The Impact of Agricultural Finance and Public Support on Employment Dynamics: A Comparative Panel Data Analysis by Income Groups
DOI:
https://doi.org/10.63556/tisej.2026.1941Keywords:
Agricultural Finance, Public Expenditure, Labour Market, GMM, Panel Data, Financial InclusionAbstract
This study aims to comparatively analyze the relationship between agricultural financing and employment based on income group differences. Within the framework of the World Bank's income classification, two separate panel datasets were constructed: Panel A, consisting of 33 upper-middle-income countries, and Panel B, covering 41 lower-middle and low-income countries. Unbalanced panel data for the 2011–2023 period, compiled from FAOSTAT and WDI databases, were analyzed using the Two-Step Difference GMM (Arellano-Bond) estimator to address endogeneity and dynamic inertia. The findings reveal that the impact of financing tools varies depending on the income group. In upper-middle-income countries, an increase in agricultural credit shares boosts agricultural employment and reduces general unemployment, indicating a functional credit transmission mechanism. Conversely, the share of public expenditures allocated to agriculture reduces agricultural employment and increases general unemployment in this group. This paradoxical effect is explained by the substitution hypothesis. In lower-middle and low-income countries, neither financing tool has a statistically significant effect on employment, which is associated with financial exclusion and inadequate policy transmission. Finally, strong labor market inertia was detected in all models, confirming the hysteresis effect and showing that policies should be country-specific.
References
Acemoğlu, D., & Restrepo, P. (2018a). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488–1542. https://doi.org/10.1257/aer.20160696
Acemoğlu, D., & Restrepo, P. (2018b). Artificial intelligence, automation, and work. In A. Agrawal, J. Gans, & A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 197–236). Chicago, IL: University of Chicago Press.
Acemoğlu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. https://doi.org/10.2307/2297968
Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51. https://doi.org/10.1016/0304-4076(94)01642-D
Balana, B. B., Mekonnen, D., Haile, B., Hagos, F., Yimam, S., & Ringler, C. (2022). Demand and supply constraints of credit in smallholder farming: Evidence from Ethiopia and Tanzania. World Development, 159, 106033. https://doi.org/10.1016/j.worlddev.2022.106033
Balana, B. B., & Oyeyemi, M. A. (2022). Agricultural credit constraints in smallholder farming in developing countries: Evidence from Nigeria. World Development Sustainability, 1, 100012. https://doi.org/10.1016/j.wds.2022.100012
Beck, T., Demirgüç-Kunt, A., & Levine, R. (2007). Finance, inequality and the poor. Journal of Economic Growth, 12(1), 27–49. https://doi.org/10.1007/s10887-007-9010-6
Blanchard, O. J., & Summers, L. H. (1986). Hysteresis and the European unemployment problem. NBER Macroeconomics Annual, 1, 15–78.
Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143. https://doi.org/10.1016/S0304-4076(98)00009-8
Burgess, R., & Pande, R. (2005). Do rural banks matter? Evidence from the Indian social banking experiment. American Economic Review, 95(3), 780–795.
Bustos, P., Caprettini, B., & Ponticelli, J. (2016). Agricultural productivity and structural transformation: Evidence from Brazil. American Economic Review, 106(6), 1320–1365. https://doi.org/10.1257/aer.20131061
Carter, M. R., Laajaj, R., & Yang, D. (2021). Subsidies and the African Green Revolution: Direct effects and social network spillovers of randomized input subsidies in Mozambique. American Economic Journal: Applied Economics, 13(2), 206–229. https://doi.org/10.1257/app.20190396
Christiaensen, L., Demery, L., & Kuhl, J. (2011). The (evolving) role of agriculture in poverty reduction: An empirical perspective. Journal of Development Economics, 96(2), 239–254. https://doi.org/10.1016/j.jdeveco.2010.10.006
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.1080/01621459.1979.10482531
Fan, S., Gulati, A., & Thorat, S. (2008). Investment, subsidies, and pro-poor growth in rural India. Agricultural Economics, 39(2), 163–170. https://doi.org/10.1111/j.1574-0862.2008.00328.x
Fan, S., Hazell, P., & Thorat, S. (2000). Government spending, growth and poverty in rural India. American Journal of Agricultural Economics, 82(4), 1038–1051. https://doi.org/10.1111/0002-9092.00101
Feder, G., Lau, L. J., Lin, J. Y., & Luo, X. (1990). The relationship between credit and productivity in Chinese agriculture: A microeconomic model of disequilibrium. American Journal of Agricultural Economics, 72(5), 1151–1157. https://doi.org/10.2307/1242524
Food and Agriculture Organization. (2018). Nutrition and food systems (HLPE report, 12). A report by the High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security. Rome, Italy: FAO. Erişim Adresi: https://www.fao.org/documents/card/en/c/I7846E
Food and Agriculture Organization. (2024). FAOSTAT statistical database. Erişim Adresi: https://www.fao.org/faostat
Foster, A. D., & Rosenzweig, M. R. (2008). Economic development and the decline of agricultural employment. In T. P. Schultz & J. Strauss (Eds.), Handbook of development economics (Vol. 4, pp. 3051–3083). Amsterdam, Netherlands: Elsevier.
