Do Village Funds Improve Access to Finance? Evidence from Thailand

Menkhoff, Lukas; Rungruxsirivorn, Ornsiri

doi:10.1016/j.worlddev.2010.09.002

Cited by 64 publications

(46 citation statements)

References 27 publications

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“…On the other hand, Sinha and Matin (1998) find that microfinance member households do not reduce borrowing from informal credit sources. Menkhoff and Rungruxsirivorn (2011) analyse the role of village funds in rural Thailand. Their findings suggest that loans from informal sources are more likely to be used for shock absorption than loans from village funds.…”

Section: Introductionmentioning

confidence: 99%

Does microfinance change informal lending in village economies? Evidence from Bangladesh

Islam

Nguyen

Smyth

2015

Journal of Banking & Finance

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Section: Introductionmentioning

confidence: 99%

Does microfinance change informal lending in village economies? Evidence from Bangladesh

Islam

Nguyen

Smyth

2015

Journal of Banking & Finance

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“…The importance of VF loans for the poor is confirmed by the finding that although VF loans are the main source of credit for 12 percent of households, this proportion rises to 21 percent for households in the poorest quintile. This is despite the fact that VF loans are, on average, far smaller than loans from all the other main sources of credit, and make, at best, a relatively modest contribution to the overall availability of credit in Thailand (Menkhoff and Rungruxsirivorn, ).…”

Section: Explaining Village Fund Behavior and Performancementioning

confidence: 98%

Microcredit on a Large Scale: Appraising the Thailand Village Fund

Haughton

Khandker

Rukumnuaykit

2014

Asian Economic Journal

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The Thailand village fund (VF) is the second-largest microcredit scheme in the world. Nearly 80 000 elected local VF committees administer loans that reach 30 percent of all households. The value of VF loans has remained steady since 2006, even without new infusions of government funds, and loans go disproportionately to the poor. Based mainly on a custom-built survey of over 3000 local VFs conducted in 2010, we evaluate the performance of the VF. As expected, profit rates are hard to model, but our regression analysis shows that loan recovery rates, total lending and the proportion of loans going to the poor are all higher when a VF borrows additional funds from a formal bank and on-lends to households, as done by one in five VFs. An economic analysis suggests, tentatively, that VF benefits exceed the costs. A financial analysis shows that VFs more than break even, with receipts covering their cash costs. Most VFs are social rather than financial intermediaries; they have little incentive to take risks or to innovate, which explains why VF lending has not kept pace with the growth of the Thai economy.

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“…11 Average loan size is approximately USD 450 which represents roughly 25% of a households's yearly income. 12 A detailed discussion of the application process that villages were required to follow to get access to the funds and the way in which those funds were delivered is provided by Kaboski and Townsend (2012), Boonperm et al (2013), Menkhoff and Rungruxsirivorn (2011) and Haughton et al (2014).I do not address that process here as all of the villages in the sample participated in the program. in the sample villages, and modif ed signif cantly the composition of the portfolio of loans in each village (See and Appendix Figure CXI).…”

Section: The Million Baht Village Fund Programmentioning

confidence: 99%

Targeting Credit through Community Members

Vera-Cossio

2018

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Delegating the allocation of public resources to community members is an increasingly popular form of delivering development programs and are associated with a tradeoff between improved information about potential benef ciaries and favoritism towards local elites. Unlike targeting cash transfers to the poor, the optimal targeting of credit is a more complex problem involving issues of productivity, repayment, and market responses: This paper analyzes this problem using a large-scale lending program, the Thai Million Baht Credit Fund, which decentralizes the allocation of loans to an elected group of community members, and provides three main results. First, exploiting a long and detailed panel, I recover pre-program structural estimates of household total factor productivity and f nd that resources from the program were not allocated to high-productivity, poor households, which is inconsistent with poverty and productive eff ciency as targeting criteria. Second, using socioeconomic networks data, I show that actual targeting is strongly driven by connections to village elites and is related to lower program profitability, which suggests favoritism as a reason for mistargeting. Finally, I exploit quasi-experimental variation in the rollout of the program and uncover evidence that, in general equilibrium, informal credit markets compensate for targeting distortions by redirecting credit towards unconnected households, albeit at higher interest rates than those provided by the program. The results highlight the limitations of community-driven approaches to program delivery and the role of markets in attenuating potential targeting errors.

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Do Village Funds Improve Access to Finance? Evidence from Thailand

Cited by 64 publications

References 27 publications

Does microfinance change informal lending in village economies? Evidence from Bangladesh

Does microfinance change informal lending in village economies? Evidence from Bangladesh

Microcredit on a Large Scale: Appraising the Thailand Village Fund

Targeting Credit through Community Members

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