exploring the economic and technical benefits they bring. It is also essential to investigate the role of sustainability and the adoption of green standards, assessing how organic certifications and green practices influence value-added and the export market. In addition, analysis of supply chains and logistics efficiency can identify ways to optimize them, reducing transport costs and increasing competitiveness.
Author Contributions
Conceptualization, G.R.L. and I.L.P.; methodology, I.L.P.; software, I.L.P.; validation, M.L., M.P. and C.E.D.; formal analysis, S.M.; investigation, S.M.; resources, C.E.D.; data curation, G.R.L.; writing—original draft preparation, I.L.P.; writing—review and editing, G.R.L.; visualization, M.L.; supervision, G.R.L.; project administration, M.L.; funding acquisition, M.P. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest.
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- Eurostat. Real GDP per Capita. Available online: https://ec.europa.eu/eurostat/databrowser/view/sdg_08_10/default/table (accessed on 3 January 2024).
Figure 1.
Average annual export value of agri-food products in 2018–2022 for EU Member States. The map was generated using Bing.
Figure 1.
Average annual export value of agri-food products in 2018–2022 for EU Member States. The map was generated using Bing.
Figure 2.
Average annual GDP per capita at EU-27 level, analysis period 2018–2022 (euro). The map was generated using Bing.
Figure 2.
Average annual GDP per capita at EU-27 level, analysis period 2018–2022 (euro). The map was generated using Bing.
Figure 3.
The average annual value of PRODY for the 24 agri-food products at the EU-27 level, period 2018–2022.
Figure 3.
The average annual value of PRODY for the 24 agri-food products at the EU-27 level, period 2018–2022.
Table 1.
Presentation of the Combined Nomenclature of chapter names, HS codes and abbreviations used.
Table 1.
Presentation of the Combined Nomenclature of chapter names, HS codes and abbreviations used.
| Chapter Names | HS4 Code | Chapter Abbreviations |
|---|---|---|
| Live animals | 01 | Live Animals |
| Meat and edible meat offal | 02 | Meat |
| Fish and crustaceans, molluscs and other aquatic invertebrates | 03 | Fish |
| Dairy produce; birds’ eggs; natural honey; edible products of animal origin, not elsewhere specified or included | 04 | Dairy |
| Products of animal origin, not elsewhere specified or included | 05 | Animal Products |
| Live trees and other plants; bulbs, roots and the like; cut flowers and ornamental foliage | 06 | Plants |
| Edible vegetables and certain roots and tubers | 07 | Vegetables |
| Edible fruit and nuts; peel of citrus fruit or melons | 08 | Fruits |
| Coffee, tea, maté and spices | 09 | Cofee & Tea |
| Cereals | 10 | Cereals |
| Products of the milling industry; malt; starches; inulin; wheat gluten | 11 | Milling industry |
| Oil seeds and oleaginous fruits; miscellaneous grains, seeds and fruit; industrial or medicinal plants; straw and fodder | 12 | Oleaginous |
| Lac; gums, resins and other vegetable saps and extracts | 13 | Lac; Gums, Resins |
| Vegetable plaiting materials; vegetable products not elsewhere specified or included | 14 | Vegetable Plaiting Materials |
| Animal, vegetable or microbial fats and oils and their cleavage products; prepared edible fats; animal or vegetable waxes | 15 | Oils |
| Preparations of meat, of fish, of crustaceans, molluscs or other aquatic invertebrates, or of insects | 16 | Meat preparations |
| Sugars and sugar confectionery | 17 | Sugars |
| Cocoa and cocoa preparations | 18 | Cocoa |
| Preparations of cereals, flour, starch or milk; pastrycooks’ products | 19 | Cereals preparations |
| Preparations of vegetables, fruit, nuts or other parts of plants | 20 | Vegetables and fruits preparations |
| Miscellaneous edible preparations | 21 | Edible preparations |
| Beverages, spirits and vinegar | 22 | Drinks |
| Residues and waste from the food industries; prepared animal fodder | 23 | Residues and Waste–Food Industries |
| Tobacco and manufactured tobacco substitutes; products, whether or not containing nicotine, intended for inhalation without combustion; other nicotine containing products intended for the intake of nicotine into the human body | 24 | Tobacco |
Source: The Combined Nomenclature, European Commission.
Table 2.
