Time Series Analysis of Seasonal Agricultural Worker Wages in Türkiye
DOI:
https://doi.org/10.24925/turjaf.v14i6.1630-1642.8670Keywords:
Real wage , Seasonal worker , Time series analysis , ARIMA , TrendAbstract
This study aims to examine the dynamics of real daily wages of seasonal agricultural workers in Türkiye, focusing on city-level and gender dimensions. The analysis is conducted using city-level real wage series for the 2007–2024 period. Initially, stationarity and trend analyses were applied, followed by a classification of cities based on wage levels and growth trends. In the final stage, forward-looking projections up to the year 2030 were generated using ARIMA models. The findings indicate that real agricultural wages generally exhibit an upward trend in the long run; however, this increase involves significant heterogeneity across cities and regions. It was observed that real wage growth is stronger and more stable in cities within the “high-trend” group, whereas growth remains limited and forecast uncertainty increases in the “low-trend” group. Furthermore, the results reveal that the real daily wages of female agricultural workers are lower than those of male workers in all cities. Forecast results suggest that real wage increases will continue in the medium and long term. These findings demonstrate that policies regarding the agricultural labor market should be designed within a flexible and targeted framework that considers regional disparities and gender-based inequalities
References
Akçil, M. B., & Bayramoğlu, Z. (2022). Mevsimlik tarım işçilerinin asgari yaşam maliyetlerinin hesaplanması. Bahri Dağdaş Bitkisel Araştırma Dergisi, 11(2), 180–189.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2008). Time series analysis: Forecasting and control (4th ed.). Wiley.
Blau, F. D., & Kahn, L. M. (2017). The gender wage gap: Extent, trends, and explanations. Journal of Economic Literature, 55(3), 789–865. https://doi.org/10.1257/jel.20160995
Burnham, K. P., & Anderson, D. R. (2004). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). Springer.
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
Enders, W. (2014). Applied econometric time series (4th ed.). Wiley.
Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2020). Cluster analysis (6th ed.). Wiley.
Food and Agriculture Organization of the United Nations. (2011). The state of food and agriculture 2010–11: Women in agriculture – Closing the gender gap for development. FAO.
Food and Agriculture Organization of the United Nations. (2023). FAO food outlook: Biannual report on global food markets. FAO.
Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). McGraw-Hill/Irwin.
Hamilton, J. D. (1994). Time series analysis. Princeton University Press.
Harris, R., & Sollis, R. (2003). Applied time series modelling and forecasting. Wiley.
Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(1), 100–108. https://doi.org/10.2307/2346830
Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. https://otexts.com/fpp3/
International Labour Organization. (2018). Global wage report 2018/19: What lies behind gender pay gaps. ILO.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R (2nd ed.). Springer.
Klasen, S., & Lamanna, F. (2009). The impact of gender inequality in education and employment on economic growth: New evidence for a panel of countries. Feminist Economics, 15(3), 91–132. https://doi.org/10.1080/13545700902893106
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1–3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y
Martin, P. L., & Taylor, J. E. (2013). Labour market dynamics in agricultural regions: Employment, wages, and demographic change. Journal of Agricultural Economics, 64(4), 789–807. https://doi.org/10.1111/1477-9552.12033
OECD. (2022). Agricultural policy monitoring and evaluation 2022. OECD Publishing.
Pauli, E. A., & Gülçubuk, B. (2024). Mevsimlik gezici tarım işçisi hanelerin karşılaştığı yoksunlukları anlamak: Ankara ili Bala ilçesi örneği. Tekirdağ Ziraat Fakültesi Dergisi, 21(5), 1294–1307. https://doi.org/10.33462/jotaf.1524143
Stock, J. H., & Watson, M. W. (2016). Introduction to econometrics (3rd ed.). Pearson Education.
Topçu, M., & Taşçı, H. M. (2017). Export-oriented agriculture and labor demand in Turkey. Economic Modelling, 64, 21–34.
Turgut, D., Güler, B., & Vatansever, Ç. (2025). Mevsimlik tarım işçilerinin çalışma koşulları, yaşam koşulları ve sağlıkla ilişkili problemleri: Sistematik bir derleme çalışması. İş, Güç: Endüstri İlişkileri ve İnsan Kaynakları Dergisi, 27(1), 458–489.
TÜİK. (2023). TÜFE ve tarımsal göstergeler veri tabanı. Türkiye İstatistik Kurumu.
TÜİK. (2024). Tarımsal girdi fiyat endeksi, Türkiye İstatistik Kurumu.
TÜİK. (2024). Tarımsal işletme işgücü ücret yapısı, Türkiye İstatistik Kurumu.
Wooldridge, J. M. (2015). Introductory econometrics: A modern approach (6th ed.). Cengage Learning.
World Bank. (2020). Gender dimensions of agricultural and rural employment: Differentiated pathways out of poverty. World Bank Publications.
Zhang, Y. (2021). COVID-19 and labor market tightening in agriculture. World Development, 140, 105287.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.