Gollin, D., Lagakos, D., & Waugh, M. E. (2014). The agricultural productivity gap. The Quarterly Journal of Economics, 129(2), 939–993. https://doi.org/10.1093/qje/qjt056
Harris, J. R., & Todaro, M. P. (1970). Migration, unemployment and development: A two-sector analysis. The American Economic Review, 60(1), 126–142.
Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74. https://doi.org/10.1016/S0304-4076(03)00092-7
Irz, X., Lin, L., Thirtle, C., & Wiggins, S. (2001). Agricultural productivity growth and poverty alleviation. Development Policy Reviewヌ, 19(4), 449–466. https://doi.org/10.1111/1467-7679.00144
King, R. G., & Levine, R. (1993). Finance and growth: Schumpeter might be right. The Quarterly Journal of Economics, 108(3), 717–737. https://doi.org/10.2307/2118406
Lewis, W. A. (1954). Economic development with unlimited supplies of labour. The Manchester School, 22(2), 139–191. https://doi.org/10.1111/1467-923X.1954.tb01246.x
Loayza, N. V., & Raddatz, C. (2010). The composition of growth matters for poverty alleviation. Journal of Development Economics, 93(1), 137–151. https://doi.org/10.1016/j.jdeveco.2009.03.008
Mellor, J. W. (1976). The new economics of growth: A strategy for India and the developing world. Ithaca, NY: Cornell University Press.
Mohammad, A. A., Mohammad, S. I., Al-Oraini, B., Vasudevan, A., Hunitie, M. F. A., & Ismael, B. (2025). The impact of agricultural credit on farm productivity, employment, and rural development: Empirical evidence from Jordan's agricultural sector. Pakistan Journal of Agricultural Research, 38(3), 20-31. https://dx.doi.org/10.17582/journal.pjar/2025/38.3.20.31
Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica, 49(6), 1417–1426.
OECD. (2023). Agricultural policy monitoring and evaluation 2023: Adapting agriculture to climate change. Paris, France: OECD Publishing. https://doi.org/10.1787/b14de474-en
Orji, A., Ogbuabor, J. E., Alisigwe, J. N., & Anthony-Orji, O. I. (2021). Agricultural financing, agricultural output growth and employment generation in Nigeria. European Journal of Business Science and Technology, 7(1), 74–90. https://doi.org/10.11118/ejobsat.2021.002
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
Pingali, P. L. (2012). Green Revolution: Impacts, limits, and the path ahead. Proceedings of the National Academy of Sciences, 109(31), 12302–12308. https://doi.org/10.1073/pnas.0912953109
Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal, 9(1), 86–136. https://doi.org/10.1177/1536867X0900900106
Seven, U., & Tumen, S. (2020). Agricultural credits and agricultural productivity: Cross-country evidence. The Singapore Economic Review, 65(Suppl. 1), 161–183. Erişim Adresi: https://www.econstor.eu/bitstream/10419/215326/1/dp12930.pdf
Stiglitz, J. E., & Weiss, A. (1981). Credit rationing in markets with imperfect information. American Economic Review, 71(3), 393–410.
Todaro, M. P. (1969). A model of labor migration and urban unemployment in less developed countries. The American Economic Review, 59(1), 138–148.
Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126(1), 25–51. https://doi.org/10.1016/j.jeconom.2004.02.005
World Bank. (2007). World Development Report 2008: Agriculture for development. Washington, DC: World Bank. Erişim Adresi: https://openknowledge.worldbank.org/handle/10986/5990
World Bank. (2024). World Development Indicators. Erişim Adresi: https://databank.worldbank.org
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Third Sector Social Economic Review

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.