Descriptive statistics on the value of agri-food exports at the EU-27 level.
| Descriptive Statistics | 2018 | 2019 | 2020 | 2021 | 2022 |
|---|---|---|---|---|---|
| Mean | 18,188,858 | 19,053,591 | 18,916,841 | 19,088,059 | 21,911,914 |
| Median | 7,255,626 | 7,297,017 | 7,218,521 | 8,325,123 | 8,550,244 |
| Standard Deviation | 24,288,585 | 25,307,743 | 24,887,642 | 25,000,116 | 28,527,506 |
| Kurtosis | 1.803 | 1.795 | 1.702 | 1.594 | 1.239 |
| Skewness | 1.652 | 1.646 | 1.617 | 1.608 | 1.548 |
| Range | 86,343,918 | 90,637,266 | 89,665,533 | 89,693,711 | 98,974,096 |
| Minimum | 373,540 | 316,639 | 298,403 | 273,056 | 142,846 |
| Maximum | 86,717,458 | 90,953,905 | 89,963,936 | 89,966,767 | 99,116,942 |
| Sum | 491,099,155 | 514,446,947 | 510,754,703 | 515,377,601 | 591,621,672 |
| Count | 27 | 27 | 27 | 27 | 27 |
Source: Own calculations based on ITC data.
Table 3.
Descriptive statistics on Gross Domestic Product at the EU-27 level.
| Descriptive Statistics | 2018 | 2019 | 2020 | 2021 | 2022 |
|---|---|---|---|---|---|
| Mean | 27,210 | 27,755 | 26,626 | 28,486 | 29,378 |
| Median | 22,510 | 23,180 | 20,830 | 23,320 | 24,650 |
| Standard Deviation | 17,393 | 17,554 | 17,492 | 18,790 | 19,144 |
| Kurtosis | 2.896 | 2.862 | 2.889 | 2.896 | 2.684 |
| Skewness | 1.497 | 1.502 | 1.586 | 1.629 | 1.609 |
| Range | 77,180 | 77,650 | 75,730 | 79,740 | 78,450 |
| Minimum | 6330 | 6630 | 6400 | 6950 | 7680 |
| Maximum | 83,510 | 84,280 | 82,130 | 86,690 | 86,130 |
| Sum | 734,670 | 749,390 | 718,890 | 769,120 | 793,210 |
| Count | 27 | 27 | 27 | 27 | 27 |
Source: Own calculations based on EUROSTAT data [26].
Table 4.
PRODY value for agri-food products at EU-27 level (first 24 chapters HS1–24).
| Product (HS Code) | 2018 | 2019 | 2020 | 2021 | 2022 | AVG. | Variation |
|---|---|---|---|---|---|---|---|
| 01 | 27,028 | 28,483 | 27,046 | 29,168 | 29,854 | 28,316 | ±4.5% |
| 02 | 31,225 | 32,265 | 32,673 | 34,866 | 35,891 | 33,384 | ±5.8% |
| 03 | 28,403 | 28,995 | 27,640 | 29,861 | 31,207 | 29,221 | ±4.7% |
| 04 | 34,098 | 34,712 | 33,723 | 36,518 | 38,466 | 35,503 | ±5.6% |
| 05 | 25,499 | 25,857 | 25,603 | 27,327 | 28,869 | 26,631 | ±5.5% |
| 06 | 31,782 | 32,890 | 31,925 | 33,562 | 35,056 | 33,043 | ±4.1% |
| 07 | 25,000 | 25,997 | 24,634 | 26,240 | 27,316 | 25,838 | ±4.1% |
| 08 | 24,116 | 24,887 | 22,839 | 24,534 | 25,316 | 24,338 | ±3.9% |
| 09 | 29,582 | 29,642 | 28,163 | 30,379 | 32,169 | 29,987 | ±4.9% |
| 10 | 16,381 | 16,459 | 16,445 | 16,784 | 17,604 | 16,735 | ±3.1% |
| 11 | 29,045 | 29,582 | 29,264 | 30,737 | 31,249 | 29,975 | ±3.2% |
| 12 | 17,962 | 18,717 | 18,778 | 19,659 | 20,206 | 19,064 | ±4.6% |
| 13 | 31,392 | 31,593 | 29,843 | 31,700 | 31,691 | 31,244 | ±2.5% |
| 14 | 26,960 | 23,886 | 29,578 | 28,880 | 28,615 | 27,584 | ±8.3% |
| 15 | 22,914 | 23,610 | 21,978 | 23,119 | 22,730 | 22,870 | ±2.6% |
| 16 | 26,292 | 26,429 | 25,346 | 27,621 | 28,743 | 26,886 | ±4.9% |
