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The Convergence Dynamics of Agricultural Labor Productivity in OECD Countries

Year 2023, Volume: 7 Issue: 1, 273 - 287, 26.06.2023
https://doi.org/10.33399/biibfad.1267854

Abstract

This study aims to examine the convergence dynamics of agricultural labor productivity in 38 OECD countries. To this end, the data for the period 1995-2019 were analyzed using the log-t convergence test proposed by Phillips and Sul (2007, 2009). The findings indicate that agricultural labor productivity in OECD countries does not converge as a whole, but rather, cross-country differences in agricultural labor productivity have increased over time. The clustering algorithm of the log-t test indicates that four final clubs of convergence characterize OECD countries in terms of agricultural labor productivity. While the first club with the best performance has followed a positive trend in which agricultural labor productivity has increased over time; the second club is positioned around the average limit. However, the long-term trend of agricultural labor productivity of the club, which includes Japan, Costa Rica, Poland, Greece, Chile and Turkey, points to a negative divergence. This negative divergence is more severe in the last club, which includes Colombia and Mexico. As a result, the fact that the clubs that are negatively diverged in terms of agricultural labor productivity include developed countries, as well as developing countries, implies that a high level of development will not guarantee an increase in agricultural labor productivity over time.

References

  • ABD Tarım Örgütü, (2022). International Agricultural Productivity. Erişim tarihi: 17 Şubat 2023. https://www.ers.usda.gov/data-products/international-agricultural-productivity/#:~:text=One%20of%20the%20most%20informative,resources%20employed%20in%20farm%20production.
  • Akyol, M. (2018). Tarımsal teşviklerle tarımsal katma değer arasındaki ilişkinin incelenmesi: yeni endüstrileşen ülkeler için panel eşanlı denklemler sistemi analizi. The Journal of International Scientific Researches, 3(3), 226–236.
  • Avrupa Komisyonu, (2020). Financial needs in the agriculture and agri-food sectors in Slovakia. European Investment Bank, fi-compass study on the use of EMFF financial instruments : final report, Publications Office, 2020.
  • Balezentis, T., & Sapolaite, V. (2022). Productivity surplus and its distribution in Lithuanian agriculture. Empirica, 49(3), 721–740. https://doi.org/10.1007/s10663-022-09544-x
  • Barrett, C. B., Christian, P., & Shiferaw, B. A. (2017). The structural transformation of African agriculture and rural spaces: Introduction to a special section. Agricultural Economics, 48(S1), 5-10.. https://doi.org/10.1111/agec.12382
  • Blanco, C., & Raurich, X. (2022). Agricultural composition and labor productivity. Journal of Development Economics, 158, 102934. https://doi.org/10.1016/j.jdeveco.2022.102934
  • Caixa Bank. (2019). Portugal’s agriculture sector: still dual, but promising. Erişim tarihi: 03 Mart 2023. https://www.caixabankresearch.com/en/portugals-agriculture-sector-still-dual-promising.
  • Cao, K. H., & Birchenall, J. A. (2013). Agricultural productivity, structural change, and economic growth in post-reform China. Journal of Development Economics, 104, 165–180. https://doi.org/10.1016/j.jdeveco.2013.06.001
  • Caselli, F. (2016). Accounting for cross-country income differences. Accounting for cross-country income differences, December. https://doi.org/10.1596/26105
  • Chen, P. C., Yu, M. M., Chang, C. C., & Hsu, S. H. (2008). Total factor productivity growth in China’s agricultural sector. China Economic Review, 19(4), 580–593. https://doi.org/10.1016/j.chieco.2008.07.001
  • Chloupkova, J. (2002). Polish Agriculture: Organisational Structure and Impacts of Transition. Unit of Economics Working Papers. https://ageconsearch.umn.edu/bitstream/24186/1/ew020003.pdf
  • Djoumessi, Y. F. (2022). New trend of agricultural productivity growth in sub-Saharan Africa. Scientific African, 18, e01410. https://doi.org/10.1016/j.sciaf.2022.e01410
  • Dünya Bankası (2021). Metadata Glossary. Erişim tarihi: 01 Mart 2023. https://databank.worldbank.org/metadataglossary/jobs/series/NV.AGR.TOTL.ZS
  • Dünya Bankası (2022). Agriculture and Food Overview. Erişim tarihi: 01 Mart 2023. https://www.worldbank.org/en/topic/agriculture/overview
