Does microfinance reduce rural poverty? Evidence based on long term household panel data from Ethiopia

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International Food Policy Research Institute (IFPRI)/ Ethiopia Strategy Support Program-II (ESSP-II), Candidate Seminar, 17-November-2009

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Does microfinance reduce rural poverty? Evidence based on long term household panel data from Ethiopia

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

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