Does credit access lead to expansion of income and multidimensional poverty? A study of rural Assam. Issue 2 (7th September 2018)

• Record Type: Journal Article
• Authors: Das, Tiken
• Is Part Of: International journal of social economics. Volume 46:Issue 2(2019)
• Page Start: 252
• Page End: 270
• Publication Date: 2018-09-07
• Subjects: Rural credit -- Heckit procedure -- Income poverty -- Multidimensional poverty -- Tobit selection equation; Economics -- Periodicals; Social sciences -- Periodicals; 330.05
• DOI: 10.1108/IJSE-12-2017-0592
• Languages: English
• ISSNs: 0306-8293

Abstract

Abstract : Purpose: The purpose of this paper is to evaluate the impact of credit access on income and multidimensional poverty by providing an econometric framework. Design/methodology/approach: The study is conducted in Assam, India and uses a quasi-experiment design to gather primary data. Econometric tools like Heckit procedure, Tobit selection equation and probit model are used for empirical purpose. Findings: The paper finds that the level of individual welfare is influenced by equivalent factors. In addition, the study observes a larger incidence of poverty among treatment households of semiformal and informal borrowers. The study argues that formal sources are more effective in reducing the number of poor households by lifting those who are closest to the poverty line. Research limitations/implications: The study indicates a vicious circle of income and multidimensional poverty among semiformal and informal borrowers. By tradition, as rural Assam gets a dominant role of traditional community-based financial institutions, we should develop the banking structure by involving these institutions. The study excludes other probable explanatory variables while evaluating the impact of credit access on income and multidimensional poverty, and this limitation is left to future research. Originality/value: This is probably the first empirical paper in Assam showing the impact of credit access on multidimensional poverty by adjusting for endogeneity and selection bias.