Transcript 6 ADB Study-Low Income Housing Finance in SRI_B. Nguyen

Impact Evaluation
Low-Income Housing Finance
in Sri Lanka
Binh T. Nguyen
Independent Evaluation Department
Asian Development Bank
17 Oct 2010
Contents
Overview of ADB Assistance in Low-Income Housing
Case Project: Urban Development and Low-Income
Housing Project in Sri Lanka
The Impact Evaluation
The Importance of Housing for Achieving the Millennium Development Goals
Millennium Development
Goals (MDGs)
Goal 1: Eradicate extreme
poverty and hunger



Goal 2: Achieve universal
primary education



Goal 3: Promote gender
equality and empower
women
Goal 4: Reduce child
mortality
Goal 5: Improve maternal
health
Goal 6: Combat HIV/AIDS,
malaria and other diseases








Goal 7: Ensure
environmental
sustainability




Goal 8: Develop a global
partnership for
development



Role of Housing in Achieving MDGs
Residential activities can provide job opportunities and income and
thereby allow urban poor to invest in food and other basic needs.
Improved housing conditions raise worker productivity.
Residential activities improve a nation’s wealth (e.g. taxes and
savings) and allow governments and agencies to invest in social
oriented programs to reduce poverty.
Improved, and access to, housing in appropriate locations lowers
absenteeism from school.
Improved, and access to, housing increases educational productivity.
Secure tenure allows parents to engage in income-generation
activities allowing them to cater for educational expenses.
Secure tenure contributes to household stability and provides women
with peaceful atmosphere to engage in economic-generating activities.
Good housing reduces stress and contributes to women’s productivity.
Good housing and related services (e.g. water, electricity, and
sanitation) reduces the risk of diseases among children.
Improved housing lowers the need for health services for women.
Secure tenure reduces stress among slum dwellers, especially
women.
Safeguard procreation and nurturing of the young.
Access to housing reduces homelessness and risks of social vices
associated with street people.
Good housing brings comfort, reduces overcrowding and limits the
transmission of communicable diseases (e.g. tuberculosis), it
facilitates and enhances care-giving.
Health conditions depend largely on good living environment.
Good housing conditions and related services contribute to a good
environment.
Use of environmentally friendly building materials, including energyefficient materials, contributes to environmental protection.
Good housing and urban design are cornerstones for mitigating
ecological footprints of settlements and reducing vulnerability to
climate change.
Partnership between national government and international
development agencies creates synergy and reduces duplication of
programs.
Partnership between national government and international
development agencies for housing ensures realistic policies and
programs, and sharing of best practices.
Programs that involve partnerships among national governments,
international development agencies, local communities and slum
dwellers have a better chance of long-term sustenance.
Source: Tibaijuka, Ana Kajumulo, “Building Prosperity: Housing and Economic Development”, UN-Habitat and
Earthscan, London and Sterling, Virginia, 2009.
ADB Loans and Grants for LIUH
All ADB Loans &
Grants
LIUH Loans & Grants
Amount
Year of
Approval
Amount
($, M)
Number
% of All
Loans
Number
Amount
($, M)
1966-1969
-
-
-
21
99.68
1970-1979
4
98.80
2.45
239
4,040.57
1980-1989
7
269.60
2.41
262
11,206.53
1990-1999
9
612.00
1.92
323
31,915.58
2000-2010
19
346.30
0.39
2,046
88,968.94
Total
39
1,326.7
0.94
2,891
141,513.21
Project Performance Ratings
Rating
Highly Successful
Successful
Partly Successful
Unsuccessful
Not Rated
Total
Number
%
1
13
4
1
4.5
59.1
18.2
4.5
3
13.6
22
100
Case Project
Loan 1632-SRI: Urban Development and
Housing Project
Basic Data:
Approved 1998
Completed 2005
Total cost $102.99M
ADB loan $67.02M Govt/Banks $35.97M
Four Components:
(i)
Urban Development: roads, traffic improvement, water
supply, drainage, etc.
(ii) Community Development: basic infra, tenure regulations
(iii) Housing Finance: housing loans to low-income households
(iv) Institutional Development: training on staff skills in municipal
management, environment management, etc.
Project Expenditure ($, Million)
Component
ADB
Govt
Total
Percent
Urban
Infrastructure
40.28
23.6
63.88
62.0
Community
Development
1.41
1.6
3.01
2.9
19.93
7
26.93
26.1
Institutional
Development
3.53
3.77
7.3
7.1
Charges
1.87
0
1.87
1.8
67.02
35.97
102.99
100
Housing
Finance
Total
Housing Finance Component
Objectives:
• Increase access of low-income households (LIHs)
to market-based housing finance through the
formal sector;
• Facilitate improvements of housing conditions and
quality of life; and
• Promote formal banking sector interest in
financing low-income housing market segment.
LIHs = households with monthly income below the
55th income percentile, i.e., below Rs12,500 (appr.
$200) per month.
Housing Finance Component (Cont.)
Basic Data:
• Total Amount = $26.93M
ADB loan = $19.93M
PCIs = $7M
• Total Borrowers = 28,378
• PCIs = 7 participating credit institutions
- Housing Finance Development Corporation =
68.6%
- 3 Regional Development Banks = 27.9%
- 3 Commercial Banks (BOC, Hatton, National) =
3.5%
Housing Finance Component (Cont.)
Loan Disbursement by Province
Loan
Amount
(SLRs
million)
Number
of Loans
(%)
Western
5,097
18.0
604.6
25.1
Southern
8,109
28.6
561.2
23.3
Central
7,051
24.9
500.0
20.8
Sabaragamuwa
2,154
7.6
216.8
9.0
North Western
1,658
5.8
182.5
7.5
North Central
2,506
8.8
179.5
7.5
UVA
1,420
5.0
125.3
5.2
383
1.3
38.3
1.6
28,378
100.0
2,408.2
100.0
Province
North and East
Total
(%)
Housing Finance Component (Cont.)
Loan Disbursement by Income Group
Income Group
(SLRs per month)
(%)
SLRs
Million
(%)
362
1.3
12.6
0.5
2,501–5,000
4,198
14.8
219.6
9.1
5,001–7,500
9,630
33.9
719.6
29.9
7,501–10,000
8,948
31.5
825.8
34.3
10,001–12,500
5,240
18.5
630.6
26.2
Total
28,378
100.0
2,408.2
100.0
less than 2,500
Number
of Loans
Housing Finance Loan (Cont.)
Loan Disbursement by Purpose
Number
of Loans
(%)
19,751
69.6
Renovation of Existing Houses
6,130
21.6
Purchase of Land for New House
Construction
2,128
7.5
312
1.1
57
0.2
28,378
100.0
Purpose
Construction of New House and
Extensions
Service Connections
Purchase of House
Total
Why This Project Was Chosen?
• Findings of the evaluation will provide insights and
lessons for urban development operations guidelines
being prepared by the Urban COP
• The project had clear assignment rule: Households
with income below the 55th income percentile (this
was confirmed during the Reconnaissance Mission in
preparing for the evaluation)
• The project appeared to be the best among ADB
housing projects in terms of baseline data: loan
applicants were required to submit a detailed
household profile, and these are kept in PCIs
The Impact Evaluation
Objectives: In addition to assessing the extent to which the
housing finance component met its stated objectives by
using the standard evaluation criteria, this impact evaluation
will:
• Empirically assess the welfare change of the
beneficiaries that can be attributed to the housing
finance component
• Identify factors (social, economic, project design and
implementation) influencing the project outcomes
• Propose a sensible set of outcome indicators and
benchmarks that can be used in future project design,
monitoring and evaluation
Evaluation Framework
Hypothesis:
Improved housing conditions will lead to increased labor
productivity, income, and overall social well-being of the
project beneficiaries.
Logic Model:
Inputs  Activities  Outputs  Outcomes  Impacts
Impact Indicators
Follow IADB study (2008) and Field and Kremer (2006)
Household-level outcomes:
• Housing quality index (HQI),[1]
• Per capita household consumption expenditure (per year),
• Household completeness (presence of spouse and formally
married),
• Occupation ratio (percent of working household members),