  • Erdinç, Z., & Aydınbaş, G. (2021). Tarımsal katma değer belirleyicilerinin panel veri analizi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 21(1), 213–232. https://doi.org/10.18037/ausbd.902602
  • Eştürk, Ö., & Mert, N. (2022). Analyzing the determinants of agricultural value added in EU15 countries and Turkey by panel data. International Journal of Social Sciences, 6(26), 1–20. https://doi.org/10.52096/usbd.6.26.1
  • Gaspar, J., Pina, G., & Simões, M. (2022). Revue d ’ études en Agriculture et Environnement Agriculture in Portugal : linkages with industry and services (Vol. 95).
  • Gıda ve Tarım Organizasyonu. (2021). Gross domestic product and agriculture value added 1970–2019. Global and regional trends. FAOSTAT Analytical Briefs. https://www.fao.org/documents/card/en/c/cb4651en/
  • Giannakis, E., & Bruggeman, A. (2018). Exploring the labour productivity of agricultural systems across European regions: A multilevel approach. Land Use Policy, 77, 94–106. https://doi.org/10.1016/j.landusepol.2018.05.037
  • Grabowski, R., & Self, S. (2023). Agricultural productivity growth and the development of manufacturing in developing Asia. Economic Systems, January 2022, 101075. https://doi.org/10.1016/j.ecosys.2023.101075
  • Hu, Y., Liu, C., & Peng, J. (2021). Financial inclusion and agricultural total factor productivity growth in China. Economic Modelling, 96, 68–82. https://doi.org/10.1016/j.econmod.2020.12.021
  • Kotulic, R., Vozarova, I. K., Nagy, J., Huttmanova, E., & Vavrek, R. (2015). Performance of The Slovak Economy in Relation to Labor Productivity and Employment. Procedia Economics and Finance, 23, 970–975. https://doi.org/10.1016/s2212-5671(15)00444-x
  • Lambert, D. K., Lim, S. H., Tweeten, K., Leistritz, F. L., Wilson, W. W., McKee, G. J., Nganje, W. E., DeVuyst, C. S., & Saxowsky, D. M. (2006). Agricultural Value Added: Prospects for North Dakota. AgEcon Search.
  • Latin Amerika Kalkınma Bankasi (2022). Kolombiya Tarımının Verimliliğini Artırma İhtiyacı. Erişim tarihi: 25 Şubat 2023. https://www.caf.com/en/knowledge/views/2021/04/the-need-to-boost-productivity-of-colombian-agriculture/
  • Maris, M. (2019). Structural and productivity shift of industries in Slovakia and Czech Republic: A comparative study. Journal of International Studies, 12(1), 313–323. https://doi.org/10.14254/2071-8330.2019/12-1/21
  • McErlean, S., & Wu, Z. (2003). Regional agricultural labour productivity convergence in China. Food Policy, 28(3), 237–252. https://doi.org/10.1016/S0306-9192(03)00035-6
  • Phillips, P.C.B., Sul, D., 2007. Transition modeling and econometric convergence tests. Econometrica, 75, 1771–1855. https://doi.org/10.1111/j.1468-0262.2007.00811.x.
  • Phillips, P.C.B., Sul, D., 2009. Economic transition and growth. Journal of Applied Econometrics, 24, 1153–1185. https://doi.org/10.1002/jae.1080.
  • Smędzik-Ambroży, K., Rutkowska, M., & Kirbaş, H. (2019). Productivity of the Polish Agricultural Sector Compared To European Union Member States in 2004-2017 based on fadn farms. Annals of the Polish Association of Agricultural and Agribusiness Economists, XXI(3), 422–431. https://doi.org/10.5604/01.3001.0013.3447
  • Soyyiğit, S., & Aslan, Y. K. (2019). An investigation on the factors affecting agricultural value added: the case of E7 countries. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10, 403–429. https://doi.org/10.9775/kauiibfd.2019.017
  • Thirtle, C., Lin, L., & Piesse, J. (2003). The impact of research-led agricultural productivity growth on poverty reduction in Africa, Asia and Latin America. World Development, 31(12), 1959–1975. https://doi.org/10.1016/j.worlddev.2003.07.001
  • Uluslararası Ticaret İdaresi (2022). Slovakya - Ülke Ticari Rehberi. Erişim tarihi: 25 Şubat 2023. https://www.trade.gov/country-commercial-guides/slovakia-agricultural-sectors
  • UNCTAD. (2014). Mexico’s Agricultural Development: Perspectives and Outlook
  • Veveris, A., Sapolaite, V., & Dambina, L. (2016). Productivity of Latvian and Lithuanian rural farms and main factors influencing it. Research for Rural Development, 2, 113–119.
  • Yuan, L., Zhang, S., Wang, S., Qian, Z., & Gong, B. (2021). World agricultural convergence. Journal of Productivity Analysis, 55(2), 135–153. https://doi.org/10.1007/s11123-021-00600-5

