Why and for what? An evidence of agriculture credit demand among rice farmers in Pakistan - Muhammad Rizwan; Ping Qing; Saleem Iram; Adnan Nazir; Qing Wang (Asian Development Bank Institute (ADBI), 2019)
Credit is a crucial factor in agricultural development. This study explores the reality in the commonly practiced sources by farmers for acquiring agricultural credit, its uses and environmental and socio-economic determinants. A total of 236 rice farmers with agricultural credit activity at least last two years were randomly selected and interviewed in the Punjab province of Pakistan using a multistage cluster sampling technique. The collected data were analyzed using ordinary linear regression (OLS) regression and multivariate probit regression (MPR) models. The results indicate that 73.7% of farmers used informal sources to acquire agricultural credit, while 22.1% of farmers acquired loans through public and private institutional sources, and 4.2% of farmers used both sources simultaneously. The acquired agricultural credit amount was not completely invested in the agricultural sector: 64.8% was invested in the agricultural sector, while 25.5% and 9.7% of the credit was used to manage their livelihood and business activities, respectively. OLS indicates that socio-economic and environmental factors are responsible for agricultural credit demand. Moreover, the MPR results indicate that different factors are responsible for requesting credit from formal and informal institutes. The findings provide suggestions for sustainable development in the agricultural sector, tackling environmental issues and socio-economic development to reduce poverty. As an emerging economy, formal and informal credit policies should be revisited and the rules regarding environmental and institutional guidelines for farmers should be softened, which would probably support development of credit policies in developing countries.
Keywords: D24; G2; Q14; 330; agriculture credit; socioeconomic and environmental factors; flood; rice farmers; multivariate probit model; OLS regression model