Tarım ihracatında performansın anatomisi: Türkiye üzerine sektörel bulgular

Year 2025, Volume: 31 Issue: 2, 349 - 361, 19.12.2025

Taner Taş , Kubilay Çağrı Yılmaz

https://doi.org/10.24181/tarekoder.1748164

Abstract

Amaç: Bu çalışma, Türkiye’nin sekiz tarımsal alt sektörüne ait ihracat performansını etkileyen belirleyicileri sektörel düzeyde analiz etmeyi amaçlamaktadır. Çalışmanın temel hedefi, döviz kuru, alıcı ülke geliri ve tarımsal verimlilik gibi makro değişkenlerin sektörel ihracat üzerindeki etkilerini ortaya koymaktır.
Tasarım/Metodoloji/Yaklaşım: –2022 dönemine ait panel veri kullanılarak sabit etkiler ve IV (2SLS) tahmin yöntemleri temel analiz yöntemleri olarak uygulanmıştır. Ayrıca GMM tahmincileri ek bir sağlamlık kontrolü amacıyla denenmiş, ancak sonuçlar ekonometrik varsayımlarla tutarlı bulunmadığından raporlanmamıştır. Bağımlı değişken olarak ihracat değeri, bağımsız değişkenler olarak reel efektif döviz kuru (REER), alıcı ülkelerin GSYH’si (GDP) ve tarımsal toplam faktör verimliliği (TFP) değişkenleri kullanılmıştır.
Bulgular: Döviz kurunun ihracat üzerindeki etkisi çoğu sektörde anlamlı ve negatiftir. GDP değişkeni tüm sektörlerde pozitif ve anlamlı bulunmuştur. TFP değişkeni yalnızca tütün sektöründe anlamlıdır. Sonuçlar, Türkiye'nin bazı sektörlerde karşılaştırmalı üstünlüğe sahip olduğunu göstermektedir.
Araştırma Sınırlamaları/Etkileri: Bu çalışmada odaklanılan kavramsal çerçeve doğrultusunda, üretim kapasitesini temsilen yalnızca tarımsal toplam faktör verimliliği (TFP) değişkeni modele dâhil edilmiştir; diğer üretim faktörleri ise araştırma kapsamı dışında bırakılmıştır.
Özgünlük/Değer: Çalışma, ürün bazlı sektörel ayrım yaparak, döviz kuru ve dış talep kanalları üzerinden ihracat performansına dair yeni ve detaylı ampirik bulgular sunmaktadır.

Keywords

Tarımsal ihracat , döviz kuru , dış talep , panel veri , verimlilik

Anatomy of agricultural export performance: Sectoral evidence from Türkiye

Abstract

Purpose: This study aims to investigate the determinants of agricultural export performance across eight sub-sectors in Türkiye. The main objective is to identify the sector-specific effects of macroeconomic factors such as exchange rates, income levels of importing countries, and agricultural productivity.
Design/Methodology/Approach: Using panel data from 2012 to 2022, Fixed Effects (FE) and Instrumental Variables (IV, 2SLS) are employed as the main estimation techniques. In addition, Generalized Method of Moments (GMM) estimators were tested as a robustness check; however, since the results were not consistent with econometric assumptions, they are not reported. The dependent variable is the sectoral export value, while REER, GDP, and TFP are included as key explanatory variables.
Findings: The effect of the exchange rate on exports is significant and negative in most sectors. The GDP variable is found to be positive and significant across all sectors. The TFP variable is significant only in the tobacco sector. The results indicate that Türkiye holds comparative advantages in certain sectors.
Research Limitations/Implications: In line with the conceptual framework adopted in this study, only agricultural total factor productivity (TFP) is included to represent production capacity, while other production-related variables are deliberately excluded from the scope of analysis.
Originality/Value: The study provides novel empirical insights into sectoral heterogeneity by examining agricultural export performance through the lens of exchange rate competitiveness and demand-side factors at the product level.

