This study explores the interval-valued data analysis techniques to witness the spatial disparity in the wage rates of farm labourers in India. Farm labourers constitute more than half of the total workforce engaged in Indian agriculture. Also, farmers' expenses towards labour charges account for more than 50 per cent of the total variable cost of production for most crops.Using the time series data on the nominal farm wage rates paid at different agriculturally important states, the interval-valued series are built. The inflation-adjusted real wage rates are found and both nominal and real wage rate data are used to find the average range of the farm wage rates over the agricultural years for a decade. Using the time series analysis techniques, viz. autoregressive integrated moving average-artificial neural network (ARIMA-ANN) hybrid model and vector autoregressive moving average (VARMA) model, the interval-valued data on nominal wage rates are modelled and the best model for forecasting is identified using forecast evaluation methods. The results established the presence of spatial disparity and the forecasts indicated that this disparity is not going to narrow down in future unless some policy intervention takes place. © 2018 Indian Council of Agricultural Research. All rights reserved.