Micro-credit and Poverty:The Role of Programme Placement Bias(*)

Twyeafur RahmanRobert E. Wright

(*) DSA Scotland Mini Conference, University of Strathclyde, Glasgow, May 30, 2014

Overview

(1)What is micro-credit?

(2)Empirical evaluation of the impact of microcredit on poverty

(3)Programme placement bias

(4)Evidence from Bangladesh

(5)Concluding Comments

(1) What is Micro-credit?

• “Small loans for poor people”

• Has been around for some time in both high- and low-income countries

• Has grown rapidly in low-income countries

• Particularly in Bangladesh where it has been spurred on by M. Yunus who established the Grameen Bank

View that the formal banking and financial sector in low-income countries does not benefit the poor since:

•Require significant collateral

•Have a preference to high-income, big loan clients

•Have lengthy and bureaucratic lending procedures

•High transaction costs

Outcome is that the poor often turn to informal sector (money lenders/”loan sharks”) who tend to:

•Charge excessively high interest rates

•Undervalue collateral

•Let racist/sexist attitudes guide lending decisions

•Employ threats and violence to ensure repayment

• This suggests that the failure of both the formal and informal financial sectors to provide affordable credit to the poor is often viewed as a one of the main factors that in fact reinforces poverty.

• Somewhat counter-intuitive

• Make poor people “better-off” by getting them in debt!

• Micro-credit is a response to this “market failure”

• “Micro-credit is essentially the dispersion of small collateral-free loans to groups of jointly liable borrowers in order to foster income generation and hence poverty reduction” J.Morduch

• The loan is supposed to be invested in capital or skills development and not just spent on consumption.

• The loans must be paid back with interest

• Mandatory savings

• Group lending is central as is lending primarily to groups of women

• “Social collateral” i.e. joint default risk

• Does micro-credit lead to lower poverty?

• There is a strong belief that it does: “Micro-credit Miracle”

• Yunus receiving the Nobel Peace Prize

• Development budgets being redirected to micro-credit and away from more traditional development activities (such as infrastructure)

• Problem: Short-run versus long-run effects

• Evaluation of micro-credit programmes has not been rigorous and many of these studies are very weak from a scientific rigour point of view

• These programmes need to be rigorously evaluated

(2) Empirical Evaluation of the Impact of Micro-credit on Poverty

Basic model:

Prob (Poor=1) = f(XP, XH, XV)

XP = Vector of micro-credit programme variables

XH = Vector of household-level and individual-level socio-economic characteristics

XV = Vector of village-level characteristics

Problems with this Approach—Lots!

• Self-selection of participants

• Non-successful applicants

• Lack of suitable control group. What is the relevant control group?

• Participation of non-eligible participants

• No reaching the “poorest poor”

• Unobserved heterogeneity

• Programme placement bias

• Need quasi-experimental design

• With or without matching

• Problem: Banks are only interested in paying for surveys of those who they have loaned money to.

Quasi Experimental Design

Group 1: Village with no micro-credit outlet

Group 2: Village with micro-credit outlet Applied for loan

Application was successful

Group 3: Village with micro-credit outlet Applied for loan Application was not successful

Group 4: Village with micro-credit outlet Did not apply for loan

Some hypotheses that could be tested with this design. There are others:

H1: Micro-credit reduces poverty, non-random outlet placement, applicant self-selection

Poverty(1) > Poverty(2) > Poverty(3) > Poverty(4)

H2: Micro-credit reduces poverty, random outlet place, applicant self-selection

Poverty(1) = Poverty(4) > Poverty(2) > Poverty(3)

H3: Micro-credit reduces poverty, random outlet place, no applicant self-selection

Poverty(1) = Poverty(4) = Poverty(2) > Poverty(3)

H4: Micro-credit doesn’t reduces poverty, random outlet place, no applicant self-selection

Poverty(1) = Poverty(4) = Poverty(2) = Poverty(3)

Or maybe!

Poverty(1) = Poverty(4) = Poverty(2) < Poverty(3)

Aside:

• “Pipe-line borrowers”

• Compare participants who have got a loan but have not got the money yet with those who got the loan and have the money

• Assume unobservables are the same for both

(3) Programme Placement Bias

Concerns the decision-making process of deciding where to place branches

View 1: Main goal of micro-credit institutions is to reduce poverty by providing loans to poor people. Therefore, branches are placed in regions where the rate of poverty is “high(er)”.