OECD Ülkelerinde Tarımsal İşgücü Verimliliğinin Yakınsama Dinamikleri

Year 2023, Volume: 7 Issue: 1, 273 - 287, 26.06.2023
https://doi.org/10.33399/biibfad.1267854

Abstract

Bu çalışma, 38 OECD ülkesinin tarımsal işgücü verimliliğinin yakınsama dinamiklerini incelemeyi amaçlamaktadır. Bu amaçla, 1995-2019 dönemine ait veriler Phillips ve Sul (2007, 2009) tarafından önerilen log-t yakınsama testi kullanılarak incelenmiştir. Çalışma bulguları, OECD ülkelerinde tarımsal işgücü verimliliğinin bir bütün olarak yakınsama trendi takip etmediği, bunun yerine tarımsal işgücü verimliliğinde ülkeler arası farklılıklarının zaman içinde arttığına işaret etmektedir. Log-t testinin kümeleme algoritması, tarımsal işgücü verimliliği bakımından OECD ülkelerinin dört nihai yakınsama kulübüyle karakterize edildiğini göstermektedir. En iyi performans gösteren ilk kulüp, dönem başından itibaren tarımsal işgücü verimliliğinin arttığı pozitif bir trendi takip ederken; ikinci kulüp, dönem boyunca ortalama sınırın etrafında konumlanmıştır. Ancak aralarında Türkiye’nin de bulunduğu Japonya, Kosta Rika, Polonya, Yunanistan ve Şili’yi içeren kulübün tarımsal işgücü verimliliğinin uzun dönemli eğilimi, negatif bir ayrışmaya işaret etmektedir. Bu negatif ayrışma, Kolombiya ve Meksika’yı içeren son kulüpte daha şiddetlidir. Sonuç olarak, tarımsal işgücü verimliliği bakımından negatif ayrışan kulüplerin gelişmekte olan ülkelerin yanı sıra gelişmiş ülkeleri de içermesi, yüksek bir gelişmişlik düzeyinin zaman içinde artan bir tarımsal işgücü verimliliğini garanti etmeyeceğini ima etmektedir.

