Wyznaczniki relacji cenowych na rynkach rolnych w różnych strukturach agrarnych UE

• Authors: Czyżewski Bazyli , Nicula Alexandra , Nicula Amalia

Title variants

EN

Drivers for the agricultural price gap in the different agrarian structures of the EU

RU

Факторы, влияющие на разрыв цен на сельскохозяйственные в различных структурах аграрного ЕС

• Languages of publication: EN

Abstracts

EN

The index of agricultural goods output comprises weighted changes of prices of agricultural commodities whereas the index of intermediate consumption describes fluctuations of outlays’ prices such as seeds and planting stock, energy, fertilizers, soil improvers, plant protection products or feedingstuffs. The relation of these two indices is defined as “price gap” or “price scissors”. There is a lot of price models for agricultural goods investigated in the subject literature. However, the issue of modeling drivers for the price gap has been rarely explored. For that reason authors aim to estimate long-term regression models of the agricultural price gap for different European countries that represent varied agrarian structures. The analysis entails few stages. In the first stage, the long-term price indices (from 1980 to 2014) were computed basing on EUROSTAT and FAOSTAT agricultural prices data for all available agricultural products and outlays in the EU-27 countries. Then, the aggregated indices were weighted with a volume of production or intermediate consumption on the basis of the average price indices for the respective outputs or inputs. In the second stage, a cluster analysis was performed with regard to the utilization of a land factor by individual farms in the subsequent European countries. In the third stage, three countries were chosen for case studies from the each of the distinguished clusters and the econometric models of price gap were estimated where the indices of outputs and inputs are independent variables. An interesting finding was discovered that marginal effects for price gap drivers are much stronger in the countries of an intensive and large scale agriculture (as France, Great Britain and Denmark) than in the countries of fragmented agrarian structures such as Greece, Portugal and Ireland.

PL

Indeks produkcji towarów rolniczych obejmuje ważone zmiany towarów rolnych podczas gdy indeks konsumpcji pośredniej opisuje fluktuacje cen produktów takich jak nasiona i rośliny, energia, nawozy, polepszacze gleby, środki ochrony roślin i pasze. Relacja między tymi dwoma indeksami jest definiowana jako “luka cenowa” lub “nożyce cenowe”. Istnieje wiele modeli cenowych przebadanych dla towarów rolniczych w literaturze przedmiotu, jednakże sprawa czynników modelujących lukę cenową jest rzadko poruszana. Z tego powodu autorzy zamierzają oszacować długoterminowy model regresji luki cenowej dla rolnictwa różnych krajów europejskich reprezentujących zróżnicowane struktury agrarne. Niniejsza analiza zawiera kilka etapów. W pierwszym, długoterminowe indeksy cenowe (od 1980 do 2014) zostały obliczone przy użyciu danych cen rolnictwa według EUROSTAT i FAOSTAT dla wszystkich dostępnych produktów i nakładów w krajach EU-27. Następnie indeksy są ważone zgodnie z poziomem produkcji lub konsumpcji pośredniej na podstawie średnich indeksów cenowych dla odpowiedniego wielkości wejściowej lub wyjściowej. W drugim etapie została dokonana analiza klasterowa w odniesieniu do czynnika ziemi przez indywidualne farmy w kolejnych krajach europejskich. W trzecim kroku zostały wybrane trzy kraje z wybranych grup (klasterów) i modele ekonomiczne luk cenowych zostały oszacowane gdzie indeksy wartości wejściowych i wyjściowych były zmiennymi niezależnymi. Dokonano interesującego spostrzeżenia, że efekty marginalne czynników luk cenowych są znacznie silniejsze w krajach o intensywniejszym i wielkoskalowym rolnictwie (jak Francja, Wielka Brytania i Dania) niż w krajach of rozczłonkowanej strukturze rolnej jak Grecja, Portugalia i Irlandia.

