does microfinance reduce rural poverty? evidence based on long term household panel data from...

Does microfinance reduce rural poverty?

Evidence based on long�term household panel data from Ethiopia*

Guush Berhane

Presented at IFPRI Job Seminar, Addis AbabaNov 17, 2009

*An earlier version of this paper has been submitted to AJAE for publication as Berhane, G. & Gardebreok, C.

Background

� Microfinance Institutions (MFIs) – considered as effective tools to tackle poverty� 3,133 MFIs globally

� The “100 million families” global target reached in 2007!

� Global Targets by 2015:

� Reach 175 million poorest families,

� Lift 100 million of them to above ‘$1 a day’ threshold

� Ethiopia: 29 MFIs; reaching ≥ 2.2 million families

� The hope: repeated loans would eventually trickle down to measurable welfare gains over the long�term

� long�term impact evidence largely missing (partly because)

� long�term impact evaluation is challenging, for two reasons:

Challenges in evaluating long�term credit impact?

� The question: whether and to what extent these gains are realized over the long�term?

1. Data requirements: long�term/panel/data

� Existing studies rely on either

� cross�sectional, quasi�experimental – IV, or

� classical, two�period (before & after) panel data methods

� This may arise due to:

� Borrower self�selection &/or program placement biases

� Observed ‘effects’ may not be simply attributable to credit only.

� i.e., effects can be attributable to ‘other unobserved’ factors that maybe potentially endogenous to borrowing decision and hence the outcome of interest.

� This is more so with ‘long�term’ impact evaluation because of

�Time � invariant and time � varying effects!

2. Methodological complexities to identify long�term impact


itiititit uMprogXC +++= αγβ

itiitit vWZprog ++= φψ

� To see this, consider this simple equation of interest:

Where� Xit = All exogenous regressors� Progit =1, if household i participated in year t, zero otherwise.� Mi = time�invariant unobservables� uit = error term, includes time�varying unobservables

�But program participation can, in turn, be determined by:

where Wi = time�invariant unobservables

Selection bias arises if Wi &/or vit is correlated with Mi , uit, or both

� OLS estimates are biased


� AIM of this paper: evaluate long�term impact of MFI credit & contribute to addressing methodological challenges.

1. Since standard panel data methods – such as FE � are also subject to biases if unobservables are time�varying (very likely in long�term impact), a more robust specification/modeling is needed!

2. Studies focus on comparing participant vs. non�participant to identify impact. However, identifying impact from ‘intensity of participation’ is equally important for gov’ts, donors, & MFI enthusiasts!

� In this paper, the standard FE method is innovatively modeled to address these concerns

Aim and contributions of this paper?

Empirical method & estimation

1. Fixed Effects (FE) model – as a reference

yields unbiased estimates iff unobservables that cause selection bias are time�invariant – ‘strict exogeneity asspn’)

�Estimation: transform data/first � differences

( ) ( ) ( ) ( ).... iitiiitiitiit uugoprprogXXCC −+−+−=− γβ

�Applying OLS on transformed data,


2. Random trend model

� Specify a time�trend to capture time�varying unobservables!

itiiititit utgMprogXC ++++= αγβ

t = individual trend, g = trend parameter

� Estimation: FE after first�differencing; or OLS after twice differencing

3. Flexible random trend model


� Modeling the FE model more flexibly to account for intensity/degree of participation

Prog jit = 1; otherwise, = 0

itiiitkititit uMtgprogkprogXC ++++++= αγγβ ,...,11

� Dedebit Credit and Saving Institution (DECSI)

� One of 29 MFIs operating in Ethiopia, mostly rural areas!

� Covers almost all villages in the region� Provides one year loans for farm and off�farm activities

� DECSI’s global aim: � increase productivity, manage shocks, eventually improve

standard of living (e.g., improve household consumption and life style such as housing)

� We measure welfare using these two indicators in this study

Data: Microfinance in northern Ethiopia

Data: borrowers and non�borrowers

� Mainly

� Annual household consumption expenditures &

� Improvements on housing (e.g., Roofing ).

� Panel data used is a sub�sample of a bigger study by ILRI �IFPRI – MU – UMB � Norway in Tigray, Ethiopia.

� 4 round surveys, 3 year intervals (1997�2006)

� Sample: 4 zones � 4 villages per zone � 25 households per village (=400 households)

� Balanced panel of 351 households in 4 years � 1404 obs.

Households’ participation and changes in borrowing status

Data: borrowers and non�borrowers

How many times participated so far?Survey year Never Once Twice Thrice Always

1997 140 211 � � �2000 87 182 82 � �2003 61 143 112 35 �2006 40 102 130 46 33

Data: evolution of outcome variables of interest

Summary statistics of annual consumption and housing improvements (ETB)

Survey years 1997 2000 2003 2006Participants 211 135 126 160Annual household consumption

Mean 1957 2931 2527 8041 Std. Dev. 1158 2894 1235 5809

Housing improvementsMean 0.0332 0.1926 0.4286 0.5938Std. Dev. 0.1795 0.3958 0.4968 0.4927

Non-participants 140 216 225 191Annual household consumption

Mean 1481 2625 2140 6618Std. Dev. 800 2398 1406 7214

Housing improvementsMean 0.0286 0.0417 0.1022 0.1152Std. Dev. 0.1672 0.2003 0.3036 0.3201

�18%

�14%

Results

1. Results suggest, for 1 (additional) year of borrowing (≈ 3 years interval):

� per capita annual consumption increases by:� ETB 415 (≈$48) in the (Standard) FE model� ETB 199 (≈$ 23) in the Random Trend Model ≈ 2 $ cent/day

� prob. of house improvements increases by: � 0.27 (similar results in both models)

� FE overestimates impact …due to time�varying unobservables.!

2. Flexible Random Trend Model shows credit impact lasts longer!

Results � flexible random trend model

Dependent variables Household per capita

annual consumption Housing improvements

One year borrowing 273.936** (107.526) -0.004 (0.075)

Two years borrowing 319.132** (137.706) 0.244** (0.097)

Three years borrowing 310.697* (213.204) 0.555*** (0.149)

Four years borrowing 665.024** (337.707) 0.457* (0.237)

Year 2006 dummy 326.079*** (31.954) -0.019 (0.022)

Age of household head 2.578 (9.432) -0.007 (0.007)Age-squared -0.027 (0.089) 0.531×10-4 (0.623×10-4)Cultivated land size(in Tsimad = 0.25hectare)

-0.887 (13.250) -0.004 (0.009)

Land size-squared 0.175 (0.463) -0.159 ×10-3 (0.3245×10-3)

Intercept 16.268 (70.153) -0.017 (0.049)R-squared 0.170 0.044F(9, 692) 15.76*** 3.560***Number of obs. 702 702

*, ** ,*** significant at 10%, 5% and 1%, respectively; standard errors in parentheses

Conclusions

� After controlling for biases, loans have significantly improved both household outcomes

� Controlling for unobserved trends slashes impact significantly!

� For consumption: the higher the frequency of borrowing, the higher the impact !� Early graduation (e.g., before 10 yrs) maybe too short to exert

meaningful impact on rural poverty

� For house improvement: significant after some years!

� Impact is non�monotonic on different hhld outcomes! � impact based on a ‘single outcome’ and ‘single�shot’ observation does not provide the complete picture!

� Maybe – one reason for conflicting results of studies so far?

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