Makineler ve İhracat: Tarımsal Verimliliği Destekliyor mu, Engelliyor mu?

Öz

Bu çalışma, tarımsal mekanizasyon, gıda ihracatı, kişi başına düşen gelir ve beşeri sermaye gelişiminin tarımsal verimlilik üzerindeki etkilerini incelemektedir. 2000-2019 yıllarını kapsayan 55 gelişmekte olan ülkeye ait panel veriler kullanılarak, heterojenlik ve kesitler arası bağımlılığı dikkate alan AMG ve CCE-MG yöntemleri uygulanmıştır. Elde edilen bulgular, tarımsal mekanizasyonun verimlilik üzerinde beklenenin aksine negatif bir etkiye sahip olduğunu ortaya koymaktadır. Bu durum, altyapı eksiklikleri, finansmana erişim zorlukları ve küçük ölçekli çiftçilerin teknolojiye adaptasyonundaki yetersizliklerle açıklanabilir. Öte yandan, kişi başına düşen gelirin tarımsal verimliliği artırdığı görülmektedir; bu, ekonomik büyümenin modern tarımsal tekniklerin benimsenmesini teşvik ettiğini göstermektedir. Gıda ihracatının ise tarımsal verimliliği artırdığı tespit edilmiştir; ancak, dış pazarlara aşırı bağımlılığın fiyat dalgalanmalarına ve gıda güvenliği risklerine yol açabileceği unutulmamalıdır. Beşeri sermayenin (eğitim) etkisi ise karmaşık bir tablo sergilemektedir; genel eğitim seviyesinin artması kırsal iş gücünün tarım dışı sektörlere kaymasına neden olabileceğinden, eğitimin tarım odaklı becerilerle desteklenmesi gerekmektedir. Bu bulgular, gelişmekte olan ülkelerde tarımsal verimliliği artırmak için çok yönlü bir politika çerçevesinin gerekli olduğunu göstermektedir.

Anahtar Kelimeler

• Tarımsal Verimlilik
• Tarımsal Mekanizasyon
• Gıda İhracatı
• AMG
• CCE-MG

Machines and Exports: Do They Complement or Counteract Agricultural Productivity?

Abstract

This study examines the impact of agricultural mechanization, food exports, per capita income, and human capital development on agricultural productivity. Utilizing panel data from 55 developing countries covering the period 2000–2019, the analysis employs the AMG and CCE-MG estimation techniques, accounting for heterogeneity and cross-sectional dependence. The findings reveal that agricultural mechanization, contrary to expectations, has a negative impact on productivity. This may be attributed to inadequate infrastructure, limited financial access, and smallholder farmers' difficulties in adopting new technologies. In contrast, per capita income positively influences agricultural productivity, indicating that economic growth facilitates the adoption of modern agricultural practices. Food exports also enhance productivity, though excessive reliance on external markets may introduce risks related to price volatility and food security. The effect of human capital (education) presents a more complex picture; while higher education levels are generally associated with increased efficiency, they may also drive the rural workforce to shift to non-agricultural sectors. This underscores the need for education systems to incorporate agricultural-specific skills and technological training. These findings highlight the necessity of a comprehensive policy framework to enhance agricultural productivity in developing economies.

Keywords

• Agricultural Productivity
• Agricultural Mechanization
• Food Exports
• AMG
• CCE-MG

References

1. Anderson, K., & Martin, W. (2005). Agricultural trade reform and the Doha Development Agenda. World Economy, 28(9), 1301-1327. https://doi.org/10.1111/j.1467-9701.2005.00735.x
2. Asadullah, M. N., & Rahman, S. (2009). Farm productivity and efficiency in rural Bangladesh: the role of education revisited. Applied Economics, 41(1), 17-33. https://doi.org/10.1080/00036840601019125
3. Balassa, B. (1978). Exports and economic growth: Further evidence. Journal of Development Economics, 5(2), 181–189. https://doi.org/10.1016/0304-3878(78)90006-8
4. Baltagi, B. (2008). Econometric analysis of panel data. Springer Cham. https://doi.org/10.1007/978-3-030-53953-5
5. Beine, M., Docquier, F., & Rapoport, H. (2008). Brain drain and human capital formation in developing countries: Winners and losers. The Economic Journal, 118(528), 631–652. https://doi.org/10.1111/j.1468-0297.2008.02135.x
6. Binswanger, H. (1986). Agricultural mechanization: A comparative historical perspective. The World Bank Research Observer, 1(1), 27–56. https://doi.org/10.1093/wbro/1.1.27
7. Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, 47(1), 239-253. https://doi.org/10.2307/2297111
8. Chu, A. C., Furukawa, Y., Peretto, P., & Xu, R. (2024). Agricultural trade and industrial development. MPRA Paper, 124669. https://mpra.ub.uni-muenchen.de/124669/