| 17 | 24,651 | 25,378 | 24,540 | 26,187 | 26,409 | 25,433 | ±3.4% |
| 18 | 25,059 | 25,907 | 24,665 | 26,586 | 27,261 | 25,896 | ±4.1% |
| 19 | 30,848 | 30,971 | 29,859 | 31,382 | 32,435 | 31,099 | ±3.0% |
| 20 | 30,590 | 31,332 | 30,567 | 31,466 | 30,196 | 30,830 | ±1.8% |
| 21 | 26,166 | 26,496 | 25,504 | 27,419 | 28,250 | 26,767 | ±4.0% |
| 22 | 28,847 | 29,750 | 28,510 | 31,190 | 32,550 | 30,169 | ±5.6% |
| 23 | 24,984 | 26,555 | 25,355 | 27,165 | 27,581 | 26,328 | ±4.3% |
| 24 | 23,546 | 23,684 | 21,659 | 24,466 | 25,090 | 23,689 | ±5.5% |
Source: Authors’ calculations according to Wang’s et al. (2023) methodology [19].
Table 5.
Centralisation of agri-food products exported by EU-27 according to PRODY indicator quartiles.
Table 5.
Centralisation of agri-food products exported by EU-27 according to PRODY indicator quartiles.
| Quartile | Interval ($) | Number of Products Included | Short Description of HS4 Product Groups |
|---|---|---|---|
| Q4 (High) | PRODY > 30,193 | 6 | 04-Dairy; 02-Meat; 06-Plants; 13-Lac; Gums, Resins; 19-Cereals preparations; 20-Vegetables and fruits preparations |
| Q3 (Medium-High) | 26,932 < PRODY < 30,192 | 6 | 22-Drinks; 09-Cofee&Tea; 11-Milling industry; 03-Fish; 01-Live animals; 14-Vegetable Plaiting Materials |
| Q2 (Medium-Low) | 25,248 < PRODY < 26,931 | 7 | 16-Meat preparations; 21-Edible preparations; 05-Animal Products; 23-Residues and Waste–Food Industries; 18-Cocoa; 07-Vegetables; 17-Sugars |
| Q1 (Low) | PRODY < 25,247 | 5 | 08-Fruits; 24-Tobacco; 15-Oils; 12-Oleaginous; 10-Cereals |
Source: Own classification.
Table 6.
EXPY value for each EU-27 Member State on agri-food products.
| Country | 2018 | 2019 | 2020 | 2021 | 2022 | AVG. | Variation |
|---|---|---|---|---|---|---|---|
| Austria | 28,129 | 28,814 | 27,796 | 29,761 | 30,662 | 29,032 | ±4.1% |
| Belgium | 27,795 | 28,538 | 27,360 | 29,213 | 29,933 | 28,568 | ±3.6% |
| Bulgaria | 23,321 | 23,546 | 23,073 | 23,755 | 24,549 | 23,649 | ±2.4% |
| Croatia | 25,650 | 26,090 | 24,651 | 26,393 | 26,984 | 25,954 | ±3.4% |
| Cyprus | 30,043 | 30,838 | 30,330 | 32,558 | 33,949 | 31,543 | ±5.3% |
| Czech Republic | 26,478 | 27,079 | 25,764 | 27,641 | 28,348 | 27,062 | ±3.7% |
| Denmark | 28,673 | 29,361 | 28,470 | 30,433 | 31,465 | 29,680 | ±4.2% |
| Estonia | 27,047 | 27,093 | 25,801 | 27,666 | 27,989 | 27,119 | ±3.1% |
| Finland | 28,826 | 29,281 | 28,368 | 30,529 | 31,953 | 29,791 | ±4.9% |
| France | 27,090 | 27,775 | 26,693 | 28,727 | 29,152 | 27,887 | ±3.8% |
| Germany | 27,902 | 28,633 | 27,488 | 29,258 | 30,210 | 28,698 | ±3.8% |
| Greece | 27,181 | 28,034 | 26,656 | 28,346 | 28,832 | 27,810 | ±3.2% |
| Hungary | 25,584 | 26,116 | 25,070 | 26,891 | 27,692 | 26,271 | ±4.0% |
| Ireland | 30,143 | 30,916 | 30,188 | 32,297 | 33,751 | 31,459 | ±4.9% |
| Italy | 28,068 | 28,742 | 27,521 | 29,487 | 30,354 | 28,834 | ±3.9% |
