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This paper examines the patterns, determinants, and challenges of rural credit access in the state of Assam, India, with a specific focus on four villages from Baksa district. Drawing on cross-sectional primary data from 510 households, the analysis investigates both institutional and non-institutional borrowing behaviour using a two-stage probit regression model. Estimation results signify that despite major policy initiatives, access to credit from formal lending sources remains uneven and exclusionary. Several key factors like operational landholding, gender of household head, educational attainment, caste, and proximity to financial institutions significantly influence access to institutional credit. Socio-economically disadvantaged groups such as Scheduled Tribes (STs), Scheduled Casts (SCs), landless laborers, women-headed households, and the poorly educated continue to face structural barriers to formal financial services. As a result, informal lenders remain prominent, accounting for over 50% of total borrowings in study villages, in spite of often exploitative interest rates exceeding 100% per annum being charged. This research recommends localised and inclusive financial reforms that address socio-regional disparities, simplify credit access, and expand institutional outreach. Expanding institutional credit accessibility and designing targeted interventions for excluded groups are crucial measures toward reducing rural indebtedness and promoting inclusive economic development.
Keywords:
Pattern of Rural Credit Access: A Micro-Level Study from Baksa District, Assam
Cite Article:
"Pattern of Rural Credit Access: A Micro-Level Study from Baksa District, Assam", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 7, page no.1095-1102, July-2023, Available :http://www.ijrti.org/papers/IJRTI2307153.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator