The effect of cash constraints on smallholder farmer revenue

Many smallholder farmers in developing countries struggle to make ends meet. We develop a model that examines how markets catering to numerous smallholder farmers reach an equilibrium, while incorporating real world challenges that smallholder farmers face, namely a lack of long term planning and cash constraints. Through this, we analyze the effectiveness of two common forms of government intervention, storage and loan provision. We fully characterize market equilibrium conditions under the base scenario of no government intervention, analyzing how price conditions, number of farmers, and severity of cash constraints impact farmer behaviour. We then illustrate how these results change when storage and loans are integrated into the model. The analysis demonstrates that myopic optimization and cash constraints induce farmers to make sub-optimal decisions, resulting in farmers not receiving the full benefit of government interventions. We show that while storage is always useful in situations where farmers have excess quantity, providing overly generous loan terms can negatively impact farmer revenue by disincentivizing farmers from selling their produce on the market. We also show that attempting to improve equality by alleviating farmer cash constraints can result in negative externalities like increased wastage. Empirical analysis with Bengal gram farmers in India shows that farmers are in dire need of government assistance to meet their cash constraints. However, improving loan terms only boosts farmer revenue up to a point, after which revenue declines. The analysis shows that while loan schemes are widely popular and sometimes necessary in aiding struggling farmers, governments should be aware that the strategic response of different farmers can result in adverse effects.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Center for Computational Science and Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Pay, Kenneth(Wen Hong Kenneeth)

Advisor dc:contributor.advisor

• Y. Karen Zheng.

• Center for Computational Science and Engineering.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/128974

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/128974