7th Annual MicroEnterprise

Conference

Provo, UTMarch 12, 2004

The Impact of Microfinance on Economic Wellbeing:

Contributions from Azerbaijan

Kris Johnson

Outline

1. Methodology

2. Results

3. Implications

4. Questions

Central Hypothesis

The more credit cycles a client completes, the better his/her household’s economic wellbeing.

In Azerbaijan…

Clients are 46% male. Overwhelmingly urban poor. Village banks are loose affiliations. Difficult for clients to give up time. 97% literacy rate allows for a written

survey.

The Instrument (English)

Sampling Distribution

City/Area # Target % ∆

Baku 239 69.5% 67.5% -2.0%

Imishli 80 22.3% 22.6% 0.3%

Masalli 35 8.2% 9.9% 1.7%

Total 354 100% 100.0% 0%

69.5%

22.3%

8.2%

Respondent # %

Current 219 61.9%

New 101 28.5%

Non 34 9.6%

Total 354 100.0%

Methodology 2 banks per credit officer randomly selected. Members of selected banks formally invited

to event in prestigious locale. Credit officers incented to have high turnout

(resulted in roughly 85%). Instruction and explanation given. Each survey triple-checked for completeness

and plausibility. Raffle prizes given after all surveys pass

inspection.

Positive Externalities

Clients honored to be gathered in such a prominent location.

Good PR within circles of influence. Clients received instruction and

practice on general accounting. Local staff actually enjoyed doing it.

Econometric model

Y1 = f (T2, C, O, G, E, A, S)

DPCI = f (Time, Client Type, Age, Gender, Education, Assets, Social Capital)

Initial OLS Regression

Explanatory Variables

Dependent Variables

DPCI ($) (Y1)

R2 = 0.1241

Coef. P>|t|

Age (O) -0.0010153 0.956

Education (E) 0.0923308 0.155

Gender (G) 0.8126927 0.016

Client Type (C) -0.2876257 0.633

Assets (A) 0.0000383 0.000

Social capital (S) 0.4136135 0.015

Cycles (T2) 0.1287165 0.037

Data Plots

3 Stage SS (log)

No significant correlations found between measures of household economic wellbeing and time in the microfinance program.

Interaction terms of time and age, and time and client type are significantly correlated with economic wellbeing.

The Crossroads

Correlated interaction terms suggest that time spent by female clients does increase DPCI.

A cross-sectional snapshot will never allow us to account for differences between individuals and their response to time spent in the program. (Ex.)

Implications1. For the NIS, we have a methodology and an

instrument that will allow us to collect valid panel data at very low cost.

2. Panel data allows us to track individual clients through their microcredit history and precisely measure impact.

3. Time series data empower us with information for essentially all “proving” and “improving” audiences.

Questions?