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Convergence of food consumption across Ukrainian regions: approach using spatial panel data models

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  • Osypova, Olha
  • Horna, Maryna
  • Vashchaiev, Serhii
  • Ishchuk, Yaroslava
  • Pomazun, Oksana

Abstract

Purpose. The article studies the convergence between the regions of Ukraine in terms of the basic foodstuff consumption per capita during 2000–2019, taking into account the effects of spatial interaction across regions. Methodology / approach. The convergence analysis between regions of Ukraine is based on the concept of β-convergence which can be tested using spatial econometric models namely spatial autoregressive models and spatial error models. The need for considering spatial interaction can be explained by the fact that regions are characterized by constant interaction with each other. Therefore, region should not be considered as isolated objects in space in empirical research with usage of panel data. Ignoring the spatial interaction between regions and using standard evaluation procedures can reduce the reliability and validity of the obtained results to some extent. Results. The results of our calculation confirm the process of β-convergence of average per capita consumption of all food groups, which means that food consumption in regions with an initial low level of consumption is growing faster than in regions with high initial levels of consumption. Also, as part of the use of spatial econometric models the convergence process was determined to be influenced by spatial interaction between regions while the influence of neighbouring regions has a positive effect on food consumption in particular region. Originality / scientific novelty. The article further develops the main ideas of modeling interregional differentiation based on convergence theory and for the first time, spatial econometric models were used to estimate β-convergence of Ukrainian regions by the levels of consumption of basic foodstuffs. Practical value / implications. The approach proposed by the authors and the obtained results can be used both by state authorities on agrarian policy and food issues, and by enterprises of the agricultural sector in the analysis and forecasting of trends in the consumption of basic foodstuffs at the regional level; when planning the production, processing and delivery of agricultural products, when planning state or regional trade policy in the field of food. At the same time, the inclusion of spatial effects in the model of evaluating convergence will allow policymakers to take into account the geographical features of the convergence process and, accordingly, make more informed decisions to reduce the differentiation of regions of Ukraine by the levels of consumption of basic foodstuffs.

Suggested Citation

  • Osypova, Olha & Horna, Maryna & Vashchaiev, Serhii & Ishchuk, Yaroslava & Pomazun, Oksana, 2023. "Convergence of food consumption across Ukrainian regions: approach using spatial panel data models," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 9(01), March.
  • Handle: RePEc:ags:areint:337422
    DOI: 10.22004/ag.econ.337422
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    References listed on IDEAS

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    1. Giuseppe Arbia & Julie Le Gallo & Gianfranco Piras, 2008. "Does Evidence on Regional Economic Convergence Depend on the Estimation Strategy? Outcomes from Analysis of a Set of NUTS2 EU Regions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 209-224.
    2. Giuseppe Arbia & Gianfranco Piras, 2004. "Convergence in per-capita GDP across European regions using panel data models extended to spatial autocorrelation effects," ERSA conference papers ersa04p524, European Regional Science Association.
    3. David M. Drukker & Hua Peng & Ingmar Prucha & Rafal Raciborski, 2013. "Creating and managing spatial-weighting matrices with the spmat command," Stata Journal, StataCorp LLC, vol. 13(2), pages 242-286, June.
    4. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
    5. Domazet Anto & Sendić Rusmir & Alić Adi, 2012. "Convergence analysis of household expenditures using the absolute β-convergence method," Business Systems Research, Sciendo, vol. 3(1), pages 23-29, June.
    6. Maria ABREU & Henri L.F. DE GROOT & Raymond J.G.M. FLORAX, 2005. "Space And Growth: A Survey Of Empirical Evidence And Methods," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 21, pages 13-44.
    7. Baltagi, Badi H. & Heun Song, Seuck & Cheol Jung, Byoung & Koh, Won, 2007. "Testing for serial correlation, spatial autocorrelation and random effects using panel data," Journal of Econometrics, Elsevier, vol. 140(1), pages 5-51, September.
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    1. Borisov, Petar & Petrov, Kamen & Tsonkov, Nikolay, 2024. "Integration perspectives for improving regional policy in rural areas of Bulgaria," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 10(01), March.

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