Farm Exit Among Smallholder Farmers of Nepal: A Bayesian Logistic Regression Models Approach
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FULL-LENGTH RESEARCH ARTICLE
Farm Exit Among Smallholder Farmers of Nepal: A Bayesian Logistic Regression Models Approach Keshav P. Pokhrel1 • Taysseer Sharaf1 • Prem Bhandari2 • Dirgha Ghimire2
Received: 8 March 2019 / Accepted: 1 February 2020 Ó NAAS (National Academy of Agricultural Sciences) 2020
Abstract To leave or not to leave farming? This is a dilemma facing a large number of farm households in a rural agrarian setting of Nepal where nearly two-thirds of the population is smallholder farmers. Using the uniquely detailed retrospective panel data collected in 2015 from farming households, we examine the influence of the access to cultivated land holding and land tenure on subsequent farm exit. We address the statistical modeling issue of complete separation by developing a robust Bayesian predictive model to predict the probability of farm exit. We use the Bayesian framework with weakly informative prior to carry out the logistic regression model and compare it with other available binary response models. Our results show that the size of cultivated land has a strong negative influence on farm exit, net of all other controls. Moreover, farmers who rented farmland from others or who rented farmland to others were significantly more likely to exit farming. We estimate that a farm household required at least 5 Katha of land (one-sixth of a hectare) per year to stay in farming. Keywords Complete separation Farming Total land farmed Overall land rented Statistical models Regression model
Introduction The issue of complete separation is common in binary response regression models with correlated features. In general, results with unusually high coefficients or close to perfect accuracy in the prediction are some of the key indicators of complete separation [10]. We attempt to & Keshav P. Pokhrel [email protected] Taysseer Sharaf [email protected] Prem Bhandari [email protected] Dirgha Ghimire [email protected] 1
Department of Mathematics and Statistics, University of Michigan-Dearborn, Dearborn, USA
2
Institute of Social Research, University of Michigan, Ann Arbor, 426 Thompson St, Ann Arbor, MI 48104, USA
address this issue by screening different binary classification methods to model farm exit probability. The source of data and study area for this report is the South Asian country of Nepal. However, the proposed procedure can be applied to similar data that pose complete separation when modeled with a classical logistic regression method. Understanding the dynamics of farm is important for making sound decision regarding policy and allocating resources. We investigate potential factors that contribute to farm exit in Nepal and develop statistical models to estimate exit probabilities. The farm industry in Nepal is yet to be commercialized. In traditional Nepali farming culture, farmers mostly live in a joint family and plant rice, wheat, corn, vegetables, and most likely raise cattle (crop– livestock mixed farming). In Nepal, use of land is an important indicator of farming. Thus, we examine th
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