Download Does Microfinance Matter?

Cross-country Variation in Household
Access to Financial Services
Patrick Honohan,
World Bank and Trinity College, Dublin
Access to Finance Conference, World Bank,
March 15-16
A new cross-country series on financial
access
• Household rather than firm level
• Combines micro and mainstream finance
• Concept is:
Proportion of the adult population with an account at a
formal or semi-formal financial intermediary
Deposit or loan?
Semi-formal:
e.g. NGO-sponsored credit-only MFI, pays taxes, but is
unregulated by financial regulator 
Susu collector operating out of a roadside kiosk 
Different sources
• Microfinance institutions: CGAP “big numbers”
– (AFIs – with a double bottom line) 2004.
– No. of accounts/members.
• Augmented by WSBI (2005) for savings banks
• And survey of commercial bank account nos. (Beck et
al., 2005)
• And household surveys
Cleaning the raw sources
• Double counting!
– Caisse d’Epargne, CCP etc.
– Strategy adopted: go through all individual MFIs with more than
100,000 a/cs for duplication
• Incredible imputation methods
– WSBI: total assets/(0.24 x GDP per cap)
– Strategy adopted: go through all countries where WSBI
imputation gives a 10 per cent figure and find independent info
about the savings bank
How to combine different sources
• Problems
– varying incidence of multiple accounts – less serious for MFIs
than for banks
(ICBC China – 430 million a/cs, 150 million customers)
– MFIs, savings banks and commercial bank categories overlap at
the boundaries
China: ICBC is in WSBI data, CCB with 143 mn customers is not
– dead accounts (or in one case a dead bank)
– the poor hold little of the total (bottom half of wealth distribution hold 310% of financial assets), so inferring from total assets risky
How to combine different sources (2)
• But household survey-based data on access
percentages is quite closely correlated with data
on bank account numbers and on average bank
account size (% GDP)
• Regressing the former on the latter two we get
an equation which can be used to project access
percentages where we have the bank account
data (see chart)
Actual and fitted access indicators
100
90
80
Fitted
70
60
50
40
30
20
10
0
0
20
40
60
Actual
80
100
How to combine different sources (3)
• We have MFI and WSBI account nos for 160
countries; the commercial bank data for only 43
countries.
• Regressing bank deposit nos. on MFI nos; and
average bank deposit size on GDP, we have
adequate projection equations which can be
used for all 160 countries (chart)
• Some issues around functional form
for country i
mi = # of MF accounts per adult population
bi = # of bank accounts per adult population
hi = household survey-based percentage access
zi = average deposit size
yi = per capita GDP
̂ k = estimated coefficients from regression bi   0  1 log( mi )  ui
̂ k = estimated coefficients from regression z i   0  1 yi  ui .
Let bˆi  bi for countries where data on bi is available; bˆi  ˆ 0  ˆ1 log( mi ) otherwise;
Let zˆi  z i for countries where data on zi is available; zˆi  ˆ0  ˆ1 yi otherwise.
Let ˆ k be the estimated coefficients from regression h   0   1 log bˆ   2 log zˆ  ui .
The synthetic access percentages are = ˆ0  ˆ1 log bˆ  ˆ2 log zˆ .
Actual and fitted access indicators
100
90
80
70
Fitted
60
50
40
30
20
10
0
0
20
40
60
Actual
80
100
Developing country access to finance - deciles
70
CGAP series
WSBI series
Synthetic series
Composite series
60
50
%
40
30
20
10
0
1
2
3
4
5
6
7
8
9
Access by region
100
90
80
70
%
60
50
40
30
20
10
0
AFR
EAP
ECA
LAC
MNA
SAR
Access vs. financial depth
• Correlated but not the same (see chart)
Access and depth
Private credit % of GDP
140
120
100
80
60
40
20
0
0
20
40
60
80
Access % of adult population
100
Using the data
• Is higher access (as measured) associated with
less poverty?
• Or is mainstream financial depth more
important?
• How about inequality?
Access and GNI per capita
100
% access composite data
90
80
70
60
50
40
30
20
10
0
0
5000
10000
15000
20000
GNI per capita PPP
25000
30000
35000
Table 2. Poverty and Financial Access
This table shows regressions relating the $1 per day poverty percentage
to financial access percentages across countries
Equation:
Constant
GNI per cap (log)
Access (log)
R-squared / NOBS
Adjusted R-squared
S.E. of regression
Log likelihood
2.A
2.D
Coeff. t-Stat Coeff.
173.6 **11.6
65.6
-18.8 **10.4
-13.7
0.546
91 0.175
0.541
0.166
15.2
20.7
-375.7
-395.0
t-Stat
**6.1
**4.3
89
2.E
Coeff. t-Stat
175.5 **11.5
-19.7 **8.4
1.6
0.5
0.549
89
0.538
15.4
-368.1
Table 4. Poverty and Financial Access – additional variables [Continued]
(b) Removing outliers
Equation:
Constant
GNI per cap lower 90% (log)
Share of top 10%
Access (log)
Private credit (log % of GDP)
Inflation (log)
Institutions (KKZ index)
Institutions (Freedom house bank)
SS Africa not ZAF dummy
Which measure?
Outliers omitted?
R-squared / NOBS
Adjusted R-squared
S.E. of regression
Log likelihood
4.H
4.J
4.K
4.P
Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff.