Keywords

Agricultural exports , exchange rate , external demand , panel data , productivity

References

• Amiti, M., Itskhoki, O., and Konings, J. (2014), “Importers, exporters, and exchange rate disconnect”, American Economic Review, 104(7), 1942–1978. https://doi.org/10.1257/aer.104.7.1942.
• Angrist, J. D., and Pischke, J.-S. (2009), Mostly harmless econometrics: An empiricist's companion, Princeton University Press.
• Arellano, M., and 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.
• Arellano, M., and Bover, O. (1995), “Another look at the instrumental variable estimation of error-components models”, Journal of Econometrics, 68(1), 29–51.
• Bahmani-Oskooee, M., and Ratha, A. (2004), “The J-curve: A literature review”, Applied Economics, 36(13), 1377–1398.
• Balassa, B. (1965), Trade liberalisation and “revealed” comparative advantage, The Manchester School, 33(2), 99–123.
• Baltagi, B. H. (2005), Econometric analysis of panel data (3rd ed.), Chichester: John Wiley and Sons.
• Baltagi, B. H. (2013), Econometric analysis of panel data, Wiley.
• Baltagi, B. H., and Wu, P. X. (1999), “Unequally spaced panel data regressions with AR(1) disturbances”, Econometric Theory, 15(6), 814–823.
• Barrett, C. B., Reardon, T., and Webb, P. (2001), “Nonfarm income diversification and household livelihood strategies in rural Africa: concepts, dynamics, and policy implications”, Food Policy, 26(4), 315–331.
• Baum, C. F., Schaffer, M. E., and Stillman, S. (2007), “Enhanced routines for instrumental variables/2SLS and GMM estimation and testing”, Stata Journal, 7(4), 465–506.
• Berman, N., Martin, P., and Mayer, T. (2012), “How do different exporters react to exchange rate changes?”, The Quarterly Journal of Economics, 127(1), 437–492.
• Birleşmiş Milletler Gıda ve Tarım Örgütü [FAO]. (2017), The future of food and agriculture – Trends and challenges, Rome. 02.07.2023 tarihinde https://www.fao.org/3/i6583e/i6583e.pdf adresinden alınmıştır.
• Birleşmiş Milletler Gıda ve Tarım Örgütü [FAO]. (2023), World food situation, 30.03.2023 tarihinde http://www.fao.org/worldfoodsituation/foodpricesindex/en/ adresinden alınmıştır.
• Bound, J., Jaeger, D. A., and Baker, R. M. (1995), “Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak”, Journal of the American Statistical Association, 90(430), 443–450.
• Cameron, A. C., and Miller, D. L. (2015), “A practitioner’s guide to cluster-robust inference”, Journal of Human Resources, 50(2), 317–372.
• Chen, N. F., and Juvenal, L. (2016), “Quality pricing-to-market”, Journal of International Economics, 100, 61–80.
• Cho, G., Sheldon, I.M. and McCorriston, S. (2002), “Exchange rate uncertainty and agricultural trade”, American Journal of Agricultural Economics, Vol. 84 No. 4, pp. 931-942.
• Demirağ, İ., and Sağır, M. (2023), “Gıda Fiyatları Neden Yükseliyor? Türkiye’de Üretici Ve Döviz Kuru Etkisinin ARDL İle İncelemesi”, Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 42(1), 33-46.
• Driscoll, J. C., and Kraay, A. C. (1998), “Consistent covariance matrix estimation with spatially dependent panel data”, Review of Economics and Statistics, 80(4), 549–560.
• Edwards, S. (1989), Real exchange rates, devaluation, and adjustment: Exchange rate policy in developing countries. MIT Press.
• El-Shagi, M., Sawyer, W.C. and Tochkov, K. (2022), “The income elasticity of import demand: A meta-survey”, Pacific Economic Review, 27(1), pp.18–41.
• Engel, E. (1895), Die Lebenskosten belgischer Arbeiterfamilien früher und jetzt [The cost of living of Belgian working-class families then and now] (Original work published 1857).
• FAO. (2020), Iraq agriculture sector note, Food and Agriculture Organization of the United Nations.
• FAO. (2021), FAO statistical yearbook 2021: World food and agriculture. Food and Agriculture Organization of the United Nations.
• FAOSTAT. (2023), Crops and livestock products database. Food and Agriculture Organization of the United Nations.
