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IFPRI's Poverty Scorecard
1. 1
Poverty Sensitive Scorecards to
prioritize grants and lending
Maximo Torero
M.Torero@cgiar.org
International Food Policy Research Institute
Manuel Hernandez
M.A.Hernandez@cgiar.org
International Food Policy Research Institute
2. What we know
• Access to credit and grants helps improving
economic opportunities
• Micro and small enterprises play a central role
in economic development
• Agriculture is essential in reducing poverty
2
3. What we know
• In underdeveloped markets lending risks are
high because:
– contracts are difficult to enforce
– problem of adverse selection (wrong choices
when the type of the borrower is unknown)
• In developed financial markets a system of
score cards are used to mitigate the problem
of adverse selection by identifying
creditworthiness
3
4. What is missing
• Riskiness of a borrower or a grantee is not the
only criterion in case of development lending
• If the objective is development the menu of
projects has to be assessed also in terms of
their potential for reducing poverty
• Thus, for the optimal use of funds there might
be a possible trade off between profitability
and poverty impacts
4
5. What we have done
• We have implemented a two dimensional
score card:
–risk score of the grantees
–poverty score card
• We combine both score cards so that project
selection will not only focus on targeting the
poor but also in assuring sustainability
5
9. Building the risk score card
• Develop a risk scoring algorithm that is suitable for
lending to the SMEs or small Rural projects
• Generate a working database on risk scores based on
data to be collected in the future to improve model
prediction
• Operationalize the risk scoring mechanism through a
simple implementable program in open source
software (spreadsheets)
9
10. Building the risk score card - Steps
Step1
• Information on similar grants and specific characteristics is
collected
• A proxy of performance (default or more specifically
sustainability for grants) will be used
Step 2
• Estimation of performance as a function of characteristics
• Traditionally is done parametrically, we implemented a semi
parametric model following Klein and Spady (1993) to
capture non linearities
Step 3
• Use the estimated parameter to prioritize projects that apply
and select the ones that assure more sustainability
10
11. Building the risk score card - Steps
Step1
Yi = past performance of grant (sustainability)
Step 2
Yi = 0 + X i + I Traditional method
Yi = g( Xi ) + i Semi-parametric method
We estimate and g where g is an unknown function
Step 3
Use predict and g to estimate predicted Yi to prioritize projects
that apply and select the ones that assure more sustainability
11
12. Examples of characteristics
12
Personal characteristics Financial and Entrepreneurial characteristics
Age of the beneficiary Experience in the enterprise for which grant/loan is
being sought
Gender of the beneficiary Experience as an entrepreneur in any enterprise
Marital status of the beneficiary Degree of specialization in the intended enterprise
Education level of the entrepreneur Number of business organizations that entrepreneur is a
part of
Geographical location (nearness to economic centers) Number of certifications and vocational qualifications
that the entrepreneur holds
Borrowing situation (level of indebtedness) Has there been a history of default on the borrowings by
the beneficiary
Asset details of the beneficiary
(Examples: Housing/Bank Balance/Other durable assets)
What has been the amount on which there has been
default
Has there been any criminal proceeding against the
beneficiary in the past
Frequency of borrowing in the last five years from any
source
Does the borrower own land? Amount of repayment in last five years from any source
Has the beneficiary borrowed from informal sources
Is this project form the main source of income
Has the beneficiary worked with the government or has
political affiliation with the ruling
13. Variable Coeff
Edad 0.0082
Sexo 0.1148
Educacion superior universitaria -0.1358
Casado -0.1115
Anos de la empresa -0.0048
Tamaño empresa (nro. trabajadores) -0.0187
Si vivienda es propia -0.2837
Monto prestamos (en miles soles) 0.0116
Tasa de interes (en %) 0.0016
Plazo del prestamo (en meses) 0.0465
Constante -0.0560
% total de aciertos 47.8%
Variable Coeff
Edad 1.0000
Sexo 0.1361
Educacion superior universitaria -0.2497
Casado 0.1867
Anos de la empresa 0.0004
Tamaño empresa (nro. trabajadores) 0.0158
Si vivienda es propia 0.0222
Monto prestamos (en miles soles) 0.0090
Tasa de interes (en %) 0.1578
Plazo del prestamo (en meses) 0.0324
% total de aciertos 71.8%
Building the risk score card
Traditional Method: Probit Semi Parametric model: Single
Index
The single index model have better performance in terms of
prediction than the Probit model.
