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Open AccessJournal ArticleDOI

Microfinance and household poverty reduction:: New evidence from India

TLDR
In this article, a treatment effects model is employed to estimate the poverty-reducing effects of Micro Finance Institutions (MFIs) loans for productive purposes, such as investment in agriculture or non-farm businesses on household poverty levels.
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This article is published in World Development.The article was published on 2010-12-01 and is currently open access. It has received 335 citations till now. The article focuses on the topics: Microfinance & Poverty.

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Citations
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Journal ArticleDOI

Does Microfinance Reduce Poverty in Bangladesh? New Evidence from Household Panel Data

TL;DR: In this paper, the authors examined whether loans from micro-finance institutions (MFI) reduce poverty in Bangladesh drawing upon the nationally representative household panel with four rounds from 1997 to 2004.
Book

What is the evidence of the impact of microfinance on the well-being of poor people?

TL;DR: In this article, the authors revisited the evidence of micro-finance evaluations focusing on the technical challenges of conducting rigorous microfinance impact evaluations, and concluded that no well-known study robustly shows any strong impacts of micro finance, while anecdotes and other inspiring stories purported to show that micro finance can make a real difference in the lives of those served.
Journal ArticleDOI

Microfinance and Poverty—A Macro Perspective

TL;DR: In this article, the authors test the hypothesis that micro-finance reduces poverty at the macro level using cross-country and panel data which are constructed by the Microfinance Information Exchange data on microfinance Institutions (MFIs) and the World Bank data.
Book ChapterDOI

Evidence from India

Ashish Malik
Journal ArticleDOI

Microfinance, financial inclusion and ICT: Implications for poverty and inequality

TL;DR: In this article, the role of information and communication technologies (ICT) in poverty and inequality reduction by fostering financial inclusion, using panel dataset of sixty-two countries between 2001 and 2012, was assessed.
References
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Journal ArticleDOI

Sample Selection Bias as a Specification Error

James J. Heckman
- 01 Jan 1979 - 
TL;DR: In this article, the bias that results from using non-randomly selected samples to estimate behavioral relationships as an ordinary specification error or "omitted variables" bias is discussed, and the asymptotic distribution of the estimator is derived.
Book

Limited-Dependent and Qualitative Variables in Econometrics

G. S. Maddala
TL;DR: In this article, the authors present a survey of the use of truncated distributions in the context of unions and wages, and some results on truncated distribution Bibliography Index and references therein.
Journal ArticleDOI

Tobit models: A survey

TL;DR: Tobin's model is also known as censored or truncated regression models as discussed by the authors, where the observations outside a specified range are totally lost and censored if one can at least observe the exogenous variables, and truncation occurs if a patient is still alive at the last observation date or if he or she cannot be located.
Posted Content

Microfinance and poverty - evidence using panel data from Bangladesh

TL;DR: In this paper, the authors used household level panel data from Bangladesh and found that micro-finance benefits the poorest and has sustained impact in reducing poverty among program participants, but the effect is more pronounced in reducing extreme rather than moderate poverty.
Journal ArticleDOI

Microfinance and poverty : evidence using panel data from Bangladesh

TL;DR: In this article, the authors used household level panel data from Bangladesh and found that micro-finance benefits the poorest and has sustained impact in reducing poverty among program participants, but the effect is more pronounced in reducing extreme rather than moderate poverty.
Frequently Asked Questions (14)
Q1. What are the contributions in this paper?

The objective of the present study is to examine whether household access to microfinance reduces poverty. This leads to exploring service delivery opportunities that provide an additional avenue to monitor the usage of loans to enhance the outreach. Despite some limitations, such as those arising from potential unobservable important determinants of access to MFIs, significant positive effect of MFI productive loans on multidimensional welfare indicator has been confirmed. 

Although the proportion of persons below the poverty line has declined from around 36 per cent of the population in 1993-94 to 28 per cent in 2004-05, poverty reduction remains the country’s major challenge in the 21st century. 

The hypothesis of their study is: (1) access to microfinance institutions (MFIs) andproductive loan reduces poverty and (2) amount of productive loan has a poverty reducing effect. 

Underlying the estimation of equation (8), is a latent variable which is assumed to be linearly related to the vector of independent variables. 

Households with older household heads tend to have higher IBR indicators with some non-linear effects, that is, the IBR indicator first increases as the household head gets older and then decreases. 

The merit of the treatment effects model is that sample selection bias is explicitlyestimated by using the results of the probit model. 

In the 1990s, MFIs became increasingly important in India mainly due to their betteraccess to local knowledge and information at community level and their use of peer group monitoring. 

Because of the fundamental differences of environment, industrial structures, household characteristics and activities between urban and rural areas, the authors first derive the estimations for total households and then for urban areas and rural areas separately. 

Despite the exceptional growth of the microfinance sector during the last three decades in serving around 40 million clients, most parts of the developing world would still remain characterised by huge demand for micro financial services. 

According to Cull et al. (2009), the argument that microfinance institutions should seek profits has an appealing ‘win-win’ resonance, admitting little trade-off between social and commercial objectives. 

The availability of non-farm business is highly significant in all cases as this increases the demand for loans for productive purposes. 

For households in rural areas, a larger poverty reducing effect of MFIs is observed when access to MFIs is defined as taking loans from MFIs for productive purposes than in the case of simply having access to MFIs. 

Within each sample area, a stratified random sample of clients, non-clients and dropouts was drawn using wealth ranking as a basis for stratification7 

Because the authors have only cross-sectional data, the authors can compare IBR indicators of households with access to MFIs and those without, as long as MFIs are randomly distributed across the sample.