| Latvia | 26,480 | 26,356 | 24,792 | 26,879 | 27,604 | 26,422 | ±3.9% |
| Lithuania | 26,473 | 26,524 | 24,804 | 27,129 | 28,036 | 26,593 | ±4.4% |
| Luxembourg | 29,919 | 30,364 | 29,204 | 31,507 | 33,242 | 30,847 | ±5.1% |
| Malta | 27,893 | 28,155 | 27,246 | 29,290 | 30,461 | 28,609 | ±4.4% |
| Netherlands | 27,920 | 28,731 | 27,582 | 29,494 | 30,501 | 28,846 | ±4.1% |
| Poland | 27,644 | 28,145 | 26,682 | 28,667 | 29,633 | 28,154 | ±3.9% |
| Portugal | 26,856 | 27,597 | 26,212 | 28,178 | 28,610 | 27,491 | ±3.5% |
| Romania | 21,662 | 22,051 | 21,675 | 22,273 | 23,068 | 22,146 | ±2.6% |
| Slovakia | 26,036 | 26,792 | 25,266 | 26,892 | 27,362 | 26,470 | ±3.1% |
| Slovenia | 27,358 | 27,909 | 26,760 | 28,590 | 29,802 | 28,084 | ±4.2% |
| Spain | 26,906 | 27,809 | 26,773 | 28,520 | 29,247 | 27,851 | ±3.8% |
| Sweden | 27,594 | 28,103 | 26,664 | 28,746 | 29,820 | 28,186 | ±4.2% |
Source: Authors’ calculations according to Wang’s et al. (2023) methodology [19].
Table 7.
Centralisation of EU-27 Member States by EXPY quartiles.
| Quartile | Interval ($) | Number of States | Short Description of HS4 Product Groups |
|---|---|---|---|
| Q4 (High) | EXPY > 28,443 | 11 | Cyprus; Ireland; Luxembourg; Finland; Denmark; Austria; Netherlands; Italy; Germany; Malta; Belgium |
| Q3 (Medium-High) | 27,688 < EXPY < 28,442 | 6 | Sweden; Poland; Slovenia; France; Spain; Greece; |
| Q2 (Medium-Low) | 26,579 < EXPY < 27,687 | 4 | Portugal; Estonia; Czech Republic; Lithuania |
| Q1 (Low) | EXPY < 26,578 | 6 | Slovakia; Latvia; Hungary; Croatia; Bulgaria; Romania |
Source: Own classification.
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MDPI and ACS Style
Lădaru, G.R.; Lombardi, M.; Petre, I.L.; Dobrotă, C.E.; Platania, M.; Mocanu, S. Analysis of Export Competitiveness of Agri-Food Products at the EU-27 Level through the Perspective of Technical Complexity. Sustainability 2024, 16, 5807. https://doi.org/10.3390/su16135807
AMA Style
Lădaru GR, Lombardi M, Petre IL, Dobrotă CE, Platania M, Mocanu S. Analysis of Export Competitiveness of Agri-Food Products at the EU-27 Level through the Perspective of Technical Complexity. Sustainability. 2024; 16(13):5807. https://doi.org/10.3390/su16135807
Chicago/Turabian StyleLădaru, Georgiana Raluca, Mariarosaria Lombardi, Ionut Laurentiu Petre, Carmen Elena Dobrotă, Marco Platania, and Steliana Mocanu. 2024. "Analysis of Export Competitiveness of Agri-Food Products at the EU-27 Level through the Perspective of Technical Complexity" Sustainability 16, no. 13: 5807. https://doi.org/10.3390/su16135807
APA StyleLădaru, G. R., Lombardi, M., Petre, I. L., Dobrotă, C. E., Platania, M., & Mocanu, S. (2024). Analysis of Export Competitiveness of Agri-Food Products at the EU-27 Level through the Perspective of Technical Complexity. Sustainability, 16(13), 5807. https://doi.org/10.3390/su16135807
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