Spatio-Temporal Analysis of Agricultural Labour Wages Using Vector Error Correction Model: An Integrated Approach to Environment and Climate Change

This study attempted to explore the interactive relations among agricultural labour wage rates in five neighbouring Indian states viz., Andhra Pradesh, Karnataka, Tamilnadu Telangana and Chhattisgarh using monthly time series data of 2005-2020. The objective this was examine degree integration labourers states. Integration with outside markets may partly mitigate costs climate change, as individuals respond warming temperature by migrating urban areas internationally search employment. We built vector error correction model (VECM) conducting stationarity test cointegration test. Granger Causality employed check whether different influence each other. For building VEC Model, complete set (180 points) split into training (168 testing (12 sets. nonstationarity established Augmented Dickey Fuller purpose forecasting, VECM (1) tested for goodness fit Mean Absolute Percentage Error (MAPE) which were found be < 10% all suggesting good model. A growing body literature suggests that economic change substantial far‐reaching, impacting agriculture, mortality, productivity, growth, civil conflict migration.