Credit off-take from formal financial institutions in rural India: Quantile regression results

Artikel

Extending financial services to unbanked population in India has remained a central part of the policy thrust of the Indian government for decades. To that effect, a widespread formal credit delivery mechanism has been established to meet the credit requirements of rural communities. However, the Indian government-backed formal financial sector has had limited success in providing resources to poor rural households, which has led to strong criticism of the policy and its implementation. In this study, we use data from 600 rural households spread across six Indian states to examine the changing distribution of credit off-take among borrowers of formal financial institutions. By using quantile regression, we find that even among rural households that could access loans from the formal banking sector, the distribution of credit off-take is skewed towards resource-rich households. We also find that even among borrowers in the upper quantiles of the conditional loan distribution, marginal farmers received substantially less loan amounts than those belonging to the category of medium and large farmers.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Agricultural and Food Economics ; ISSN: 2193-7532 ; Volume: 2 ; Year: 2014 ; Pages: 1-20 ; Heidelberg: Springer

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models ; Agricultural Finance
• Subject: Rural credit ; Formal finance ; Quantile regression ; India

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Pal, Debdatta ; Laha, Arnab
• Event: Veröffentlichung
• (who): Springer
• (where): Heidelberg
• (when): 2014

Further information

• DOI: doi:10.1186/s40100-014-0009-y
• Handle: http://hdl.handle.net/10419/108920
• Last update: 10.03.2025, 11:44 AM CET