9. De Monteiro Jales, M. Q., Jank, M. S., Yao, S., & Carter, C. A. (2019). Agricultural in Brazil and China Intal ITD Occasional Paper 44.
10. Diao, X., Hazell, P., & Thurlow, J. (2010). The role of agriculture in African development. World Development, 38(10), 1375–1383. https://doi.org/10.1016/j.worlddev.2009.06.011
11. Dollar, D., & Kraay, A. (2004). Trade, growth, and poverty. The Economic Journal, 114(493), F22–F49. https://doi.org/10.1111/j.0013-0133.2004.00186.x
12. Eberhardt, M., & Bond, S. (2009). Cross-section dependence in nonstationary panel models: A novel estimator. MPRA Paper No. 17692.
13. Eberhardt, M., & Teal, F. (2011). Econometrics for grumblers: A new look at the literature on cross-country growth empirics. Journal of Economic Surveys, 25(1), 109–155. https://doi.org/10.1111/j.1467-6419.2010.00624.x
14. Foster, A. D., & Rosenzweig, M. R. (1996). Technical change and human-capital returns and investments: Evidence from the Green Revolution. The American Economic Review, 86(4), 931–953. https://www.jstor.org/stable/2118312
15. Foster, A. D., & Rosenzweig, M. R. (2010). Microeconomics of technology adoption. Annual Review of Economics, 2(1), 395–424. https://doi.org/10.1146/annurev.economics.102308.124433
16. Fuglie, K. (2018). Is agricultural productivity slowing?. Global Food Security, 17, 73–83. https://doi.org/10.1016/j.gfs.2018.05.001
17. Gollin, D. (2010). Agricultural productivity and economic growth. Handbook of Agricultural Economics, 4, 3825–3866. https://doi.org/10.1016/S1574-0072(09)04073-0
18. Gollin, D., Parente, S., & Rogerson, R. (2002). The role of agriculture in development. The American Economic Review, 92(2), 160–164. https://doi.org/10.1257/000282802320189177
19. Im K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogenous panels. Journal of Econometrics, 115(1), 53-74. https://doi.org/10.1016/S0304-4076(03)00092-7
20. Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90(1), 1-44. https://doi.org/10.1016/S0304-4076(98)00023-2
21. McNamara, K. T., Wetzstein, M. E., & Douce, G. K. (1991). Factors affecting peanut producer adoption of integrated pest management. Applied Economic Perspectives and Policy, 13(1), 129-139. https://doi.org/10.2307/1349563
22. Mottaleb, K. A., Krupnik, T. J., & Erenstein, O. (2016). Factors associated with small-scale agricultural mechanization in Bangladesh: Census findings. Journal of Rural Studies, 46, 155–168. https://doi.org/10.1016/j.jrurstud.2016.06.012
23. Nin-Pratt, A., & McBride, L. (2014). Agricultural intensification in Ghana: Evaluating the optimist’s case for a Green Revolution. Food Policy, 48, 153–167. https://doi.org/10.1016/j.foodpol.2014.05.004
24. Our World in Data. (2025). https://ourworldindata.org
25. Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61, 653–670. https://doi.org/10.1111/1468-0084.0610s1653
26. Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory, 20(3), 597-625. https://doi.org/10.1017/S0266466604203073
27. Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics No. 0435, Faculty of Economics, University of Cambridge.
28. Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967-1012. https://doi.org/10.1111/j.1468-0262.2006.00692.x
29. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross‐section dependence. Journal of Applied Econometrics, 22(2), 265-312. https://doi.org/10.1002/jae.951
30. Pesaran, M. H., & Chudik, A. (2013). Large panel data models with cross-sectional dependence: A survey. Unpublished, Cambridge, UK.
31. Pesaran, M. H., & Tosetti, E. (2011). Large panels with common factors and spatial correlation. Journal of Econometrics, 161(2), 182-202. https://doi.org/10.1016/j.jeconom.2010.12.003
32. Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50-93. https://doi.org/10.1016/j.jeconom.2007.05.010
33. Pesaran, M. H., Shin, Y., & Smith, R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621-634.
34. Pesaran, M. H., Ullah, A., & Yamagata, T. (2008). A bias‐adjusted LM test of error cross‐section independence. The Econometrics Journal, 11(1), 105-127. https://doi.org/10.1111/j.1368-423X.2007.00227.x
35. Pingali, P. (2007). Agricultural mechanization: Adoption patterns and economic impact. Handbook of Agricultural Economics, 3, 2779–2805. https://doi.org/10.1016/S1574-0072(06)03054-4
36. Schultz, T. W. (1964). Transforming traditional agriculture. Yale University Press.
37. Sowrov, S. M. (2024). Trade openness, tariffs and economic growth: An empirical study from countries of G-20. arXiv:2405.08052. https://doi.org/10.48550/arXiv.2405.08052
38. Swinnen, J., & Squicciarini, P. (2012). Mixed messages on prices and food security. Science, 335(6067), 405–406. https://doi.org/10.1126/science.1210806
39. Takeshima, H. (2017). Custom-hired tractor services and returns to scale in smallholder agriculture: A production function approach. Agricultural Economics, 48(3), 363–372. https://doi.org/10.1111/agec.12339
40. Timmer, C. P. (2009). A world without agriculture: The structural transformation in historical perspective. American Enterprise Institute Press.
41. Toaha, M., & Mondal, L. (2023). Agriculture credit and economic growth in Bangladesh: A time series analysis. arXiv preprint arXiv:2309.04118. https://doi.org/10.48550/arXiv.2309.04118
42. van Huyssteen, T., Thiam, D., & Nonhebel, S. (2025). The sustainability of agricultural trade: The case of South Africa. Cleaner Production Letters, 8, 100092. https://doi.org/10.1016/j.clpl.2025.100092
43. Westerlund, J. (2005). New simple tests for panel cointegration. Econometric Reviews, 24, 297–316. https://doi.org/10.1080/07474930500243019
44. Westerlund, J. (2008). Panel cointegration tests of the Fisher effect. Journal of Applied Econometrics, 23(2), 193-233. https://doi.org/10.1002/jae.967
45. World Bank. (2025). World Development Indicators (WDI). https://databank.worldbank.org/source/world-development-indicators
46. Zeng, H., Chen, S., Zhang, H., & Xu, J. (2025). The effects and mechanisms of deep free trade agreements on agricultural global value chains. Frontiers in Sustainable Food Systems, 8, 1523091. https://doi.org/10.3389/fsufs.2024.1523091