ROLE OF SOCIO-ECONOMIC FACTORS ON SCALE EFFICIENCY OF COTTON FARMS

M. A. Bashir, M. A. Kamran, Kh. Bakhsh, M. S. Hashmi

Abstract: The increasing need of food and fibre is a big challenge around the globe especially for the researchers. The cotton is one of the important crops all over the world as well as in the study area which contribute in textile industry as well as a number of food items. In context of Pakistan, cotton contributes directly or indirectly in exports, hence, earning foreign exchange. In this regard, socio-economic factors play important role in the agricultural production and Scale Efficiency (SE) measure is one of the most important indicators which can show the influences of varying range of the endowment and socio-economic factors. This piece of work contributes by estimating the SE of the cotton farms in selected study area. Moreover, it describes the influences of socio-economic factors on SE of cotton farms. To estimate the SE, Data Envelopment Analysis (DEA) method has been used. For the determination of influences of socio-economic factors, Tobit censored linear regression (parametric) and Kruskal Wallis & Bonferroni comparison tests (non-parametric) analyses have been considered. In total seven socio-economic factors; agriculture farm type, farm machinery, farm size, farmers’ age, qualification, experience and working style of the farmers have been considered. It was found that farm size and farmers working style have, statistically, very significant influences on SE of cotton farms and farmers working as part time are the most efficient. Moreover, renters’ cotton farms are more efficient than owners’ farms. It was also concluded that social factors influence SE, statistically, insignificantly. However, old aged farmers are more efficient than the young farmers, farmers having university education are more efficient than the other
levels, and most experienced farms are the most efficient.

Keywords: agricultural farms; data envelopment analysis; Kruskal Wallis test; socio-economic factors; Tobit regression analysis

Date published: 2017-08-29