RU

Индекс производства сельскохозяйственной продукции включает в себя взвешенное изменение сельскохозяйственных товаров, в то время как индекс промежуточного потребления описывает колебания цен на продукты, такие как семена и растения, энергии, удобрений, почвы улучшителей, пестицидов и кормов для животных. Отношения между этими двумя показателями определяется как «ценовой разрыв» или «ножниц цен». Есть много моделей ценообразования, проверенные на сельскохозяйственные товары в литературе, однако, факторы материи моделирования ценовой разрыв редко перемещается. По этой причине авторы намерены оценить долгосрочную регрессионной модели ценовой разрыв для сельского хозяйства различных европейских стран, представляющих разнообразную аграрную структуру. Этот анализ включает в себя несколько этапов. В первом случае, долгосрочных индексов цен (с 1980 до 2014 года) не были рассчитаны с использованием данных цен сельского хозяйства Евростатом и ФАОСТАТ для всех доступных продуктов и инвестиций в ЕС-27. Затем индексы взвешиваются в соответствии с уровнем производства или промежуточного потребления на основе средних индексов цен для соответствующего размера входа или выхода. На втором этапе был сделан Кластерный анализ в отношении фактора земли отдельными хозяйствами в других европейских странах. На третьем этапе были отобраны из трех стран, выбранных групп (кластеров) и экономические модели были оценены ценовых разрывов, где индексы входных и выходных значений были независимыми переменными. Там было интересное наблюдение, что влияние маргинальных факторов ценовых разрывов гораздо сильнее в странах с интенсивным и крупного сельского хозяйства (как Франции, Великобритании и Дании), чем в фрагментированной структуры сельского хозяйства, как Греции, Португалии и Ирландии.

Keywords

EN

agricultural prices; price gap; price scissors

PL

ceny produktów rolnych; luka cenowa; nożyce cenowe

RU

цены на сельскохозяйственную продукцию; ценовой разрыв; ножницы цена

• Publisher: Wydawnictwo Państwowej Wyższej Szkoły Zawodowej im. Stanisława Staszica w Pile
• Journal: Progress in Economic Sciences
• Year: 2016
• Volume: 3
• Pages: 13-40