162.1 **14.2 187.5 **10.2 187.1 **10.0 136.1
-16.5 **7.6 -20.3 **8.0 -20.2 **7.8 -15.5
0.609 **2.9 0.355
*2.4 0.358
*2.4 0.339
3.67
1.0
2.31
0.7
2.98
0.8
3.48
-6.67
-3.2 -7.46 **3.6 -6.78 **3.0 -5.14
-1.86
-1.3 -1.26
0.4 -1.02
0.7 -1.38
6.96
*2.1
7.07
*2.1
-1.75
0.9
8.96
Comp
Comp
Comp
Comp
62,129,139,
184,192,204
0.791
0.774
9.9
-238.0
t-Stat
**7.2
**5.8
*2.3
0.3
*2.4
1.0
*2.0
ETH,MNG,
ETH,MNG,
ETH,MNG,
ETH,MNG,
62,129,139,
62,129,139,
62,129,139,
NIC,TZA,
NIC,TZA,
NIC,TZA,
NIC,TZA,
184,192,204
184,192,204
184,192,204
UGA,YEM
UGA,YEM
UGA,YEM
UGA,YEM
65
0.806
0.786
9.6
-235.7
65
0.793
0.767
9.7
-231.8
64
0.805
0.785
9.6
-235.8
65
Table 4. Poverty and Financial Access – additional variables [Continued]
(c) Interaction term: credit depth x access
Equation:
Constant
GNI per cap lower 90% (log)
Share of top 10%
Access (log)
Private credit (log % of GDP)
Access x private credit (log)
Inflation (log)
Institutions (KKZ index)
SS Africa not ZAF dummy
Which measure?
Outliers omitted?
R-squared / NOBS
Adjusted R-squared
S.E. of regression
Log likelihood
4.Q
Coeff.
265.3
-18.2
0.430
-27.1
-41.1
9.94
-1.19
t-Stat
**6.8
**8.0
*3.1
*2.3
**3.3
**2.8
0.9
Comp
4.R
Coeff.
271.1
-19.8
0.376
-24.3
-37.3
8.69
-0.83
5.16
t-Stat
**7.0
**8.1
*2.7
*2.1
**3.0
*2.4
0.6
1.6
Comp
ETH,MNG,
ETH,MNG,
62,129,139,
62,129,139,
NIC,TZA,
NIC,TZA,
184,192,204
184,192,204
UGA,YEM
UGA,YEM
0.817
0.798
9.3
-233.8
65
0.824
0.803
9.2
232.4
65
Table 6. Poverty and Financial Access
This table shows regressions relating the Gini coefficient
to financial access percentages across countries
Equation:
6.A
6.B
Coeff. t-Stat Coeff.
51.7 **11.0
68.9
-3.49
-3.15
*2.4
0.42
Constant
GNI per cap (log)
Access (log)
Private credit (log % of GDP)
Inflation (log)
Institutions (KKZ index)
Institutions (Freedom house bank)
Population (log)
SS Africa dummy
Which measure?
Comp
Comp
Outliers omitted?
None
R-squared / NOBS
0.049
112 0.104
Adjusted R-squared
0.040
0.087
S.E. of regression
10.6
10.3
Log likelihood
-422.4
-419.1
** and * indicate significance at the 1% and 5% levels, respectively
t-Stat
**8.5
**2.6
0.2
6.C
Coeff.
50.7
-1.28
-0.25
7.76
Comp
None
112
0.162
0.138
10.0
-415.4
t-Stat
**4.9
0.8
0.1
**2.7
None
112
Equation:
6.D
Coeff.
19.4
4.20
-6.21
2.48
0.04
t-Stat
1.4
1.9
*2.2
1.4
0.0
6.E
6.F
Coeff. t-Stat Coeff.
49.3 **4.4 17.7
1.84
0.9 3.54
-6.17 *2.1 -6.21
3.36
0.70
0.08
t-Stat
0.9
1.4
*2.2
1.8
0.5
0.0
6.G
Coeff.
20.4
3.34
-5.96
3.62
0.74
-0.21
t-Stat
0.8
1.3
*2.0
1.8
0.5
0.1
Constant
GNI per cap (log)
Access (log)
Private credit (log % of GDP)
Inflation (log)
Institutions (KKZ index)
Institutions (Freedom house
0.78
0.4 0.65
0.3
bank)
Population (log)
-0.14
0.1
SS Africa dummy
10.25 **3.3
10.25 **2.9 9.56 **2.8
Which measure?
Comp
Comp
Comp
Comp
See note
Outliers omitted?
None
None
None
R-squared / NOBS
0.223
74 0.073
74 0.221
72 0.220
72
Adjusted R-squared
0.166
0.047
0.136
0.119
S.E. of regression
9.5
10.1
9.5
9.6
Log likelihood
-268.2
-274.7
-260.0
-256.6
NB: The sample for equation 6.E was the set of countries for which all the data for regression 6D
was available. There were no large outliers in 6D-G.
Issues /next steps
• More comprehensive data on control
variables
• Issue of endogeneity – does it really not
matter much here?
Conclusion
• Even if does not robustly help explain absolute
poverty, financial access is negatively correlated
with income inequality (Gini).
• (Access does more for those somewhat higher
up the ladder).
• Whatever about impact of direct access,
regressions confirm favorable inverse
association between financial depth and poverty.