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Empirical measurement of credit rationing in agriculture: a methodological survey
Author(s) -
Petrick Martin
Publication year - 2005
Publication title -
agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.29
H-Index - 82
eISSN - 1574-0862
pISSN - 0169-5150
DOI - 10.1111/j.1574-0862.2005.00384.x
Subject(s) - credit rationing , economics , agriculture , empirical research , agricultural economics , rationing , agribusiness , financial economics , monetary economics , interest rate , economic growth , geography , statistics , mathematics , health care , archaeology
Abstract Empirical analysis of rural credit market failure has been of key scientific and political interest in recent years. The aim of this article is to give an overview of the various methods for measuring credit rationing that are employed in the literature. Furthermore, the methods are subjected to a comparative evaluation of their specific strengths or shortcomings. Six approaches are distinguished: measurement of loan transaction costs, analysis of qualitative information collected in interviews, analysis of quantitative information collected in interviews using the credit limit concept, analysis of spill‐over effects with regard to secondary credit sources, econometric household modeling, and the econometric analysis of dynamic investment decisions. An explicit comparison with a first‐best solution is impossible in the first three approaches, since they essentially rely on a subjective assessment of borrowers' access to credit, based on qualitative or quantitative indicators. The fifth and sixth approaches allow a rigorous interpretation in the framework of neoclassical equilibrium theory. The fourth approach takes an intermediate position, since spill‐over on segmented loan markets reveals a willingness to pay with regard to the supposedly less expensive but rationed primary source. The approaches are fairly data demanding in general, usually requiring specific data on loan transactions. Even so, most approaches are applicable to cross‐sectional household data. With the exception of the first, all methods surveyed might plausibly be used to empirically detect credit rationing.

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