Dates

printed

2016

Contributors

author

Czyżewski Bazyli

• Uniwersytet Ekonomiczny w Poznaniu

author

Nicula Alexandra

• „1 Decembrie 1918” University of Alba Iulia

author

Nicula Amalia

• „1 Decembrie 1918” University of Alba Iulia

References

• ADALTO ACIR ALTHAUS JUNIOR, MARCELO S. BEGO, ADALTO BARBACEIA GONÇALVES. (2014). Forecasting Agricultural Commodities Spot Prices: A Jointly Approach, WORKING PAPER 25/04/2014. Instituto de Ensino e Pesquisa. ASHUTOSH KUMAR TRIPATHI. (2013). Agricultural Price Policy, Output, and Farm Profitability - Examining Linkages during Post-Reform Period in India, Asian Journal of Agriculture and Development, Vol. 10, No. 1,. pp. 91-111. BOLLERSLEV, T. A. (1987). Conditionally Heteroskedastic Time-Series Model for Speculative Prices and Rates of Return. The Review of Economics and Statistics. 69: 542-47. Bucharest University of Economic Studies (2015), Faculty of Economics, Romanian Economists General Association, Romanian Association of Economic Faculties, Theoretical and Applied Economics, Volume XXII, Bucharest, Special Issue 2015 (http://store.ectap.ro/suplimente/Post-crisis-developments-in-Economics-nov-2014.pdf; accessed 11.03.2016) Co, Henry C., Boosarawongse Rujirek. (2007). Forecasting agricultural exports and imports in South Africa Article in Applied Economics 39 (16):2069-2084. COM (2010) Communication From The Commission To The European Parliament. The Council, The European Economic And Social Committee and The Committee of The Regions. The CAP towards 2020: Meeting The Food, Natural Resources And Territorial Challenges of The Future. COM/2010/0672. ENKE, D. AND THAWORNWONG, S. (2005) The Use of Data Mining and Neural Networks for Forecasting Stock Market Returns. Expert Systems with Applications, 29, 927-940. Eurostat statistics explained (2015), Agricultural output, price indices and income, (http://ec.europa.eu/eurostat/statistics-explained/index.php/Agricultural_output,_price_indices_and_income#Price_indices ; accessed 11.03.2016) Eurostat statistics explained. (2016), Agricultural products, (http://ec.europa.eu/eurostat/statistics-explained/index.php/Agricultural_products. accessed 11.03.2016 ) Eurostat.(2013). http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Glossary:ESU (accessed16 July 2013). FISCHER, P. (2006). Rent-seeking, institutions and reforms in Africa: theory and empirical evidence for Tanzania. Springer, New York. Food and Agriculture Organization of the United Nations (1988), Manual on agricultural price index numbers, Economic and Social development paper, 74, Rome, (http://www.fao.org/fileadmin/templates/ess/ess_test_folder/World_Census_Agriculture/Publications/FAO_ESDP/ESDP_74_Manual_on_agricultural_price_index_numbers.pdf. accessed 11.03.2016) GATNAR, E., WALESIAK, M. (2004). Metody statystycznej analizy wielowymiarowej w badaniach marketingowych [Methods of multivariate statistical analysis in marketing research] LABYS, W. C. (2006). Modeling and Forecasting Primary Commodity Prices. Co., Burlington. MALPEZZI, S. (2003), Hedonic Pricing Models: A Selective and Applied Review, in Housing Economics and Public Policy: Essays in honor of Duncan Maclennan, red. T. O’Sullivan, K. Gibb, Oxford: Blackwell. MATUSZCZAK A. (2013). Zróżnicowanie rozowju rolnictwa w regionach unii europejskiej w aspekcie jego zrównoważenia. PWN. Warszawa. pp. 139. MELLOR J. W., RAISUDDIN A.. (1989). Agricultural price policy for developing countries, The International Food Policy Research Institute, London. MOSS, C.B. (1992). The Cost Price Squeeze in Agriculture: An Application of Cointegration. Review of Agricultural Economics 14(1) 209-217. MOSS, C.B., Shonkwiler J.S., Ford S.A. (1990). A Risk Endogenous Model of Aggregate Agricultural Debt. Agricultural Finance Review 50:73-79. OCTAVIO A. R., MOHAMADOU F. (2003), Forecasting Agricultural Commodity Prices with Asymmetric-Error GARCH Models, Journal of Agricultural and Resource Economics 28(1):71-85. Office for Official Publications of the European Communities (2002), Handbook for EU agricultural price statistics, Luxembourg POCZTA, W., MRÓWCZYŃSKA, A. (2002). Regionalne zróżnicowanie polskiego rolnictwa [Regional diversification of Polish agriculture] in: Poczta, W., Wysocki, F. (eds.), Zróżnicowanie regionalne gospodarki żywnościowej w Polsce w procesie integracji z Unią Europejską [The diversity of agribusiness in Poland in the integration process with the European Union]. Wydawnictwo AR im. Augusta Cieszkowskiego, Poznań, p. 126. POCZTA-WAJDA A. (2013). The Role of Olson's Interest Groups Theory in Explaining the Different Level of Agricultural Support in Countries with Different Development Level, Production and Cooperation in Agriculture Finance and Taxes; no. 30, Jelgava: Ministry of Rural. Development and Food. POCZTA-WAJDA A.