References

  • ABD Tarım Örgütü, (2022). International Agricultural Productivity. Erişim tarihi: 17 Şubat 2023. https://www.ers.usda.gov/data-products/international-agricultural-productivity/#:~:text=One%20of%20the%20most%20informative,resources%20employed%20in%20farm%20production.
  • Akyol, M. (2018). Tarımsal teşviklerle tarımsal katma değer arasındaki ilişkinin incelenmesi: yeni endüstrileşen ülkeler için panel eşanlı denklemler sistemi analizi. The Journal of International Scientific Researches, 3(3), 226–236.
  • Avrupa Komisyonu, (2020). Financial needs in the agriculture and agri-food sectors in Slovakia. European Investment Bank, fi-compass study on the use of EMFF financial instruments : final report, Publications Office, 2020.
  • Balezentis, T., & Sapolaite, V. (2022). Productivity surplus and its distribution in Lithuanian agriculture. Empirica, 49(3), 721–740. https://doi.org/10.1007/s10663-022-09544-x
  • Barrett, C. B., Christian, P., & Shiferaw, B. A. (2017). The structural transformation of African agriculture and rural spaces: Introduction to a special section. Agricultural Economics, 48(S1), 5-10.. https://doi.org/10.1111/agec.12382
  • Blanco, C., & Raurich, X. (2022). Agricultural composition and labor productivity. Journal of Development Economics, 158, 102934. https://doi.org/10.1016/j.jdeveco.2022.102934
  • Caixa Bank. (2019). Portugal’s agriculture sector: still dual, but promising. Erişim tarihi: 03 Mart 2023. https://www.caixabankresearch.com/en/portugals-agriculture-sector-still-dual-promising.
  • Cao, K. H., & Birchenall, J. A. (2013). Agricultural productivity, structural change, and economic growth in post-reform China. Journal of Development Economics, 104, 165–180. https://doi.org/10.1016/j.jdeveco.2013.06.001
  • Caselli, F. (2016). Accounting for cross-country income differences. Accounting for cross-country income differences, December. https://doi.org/10.1596/26105
  • Chen, P. C., Yu, M. M., Chang, C. C., & Hsu, S. H. (2008). Total factor productivity growth in China’s agricultural sector. China Economic Review, 19(4), 580–593. https://doi.org/10.1016/j.chieco.2008.07.001
  • Chloupkova, J. (2002). Polish Agriculture: Organisational Structure and Impacts of Transition. Unit of Economics Working Papers. https://ageconsearch.umn.edu/bitstream/24186/1/ew020003.pdf
  • Djoumessi, Y. F. (2022). New trend of agricultural productivity growth in sub-Saharan Africa. Scientific African, 18, e01410. https://doi.org/10.1016/j.sciaf.2022.e01410
  • Dünya Bankası (2021). Metadata Glossary. Erişim tarihi: 01 Mart 2023. https://databank.worldbank.org/metadataglossary/jobs/series/NV.AGR.TOTL.ZS
  • Dünya Bankası (2022). Agriculture and Food Overview. Erişim tarihi: 01 Mart 2023. https://www.worldbank.org/en/topic/agriculture/overview
  • Erdinç, Z., & Aydınbaş, G. (2021). Tarımsal katma değer belirleyicilerinin panel veri analizi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 21(1), 213–232. https://doi.org/10.18037/ausbd.902602
  • Eştürk, Ö., & Mert, N. (2022). Analyzing the determinants of agricultural value added in EU15 countries and Turkey by panel data. International Journal of Social Sciences, 6(26), 1–20. https://doi.org/10.52096/usbd.6.26.1
  • Gaspar, J., Pina, G., & Simões, M. (2022). Revue d ’ études en Agriculture et Environnement Agriculture in Portugal : linkages with industry and services (Vol. 95).
  • Gıda ve Tarım Organizasyonu. (2021). Gross domestic product and agriculture value added 1970–2019. Global and regional trends. FAOSTAT Analytical Briefs. https://www.fao.org/documents/card/en/c/cb4651en/
  • Giannakis, E., & Bruggeman, A. (2018). Exploring the labour productivity of agricultural systems across European regions: A multilevel approach. Land Use Policy, 77, 94–106. https://doi.org/10.1016/j.landusepol.2018.05.037
