Farmers in developing countries struggle to invest in modern equipment and quality inputs that can improve outputs. This can lead to lower productivity and eventually, reduces farm level income. This paper aims at dete rmining the precise amount of credit needed to achieve a targeted increase in productivity based on data collected through direct interviews of 452 prawn farms in Bangladesh. This issue has been overlooked in the discussion of farm credit literatures. The study is
The output distance function is used to estimate the farm level technical efficiency (TE), and quantity of output needed to reach a target efficiency level based on current production and TE. Further Harod-Domar growth equation is used to calculate the finance needed to achieve this target efficiency, and to determine the financing gap. The probit regression calibrated with Heckman’s two-stage regression is used to identify the determinants of access to credit and its impact on credit size.
It is found that it is possible to estimate the actual amount required to enhance farm level productivity to specific TE levels linking credit amount to production efficiency. Access to credit is influenced by factors like interest rates, education, credit process duration, land ownership, and asset value.This study shows that the Harrod-Domar theory can also be used in defining the relationship between financing needs and productivity.
The practical implications are that farm level TE significantly influences the amount of credit needed to bridge financing gaps and boost productivity. The study guides that tying credit grants to specific production increase targets to reduce mismanagement and indiscipline in credit use especially agricultural credits schemes in many of the developing countries like Bangladesh.
The study is unique in its approach; using TE and applying the Harrod-Domar equation at the farm level to estimate the exact credit amount required for specific production increase, thereby identifying a threshold beyond which mismanagement may occur.