(2015) Why "rich " farmers demand financial support, Annals of the Polish Association of Agricultural and Agribusiness Economists 4. SAAB M. (2011).Define agricultural price policy and what are the objectives of agricultural price policy, Study Points, Easy notes and assignments, (http://studypoints.blogspot.ro/2011/07/define-agricultural-price-policy-and_8377.html. accessed 11.03.2016) SHAHWAN T., ODENING M. (2007). Forecasting Agricultural Commodity Prices using Hybrid Neural networks. In: Chen, S-H, Wang, P.P., Kuo, T-W (EDs.): Computational Intelligence in Econmics and Finance, Vol. 2, Springer-Verlag Berlin Heidelberg, Germany. SWINNEN, J. (2008). The Political Economy of Agricultural Distortions: The Literature to Date. Paper for the IATRC Meeting, Scotsdale. TICLAVILCA ANDRES M., FEUZ DILLON M., McKee Mac. (2010). Forecasting Agricultural Commodity Prices Using Multivariate Bayesian Machine Learning Regression, St. Louis, Missouri. WANG, K.L., FAWSON C., BARRETT C.B, MCDONALD J.B. (2002). A Flexible Parametric GARCH Model with an Application to Exchange Rates. Journal of Applied Econometrics 16: 521-36. Wiking Educational Publishers (2013). http://www.wiking.edu.pl/article.php?id=272 (accessed 15 January 2013). World Bank Group (2015), Global economic prospects. Having fiscal space and using it, Washington, DC, 2015 (https://www.worldbank.org/content/dam/Worldbank/GEP/GEP2015a/pdfs/GEP15a_web_full.pdf; accessed 11.03.2016) YANG, S-R. and B.W. BRORSEN. (1992). Nonlinear Dynamics of Daily Cash Prices. American Journal of Agricultural Economics. 74(3):706-15
• ADALTO ACIR ALTHAUS JUNIOR, MARCELO S. BEGO, ADALTO BARBACEIA GONÇALVES. (2014). Forecasting Agricultural Commodities Spot Prices: A Jointly Approach, WORKING PAPER 25/04/2014. Instituto de Ensino e Pesquisa.
• ASHUTOSH KUMAR TRIPATHI. (2013). Agricultural Price Policy, Output, and Farm Profitability - Examining Linkages during Post-Reform Period in India, Asian Journal of Agriculture and Development, Vol. 10, No. 1,. pp. 91-111.
• BOLLERSLEV, T. A. (1987). Conditionally Heteroskedastic Time-Series Model for Speculative Prices and Rates of Return. The Review of Economics and Statistics. 69: 542-47.
• Bucharest University of Economic Studies (2015), Faculty of Economics, Romanian Economists General Association, Romanian Association of Economic Faculties, Theoretical and Applied Economics, Volume XXII, Bucharest, Special Issue 2015 (http://store.ectap.ro/suplimente/Post-crisis-developments-in-Economics-nov-2014.pdf; accessed 11.03.2016)
• Co, Henry C., Boosarawongse Rujirek. (2007). Forecasting agricultural exports and imports in South Africa Article in Applied Economics 39 (16):2069-2084.
• COM (2010) Communication From The Commission To The European Parliament. The Council, The European Economic And Social Committee and The Committee of The Regions. The CAP towards 2020: Meeting The Food, Natural Resources And Territorial Challenges of The Future. COM/2010/0672.
• ENKE, D. AND THAWORNWONG, S. (2005) The Use of Data Mining and Neural Networks for Forecasting Stock Market Returns. Expert Systems with Applications, 29, 927-940.
• Eurostat statistics explained (2015), Agricultural output, price indices and income, (http://ec.europa.eu/eurostat/statistics-explained/index.php/Agricultural_output,_price_indices_and_income#Price_indices ; accessed 11.03.2016)
• Eurostat statistics explained. (2016), Agricultural products, (http://ec.europa.eu/eurostat/statistics-explained/index.php/Agricultural_products. accessed 11.03.2016 )
• Eurostat.(2013). http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Glossary:ESU (accessed16 July 2013).