• School attendance (of school age children), and
• Nourishment ratio (percent of children under 6 who are not undernourished).
[1]
HQI  
i
ai
, where ai equals unity if the house has condition i, and zero
7
otherwise; and i runs through the seven dwelling quality indicators: potable water access,
sewage connection, electricity connection, walls, floors, ceilings, and overcrowding
problems (more than 2 persons living per room).
Impact Indicators (Cont.)
Community-level outcomes:
• Poverty rate (percent of households below the
poverty line),
• Housing shortage (percent of households without
a house),
• Loan default ratio,
• Crime rates, and
• Net migration (difference between migration in
and out).
Estimation Methods
Regression Discontinuity Design:
Y
Before the treatment
Y
********************
******************
+++++++++++
***************
++++++++++++++++++
++++++++++++++++++++ **********
++++++++++++++++++++
+++++++++++++++
+++++++
Participants
Non-participants
Cutoff
After the treatment
+++++++++++ ************************
++++++++++++++++++ *******************
++++++++++++++++++++ *******************
++++++++++++++++++++
************
+++++++++++++++
+++++++
Participants
Score
Non-participants
Cutoff
Score
RDD (cont.)
The program impact is estimated by the mean difference in
outcomes for persons above and below the cut-off point.
Probability of Borrowing
Before treatment:
.55
income
.55
income
After treatment:
Expected Outcome
Before Treatment:
.55
income
.55
income
After Treatment:
Fuzzy Regression Discontinuity Design
Estimator:
lim E [ Yi | X i  x ] - lim E [ Yi | Xi  x ]
x ↑x *
x ↓x *
lim E [ Wi | Xi  x ] - lim E [ Wi | X i  x ]
x ↑x *
x ↓x *
where E[.] is the expectation operation; x* is the cutoff (i.e., the 55th income
percentile); Yi and Xi are the outcome and forcing (treatment-determining)
Wis
1[ Xassignment
variable of household i, respectively; and
the
i 
i  x*]
variable with 1[.] being the index function taking value 1 if the condition in
the square brackets is correct, and zero otherwise. The denominator
represents the jump in borrowing probability due to treatment assignment.
We will follow Imbens and Lemieux (2008)[1] to use the local linear regression
estimation method, where both the difference in the outcome (numerator)
and the difference in the borrowing probability (denominator) will be
estimated by the fitted values of Y and W at both sides of the cutoff.
Imbens, G. and T. Lemieux. 2008. Regression discontinuity designs: a guide to practice. Journal of
Econometrics, vol. 142 (2): 615 – 635.
[1]
Other Estimators: PSM and DD
The RDD estimator gives an unbiased estimate of the
project impact. However, it only estimates the project
effect near the cutoff.
If the project effect is constant, this poses no problem.
To give an estimate of the overall average treatment
effect, we will also use the propensity score matching
(PSM) method combined with a double/single difference
estimator, pending data availability and quality.
Data Requirement
Province
Community
Treatment Comparison
Survey
FGD
Southern
495
495
10
10
Central
420
420
10
10
Western
300
300
10
10
North Central
150
150
10
10
Sabaragamuwa
135
135
10
10
1,500
1,500
50
50
Total
KII
100
Household Sample
Time
Treatment Group
Comparison Group
1998
T0 = 1,500 households with
income below the cutoff;
households actually borrowed
will be drawn from client profiles
in PCIs’ databases; households
that did not borrow will be
drawn from household profiles
kept in the village statistics
archives
2010
T1 = the same 1,500 households C1 = the same 1,500 households of
of T0 to be surveyed for the
C0 to be surveyed for the study
study
C0 = 1,500 households with income
above cutoff drawn from household
profiles in the village statistics
archives
Implementation Plan
Milestones
Preparations
Data Collection
Data Analysis
Drafting Report
Review/Revision
Completion
OCT
NOV
DEC
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
Thank you.
For more information:
http://www.adb.org/Evaluation/resources.asp