1) The study evaluates the long-term impact of microfinance loans on rural poverty in Ethiopia using a panel dataset over 1997-2006. 2) Results show microfinance loans modestly increased annual household consumption by $23-48 and likelihood of housing improvements by 0.27, but impact is smaller when controlling for time-varying factors. 3) Higher frequency and longer duration of borrowing is associated with larger impacts on consumption and improvements become significant only after several years of borrowing.

1. Does microfinance reduce rural poverty?
Evidence based on long term household panel data from Ethiopia*
Guush Berhane
Presented at IFPRI Job Seminar, Addis Ababa
Nov 17, 2009
*An earlier version of this paper has been submitted to AJAE for publication as Berhane, G. & Gardebreok, C.

2. 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

3. Challenges in evaluating long term credit impact?
The question: whether and to what extent these gains are
realized over the long term?
long term impact evidence largely missing (partly because)
long term impact evaluation is challenging, for two reasons:
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

4. Challenges in evaluating long term credit impact?
2. Methodological complexities to identify long term impact
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!
This may arise due to:
Borrower self selection &/or program placement biases

5. Challenges in evaluating long term credit impact?
To see this, consider this simple equation of interest:
Cit = X it β + prog it γ + M iα + uit
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:
prog it = Z itψ + Wiφ + vit
where Wi = time invariant unobservables
Selection bias arises if Wi &/or vit is correlated with Mi , uit, or both
OLS estimates are biased

6. Aim and contributions of this paper?
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

7. Empirical method & estimation
1. Fixed Effects (FE) model – as a reference
Estimation: transform data/first differences
(C it − Ci . )= (X it − X i. ) + (prog it − pr o g i . ) i + (u it − u i . )
β γ
Applying OLS on transformed data,
yields unbiased estimates iff unobservables that cause
selection bias are time invariant – ‘strict exogeneity asspn’)

8. Empirical method & estimation
2. Random trend model
Specify a time trend to capture time varying unobservables!
Cit = X it β + prog it γ + M iα + g i t + uit
t = individual trend, g = trend parameter
Estimation: FE after first differencing; or OLS after twice
differencing

9. Empirical method & estimation
3. Flexible random trend model
Modeling the FE model more flexibly to account for
intensity/degree of participation
C it = X it β + γ 1 prog1it +,...,+γ k progk it + g i t + M iα + uit
Prog jit = 1; otherwise, = 0

10. Data: Microfinance in northern Ethiopia
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

11. 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.

12. Data: borrowers and non borrowers
Households’ participation and changes in borrowing status
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

13. Data: evolution of outcome variables of interest
Summary statistics of annual consumption and housing improvements (ETB) 14%
Survey years 1997 2000 2003 2006
Participants 211 135 126 160
Annual household consumption
Mean 1957 2931 2527 8041
Std. Dev. 1158 2894 1235 5809
Housing improvements
Mean 0.0332 0.1926 0.4286 0.5938
Std. Dev. 0.1795 0.3958 0.4968 0.4927
Non-participants 140 216 225 191
Annual household consumption
Mean 1481 2625 2140 6618
Std. Dev. 800 2398 1406 7214
Housing improvements
Mean 0.0286 0.0417 0.1022 0.1152
18%
Std. Dev. 0.1672 0.2003 0.3036 0.3201

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!

15. 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 -0.887 (13.250) -0.004 (0.009)
(in Tsimad = 0.25hectare)
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 0.044
F(9, 692) 15.76*** 3.560***
Number of obs. 702 702
*, ** ,*** significant at 10%, 5% and 1%, respect
ively; standard errors in parentheses

16. 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?

17. Thank you!
guush.berhane@wur.nl
guush.berhane@yahoo.com
© Wageningen UR