14. Semi-parametric Model: Single
Index Model
Probability of default: P(y=1|x) = E(y|x) = g(x΄ )
estimated= argmaxβ
where g is aproximated for each i as:
βestimado
Edad promedio beneficiarios 1.000
% Hombres 0.136
% Educación sec. o sup. -0.250
% Casados 0.187
Antigüedad de asociación 0.000
Tamaño de asociación 0.016
Si posee activos 0.022
Monto solicitado/deuda 0.009
Tasa de interés 0.158
Plazo de deuda 0.032
Kernel type (k): second order Gaussian
To estimate the risk score for each project i
with characteristics Xi we use these
βestimated (“weighting factors”) .
16. • 24 projects passed the minimum risk threshold and they
requested a total value of US$ 4,469,400.
In parenthesis is the number of projects that were submitted and completed their application.
Country # Project Amount (US$)
El Salvador 3 (4) 586,713
Guatemala 6 (11) 968,946
Honduras 10 (14) 1,958,293
Nicaragua 5 (10) 955,448
Total 24 (39) 4,469,400
First Stage: Results of Risk scoring
17. Building the poverty score card
• Keep projects that meet the threshold of the
risk score card (we initially use 67%)
• Project selection will involve assessing the
poverty reducing potential of the projects
• The projects with the highest poverty score
card will be the ones prioritized
17
18. Building the poverty score card
18
Information Criteria
Geographical indicators
1. Project located in high poverty
areas
If it falls in an extreme poor or district with poverty
level over 50%.
2. Access to markets If it falls in a district which has a very low accessibility
to main road, i.e. equal or more of an hour to the main
road.
Employment indicators
1. Labor intensive project Number of new employment generated by the project
2.Low skill labor intensive
project
% of low skill labor in the project
3.Potential female employment
intensity of the enterprise
% of female labor in the project
Spillover indicators
1.Effect on supply chain
participants
Number of potential beneficiaries by the project. The
ration will be calculated based on dollars invested.
2.Other spillover effects (for
example provision of some public
good)
Number of potential beneficiaries by the project. The
ration will be calculated based on dollars invested.
23. Weighting the criteria
• Step 1: Collect for all projects that apply the information
for all the variables
• Step 2: Normalize/Adjust the data by subtracting the
mean to it
• Step 3: Calculate the covariance matrix
• Step 4: Calculate the eigenvectors and eigenvalues of the
covariance matrix
• Step 5: Choose the first and second principal components
(PCPov1 and PCPov2)
• Step 6: Use PCPov 1 and PCPov 2 to rank the projects on
the poverty dimension.
23
24. 24
- 10 proyectos (en Honduras) que aprobaron el corte mínimo de riesgo.
- Se dispone de 1 millón de dólares americanos.
Caracteristicas de los proyectos
Monto # empleos # empleos # empleos # empleos # total empleos no empleos # empleos # empleos Pobreza Acceso
(miles directos indirectos no femeninos de empleos calificados / femeninos / directos / indirectos /
US$) calificados total empleos total empleos inversion inversion
var 1 var 2 var 3 var 4 var 5 var 6 var 7
Proyecto 1 (La Ceiba, Atlantida) 100 100 200 100 150 300 0.33 0.50 1.00 2.00 0.191 6.325
Proyecto 2 (El Paraiso, Copan) 200 220 500 300 500 720 0.42 0.69 1.10 2.50 0.426 7.994
Proyecto 3 (Choluteca, Choluteca) 170 150 50 50 50 200 0.25 0.25 0.88 0.29 0.336 2.233
Proyecto 4 (Choluteca, Choluteca) 100 120 400 400 100 520 0.77 0.19 1.20 4.00 0.336 2.233
Proyecto 5 (El Corpus, Choluteca) 180 80 240 150 200 320 0.47 0.63 0.44 1.33 0.541 1.743
Proyecto 6 (Vado Ancho, Paraiso) 120 50 300 200 300 350 0.57 0.86 0.42 2.50 0.477 4.155
Proyecto 7 (Brus Laguna, Gracias a Dios) 300 400 800 1,000 200 1200 0.83 0.17 1.33 2.67 0.527 38.062
Proyecto 8 (Colomoncagua, Intibuca) 120 240 80 200 150 320 0.63 0.47 2.00 0.67 0.445 13.895
Proyecto 9 (San Antonio, Intibuca) 150 130 150 140 100 280 0.50 0.36 0.87 1.00 0.332 23.596
Proyecto 10 (Santa Cruz, Lempira) 250 300 300 400 500 600 0.67 0.83 1.20 1.20 0.496 11.707
Example of poverty scoring
25. 25
Example of poverty scoring (cont.)
