Structural modeling of the financial support for the Ukrainian agrarian sector
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DOIhttp://dx.doi.org/10.21511/imfi.15(3).2018.17
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Article InfoVolume 15 2018, Issue #3, pp. 199-211
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Financial support for the agrarian sector is the priority of economic policy in many countries of the world, as it plays a key role in achieving the goals of sustainable development, in particular poverty reduction, food security, environmental improvement, including reducing CO2 emissions, reducing water pollution, etc. In the main, the financial support for the agrarian sector of the various countries is multi-channel and combines budget financing and financial market opportunities. At the same time, for many countries, including Ukraine, the issue of the ratio of these sources of financing and their influence on the development of agricultural production remains unresolved. The analysis of budget financing has shown a lack of stability in the implementation of financial support programs for the agrarian sector of Ukraine, which affects the financial sustainability of enterprises and their ability to attract market financing. In the article, using the structural modeling, the necessary amount of financing for the agrarian sector was determined through budget financing, bank lending and agro-insurance. The results of the calculations showed that the actual size of bank lending to agrarian enterprises is significantly lower than the simulated values. At the same time, budget financing creates conditions for ensuring financial sustainability of agrarian enterprises and encourages them to use bank lending, while increasing budget financing reduces the need for agro-insurance operations, which is a negative consequence of its use.
- Keywords
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JEL Classification (Paper profile tab)G21, G22, Q14, R51
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References27
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Tables4
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Figures6
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- Figure 1. The structural modeling formation
- Figure 2. Regression relationships between latent variables for the first model, where ZETA1 and ZETA2 are the residuals
- Figure 3. Regression relationships between latent variables for the second model, where ZETA2 and ZETA3 are the residuals
- Figure 4. Overall view of the structural equations model for both cases
- Figure 5. Dynamics of changes in the real and simulated levels of lending to agriculture as to public sector financing
- Figure 6. Dynamics of changes in the real and simulated levels of agricultural insurance as to public financing of the sector
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- Table 1. Funding allocation for agriculture in Ukraine during 2009–2017
- Table 2. Results of structural modeling of the indices relationship for the overall situation in agriculture, state and non-state financing, i.e., lending
- Table 3. Results of structural modeling of the indices relationship for the overall situation in agriculture, state and non-state financing, i.e., agro-insurance
- Table 4. Simulated values of non-state financing of Ukrainian agriculture in terms of sector lending and agro-insurance
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- Ahmed, M. K., Asadullah, M. N., & Kambhampati, U. (2016) The effect of formal banks on household income and poverty in Bangladesh Malaysian Journal of Economic Studies, 53(2), 173-193.
- Anang, B. T., Bäckman, S., & Sipiläinen, T. (2016). Agricultural microcredit and technical efficiency: The case of smallholder rice farmers in Northern Ghana. Journal of Agriculture and Rural Development in the Tropics and Subtropics, 117(2), 189-202.
- Artemenko, V. (2015). Structural modeling of connections between the criteria of the effectiveness of regional socioeconomic systems. Inductive Modeling of Complex Systems, 7, 59-68.
- Asante-Addo, C., Mockshell, J., Zeller, M., Siddig, K., & Egyir, I. S. (2017). Agricultural credit provision: what really determines farmers’ participation and credit rationing? Agricultural Finance Review, 77(2), 239-256.
- Binswanger, H. P, Khandker, S. R., & Rosenzweig, M. R. (1993). How infrastructure and financial institutions affect agricultural output and investment in India. Journal of Development Economics, 41(2), 337-366.
- Carter, M., De Janvry, A., Sadoulet, E., & Sarris, A. (2017). Index Insurance for Developing Country Agriculture: A Reassessment. Annual Review of Resource Economics, 9(5), 421-438.
- Chisasa, J. (2014). A diagnosis of rural agricultural credit markets in South Africa: empirical evidence from North West and Mpumalanga provinces. Banks and Bank Systems, 9(2), 100-111.
