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

Abstract

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