  • Grabowski, R., & Self, S. (2023). Agricultural productivity growth and the development of manufacturing in developing Asia. Economic Systems, January 2022, 101075. https://doi.org/10.1016/j.ecosys.2023.101075
  • Hu, Y., Liu, C., & Peng, J. (2021). Financial inclusion and agricultural total factor productivity growth in China. Economic Modelling, 96, 68–82. https://doi.org/10.1016/j.econmod.2020.12.021
  • Kotulic, R., Vozarova, I. K., Nagy, J., Huttmanova, E., & Vavrek, R. (2015). Performance of The Slovak Economy in Relation to Labor Productivity and Employment. Procedia Economics and Finance, 23, 970–975. https://doi.org/10.1016/s2212-5671(15)00444-x
  • Lambert, D. K., Lim, S. H., Tweeten, K., Leistritz, F. L., Wilson, W. W., McKee, G. J., Nganje, W. E., DeVuyst, C. S., & Saxowsky, D. M. (2006). Agricultural Value Added: Prospects for North Dakota. AgEcon Search.
  • Latin Amerika Kalkınma Bankasi (2022). Kolombiya Tarımının Verimliliğini Artırma İhtiyacı. Erişim tarihi: 25 Şubat 2023. https://www.caf.com/en/knowledge/views/2021/04/the-need-to-boost-productivity-of-colombian-agriculture/
  • Maris, M. (2019). Structural and productivity shift of industries in Slovakia and Czech Republic: A comparative study. Journal of International Studies, 12(1), 313–323. https://doi.org/10.14254/2071-8330.2019/12-1/21
  • McErlean, S., & Wu, Z. (2003). Regional agricultural labour productivity convergence in China. Food Policy, 28(3), 237–252. https://doi.org/10.1016/S0306-9192(03)00035-6
  • Phillips, P.C.B., Sul, D., 2007. Transition modeling and econometric convergence tests. Econometrica, 75, 1771–1855. https://doi.org/10.1111/j.1468-0262.2007.00811.x.
  • Phillips, P.C.B., Sul, D., 2009. Economic transition and growth. Journal of Applied Econometrics, 24, 1153–1185. https://doi.org/10.1002/jae.1080.
  • Smędzik-Ambroży, K., Rutkowska, M., & Kirbaş, H. (2019). Productivity of the Polish Agricultural Sector Compared To European Union Member States in 2004-2017 based on fadn farms. Annals of the Polish Association of Agricultural and Agribusiness Economists, XXI(3), 422–431. https://doi.org/10.5604/01.3001.0013.3447
  • Soyyiğit, S., & Aslan, Y. K. (2019). An investigation on the factors affecting agricultural value added: the case of E7 countries. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10, 403–429. https://doi.org/10.9775/kauiibfd.2019.017
  • Thirtle, C., Lin, L., & Piesse, J. (2003). The impact of research-led agricultural productivity growth on poverty reduction in Africa, Asia and Latin America. World Development, 31(12), 1959–1975. https://doi.org/10.1016/j.worlddev.2003.07.001
  • Uluslararası Ticaret İdaresi (2022). Slovakya - Ülke Ticari Rehberi. Erişim tarihi: 25 Şubat 2023. https://www.trade.gov/country-commercial-guides/slovakia-agricultural-sectors
  • UNCTAD. (2014). Mexico’s Agricultural Development: Perspectives and Outlook
  • Veveris, A., Sapolaite, V., & Dambina, L. (2016). Productivity of Latvian and Lithuanian rural farms and main factors influencing it. Research for Rural Development, 2, 113–119.
  • Yuan, L., Zhang, S., Wang, S., Qian, Z., & Gong, B. (2021). World agricultural convergence. Journal of Productivity Analysis, 55(2), 135–153. https://doi.org/10.1007/s11123-021-00600-5
There are 35 citations in total.

Details

Primary Language Turkish
Subjects Economics
Journal Section Makaleler
Authors

Ahmet Tunç 0000-0002-0864-2695

Early Pub Date June 23, 2023
Publication Date June 26, 2023
Submission Date March 20, 2023
Published in Issue Year 2023 Volume: 7 Issue: 1

Cite

APA Tunç, A. (2023). OECD Ülkelerinde Tarımsal İşgücü Verimliliğinin Yakınsama Dinamikleri. Bingöl Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 7(1), 273-287. https://doi.org/10.33399/biibfad.1267854


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