• Fuglie, K. (2015), “Accounting for growth in global agriculture”, Bio-based and Applied Economics Journal, 4(3), 201–234.
• Fuglie, K. O. (2018), “R and D Capital, R and D Spillovers, and Productivity Growth in World Agriculture”, Applied Economic Perspectives and Policy, 40(3), 421–444.
• Grant, J. H., and Boys, K. A. (2012), “Agricultural trade and the GATT/WTO: does membership make a difference?”, American Journal of Agricultural Economics, 94(1), 1-24.
• Hansen, L. P. (1982), “Large sample properties of generalized method of moments estimators”, Econometrica, 50(4), 1029–1054.
• Hausman, J. A. (1978), “Specification tests in econometrics”, Econometrica, 46(6), 1251–1271. https://doi.org/10.2307/1913827.
• Headey, D. (2011), “Rethinking the global food crisis: The role of trade shocks”, Food Policy, 36(2), 136–146. https://doi.org/10.1016/j.foodpol.2010.10.003.
• Hechler, D. (2007), “Robust standard errors for panel regressions with cross-sectional dependence”, Stata Journal, 7(3), 281–312.
• International Olive Council (IOC). (2023), World olive oil figures.
• ITC Trade Map. (2023), Trade statistics for international business development. International Trade Centre.
• Knetter, M. M. (1989), “Price discrimination by US and German exporters”, American Economic Review, 79(1), 198–210.
• Krugman, P. R. (1987), “Pricing to market when the exchange rate changes”, In S. W. Arndt and J. D. Richardson (Eds.), Real-Financial Linkages among Open Economies (pp. 49–70). MIT Press.
• Kwon, Y., and Koo, W. W. (2009), “Price transmission channels of exchange rate changes on food prices”, Review of Agricultural Economics, 31(3), 639–657.
• Melitz, M. J. (2003), “The impact of trade on intra‐industry reallocations and aggregate industry productivity”, Econometrica, 71(6), 1695–1725.
• Pesaran, M. H. (2004), General diagnostic tests for cross section dependence in panels, CESifo Working Paper Series, No. 1229.
• Piesse, J., and Thirtle, C. (2009), “Three bubbles and a panic: An explanatory review of recent food commodity price events”, Food Policy, 34(2), 119–129.
• Ricardo, D. (2004), On the principles of political economy and taxation (P. Sraffa, Ed.). Liberty Fund. (Original work published 1817).
• Sanderson, E., and Windmeijer, F. (2016), “A weak instrument F-test in linear IV models with multiple endogenous variables”, Journal of Econometrics, 190(2), 212–221.
• Stock, J. H., and Watson, M. W. (2015), Introduction to Econometrics (3rd ed.), Pearson.
• Stock, J. H., and Yogo, M. (2005), “Testing for weak instruments in linear IV regression. In D. W. K. Andrews and J. H. Stock (Eds.), Identification and inference for econometric models: Essays in honor of Thomas Rothenberg” (pp. 80–108). Cambridge University Press.
• Umar, Z., Jareño, F., and Escribano, A. (2021), “Agricultural commodity markets and oil prices: An analysis of the dynamic return and volatility connectedness”, Resources Policy, 73, 102147.
• Verter, N., & Bečvárová, V. (2016), “The impact of agricultural exports on economic growth in Nigeria”, Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 64(2), 691–700.
• Wang, K., and Barrett, C. B. (2007), “Estimating the effects of exchange rate volatility on export volumes”, Journal of Agricultural and Resource Economics, 32(2), 225–255.
• Windmeijer, F. (2005), “A finite sample correction for the variance of linear efficient two-step GMM estimators”, Journal of Econometrics, 126(1), 25–51.
• Wooldridge, J. M. (2010), Econometric analysis of cross section and panel data (2nd ed.), Cambridge, MA: MIT Press.
• World Bank & International Monetary Fund, (2011), Price volatility in food and agricultural markets: Policy responses, Washington, DC: World Bank.
• Wu, D. (1973), “Alternative tests of independence between stochastic regressors and disturbances”, Econometrica, 41(4), 733–750.
• Yang, Q. (2022), “Agricultural economic resilience in the context of international food price fluctuations”, Sustainability, 14(21), 14102.