Paso 1: Calculo de matriz de covarianzas
Variables Var1 Var2 Var3 Var4 Var5 Var6 Var7
Var1 1 0.649 -0.187 0.293 0.500 0.457 0.674 Se observa una elevada correlacion entre numero total de
Var2 0.649 1 -0.169 0.395 0.522 0.498 0.518 empleos, proporcion de empleos no calificados sobre total
Var3 -0.187 -0.169 1 -0.331 -0.121 0.335 -0.374 de empleos, proporcion de empleos indirectos sobre total
Var4 0.293 0.395 -0.331 1 -0.071 -0.033 0.387 invertido y baja accesibilidad.
Var5 0.500 0.522 -0.121 -0.071 1 -0.023 0.000
Var6 0.457 0.498 0.335 -0.033 -0.023 1 0.256
Var7 0.674 0.518 -0.374 0.387 0.000 0.256 1
Paso 2: Estimacion de valores propios (eigenvalues) de cada componente principal (CP) y seleccion del primer componente
CP1 CP2 CP3 CP4 CP5 CP6 CP7
Eigenvalue 2.95 1.53 1.17 0.67 0.40 0.25 0.03
% Variacion explicada 42.08 21.80 16.74 9.51 5.75 3.63 0.47 El primer componente principal (CP1) explica el
Variacion acumulada 42.08 63.89 80.63 90.14 95.89 99.53 100.00 42% de la varianza observada.
Correlacion de cada componente principal con cada variable
CP1 CP2 CP3 CP4 CP5 CP6 CP7
Var1 0.893 0.150 0.079 -0.165 0.263 -0.264 -0.086 Importancia del numero total de empleos,
Var2 0.875 0.176 0.102 0.233 -0.244 0.270 -0.080 proporcion de empleos no calificados sobre total
Var3 -0.344 0.787 -0.178 0.275 0.369 0.138 -0.003 de empleos y baja accesibilidad en CP1.
Var4 0.498 -0.513 -0.335 0.595 0.096 -0.107 0.040
Var5 0.462 0.174 0.855 0.108 0.067 -0.005 0.092
Var6 0.461 0.714 -0.402 -0.042 -0.286 -0.165 0.069
Var7 0.768 -0.224 -0.343 -0.376 0.206 0.232 0.067
Paso 3: Ranking final de proyectos a partir del primer componente principal (CP1) identificado
Ranking Puntuacion Monto Acumulado
1. Proyecto 7 4.222 300 300 Se seleccionan los proyectos 7 (300,000 dólares), 4 (100,000 dólares), 8 (120,000 dólares),
2. Proyecto 4 1.075 100 400 10 (250,000 dólares) y 2 (200,000 dólares).
3. Proyecto 8 0.514 120 520
4. Proyecto 10 0.481 250 770 Monto total otorgado igual a 970,000 dolares.
5. Proyecto 2 0.024 200 970
6. Proyecto 9 -0.403 150
7. Proyecto 6 -0.825 120
8. Proyecto 5 -1.192 180
9. Proyecto 1 -1.688 100
10. Proyecto 3 -2.207 170
26. Second Stage: Results on poverty
dimension
• The first two principal components explain 52% of the variability
of the different variables.
• The first principal component (CP1) is highly correlated with the
number of new employment generated, and direct and indirect
employment generated per invested dollar
• The second principal component (CP2) has a high correlation
with the number of female employments, poverty rate, and low
accessibility to markets.
27. CP1 CP2 CP3 CP4 CP5 CP6 CP7
Eigenvalue 2.219 1.399 1.109 1.054 0.765 0.453 0.001
Variability (%) 31.70 19.98 15.84 15.06 10.94 6.48 0.01
Cumulative % 31.70 51.68 67.52 82.58 93.52 99.99 100.00
Eigenvalues (summary of information):
Eigenvectors (“weights” of the different indicators in the PC):
Indicator CP1 CP2
Number of employment 0.659 -0.023
% Non qualified labor 0.014 0.286
% Female employement -0.139 0.580
Direct employment per $ invested 0.460 -0.317
Indirect employment per $ invested 0.536 0.184
Poverty rate 0.163 0.370
Access to markets 0.141 0.557
Second Stage: Results on poverty
dimension
Riskiness of a borrower (for loans in terms of chances of default on repayment and for grants in terms of efficient and adequate use of funds) is not the only criterion in case of development lending.
