Agriculture and Regional Development Spatial Insight from Turkish Provinces
Abstract
This study investigates the impact of agricultural activities on regional economic growth in Turkey over the period 2008–2020. Utilizing a balanced panel dataset for 81 NUTS-3 provinces, the analysis is grounded in the Augmented Solow–Swan Growth Model pro-posed by Mankiw, Romer, and Weil (1992). Spatial econometric techniques are employed to assess spatial dependence and interregional linkages. Hausman test outcomes identify the Spatial Durbin Model (SDM) as the most appropriate specification. The empirical find-ings reveal that agricultural production exerts a positive and statistically significant influ-ence on regional economic growth (coefficient = 0.037). While the direct effect is signifi-cant (0.046), both the indirect (0.108) and total (0.155) effects are positive yet statistically insignificant, indicating limited spatial spillovers across provinces. These results suggest that agriculture supports local economic performance but does not induce strong cross-regional diffusion effects. The study is limited to provincial-level data for 2008–2020, and results may vary with alternative spatial weight matrices or structural changes beyond this period. Policy implications highlight the need to complement agricultural support with improve-ments in human capital, infrastructure, and technology to enhance regional convergence. The study contributes to the literature by integrating spatial econometrics with the Solow–Swan framework and offering novel evidence on agriculture–growth dynamics in Turkey.
Keywords
References
- Acemoglu, D. (2009). Introduction to modern economic growth. Princeton University Press.
- Anselin, L. (1988). Spatial econometrics: Methods and models. Kluwer Academic Publishers. https://doi.org/10.1007/978-94-015-7799-1
- Anselin, L., & Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. In A. Ullah & D. E. A. Giles (Eds.), Handbook of applied economic statistics (pp. 237–289). Marcel Dekker.
- Burnett, J. W. (2025). Bayesian spatial analysis of US agricultural land values. Journal of Agricultural Economics, 56(2), 123–145. https://doi.org/10.1111/1477-9552.12456
- 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
- Elhorst, J. P. (2014). Spatial econometrics: From cross-sectional data to spatial panels. Springer. https://doi.org/10.1007/978-3-642-40340-8
- Gollin, D. (2010). Agricultural productivity and economic growth. In P. Aghion & S. Durlauf (Eds.), Handbook of economic growth (Vol. 2, pp. 382–472). Elsevier. https://doi.org/10.1016/S1574-0684(10)02007-5
- Gollin, D., Parente, S., & Rogerson, R. (2002). The role of agriculture in development. American Economic Review, 92(2), 160–164. https://doi.org/10.1257/000282802320189212
Details
Primary Language
English
Subjects
Panel Data Analysis
Journal Section
Research Article
Authors
Sinan Çinar
*
0000-0002-2756-5875
Türkiye
Publication Date
May 31, 2026
Submission Date
December 5, 2025
Acceptance Date
March 13, 2026
Published in Issue
Year 2026 Volume: 10 Number: 1