View 2: Main goal of micro-credit institutions is to make profit by providing loans to poor people. Therefore branches are placed in regions where the rate of poverty is “low(er)”.

PROBLEM: Both will likely bias the estimates of the impact of micro-credit on poverty

Testable:

Let i = 1, 2, 3, …, N “regions”

Prob(Bi = 1) = α1X1i + α2X2i + α3X3i + … + αkXki + ei

If programme placement is random:

H0: α1 = α2 = α3 = αk = 0

• Considering the branch placement as a random process which can lead to serious bias in measuring outcome of micro-credit programme effectiveness (Pitt, Rosenzweig, and Gibbons, 1993).

• Outcome of micro-credit might be biased due to non-random programme placement. (e.g. Murdoch, 1998 and Chowdhury, Ghosh and Wright, 2005).

• MFIs in Bangladch are more likely to place their branches in the relatively developed areas where better transport facilities and government and private infrastructures are available (Sharma and Zeller, 1999).

• MFIs are more likely to place their branches where literacy is low and flood etc risk is high (Sharma and Zeller, 1999).

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(4) Evidence from Bangladesh

Data:

• ASA (Association of Social Advancement)

• Founded 1978 in Bangladesh, now international

• Owner/Founder: Shafiqual Haque Chowdhury

• 2,936 branches

• Branches “surveyed” in November/December 2013

• Response rate was 100%!

Information on the branch location specific characteristics such as

schools, hospitals, local market centre and Grameen Bank through surveying branch managers’

Information on number of borrowers and repayment rate and outstanding loan amount for each branch through the ASA Head Office, Dhaka.

Information about the poverty ratio, literacy rate, population, access to roads, area of cultivable land, number of market centres from the Bangladesh Bureau of Statistics.

Information of risk from flooding, draught, cyclone, tornados, earthquake and river erosion) from SWISS Agency for Development and Co-operation and MoD Mgt., Bangladesh.

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There are four layer of administrative Units in Bangladesh.Division (7)>Districts (64)>Thana(517)>Unions (6766)

(Sources: BBS, 2001 and 2005)

Poverty Map: Bangladesh

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TABLE 1: Descriptive Statistics 

Poverty level N of Thana % PoorN of ASA branches

(%share)Branch

density

<0.20% 63 12.2 275 (9.4%) 4.37

0.20 and 0.36% 131 25.3 686 (23.4%) 5.24

0.36 and 0.48% 141 27.3 868 (29.6%) 6.16

0.48 and 0.60% 116 22.4 737 (25.1%) 6.53

>0.60% 66 12.8 370 (12.6%) 5.61

Total 517   2,936  

TABLE 2: Variables

Variable Description N Mean Std. Dev.

NOBRANCH Number of ASA Branches 517 5.7 3.56

Pov Rate Poverty rate: 5 categories 511    

LITERACY Rate of literacy (%) 510 45.1 11.6

AREA The size of each Thana (Sq. km) 512 284.3 217

POPDENSITY Population density of Thana 512 2,895 8,190

ROADLength (km) of pucca, semi-pucca and rail road per 1,000 population

484 1.37 1.35

URBANHH Households in urban areas (%) 503 22.3 28.2

CULTILAND Acre of cultivable land per 1,000 population 512 252.6 199.5

MARKET Number of local market centres 485 35 28.5

NRISKS Number of potential risks 517 0.9 0.9

TABLE 3: Branch Density Regressions

Variable: (1) (2) (3)PovRate: 48-60% 0.724 -- 0.479PovRate: 36-48% 0.522 -- 1.012PovRate: 20-36% -0.329 -- 0.087PovRate: <20% -1.098 -- -1.287

Literacy -- 0.087*** 0.085***

Area -- 0.004*** 0.004***

PopDensity -- -0.00012*** 0.00014***

Road -- 0.010 0.094UrbanHH -- 0.015 0.017

CultLand -- -0.005*** -0.005***

Market -- 0.041*** 0.040***

NRisk -- -1.390 -1.196

Constant 5.606 1.781  

Notes: (1) Excluded category Poverty rate >60% (2) Includes fixed effects

(5) Concluding Comments

• Some evidence of programme placement bias

• Further analysis required

• Qualitative research needed

• “Marginal branch”

• Endogeneity