Measuring Technical Efficiency and Returns to Scale in Indian Agriculture Using Panel Data: A Case Study of West Bengal

The study investigates farm level technical efficiency (TE) and its determinants in the state of West Bengal in India. A stochastic production frontier model has been applied for determining technical efficiency by using panel data on 17 agricultural production units over a period of 23 years. Maximum-likelihood estimates of the Cobb-Douglas stochastic frontier production function in a time-variant truncated normal distribution is appropriate for the measurement of technical efficiency of West Bengal agriculture in India. The estimated variance ratio indicates that 48.90 percent of the differences between the observed and the estimated output is caused by differences in farms’ technical inefficiencies. However, the remaining variation is due to factors beyond farmers’ control. The study shows that the agricultural farms in West Bengal exhibit increasing returns to scale in production. The study finds that farmers’ education and agricultural extension are important determinants of technical efficiency. Other prominent determinants that have a significant contribution are farm size, crop diversification, number of available agricultural markets, the proportion of small landholders and input intensity. All these determinants, excluding the proportion of small landholders, have a largely positive impact on technical efficiency. The maximum-likelihood estimation (MLE) and principal component analysis (PCA) are applied to determine the effects of determinants on TE. Both methods give similar results

The Impact of Price and Non-Price Factors on Area Allocated to Oilseeds in India: An Application of ARDL Model

The study attempts to investigate the dynamic relationship between acreage allocation of oilseeds and price (own price and prices of competitive crops) and also search for the link between area allocation and other non-price factors including productivity, irrigation, rainfall, technology and a policy-making variable (economic liberalization). The dynamic panel data for the year 1976-77 to 2017-18 have been used in the analysis. The study has used the autoregressive distributive lag (ARDL) model to understand the relationship between the dependent and the independent variables and to investigate the long-run equilibrium relationship between them. To estimate the model, both PMG (Pooled Mean Group) and MG (Mean group) estimation methods have been used. The Hausman test has been conducted to see the difference between the PMG and the MG results. The outcomes show that PMG serves as an efficient estimator here. The error correction terms are negative and significant. The results show strong evidence of area allocation towards oilseed crops, indicate a strong co-integration among their determinants in the long run. The ARDL results indicate that the speed of adjustment towards long-run equilibrium varies from 14.8 to 40.6 percent