• FISCHER, P. (2006). Rent-seeking, institutions and reforms in Africa: theory and empirical evidence for Tanzania. Springer, New York.
• Food and Agriculture Organization of the United Nations (1988), Manual on agricultural price index numbers, Economic and Social development paper, 74, Rome, (http://www.fao.org/fileadmin/templates/ess/ess_test_folder/World_Census_Agriculture/Publications/FAO_ESDP/ESDP_74_Manual_on_agricultural_price_index_numbers.pdf. accessed 11.03.2016)
• GATNAR, E., WALESIAK, M. (2004). Metody statystycznej analizy wielowymiarowej w badaniach marketingowych [Methods of multivariate statistical analysis in marketing research]
• LABYS, W. C. (2006). Modeling and Forecasting Primary Commodity Prices. Co., Burlington.
• MALPEZZI, S. (2003), Hedonic Pricing Models: A Selective and Applied Review, in Housing Economics and Public Policy: Essays in honor of Duncan Maclennan, red. T. O’Sullivan, K. Gibb, Oxford: Blackwell.
• MATUSZCZAK A. (2013). Zróżnicowanie rozowju rolnictwa w regionach unii europejskiej w aspekcie jego zrównoważenia. PWN. Warszawa. pp. 139.
• MELLOR J. W., RAISUDDIN A.. (1989). Agricultural price policy for developing countries, The International Food Policy Research Institute, London.
• MOSS, C.B. (1992). The Cost Price Squeeze in Agriculture: An Application of Cointegration. Review of Agricultural Economics 14(1) 209-217.
• MOSS, C.B., Shonkwiler J.S., Ford S.A. (1990). A Risk Endogenous Model of Aggregate Agricultural Debt. Agricultural Finance Review 50:73-79.
• OCTAVIO A. R., MOHAMADOU F. (2003), Forecasting Agricultural Commodity Prices with Asymmetric-Error GARCH Models, Journal of Agricultural and Resource Economics 28(1):71-85.
• Office for Official Publications of the European Communities (2002), Handbook for EU agricultural price statistics, Luxembourg
• POCZTA, W., MRÓWCZYŃSKA, A. (2002). Regionalne zróżnicowanie polskiego rolnictwa [Regional diversification of Polish agriculture] in: Poczta, W., Wysocki, F. (eds.), Zróżnicowanie regionalne gospodarki żywnościowej w Polsce w procesie integracji z Unią Europejską [The diversity of agribusiness in Poland in the integration process with the European Union]. Wydawnictwo AR im. Augusta Cieszkowskiego, Poznań, p. 126.
• POCZTA-WAJDA A. (2013). The Role of Olson's Interest Groups Theory in Explaining the Different Level of Agricultural Support in Countries with Different Development Level, Production and Cooperation in Agriculture Finance and Taxes; no. 30, Jelgava: Ministry of Rural. Development and Food.
• POCZTA-WAJDA A.(2015) Why "rich " farmers demand financial support, Annals of the Polish Association of Agricultural and Agribusiness Economists 4.
• SAAB M. (2011).Define agricultural price policy and what are the objectives of agricultural price policy, Study Points, Easy notes and assignments, (http://studypoints.blogspot.ro/2011/07/define-agricultural-price-policy-and_8377.html. accessed 11.03.2016)
• SHAHWAN T., ODENING M. (2007). Forecasting Agricultural Commodity Prices using Hybrid Neural networks. In: Chen, S-H, Wang, P.P., Kuo, T-W (EDs.): Computational Intelligence in Econmics and Finance, Vol. 2, Springer-Verlag Berlin Heidelberg, Germany.
• SWINNEN, J. (2008). The Political Economy of Agricultural Distortions: The Literature to Date. Paper for the IATRC Meeting, Scotsdale.
• TICLAVILCA ANDRES M., FEUZ DILLON M., McKee Mac. (2010). Forecasting Agricultural Commodity Prices Using Multivariate Bayesian Machine Learning Regression, St. Louis, Missouri.
• WANG, K.L., FAWSON C., BARRETT C.B, MCDONALD J.B. (2002). A Flexible Parametric GARCH Model with an Application to Exchange Rates. Journal of Applied Econometrics 16: 521-36.
• Wiking Educational Publishers (2013). http://www.wiking.edu.pl/article.php?id=272 (accessed 15 January 2013).
• World Bank Group (2015), Global economic prospects. Having fiscal space and using it, Washington, DC, 2015 (https://www.worldbank.org/content/dam/Worldbank/GEP/GEP2015a/pdfs/GEP15a_web_full.pdf; accessed 11.03.2016)
• YANG, S-R. and B.W. BRORSEN. (1992). Nonlinear Dynamics of Daily Cash Prices. American Journal of Agricultural Economics. 74(3):706-15

Identifiers

DOI

10.14595/PES/03/001