- Chisasa, J. (2014). The finance-growth nexus in South Africa’s agricultural sector: a structural equation modeling approach. Banks and Bank Systems, 9(4), 38-47.
- Ender, M., & Zhang, R. (2015). Efficiency of weather derivatives for Chinese agriculture industry. China Agricultural Economic Review, 7(1), 102-121.
- Esposti, R., & Materia, V. C. (2016). The determinants of the public cofinancing rate for applied R&D: an empirical assessment on agricultural projects in an Italian region. R and D Management, 46, 521-536.
- Ibrahim, Y., Ahmed, I., & Minai, M. S. (2018). The Influence of Institutional Characteristics on Financial Performance of Microfinance Institutions in the OIC Countries. Economics and Sociology, 11(2), 19-35.
- Islam, A., Maitra, C., Pakrashi, D., & Smyth, R. (2016). Microcredit Program Participation and Household Food Security in Rural Bangladesh. Journal of Agricultural Economics, 67(2), 448-470.
- Kandilov, A. M. G., & Kandilov, I. T. (2018). The impact of bank branching deregulations on the U.S. agricultural sector. American Journal of Agricultural Economics, 100(1), 73-90.
- Karantininis, K. (2017). A new paradigm for Greek agriculture (112 p.). Swedish University of Agricultural Sciences, Uppsala.
- Karimi, L., & Meyer, D. (2014). Structural Equation Modeling in Psychology: The History, Development and Current Challenges. International Journal of Psychological Studies, 6(4).
- Khandker, S. R., & Koolwal, G. B., (2016). How has microcredit supported agriculture? Evidence using panel data from Bangladesh. Agricultural Economics (United Kingdom), 47(2), 157-168.
- Kirby, J. B., & Bollen, K. A. (2009). Using instrumental variable tests to evaluate model specification in latent variable structural equation models. Sociological Methodology, 39, 327-355.
- Kuwata, K., Mahmood, F., & Shibasaki, R. (2015). Weather index for crop insurance to mitigate basis risk. Paper Presented at International Geoscience and Remote Sensing Symposium Milan, Italy, 26-31 July 2015, (pp. 4656-4659).
- Mahmud, W., & Osmani S. R. (2016). The theory and practice of microcredit (pp. 1-274). International Growth Centre, South Asia Network of Economic Research Institutes, United Kingdom, Global Development Network, United States.
- Manta, O. (2015) Countryside microfinance opportunity for sustainable rural development (1446-1450). Proceedings of the 25th International Business Infor¬mation Management Association Conference - Innovation Vision 2020: From Regional Development Sustainability to Global Economic Growth, IBIMA.
- Novak, I., Verniuk, N., & Novak, Y. (2016). Structuring of sources of attracting capital to agricultural production as a prerequisite for the formation of an effective investment mechanism in the agricultural sector. Economic Annals-XXI, 159(5-6), 29-33.
- Popova, L. V., Korobeynikov, D. A., Korobeynikova, O. M., Shaldokhina, S. J., & Zabaznova, D. O. (2016). Concessional lending as a perspective tool of development of agribusiness. European Research Studies Journal, 19(2), 12-20.
- Tang, S., & Guo, S. (2017). Formal and informal credit markets and rural credit demand in China. 4th International Conference on Industrial Economics System and Industrial Security Engineering. IEIS 2017 20 October.
- Tung, D. T. (2018). Poverty and Ethnic Minorities: The Case of Khmer Households in the Rural Mekong Delta, Vietnam. Economics and Sociology, 11(1), 233-244.
- Vilhelm, V., Špička, J., & Valder, A. (2015). Public support of agricultural risk management – Situation and prospects. Agris On-line Papers in Economics and Informatics, 7(2), 93-102.
- Wang, X. (2016). Research on the impact of micro finance platform on farmers’ life based on ICT information technology. Journal of Computational and Theoretical Nanoscience, 13(12), 9932-9936.
- Warfield, J. (1976). Societal Systems: Planning, Policy and Complexity. New York: John Wiley & Sons, Inc.