Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/222762
Authors: 
Rizwan, Muhammad
Qing, Ping
Iram, Saleem
Nazir, Adnan
Wang, Qing
Year of Publication: 
2019
Series/Report no.: 
ADBI Working Paper Series No. 995
Abstract: 
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.
Subjects: 
agriculture credit
socioeconomic and environmental factors
flood
rice farmers
multivariate probit model
OLS regression model
JEL: 
D24
G2
Q14
Creative Commons License: 
https://creativecommons.org/licenses/by-nc-nd/3.0/igo/
Document Type: 
Working Paper

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